Development of an Haa1-Based Biosensor for Acetic Acid Sensing in Saccharomyces cerevisiae.

Transcription factor (TF)-based biosensors are important tools in strain development and screening as they can allow accurate monitoring of intracellular concentrations of a molecule. Acetic acid is one of the main inhibitors in lignocellulosic biomass and a major challenge when using yeast cell factories for biorefinery applications. Thus, developing acetic acid tolerant strains is of great importance. The acetic acid sensing biosensor developed relies on the endogenous Saccharomyces cerevisiae TF Haa1 that upon binding of acetic acid translocates to the nucleus. The acetic acid biosensor can be used as a tool for strain development and evaluation, as well as for screening of acetic acid-producing strains and for dynamic monitoring of acetic acid accumulation. This chapter describes a methodology for developing a TF-based biosensor for acetic acid sensing. Protocols for design considerations, part construction, and characterization procedures are included. The approach can potentially be adapted to any molecule where a suitable TF can be identified.

Evaluation and comparison of colorimetric outputs for yeast-based biosensors in laboratory and point-of-use settings.

Recent research has shown the potential of yeast-based biosensors (YBBs) for point-of-use detection of pathogens and target molecules in saliva, blood, and urine samples. The choice of output can greatly affect the sensitivity, dynamic range, detection time, and ease-of-use of a sensor. For visual detection without the need for additional reagents or machinery, colorimetric outputs have shown great potential. Here, we evaluated the inducible generation of prodeoxyviolacein and proviolacein as colorimetric YBB outputs and benchmarked these against lycopene. The outputs were induced via the yeast mating pathway and were compared on agar plates, in liquid culture, and on paper slips. We found that all three outputs produced comparable pigment intensity on agar plates, making them applicable for bioengineering settings. In liquid media and on paper slips, lycopene resulted in a higher intensity pigment and a decreased time-of-detection.

Saccharomyces cerevisiae strains performing similarly during fermentation of lignocellulosic hydrolysates show pronounced differences in transcriptional stress responses.

Improving our understanding of the transcriptional changes of Saccharomyces cerevisiae during fermentation of lignocellulosic hydrolysates is crucial for the creation of more efficient strains to be used in biorefineries. We performed RNA sequencing of a CEN.PK laboratory strain, two industrial strains (KE6-12 and Ethanol Red), and two wild-type isolates of the LBCM collection when cultivated anaerobically in wheat straw hydrolysate. Many of the differently expressed genes identified among the strains have previously been reported to be important for tolerance to lignocellulosic hydrolysates or inhibitors therein. Our study demonstrates that stress responses typically identified during aerobic conditions such as glutathione metabolism, osmotolerance, and detoxification processes also are important for anaerobic processes. Overall, the transcriptomic responses were largely strain dependent, and we focused our study on similarities and differences in the transcriptomes of the LBCM strains. The expression of sugar transporter-encoding genes was higher in LBCM31 compared with LBCM109 that showed high expression of genes involved in iron metabolism and genes promoting the accumulation of sphingolipids, phospholipids, and ergosterol. These results highlight different evolutionary adaptations enabling S. cerevisiae to strive in lignocellulosic hydrolysates and suggest novel gene targets for improving fermentation performance and robustness. IMPORTANCE: The need for sustainable alternatives to oil-based production of biochemicals and biofuels is undisputable. Saccharomyces cerevisiae is the most commonly used industrial fermentation workhorse. The fermentation of lignocellulosic hydrolysates, second-generation biomass unsuited for food and feed, is still hampered by lowered productivities as the raw material is inhibitory for the cells. In order to map the genetic responses of different S. cerevisiae strains, we performed RNA sequencing of a CEN.PK laboratory strain, two industrial strains (KE6-12 and Ethanol Red), and two wild-type isolates of the LBCM collection when cultivated anaerobically in wheat straw hydrolysate. While the response to inhibitors of S. cerevisiae has been studied earlier, this has in previous studies been done in aerobic conditions. The transcriptomic analysis highlights different evolutionary adaptations among the different S. cerevisiae strains and suggests novel gene targets for improving fermentation performance and robustness.

Fine-tuning of p-coumaric acid synthesis to increase (2S)-naringenin production in yeast.

(2S)-Naringenin is a key precursor for biosynthesis of various high-value flavonoids and possesses a variety of nutritional and pharmaceutical properties on human health. Systematic optimization approaches have been employed to improve (2S)-naringenin production in different microbial hosts. However, very few studies have focused on the spatiotemporal distribution of (2S)-naringenin and the related pathway intermediate p-coumaric acid, which is an important factor for efficient production. Here, we first optimized the (2S)-naringenin biosynthetic pathway by alleviating the bottleneck downstream of p-coumaric acid and increasing malonyl-CoA supply, which improved (2S)-naringenin production but significant accumulation of p-coumaric acid still existed extracellularly. We thus established a dual dynamic control system through combining a malonyl-CoA biosensor regulator and an RNAi strategy, to autonomously control the synthesis of p-coumaric acid with the supply of malonyl-CoA. Furthermore, screening potential transporters led to identification of Pdr12 for improved (2S)-naringenin production and reduced accumulation of p-coumaric acid. Finally, a titer of 2.05 g/L (2S)-naringenin with negligible accumulation of p-coumaric acid was achieved in a fed batch fermentation. Our work highlights the importance of systematic control of pathway intermediates for efficient microbial production of plant natural products.

Expanding the genetic toolbox of Rhodotorula toruloides by identification and validation of six novel promoters induced or repressed under nitrogen starvation.

BACKGROUND: The non-conventional yeast Rhodotorula toruloides is an emerging host organism in biotechnology by merit of its natural capacity to accumulate high levels of carotenoids and intracellular storage lipids from a variety of carbon sources. While the number of genetic engineering strategies that employ R. toruloides is increasing, the lack of genetic tools available for modification of this yeast is still limiting strain development. For instance, several strong, constitutive R. toruloides promoters have been characterized, but to date, only five inducible promoters have been identified. Although nitrogen-limited cultivation conditions are commonly used to induce lipid accumulation in this yeast, no promoters regulated by nitrogen starvation have been described for R. toruloides. RESULTS: In this study, we used a combination of genomics and transcriptomics methods to identify novel R. toruloides promoter sequences that are either inducible or repressible by nitrogen starvation. RNA sequencing was used to assess gene expression in the recently isolated strain R. toruloides BOT-A2 during exponential growth and during nitrogen starvation, when cultivated with either glucose or xylose as the carbon source. The genome of BOT-A2 was sequenced using a combination of long- and short-read sequencing and annotated with support of the RNAseq data. Differential expression analysis was used to identify genes with a |log2 fold change|≥ 2 when comparing their expression during nitrogen depletion to that during exponential growth. The promoter regions from 16 of these genes were evaluated for their ability to drive the expression of a fluorescent reporter gene. Three promoters that were clearly upregulated under nitrogen starvation and three that were downregulated were selected and further characterized. One promoter, derived from gene RTBOTA2_003877, was found to function like an on-off switch, as it was only upregulated under full nitrogen depletion and downregulated in the presence of the nitrogen source. CONCLUSIONS: Six new R. toruloides promoters that were either upregulated or downregulated under nitrogen-starvation were identified. These substantially contribute to the available promoters when engineering this organism and are foreseen to be particularly useful for future engineering strategies requiring specific regulation of target genes in accordance with nitrogen availability.

Targeted In Vivo Mutagenesis in Yeast Using CRISPR/Cas9 and Hyperactive Cytidine and Adenine Deaminases.

Directed evolution is a preferred strategy to improve the function of proteins such as enzymes that act as bottlenecks in metabolic pathways. Common directed evolution approaches rely on error-prone PCR-based libraries where the number of possible variants is usually limited by cellular transformation efficiencies. Targeted in vivo mutagenesis can advance directed evolution approaches and help to overcome limitations in library generation. In the current study, we aimed to develop a high-efficiency time-controllable targeted mutagenesis toolkit in the yeast Saccharomyces cerevisiae by employing the CRISPR/Cas9 technology. To that end, we fused the dCas9 protein with hyperactive variants of adenine and cytidine deaminases aiming to create an inducible CRISPR-based mutagenesis tool targeting a specific DNA sequence in vivo with extended editing windows and high mutagenesis efficiency. We also investigated the effect of guide RNA multiplexing on the mutagenesis efficiency both phenotypically and on the DNA level.

Multiplexed Guide RNA Expression Leads to Increased Mutation Frequency in Targeted Window Using a CRISPR-Guided Error-Prone DNA Polymerase in Saccharomyces cerevisiae.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology, with its ability to target a specific DNA locus using guide RNAs (gRNAs), is particularly suited for targeted mutagenesis. The targeted diversification of nucleotides in Saccharomyces cerevisiae using a CRISPR-guided error-prone DNA polymerase─called yEvolvR─was recently reported. Here, we investigate the effect of multiplexed expression of gRNAs flanking a short stretch of DNA on reversion and mutation frequencies using yEvolvR. Phenotypic assays demonstrate that higher reversion frequencies are observed when expressing multiple gRNAs simultaneously. Next generation sequencing reveals a synergistic effect of multiple gRNAs on mutation frequencies, which is more pronounced in a mutant with a partially defective DNA mismatch repair system. Additionally, we characterize a galactose-inducible yEvolvR, which enables temporal control of mutagenesis. This study demonstrates that multiplex expression of gRNAs and induction of mutagenesis greatly improves the capabilities of yEvolvR for generation of genetic libraries in vivo.

Functional genome annotation and transcriptome analysis of Pseudozyma hubeiensis BOT-O, an oleaginous yeast that utilizes glucose and xylose at equal rates.

Pseudozyma hubeiensis is a basidiomycete yeast that has the highly desirable traits for lignocellulose valorisation of being equally efficient at utilization of glucose and xylose, and capable of their co-utilization. The species has previously mainly been studied for its capacity to produce secreted biosurfactants in the form of mannosylerythritol lipids, but it is also an oleaginous species capable of accumulating high levels of triacylglycerol storage lipids during nutrient starvation. In this study, we aimed to further characterize the oleaginous nature of P. hubeiensis by evaluating metabolism and gene expression responses during storage lipid formation conditions with glucose or xylose as a carbon source. The genome of the recently isolated P. hubeiensis BOT-O strain was sequenced using MinION long-read sequencing and resulted in the most contiguous P. hubeiensis assembly to date with 18.95 Mb in 31 contigs. Using transcriptome data as experimental support, we generated the first mRNA-supported P. hubeiensis genome annotation and identified 6540 genes. 80% of the predicted genes were assigned functional annotations based on protein homology to other yeasts. Based on the annotation, key metabolic pathways in BOT-O were reconstructed, including pathways for storage lipids, mannosylerythritol lipids and xylose assimilation. BOT-O was confirmed to consume glucose and xylose at equal rates, but during mixed glucose-xylose cultivation glucose was found to be taken up faster. Differential expression analysis revealed that only a total of 122 genes were significantly differentially expressed at a cut-off of |log2 fold change| ≥ 2 when comparing cultivation on xylose with glucose, during exponential growth and during nitrogen-starvation. Of these 122 genes, a core-set of 24 genes was identified that were differentially expressed at all time points. Nitrogen-starvation resulted in a larger transcriptional effect, with a total of 1179 genes with significant expression changes at the designated fold change cut-off compared with exponential growth on either glucose or xylose.

Directed evolution of a wax ester synthase for production of fatty acid ethyl esters in Saccharomyces cerevisiae.

Wax ester synthases (WSs) utilize a fatty alcohol and a fatty acyl-coenzyme A (activated fatty acid) to synthesize the corresponding wax ester. There is much interest in developing novel cell factories that can produce shorter esters, e.g., fatty acid ethyl esters (FAEEs), with properties similar to biodiesel in order to use these as transportation fuels. However, ethanol is a poor substrate for WSs, and this may limit the biosynthesis of FAEEs. Here, we implemented a random mutagenesis approach to enhance the catalytic efficiency of a WS from Marinobacter hydrocarbonoclasticus (MhWS2, encoded by the ws2 gene). Our selection system was based on FAEE formation serving as a detoxification mechanism for excessive oleate, where high WS activity was essential for a storage-lipid free yeast to survive. A random mutagenesis library of ws2 was used to transform the storage-lipid free yeast, and mutants could be selected by plating the transformants on oleate containing plates. The variants encoding WS with improved activity were sequenced, and an identified point mutation translated into the residue substitution at position A344 was discovered to substantially increase the selectivity of MhWS2 toward ethanol and other shorter alcohols. Structural modeling indicated that an A344T substitution might affect the alcohol selectivity due to change of both steric effects and polarity changes near the active site. This work not only provides a new WS variant with altered selectivity to shorter alcohols but also presents a new high-throughput selection system to isolate WSs with a desired selectivity. KEY POINTS: • The work provides WS variants with altered substrate preference for shorter alcohols • A novel method was developed for directed evolution of WS of desired selectivity.

Genetically Encoded Whole Cell Biosensor for Drug Discovery of HIF-1 Interaction Inhibitors.

The heterodimeric transcription factor, hypoxia inducible factor-1 (HIF-1), is an important anticancer target as it supports the adaptation and response of tumors to hypoxia. Here, we optimized the repressed transactivator yeast two-hybrid system to further develop it as part of a versatile yeast-based drug discovery platform and validated it using HIF-1. We demonstrate both fluorescence-based and auxotrophy-based selections that could detect HIF-1α/HIF-1ß dimerization inhibition. The engineered genetic selection is tunable and able to differentiate between strong and weak interactions, shows a large dynamic range, and is stable over different growth phases. Furthermore, we engineered mechanisms to control for cellular activity and off-target drug effects. We thoroughly characterized all parts of the biosensor system and argue this tool will be generally applicable to a wide array of protein-protein interaction targets. We anticipate this biosensor will be useful as part of a drug discovery platform, particularly when screening DNA-encoded new modality drugs.

Identification of acetic acid sensitive strains through biosensor-based screening of a Saccharomyces cerevisiae CRISPRi library.

BACKGROUND: Acetic acid tolerance is crucial for the development of robust cell factories for conversion of lignocellulosic hydrolysates that typically contain high levels of acetic acid. Screening mutants for growth in medium with acetic acid is an attractive way to identify sensitive variants and can provide novel insights into the complex mechanisms regulating the acetic acid stress response. RESULTS: An acetic acid biosensor based on the Saccharomyces cerevisiae transcription factor Haa1, was used to screen a CRISPRi yeast strain library where dCas9-Mxi was set to individually repress each essential or respiratory growth essential gene. Fluorescence-activated cell sorting led to the enrichment of a population of cells with higher acetic acid retention. These cells with higher biosensor signal were demonstrated to be more sensitive to acetic acid. Biosensor-based screening of the CRISPRi library strains enabled identification of strains with increased acetic acid sensitivity: strains with gRNAs targeting TIF34, MSN5, PAP1, COX10 or TRA1. CONCLUSIONS: This study demonstrated that biosensors are valuable tools for screening and monitoring acetic acid tolerance in yeast. Fine-tuning the expression of essential genes can lead to altered acetic acid tolerance.

A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae.

In vivo biosensors that can convert metabolite concentrations into measurable output signals are valuable tools for high-throughput screening and dynamic pathway control in the field of metabolic engineering. Here, we present a novel biosensor in Saccharomyces cerevisiae that is responsive to p-coumaroyl-CoA, a central precursor of many flavonoids. The sensor is based on the transcriptional repressor CouR from Rhodopseudomonas palustris and was applied in combination with a previously developed malonyl-CoA biosensor for dual regulation of p-coumaroyl-CoA synthesis within the naringenin production pathway. Using this approach, we obtained a naringenin titer of 47.3 mg/L upon external precursor feeding, representing a 15-fold increase over the nonregulated system.

Controlling gene expression with deep generative design of regulatory DNA.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

An Overview on Selection Marker Genes for Transformation of Saccharomyces cerevisiae.

For genetic manipulation of yeast, numerous selection marker genes have been employed. These include prototrophic markers, markers conferring drug resistance, autoselection markers, and counterselectable markers. This chapter describes the different classes of selection markers and provides a number of examples for different applications.

CRISPR/Cas9-mediated point mutations improve α-amylase secretion in Saccharomyces cerevisiae.

The rapid expansion of the application of pharmaceutical proteins and industrial enzymes requires robust microbial workhorses for high protein production. The budding yeast Saccharomyces cerevisiae is an attractive cell factory due to its ability to perform eukaryotic post-translational modifications and to secrete proteins. Many strategies have been used to engineer yeast platform strains for higher protein secretion capacity. Herein, we investigated a line of strains that have previously been selected after UV random mutagenesis for improved α-amylase secretion. A total of 42 amino acid altering point mutations identified in this strain line were reintroduced into the parental strain AAC to study their individual effects on protein secretion. These point mutations included missense mutations (amino acid substitution), nonsense mutations (stop codon generation), and frameshift mutations. For comparison, single gene deletions for the corresponding target genes were also performed in this study. A total of 11 point mutations and seven gene deletions were found to effectively improve α-amylase secretion. These targets were involved in several bioprocesses, including cellular stresses, protein degradation, transportation, mRNA processing and export, DNA replication, and repair, which indicates that the improved protein secretion capacity in the evolved strains is the result of the interaction of multiple intracellular processes. Our findings will contribute to the construction of novel cell factories for recombinant protein secretion.

Dataset for suppressors of amyloid-ß toxicity and their functions in recombinant protein production in yeast.

The production of recombinant proteins at high levels often induces stress-related phenotypes by protein misfolding or aggregation. These are similar to those of the yeast Alzheimer's disease (AD) model in which amyloid-ß peptides (Aß42) were accumulated [1], [2]. We have previously identified suppressors of Aß42 cytotoxicity via the genome-wide synthetic genetic array (SGA) [3] and here we use them as metabolic engineering targets to evaluate their potentiality on recombinant protein production in yeast Saccharomyces cerevisiae. In order to investigate the mechanisms linking the genetic modifications to the improved recombinant protein production, we perform systems biology approaches (transcriptomics and proteomics) on the resulting strain and intermediate strains. The RNAseq data are preprocessed by the nf-core/RNAseq pipeline and analyzed using the Platform for Integrative Analysis of Omics (PIANO) package [4]. The quantitative proteome is analyzed on an Orbitrap Fusion Lumos mass spectrometer interfaced with an Easy-nLC1200 liquid chromatography (LC) system. LC-MS data files are processed by Proteome Discoverer version 2.4 with Mascot 2.5.1 as a database search engine. The original data presented in this work can be found in the research paper titled "Suppressors of Amyloid-ß Toxicity Improve Recombinant Protein Production in yeast by Reducing Oxidative Stress and Tuning Cellular Metabolism", by Chen et al. [5].

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.

Suppressors of amyloid-ß toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism.

High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-ß peptides (Aß42), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of Aß42 cytotoxicity. Here we hypothesize that these suppressors could be used as metabolic engineering targets to alleviate cellular stress and improve recombinant protein production in the yeast Saccharomyces cerevisiae. Forty-six candidate genes were individually deleted and twenty were individually overexpressed. The positive targets that increased recombinant α-amylase production were further combined leading to an 18.7-fold increased recombinant protein production. These target genes are involved in multiple cellular networks including RNA processing, transcription, ER-mitochondrial complex, and protein unfolding. By using transcriptomics and proteomics analyses, combined with reverse metabolic engineering, we showed that reduced oxidative stress, increased membrane lipid biosynthesis and repressed arginine and sulfur amino acid biosynthesis are significant pathways for increased recombinant protein production. Our findings provide new insights towards developing synthetic yeast cell factories for biosynthesis of valuable proteins.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products.

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Identification of a novel gene required for competitive growth at high temperature in the thermotolerant yeast Kluyveromyces marxianus.

It is important to understand the basis of thermotolerance in yeasts to broaden their application in industrial biotechnology. The capacity to run bioprocesses at temperatures above 40 °C is of great interest but this is beyond the growth range of most of the commonly used yeast species. In contrast, some industrial yeasts such as Kluyveromyces marxianus can grow at temperatures of 45 °C or higher. Such species are valuable for direct use in industrial biotechnology and as a vehicle to study the genetic and physiological basis of yeast thermotolerance. In previous work, we reported that evolutionarily young genes disproportionately changed expression when yeast were growing under stressful conditions and postulated that such genes could be important for long-term adaptation to stress. Here, we tested this hypothesis in K. marxianus by identifying and studying species-specific genes that showed increased expression during high-temperature growth. Twelve such genes were identified and 11 were successfully inactivated using CRISPR-mediated mutagenesis. One gene, KLMX_70384, is required for competitive growth at high temperature, supporting the hypothesis that evolutionary young genes could play roles in adaptation to harsh environments. KLMX_70384 is predicted to encode an 83 aa peptide, and RNA sequencing and ribo-sequencing were used to confirm transcription and translation of the gene. The precise function of KLMX_70384 remains unknown but some features are suggestive of RNA-binding activity. The gene is located in what was previously considered an intergenic region of the genome, which lacks homologues in other yeasts or in databases. Overall, the data support the hypothesis that genes that arose de novo in K. marxianus after the speciation event that separated K. marxianus and K. lactis contribute to some of its unique traits.