Burden Imposed by Heterologous Protein Production in Two Major Industrial Yeast Cell Factories: Identifying Sources and Mitigation Strategies

Expression of a periplasmic β-glucosidase from Yarrowia lipolytica allows efficient cellobiose-xylose co-fermentation by industrial xylose-fermenting Saccharomyces cerevisiae strains

This study aimed to compare the effects of cellobiose hydrolysis, whether occurring inside or outside the cell, on the ability of Saccharomyces cerevisiae strains to ferment this sugar and then apply the most effective strategy to industrial S. cerevisiae strains. Firstly, two recombinant laboratory S. cerevisiae strains were engineered: CEN.PK-X-Bgl1YL, expressing the periplasmic β-glucosidase BGL1 from Yarrowia lipolytica; and CEN.PK-X-B7-T2, co-expressing the intracellular β-glucosidase SpBGL7 from Spathaspora passalidarum and the cellobiose transporter MgCBT2 from Meyerozyma guilliermondii. Both engineered strains were able to grown in media with cellobiose and to ferment this disaccharide. However, CEN.PK-X-Bgl1YL, which hydrolyzes cellobiose extracellularly, exhibited faster growth and superior batch fermentation performance. Furthermore, enzymatic and transport activities revealed that sugar uptake was possibly the limiting factor in cellobiose fermentation by CEN.PK-X-B7-T2. Since extracellular hydrolysis with the periplasmic β-glucosidase was more efficient for cellobiose fermentation, we integrated the BGL1 gene into two industrial xylose-fermenting S. cerevisiae strains. The resulting strains (MP-C5H1-Bgl1YL and MP-P5-Bgl1YL) efficiently co-consumed ∼ 22 g L- 1 of cellobiose and ∼ 22 g L- 1 of xylose in 24 h, achieving high ethanol production levels (∼ 17 g L- 1 titer, ∼ 0.50 g L- 1 h- 1 volumetric productivity, and 0.40 g g- 1 ethanol yield). Our findings suggest that the expression of periplasmic β-glucosidases in S. cerevisiae could be an effective strategy to overcome the disaccharide transport problem, thus enabling efficient cellobiose fermentation or even cellobiose-xylose co-fermentation.

Marine cosmetics and the blue bioeconomy: From sourcing to success stories

As the global population continues to grow, so does the demand for longer, healthier lives and environmentally responsible choices. Consumers are increasingly drawn to naturally sourced products with proven health and wellbeing benefits. The marine environment presents a promising yet underexplored resource for the cosmetics industry, offering bioactive compounds with the potential for safe and biocompatible ingredients. This manuscript provides a comprehensive overview of the potential of marine organisms for cosmetics production, highlighting marine-derived compounds and their applications in skin/hair/oral-care products, cosmeceuticals and more. It also lays down critical safety considerations and addresses the methodologies for sourcing marine compounds, including harvesting, the biorefinery concept, use of systems biology for enhanced product development, and the relevant regulatory landscape. The review is enriched by three case studies: design of macroalgal skincare products in Iceland, establishment of a microalgal cosmetics spin-off in Italy, and the utilization of marine proteins for cosmeceutical applications.

Improving CsOAC Activity in Saccharomyces cerevisiae for Directed Production of Olivetolic Acid through Rational Design

Olivetolic acid (OA) is an essential precursor in the cannabinoid biosynthesis. It is produced through a unique interaction between the two proteins, olivetol synthase (CsOLS) and olivetolic acid cyclase (CsOAC). When the OA biosynthesis is transferred to Saccharomyces cerevisiae, olivetol (OL) is produced as a side product, even with a high enhancement of copy number of CsOAC. In order to increase the OA titer while decreasing the OL titer in S. cerevisiae, rational design was applied to CsOAC using in silico approaches such as protein-ligand docking to find potential protein variants. In vivo screening and also testing different approaches for both proteins was applied to identify the best performing variants of CsOAC. Four variants were identified that gave the desired properties. The best CsOAC variant, G82 A/L92Y, resulted in a 1.7-fold increase in OA production and a shift in the ratio between the two products towards OA.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits- shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Strategic approaches for designing yeast strains as protein secretion and display platforms

Yeast has been established as a versatile platform for expressing functional molecules, owing to its well-characterized biology and extensive genetic modification tools. Compared to prokaryotic systems, yeast possesses advanced cellular mechanisms that ensure accurate protein folding and post-translational modifications. These capabilities are particularly advantageous for the expression of human-derived functional proteins. However, designing yeast strains as an expression platform for proteins requires the integration of molecular and cellular functions. By delving into the complexities of yeast-based expression systems, this review aims to empower researchers with the knowledge to fully exploit yeast as a functional platform to produce a diverse range of proteins. This review includes an exploration of the host strains, gene cassette structures, as well as considerations for maximizing the efficiency of the expression system. Through this in-depth analysis, the review anticipates stimulating further innovation in the field of yeast biotechnology and protein engineering.

Expression and characterization of scFv-6009FV in Pichia pastoris with improved ability to neutralize the neurotoxin Cn2 from Centruroides noxius

Small single-chain variable fragments (scFv) are promising biomolecules to inhibit and neutralize toxins and to act as antivenoms. In this work, we aimed to produce a functional scFv-6009FV in the yeast Pichia pastoris, which inhibits the pure Cn2 neurotoxin and the whole venom of Centruroides noxius. We were able to achieve yields of up to 31.6 ± 2 mg/L in flasks. Furthermore, the protein showed a structure of 6.1 % α-helix, 49.1 % β-sheet, and 44.8 % of random coil by CD. Mass spectrometry confirmed the amino acid sequence and showed no glycosylation profile for this molecule. Purified scFv-6009FV allowed us to develop anti-scFvs in rabbits, which were then used in affinity columns to purify other scFvs. Determination of its half-maximal inhibitory concentration value (IC50) was 40 % better than the scFvs produced by E. coli as a control. Finally, we found that scFv-6009FV was able to inhibit ex vivo the pure Cn2 toxin and the whole venom from C. noxius in murine rescue experiments. These results demonstrated that under the conditions assayed here, P. pastoris is suited to produce scFv-6009FV that, compared to scFvs produced by E. coli, maintains the characteristics of an antibody and neutralizes the Cn2 toxin more effectively.

Recombinant monoclonal antibody production in yeasts: Challenges and considerations

Monoclonal antibodies (mAbs) are laboratory-based engineered protein molecules with a monovalent affinity or multivalent avidity towards a specific target or antigen, which can mimic natural antibodies that are produced in the human immune systems to fight against detrimental pathogens. The recombinant mAb is one of the most effective classes of biopharmaceuticals produced in vitro by cloning and expressing synthetic antibody genes in a suitable host. Yeast is one of the potential hosts among others for the successful production of recombinant mAbs. However, there are very few yeast-derived mAbs that got the approval of the regulatory agencies for direct use for treatment purposes. Certain challenges encountered by yeasts for recombinant antibody productions need to be overcome and a few considerations related to antibody structure, host engineering, and culturing strategies should be followed for the improved production of mAbs in yeasts. In this review, the drawbacks related to the metabolic burden of the host, culturing conditions including induction mechanism and secretion efficiency, solubility and stability, downstream processing, and the pharmacokinetic behavior of the antibody are discussed, which will help in developing the yeast hosts for the efficient production of recombinant mAbs.

Engineering Saccharomyces cerevisiae for targeted hydrolysis and fermentation of glucuronoxylan through CRISPR/Cas9 genome editing

BACKGROUND

The abundance of glucuronoxylan (GX) in agricultural and forestry residual side streams positions it as a promising feedstock for microbial conversion into valuable compounds. By engineering strains of the widely employed cell factory Saccharomyces cerevisiae with the ability to directly hydrolyze and ferment GX polymers, we can avoid the need for harsh chemical pretreatments and costly enzymatic hydrolysis steps prior to fermentation. However, for an economically viable bioproduction process, the engineered strains must efficiently express and secrete enzymes that act in synergy to hydrolyze the targeted polymers.

RESULTS

The aim of this study was to equip the xylose-fermenting S. cerevisiae strain CEN.PK XXX with xylanolytic enzymes targeting beechwood GX. Using a targeted enzyme approach, we matched hydrolytic enzyme activities to the chemical features of the GX substrate and determined that besides endo-1,4-β-xylanase and β-xylosidase activities, α-methyl-glucuronidase activity was of great importance for GX hydrolysis and yeast growth. We also created a library of strains expressing different combinations of enzymes, and screened for yeast strains that could express and secrete the enzymes and metabolize the GX hydrolysis products efficiently. While strains engineered with BmXyn11A xylanase and XylA β-xylosidase could grow relatively well in beechwood GX, strains further engineered with Agu115 α-methyl-glucuronidase did not display an additional growth benefit, likely due to inefficient expression and secretion of this enzyme. Co-cultures of strains expressing complementary enzymes as well as external enzyme supplementation boosted yeast growth and ethanol fermentation of GX, and ethanol titers reached a maximum of 1.33 g L- 1 after 48 h under oxygen limited condition in bioreactor fermentations.

CONCLUSION

This work underscored the importance of identifying an optimal enzyme combination for successful engineering of S. cerevisiae strains that can hydrolyze and assimilate GX. The enzymes must exhibit high and balanced activities, be compatible with the yeast's expression and secretion system, and the nature of the hydrolysis products must be such that they can be taken up and metabolized by the yeast. The engineered strains, particularly when co-cultivated, display robust growth and fermentation of GX, and represent a significant step forward towards a sustainable and cost-effective bioprocessing of GX-rich biomass. They also provide valuable insights for future strain and process development targets.

Codon-tRNA Coadaptation Bias for Identifying Strong Native Promoters in Komagataella phaffii

Promoters are crucial elements for engineering microbial production strains used in bioprocesses. For the increasingly popular chassis Komagataella phaffii (formerly Pichia pastoris), a limited number of well-characterized promoters constrain the data-driven engineering of production strains. Here, we present an in silico approach for condition-independent de novo identification of strong native promoters. The method relies on tRNA-codon coadaptation of coding sequences in the K. phaffii genome and is based on two complementary scores: the number of effective codons and the tRNA adaptation index. Genes with high codon bias are expected to be translated efficiently and, thus, also be under control of strong promoters. Using this approach, we identified promising strong promoter candidates and experimentally assessed their activity using fluorescent reporter assays characterizing 50 promoters spanning a 76-fold difference in expression levels in a glucose medium. Overall, we report several promoters that should be added to the molecular toolbox for engineering of K. phaffii and present an approach for identifying promoters in microbial genomes.

Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains

Foreign proteins are produced by introducing synthetic constructs into host bacteria for biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result in load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, and the synthetic system may suffer from suboptimal function. To help in the detection of bacterial load stress, we developed machine-learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered Escherichia coli strains.

Improving therapeutic protein secretion in the probiotic yeast Saccharomyces boulardii using a multifactorial engineering approach

The probiotic yeast Saccharomyces boulardii (Sb) is a promising chassis to deliver therapeutic proteins to the gut due to Sb's innate therapeutic properties, resistance to phage and antibiotics, and high protein secretion capacity. To maintain therapeutic efficacy in the context of challenges such as washout, low rates of diffusion, weak target binding, and/or high rates of proteolysis, it is desirable to engineer Sb strains with enhanced levels of protein secretion. In this work, we explored genetic modifications in both cis- (i.e. to the expression cassette of the secreted protein) and trans- (i.e. to the Sb genome) that enhance Sb's ability to secrete proteins, taking a Clostridioides difficile Toxin A neutralizing peptide (NPA) as our model therapeutic. First, by modulating the copy number of the NPA expression cassette, we found NPA concentrations in the supernatant could be varied by sixfold (76-458 mg/L) in microbioreactor fermentations. In the context of high NPA copy number, we found a previously-developed collection of native and synthetic secretion signals could further tune NPA secretion between 121 and 463 mg/L. Then, guided by prior knowledge of S. cerevisiae's secretion mechanisms, we generated a library of homozygous single gene deletion strains, the most productive of which achieved 2297 mg/L secretory production of NPA. We then expanded on this library by performing combinatorial gene deletions, supplemented by proteomics experiments. We ultimately constructed a quadruple protease-deficient Sb strain that produces 5045 mg/L secretory NPA, an improvement of > tenfold over wild-type Sb. Overall, this work systematically explores a broad collection of engineering strategies to improve protein secretion in Sb and highlights the ability of proteomics to highlight under-explored mediators of this process. In doing so, we created a set of probiotic strains that are capable of delivering a wide range of protein titers and therefore furthers the ability of Sb to deliver therapeutics to the gut and other settings to which it is adapted.

A roadmap to establish a comprehensive platform for sustainable manufacturing of natural products in yeast

Secondary natural products (NPs) are a rich source for drug discovery. However, the low abundance of NPs makes their extraction from nature inefficient, while chemical synthesis is challenging and unsustainable. Saccharomyces cerevisiae and Pichia pastoris are excellent manufacturing systems for the production of NPs. This Perspective discusses a comprehensive platform for sustainable production of NPs in the two yeasts through system-associated optimization at four levels: genetics, temporal controllers, productivity screening, and scalability. Additionally, it is pointed out critical metabolic building blocks in NP bioengineering can be identified through connecting multilevel data of the optimized system using deep learning.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Heterologous production of Cannabis sativa-derived specialised metabolites of medicinal significance - Insights into engineering strategies

Cannabis sativa L. has been known for at least 2000 years as a source of important, medically significant specialised metabolites and several bio-active molecules have been enriched from multiple chemotypes. However, due to the many levels of complexity in both the commercial cultivation of cannabis and extraction of its specialised metabolites, several heterologous production approaches are being pursued in parallel. In this review, we outline the recent achievements in engineering strategies used for heterologous production of cannabinoids, terpenes and flavonoids along with their strength and weakness. We provide an overview of the specialised metabolism pathway in C. sativa and a comprehensive list of the specialised metabolites produced along with their medicinal significance. We highlight cannabinoid-like molecules produced by other species. We discuss the key biosynthetic enzymes and their heterologous production using various hosts such as microbial and eukaryotic systems. A brief discussion on complementary production strategies using co-culturing and cell-free systems is described. Various approaches to optimise specialised metabolite production through co-expression, enzyme engineering and pathway engineering are discussed. We derive insights from recent advances in metabolic engineering of hosts with improved precursor supply and suggest their application for the production of C. sativa speciality metabolites. We present a collation of non-conventional hosts with speciality traits that can improve the feasibility of commercial heterologous production of cannabis-based specialised metabolites. We provide a perspective of emerging research in synthetic biology, allied analytical techniques and plant heterologous platforms as focus areas for heterologous production of cannabis specialised metabolites in the future.

Combining metabolite doping and metabolic engineering to improve 2-phenylethanol production by engineered cyanobacteria

2-Phenylethanol (2-PE) is a rose-scented aromatic compound, with broad application in cosmetic, pharmaceutical, food and beverage industries. Many plants naturally synthesize 2-PE via Shikimate Pathway, but its extraction is expensive and low-yielding. Consequently, most 2-PE derives from chemical synthesis, which employs petroleum as feedstock and generates unwanted by products and health issues. The need for "green" processes and the increasing public demand for natural products are pushing biotechnological production systems as promising alternatives. So far, several microorganisms have been investigated and engineered for 2-PE biosynthesis, but a few studies have focused on autotrophic microorganisms. Among them, the prokaryotic cyanobacteria can represent ideal microbial factories thanks to their ability to photosynthetically convert CO2 into valuable compounds, their minimal nutritional requirements, high photosynthetic rate and the availability of genetic and bioinformatics tools. An engineered strain of Synechococcus elongatus PCC 7942 for 2-PE production, i.e., p120, was previously published elsewhere. The strain p120 expresses four heterologous genes for the complete 2-PE synthesis pathway. Here, we developed a combined approach of metabolite doping and metabolic engineering to improve the 2-PE production kinetics of the Synechococcus elongatus PCC 7942 p120 strain. Firstly, the growth and 2-PE productivity performances of the p120 recombinant strain were analyzed to highlight potential metabolic constraints. By implementing a BG11 medium doped with L-phenylalanine, we covered the metabolic burden to which the p120 strain is strongly subjected, when the 2-PE pathway expression is induced. Additionally, we further boosted the carbon flow into the Shikimate Pathway by overexpressing the native Shikimate Kinase in the Synechococcus elongatus PCC 7942 p120 strain (i.e., 2PE_aroK). The combination of these different approaches led to a 2-PE yield of 300 mg/gDW and a maximum 2-PE titer of 285 mg/L, 2.4-fold higher than that reported in literature for the p120 recombinant strain and, to our knowledge, the highest recorded for photosynthetic microorganisms, in photoautotrophic growth condition. Finally, this work provides the basis for further optimization of the process aimed at increasing 2-PE productivity and concentration, and could offer new insights about the use of cyanobacteria as appealing microbial cell factories for the synthesis of aromatic compounds.

Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids

Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.

Yeast Secretes High Amounts of Human Calreticulin without Cellular Stress

The ER chaperone calreticulin (CALR) also has extracellular functions and can exit the mammalian cell in response to various factors, although the mechanism by which this takes place is unknown. The yeast Saccharomyces cerevisiae efficiently secretes human CALR, and the analysis of this process in yeast could help to clarify how it gets out of eukaryotic cells. We have achieved a secretion titer of about 140 mg/L CALR in our S. cerevisiae system. Here, we present a comparative quantitative whole proteome study in CALR-secreting yeast using non-equilibrium pH gradient electrophoresis (NEPHGE)-based two-dimensional gel electrophoresis (2DE) as well as liquid chromatography mass spectrometry in data-independent analysis mode (LC-MSE). A reconstructed carrier ampholyte (CA) composition of NEPHGE-based first-dimension separation for 2DE could be used instead of formerly commercially available gels. Using LC-MSE, we identified 1574 proteins, 20 of which exhibited differential expression. The largest group of differentially expressed proteins were structural ribosomal proteins involved in translation. Interestingly, we did not find any signs of cellular stress which is usually observed in recombinant protein-producing yeast, and we did not identify any secretory pathway proteins that exhibited changes in expression. Taken together, high-level secretion of human recombinant CALR protein in S. cerevisiae does not induce cellular stress and does not burden the cellular secretory machinery. There are only small changes in the cellular proteome of yeast secreting CALR at a high level.

Advances in Cell Engineering of the Komagataella phaffii Platform for Recombinant Protein Production

Komagataella phaffii (formerly known as Pichia pastoris) has become an increasingly important microorganism for recombinant protein production. This yeast species has gained high interest in an industrial setting for the production of a wide range of proteins, including enzymes and biopharmaceuticals. During the last decades, relevant bioprocess progress has been achieved in order to increase recombinant protein productivity and to reduce production costs. More recently, the improvement of cell features and performance has also been considered for this aim, and promising strategies with a direct and substantial impact on protein productivity have been reported. In this review, cell engineering approaches including metabolic engineering and energy supply, transcription factor modulation, and manipulation of routes involved in folding and secretion of recombinant protein are discussed. A lack of studies performed at the higher-scale bioreactor involving optimisation of cultivation parameters is also evidenced, which highlights new research aims to be considered.