Performance and robustness analysis reveals phenotypic trade-offs in yeast.

To design strains that can function efficiently in complex industrial settings, it is crucial to consider their robustness, that is, the stability of their performance when faced with perturbations. In the present study, we cultivated 24 Saccharomyces cerevisiae strains under conditions that simulated perturbations encountered during lignocellulosic bioethanol production, and assessed the performance and robustness of multiple phenotypes simultaneously. The observed negative correlations confirmed a trade-off between performance and robustness of ethanol yield, biomass yield, and cell dry weight. Conversely, the specific growth rate performance positively correlated with the robustness, presumably because of evolutionary selection for robust, fast-growing cells. The Ethanol Red strain exhibited both high performance and robustness, making it a good candidate for bioproduction in the tested perturbation space. Our results experimentally map the robustness-performance trade-offs, previously demonstrated mainly by single-phenotype and computational studies.

Quantification of Microbial Robustness in Yeast.

Stable cell performance in a fluctuating environment is essential for sustainable bioproduction and synthetic cell functionality; however, microbial robustness is rarely quantified. Here, we describe a high-throughput strategy for quantifying robustness of multiple cellular functions and strains in a perturbation space. We evaluated quantification theory on experimental data and concluded that the mean-normalized Fano factor allowed accurate, reliable, and standardized quantification. Our methodology applied to perturbations related to lignocellulosic bioethanol production showed that the industrial bioethanol producing strain Saccharomyces cerevisiae Ethanol Red exhibited both higher and more robust growth rates than the laboratory strain CEN.PK and industrial strain PE-2, while a more robust product yield traded off for lower mean levels. The methodology validated that robustness is function-specific and characterized by positive and negative function-specific trade-offs. Systematic quantification of robustness to end-use perturbations will be important to analyze and construct robust strains with more predictable functions.

Robustness: linking strain design to viable bioprocesses.

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

RNA sequencing reveals metabolic and regulatory changes leading to more robust fermentation performance during short-term adaptation of Saccharomyces cerevisiae to lignocellulosic inhibitors.

BACKGROUND: The limited tolerance of Saccharomyces cerevisiae to inhibitors is a major challenge in second-generation bioethanol production, and our understanding of the molecular mechanisms providing tolerance to inhibitor-rich lignocellulosic hydrolysates is incomplete. Short-term adaptation of the yeast in the presence of dilute hydrolysate can improve its robustness and productivity during subsequent fermentation. RESULTS: We utilized RNA sequencing to investigate differential gene expression in the industrial yeast strain CR01 during short-term adaptation, mimicking industrial conditions for cell propagation. In this first transcriptomic study of short-term adaption of S. cerevisiae to lignocellulosic hydrolysate, we found that cultures respond by fine-tuned up- and down-regulation of a subset of general stress response genes. Furthermore, time-resolved RNA sequencing allowed for identification of genes that were differentially expressed at 2 or more sampling points, revealing the importance of oxidative stress response, thiamin and biotin biosynthesis. furan-aldehyde reductases and specific drug:H+ antiporters, as well as the down-regulation of certain transporter genes. CONCLUSIONS: These findings provide a better understanding of the molecular mechanisms governing short-term adaptation of S. cerevisiae to lignocellulosic hydrolysate, and suggest new genetic targets for improving fermentation robustness.

Exploring Selective Pressure Trade-Offs for Synthetic Addiction to Extend Metabolite Productive Lifetimes in Yeast.

Engineered microbes often suffer from reduced fitness resulting from metabolic burden and various stresses. The productive lifetime of a bioreactor with engineered microbes is therefore susceptible to the rise of nonproductive mutants with better fitness. Synthetic addiction is emerging as a concept to artificially couple the growth rate of the microbe to production to tackle this problem. However, only a few successful cases of synthetic addiction systems have been reported to date. To understand the limitations and design constraints in long-term cultivations, we designed and studied conditional synthetic addiction circuits in Saccharomyces cerevisiae. This allowed us to probe a range of selective pressure strengths and identify the optimal balance between circuit stability and production-to-growth coupling. In the optimal balance, the productive lifetime was greatly extended compared with suboptimal circuit tuning. With a too-high or -low pressure, we found that production declines mainly through homologous recombination. These principles of trade-off in the design of synthetic addition systems should lead to the better control of bioprocess performance.

Genomic and transcriptomic analysis of the thermophilic lignocellulose-degrading fungus Thielavia terrestris LPH172.

BACKGROUND: Biomass-degrading enzymes with improved activity and stability can increase substrate saccharification and make biorefineries economically feasible. Filamentous fungi are a rich source of carbohydrate-active enzymes (CAZymes) for biomass degradation. The newly isolated LPH172 strain of the thermophilic Ascomycete Thielavia terrestris has been shown to possess high xylanase and cellulase activities and tolerate low pH and high temperatures. Here, we aimed to illuminate the lignocellulose-degrading machinery and novel carbohydrate-active enzymes in LPH172 in detail. RESULTS: We sequenced and analyzed the 36.6-Mb genome and transcriptome of LPH172 during growth on glucose, cellulose, rice straw, and beechwood xylan. 10,128 predicted genes were found in total, which included 411 CAZy domains. Compared to other fungi, auxiliary activity (AA) domains were particularly enriched. A higher GC content was found in coding sequences compared to the overall genome, as well as a high GC3 content, which is hypothesized to contribute to thermophilicity. Primarily auxiliary activity (AA) family 9 lytic polysaccharide monooxygenase (LPMO) and glycoside hydrolase (GH) family 7 glucanase encoding genes were upregulated when LPH172 was cultivated on cellulosic substrates. Conventional hemicellulose encoding genes (GH10, GH11 and various CEs), as well as AA9 LPMOs, were upregulated when LPH172 was cultivated on xylan. The observed co-expression and co-upregulation of genes encoding AA9 LPMOs, other AA CAZymes, and (hemi)cellulases point to a complex and nuanced degradation strategy. CONCLUSIONS: Our analysis of the genome and transcriptome of T. terrestris LPH172 elucidates the enzyme arsenal that the fungus uses to degrade lignocellulosic substrates. The study provides the basis for future characterization of potential new enzymes for industrial biomass saccharification.

Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors.

Industrial fermentation processes strive for high robustness to ensure optimal and consistent performance. Medium components, fermentation products, and physical perturbations may cause stress and lower performance. Cellular stress elicits a range of responses, whose extracellular manifestations have been extensively studied; whereas intracellular aspects remain poorly known due to lack of tools for real-time monitoring. Genetically encoded biosensors have emerged as promising tools and have been used to improve microbial productivity and tolerance toward industrially relevant stresses. Here, fluorescent biosensors able to sense the yeast intracellular environment (pH, ATP levels, oxidative stress, glycolytic flux, and ribosome production) were implemented into a versatile and easy-to-use toolbox. Marker-free and efficient genome integration at a conserved site on chromosome X of Saccharomyces cerevisiae strains and a commercial Saccharomyces boulardii strain was developed. Moreover, multiple biosensors were used to simultaneously monitor different intracellular parameters in a single cell. Even when combined together, the biosensors did not significantly affect key physiological parameters, such as specific growth rate and product yields. Activation and response of each biosensor and their interconnection were assessed using an advanced micro-cultivation system. Finally, the toolbox was used to screen cell behavior in a synthetic lignocellulosic hydrolysate that mimicked harsh industrial substrates, revealing differences in the oxidative stress response between laboratory (CEN.PK113-7D) and industrial (Ethanol Red) S. cerevisiae strains. In summary, the toolbox will allow both the exploration of yeast diversity and physiological responses in natural and complex industrial conditions, as well as the possibility to monitor production processes.

Short and long-read ultra-deep sequencing profiles emerging heterogeneity across five platform Escherichia coli strains.

Reprogramming organisms for large-scale bioproduction counters their evolutionary objectives of fast growth and often leads to mutational collapse of the engineered production pathways during cultivation. Yet, the mutational susceptibility of academic and industrial Escherichia coli bioproduction host strains are poorly understood. In this study, we apply 2nd and 3rd generation deep sequencing to profile simultaneous modes of genetic heterogeneity that decimate engineered biosynthetic production in five popular E. coli hosts BL21(DE3), TOP10, MG1655, W, and W3110 producing 2,3-butanediol and mevalonic acid. Combining short-read and long-read sequencing, we detect strain and sequence-specific mutational modes including single nucleotide polymorphism, inversion, and mobile element transposition, as well as complex structural variations that disrupt the integrity of the engineered biosynthetic pathway. Our analysis suggests that organism engineers should avoid chassis strains hosting active insertion sequence (IS) subfamilies such as IS1 and IS10 present in popular E. coli TOP10. We also recommend monitoring for increased mutagenicity in the pathway transcription initiation regions and recombinogenic repeats. Together, short and long sequencing reads identified latent low-frequency mutation events such as a short detrimental inversion within a pathway gene, driven by 8-bp short inverted repeats. This demonstrates the power of combining ultra-deep DNA sequencing technologies to profile genetic heterogeneities of engineered constructs and explore the markedly different mutational landscapes of common E. coli host strains. The observed multitude of evolving variants underlines the usefulness of early mutational profiling for new synthetic pathways designed to sustain in organisms over long cultivation scales.

The future of self-selecting and stable fermentations.

Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.

Regulatory control circuits for stabilizing long-term anabolic product formation in yeast.

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 high-performing engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-ß-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-ß-glucoside pathway intermediates in order to stabilize vanillin-ß-glucoside production over ~55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-ß-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and almost 2-fold higher vanillin-ß-glucoside production, including 5-fold increase in total vanillin-ß-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-ß-glucoside producing cells without control circuits.

Overcoming genetic heterogeneity in industrial fermentations.

Engineering the synthesis of massive amounts of therapeutics, enzymes or commodity chemicals can select for subpopulations of nonproducer cells, owing to metabolic burden and product toxicity. Deep DNA sequencing can be used to detect undesirable genetic heterogeneity in producer populations and diagnose associated genetic error modes. Hotspots of genetic heterogeneity can pinpoint mechanisms that underlie load problems and product toxicity. Understanding genetic heterogeneity will inform metabolic engineering and synthetic biology strategies to minimize the emergence of nonproducer mutants in scaled-up fermentations and maximize product quality and yield.

Systematic Evaluation of Site-Specific Recombinant Gene Expression for Programmable Mammalian Cell Engineering.

Many branches of biology depend on stable and predictable recombinant gene expression, which has been achieved in recent years through targeted integration of the recombinant gene into defined integration sites. However, transcriptional levels of recombinant genes in characterized integration sites are controlled by multiple components of the integrated expression cassette. Lack of readily available tools has inhibited meaningful experimental investigation of the interplay between the integration site and the expression cassette components. Here we show in a systematic manner how multiple components contribute to final net expression of recombinant genes in a characterized integration site. We develop a CRISPR/Cas9-based toolbox for construction of mammalian cell lines with targeted integration of a landing pad, containing a recombinant gene under defined 5' proximal regulatory elements. Generated site-specific recombinant cell lines can be used in a streamlined recombinase-mediated cassette exchange for fast screening of different expression cassettes. Using the developed toolbox, we show that different 5' proximal regulatory elements generate distinct and robust recombinant gene expression patterns in defined integration sites of CHO cells with a wide range of transcriptional outputs. This approach facilitates the generation of user-defined and product-specific gene expression patterns for programmable mammalian cell engineering.

Enhanced Metabolite Productivity of Escherichia coli Adapted to Glucose M9 Minimal Medium.

High productivity of biotechnological strains is important to industrial fermentation processes and can be constrained by precursor availability and substrate uptake rate. Adaptive laboratory evolution (ALE) of Escherichia coli MG1655 to glucose minimal M9 medium has been shown to increase strain fitness, mainly through a key mutation in the transcriptional regulator rpoB, which increases flux through central carbon metabolism and the glucose uptake rate. We wanted to test the hypothesis that a substrate uptake enhancing rpoB mutation can translate to increased productivity in a strain possessing a heterologous metabolite pathway. When engineered for heterologous mevalonate production, we found that E. coli rpoB E672K strains displayed 114-167% higher glucose uptake rates and 48-77% higher mevalonate productivities in glucose minimal M9 medium. This improvement in heterologous mevalonate productivity of the rpoB E672K strain is likely mediated by the elevated glucose uptake rate of such strains, which favors overflow metabolism toward acetate production and availability of acetyl-CoA as precursor. These results demonstrate the utility of adaptive laboratory evolution (ALE) to generate a platform strain for an increased production rate for a heterologous product.

Synthetic addiction extends the productive life time of engineered Escherichia coli populations.

Bio-production of chemicals is an important driver of the societal transition toward sustainability. However, fermentations with heavily engineered production organisms can be challenging to scale to industrial volumes. Such fermentations are subject to evolutionary pressures that select for a wide range of genetic variants that disrupt the biosynthetic capacity of the engineered organism. Synthetic product addiction that couples high-yield production of a desired metabolite to expression of nonconditionally essential genes could offer a solution to this problem by selectively favoring cells with biosynthetic capacity in the population without constraining the medium. We constructed such synthetic product addiction by controlling the expression of two nonconditionally essential genes with a mevalonic acid biosensor. The product-addicted production organism retained high-yield mevalonic acid production through 95 generations of cultivation, corresponding to the number of cell generations required for >200-m3 industrial-scale production, at which time the nonaddicted strain completely abolished production. Using deep DNA sequencing, we find that the product-addicted populations do not accumulate genetic variants that compromise biosynthetic capacity, highlighting how synthetic networks can be designed to control genetic population heterogeneity. Such synthetic redesign of evolutionary forces with endogenous processes may be a promising concept for realizing complex cellular designs required for sustainable bio-manufacturing.

Diverse genetic error modes constrain large-scale bio-based production.

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines.

Molecular Buffers Permit Sensitivity Tuning and Inversion of Riboswitch Signals.

Predictable integration of foreign biological signals and parts remains a key challenge in the systematic engineering of synthetic cellular actuations, and general methods to improve signal transduction and sensitivity are needed. To address this problem we modeled and built a molecular signal buffer network in Saccharomyces cerevisiae inspired by chemical pH buffer systems. The molecular buffer system context-insulates a riboswitch enabling synthetic control of colony formation and modular signal manipulations. The riboswitch signal is relayed to a transcriptional activation domain of a split transcription factor, while interacting DNA-binding domains mediate the transduction of signal and form an interacting molecular buffer. The molecular buffer system enables modular signal inversion through integration with repressor modules. Further, tuning of input sensitivity was achieved through perturbation of the buffer pair ratio guided by a mathematical model. Such buffered signal tuning networks will be useful for domestication of RNA-based sensors enabling tunable outputs and library-wide selections for drug discovery and metabolic engineering.

Recombination-stable multimeric green fluorescent protein for characterization of weak promoter outputs in Saccharomyces cerevisiae.

Green fluorescent proteins (GFPs) are widely used for visualization of proteins to track localization and expression dynamics. However, phenotypically important processes can operate at too low expression levels for routine detection, i.e. be overshadowed by autofluorescence noise. While GFP functions well in translational fusions, the use of tandem GFPs to amplify fluorescence signals is currently avoided in Saccharomyces cerevisiae and many other microorganisms due to the risk of loop-out by direct-repeat recombination. We increased GFP fluorescence by translationally fusing three different GFP variants, yeast-enhanced GFP, GFP+ and superfolder GFP to yield a sequence-diverged triple GFP molecule 3vGFP with 74-84% internal repeat identity. Unlike a single GFP, the brightness of 3vGFP allowed characterization of a weak promoter in S. cerevisiae. Utilizing 3vGFP, we further engineered a less leaky Cu(2+)-inducible promoter based on CUP1. The basal expression level of the new promoter was approximately 61% below the wild-type CUP1 promoter, thus expanding the absolute range of Cu(2+)-based gene control. The stability of 3vGFP towards direct-repeat recombination was assayed in S. cerevisiae cultured for 25 generations under strong and slightly toxic expression after which only limited reduction in fluorescence was detectable. Such non-recombinogenic GFPs can help quantify intracellular responses operating a low copy number in recombination-prone organisms.

Flexible metabolic pathway construction using modular and divisible selection gene regulators.

Genetic selections are important to biological engineering. Although selectable traits are limited, currently each trait only permits simultaneous introduction of a single DNA fragment. Complex pathway and strain construction however depends on rapid, combinatorial introduction of many genes that encode putative pathway candidates and homologs. To triple the utility of existing selection genes, we have developed divisible selection in Saccharomyces cerevisiae. Here, independent DNA fragments can be introduced and selected for simultaneously using a set of split hybrid transcription factors composed of parts from Escherichia coli LexA and Herpes simplex VP16 to regulate one single selectable phenotype of choice. Only when co-expressed, these split hybrid transcription factors promote transcription of a selection gene, causing tight selection of transformants containing all desired DNA fragments. Upon transformation, 94% of the selected colonies resulted strictly from transforming all three modules based on ARS/CEN plasmids. Similarly when used for chromosome integration, 95% of the transformants contained all three modules. The divisible selection system acts dominantly and thus expands selection gene utility from one to three without any genomic pre-modifications of the strain. We demonstrate the approach by introducing the fungal rubrofusarin polyketide pathway at a gene load of 11 kb distributed on three different plasmids, using a single selection trait and one yeast transformation step. By tripling the utility of existing selection genes, the employment of divisible selection improves flexibility and freedom in the strain engineering process.

Reconstruction of the biosynthetic pathway for the core fungal polyketide scaffold rubrofusarin in Saccharomyces cerevisiae.

BACKGROUND: Fungal polyketides include commercially important pharmaceuticals and food additives, e.g. the cholesterol-lowering statins and the red and orange monascus pigments. Presently, production relies on isolation of the compounds from the natural producers, and systems for heterologous production in easily fermentable and genetically engineerable organisms, such as Saccharomyces cerevisiae and Escherichia coli are desirable. Rubrofusarin is an orange polyketide pigment that is a common intermediate in many different fungal biosynthetic pathways. RESULTS: In this study, we established a biosynthetic pathway for rubrofusarin in S. cerevisiae. First, the Fusarium graminearum gene encoding polyketide synthase 12 (PKS12) was heterologously co-expressed with the Aspergillus fumigatus gene encoding phosphopantetheinyl transferase (npgA) resulting in production of YWA1. This aromatic heptaketide intermediate was converted into nor-rubrofusarin upon expression of the dehydratase gene aurZ from the aurofusarin gene cluster of F. graminearum. Final conversion into rubrofusarin was achieved by expression of the O-methyltransferase encoding gene aurJ, also obtained from the aurofusarin gene cluster, resulting in a titer of 1.1 mg/L. Reduced levels of rubrofusarin were detected when expressing PKS12, npgA, and aurJ alone, presumably due to spontaneous conversion of YWA1 to nor-rubrofusarin. However, the co-expression of aurZ resulted in an approx. six-fold increase in rubrofusarin production. CONCLUSIONS: The reconstructed pathway for rubrofusarin in S. cerevisiae allows the production of a core scaffold molecule with a branch-point role in several fungal polyketide pathways, thus paving the way for production of further natural pigments and bioactive molecules. Furthermore, the reconstruction verifies the suggested pathway, and as such, it is the first example of utilizing a synthetic biological "bottom up" approach for the validation of a complex fungal polyketide pathway.