Combining enzyme and metabolic engineering for microbial supply of therapeutic phytochemicals

The history of pharmacology is deeply intertwined with plant-derived compounds, which continue to be crucial in drug development. However, their complex structures and limited availability in plants challenge drug discovery, optimization, development, and industrial production via chemical synthesis or natural extraction. This review delves into the integration of metabolic and enzyme engineering to leverage micro-organisms as platforms for the sustainable and reliable production of therapeutic phytochemicals. We argue that engineered microbes can serve a triple role in this paradigm: facilitating pathway discovery, acting as cell factories for scalable manufacturing, and functioning as platforms for chemical derivatization. Analyzing recent progress and outlining future directions, the review highlights microbial biotechnology's transformative potential in expanding plant-derived human therapeutics' discovery and supply chains.

Can biotechnology lead the way toward a sustainable pharmaceutical industry?

The impact-intensive and rapidly growing pharmaceutical industry must ensure its sustainability. This study reveals that environmental sustainability assessments have been conducted for only around 0.2% of pharmaceuticals, environmental impacts have significant variations among the assessed products, and different impact categories have not been consistently studied. Highly varied impacts require assessing more products to understand the industry's sustainability status. Reporting all impact categories will be crucial, especially when comparing production technologies. Biological production of (semi)synthetic pharmaceuticals could reduce their environmental costs, though the high impacts of biologically produced monoclonal antibodies should also be optimized. Considering the sustainability potential of biopharmaceuticals from economic, environmental, and social perspectives, collaboratively guiding their immense market growth would lead to the industry's sustainability transition.

Yeast Platforms for Production and Screening of Bioactive Derivatives of Rauwolscine

Monoterpene indole alkaloids (MIAs) make up a highly bioactive class of metabolites produced by a range of tropical and subtropical plants. The corynanthe-type MIAs are a stereochemically complex subclass with therapeutic potential against a large number of indications including cancer, psychotic disorders, and erectile dysfunction. Here, we report yeast-based cell factories capable of de novo production of corynanthe-type MIAs rauwolscine, yohimbine, tetrahydroalstonine, and corynanthine. From this, we demonstrate regioselective biosynthesis of 4 fluorinated derivatives of these compounds and de novo biosynthesis of 7-chlororauwolscine by coexpression of a halogenase with the biosynthetic pathway. Finally, we capitalize on the ability of these cell factories to produce derivatives of these bioactive scaffolds to establish a proof-of-principle drug discovery pipeline in which the corynanthe-type MIAs are screened for bioactivity on human drug targets, expressed in yeast. In doing so, we identify antagonistic and agonistic behavior against the human adrenergic G protein-coupled receptors ADRA2A and ADRA2B, and the serotonergic receptor 5HT4b, respectively. This study thus demonstrates a proto-drug discovery pipeline for bioactive plant-inspired small molecules based on one-pot biocatalysis of natural and new-to-nature corynanthe-type MIAs in yeast.

Genome-wide host-pathway interactions affecting cis-cis-muconic acid production in yeast

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

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.

Jensen M. teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

Biosynthesis of natural and halogenated plant monoterpene indole alkaloids in yeast

Monoterpenoid indole alkaloids (MIAs) represent a large class of plant natural products with marketed pharmaceutical activities against a wide range of indications, including cancer, malaria and hypertension. Halogenated MIAs have shown improved pharmaceutical properties; however, synthesis of new-to-nature halogenated MIAs remains a challenge. Here we demonstrate a platform for de novo biosynthesis of two MIAs, serpentine and alstonine, in baker's yeast Saccharomyces cerevisiae and deploy it to systematically explore the biocatalytic potential of refactored MIA pathways for the production of halogenated MIAs. From this, we demonstrate conversion of individual haloindole derivatives to a total of 19 different new-to-nature haloserpentine and haloalstonine analogs. Furthermore, by process optimization and heterologous expression of a modified halogenase in the microbial MIA platform, we document de novo halogenation and biosynthesis of chloroalstonine. Together, this study highlights a microbial platform for enzymatic exploration and production of complex natural and new-to-nature MIAs with therapeutic potential.

HSP70-binding motifs function as protein quality control degrons

Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for proteasomal degradation, and thus protect cells against the accumulation of potentially toxic non-native proteins. Studies have shown that PQC degrons are hydrophobic and rarely contain negatively charged residues, features which are shared with chaperone-binding regions. Here we explore the notion that chaperone-binding regions may function as PQC degrons. When directly tested, we found that a canonical Hsp70-binding motif (the APPY peptide) functioned as a dose-dependent PQC degron both in yeast and in human cells. In yeast, Hsp70, Hsp110, Fes1, and the E3 Ubr1 target the APPY degron. Screening revealed that the sequence space within the chaperone-binding region of APPY that is compatible with degron function is vast. We find that the number of exposed Hsp70-binding sites in the yeast proteome correlates with a reduced protein abundance and half-life. Our results suggest that when protein folding fails, chaperone-binding sites may operate as PQC degrons, and that the sequence properties leading to PQC-linked degradation therefore overlap with those of chaperone binding.

Engineered cell differentiation and sexual reproduction in probiotic and mating yeasts

G protein-coupled receptors (GPCRs) enable cells to sense environmental cues and are indispensable for coordinating vital processes including quorum sensing, proliferation, and sexual reproduction. GPCRs comprise the largest class of cell surface receptors in eukaryotes, and for more than three decades the pheromone-induced mating pathway in baker's yeast Saccharomyces cerevisiae has served as a model for studying heterologous GPCRs (hGPCRs). Here we report transcriptome profiles following mating pathway activation in native and hGPCR-signaling yeast and use a model-guided approach to correlate gene expression to morphological changes. From this we demonstrate mating between haploid cells armed with hGPCRs and endogenous biosynthesis of their cognate ligands. Furthermore, we devise a ligand-free screening strategy for hGPCR compatibility with the yeast mating pathway and enable hGPCR-signaling in the probiotic yeast Saccharomyces boulardii. Combined, our findings enable new means to study mating, hGPCR-signaling, and cell-cell communication in a model eukaryote and yeast probiotics.

Metoprolol improves left ventricular longitudinal function at rest and during exercise in obstructive hypertrophic cardiomyopathy

Background

Hypertrophic cardiomyopathy (HCM) patients with outflow obstruction often experience symptoms of heart failure upon exertion. We aimed to characterize left ventricular (LV) systolic function by segmental and global longitudinal strain echocardiography, and to investigate changes in relation to exercise, and the effect of standard treatment with beta-blockers.

Method

Twenty-nine patients with obstructive HCM and New York Heart Association (NYHA) class ≥ II symptoms were enrolled in a double-blind, placebo-controlled, randomized crossover trial. Patients received metoprolol 150 mg or placebo for two consecutive two-week periods in random order. Echocardiographic assessment with speckle-tracking derived longitudinal strain (LS) was performed at rest and during peak exercise at the end of each treatment period. Segmental LS was calculated as the mean of the six apical, mid, and basal segments.

Results

During placebo treatment resting values of segmental LS showed an apical-basal difference of −10.3% (95% confidence interval (CI): −12.7 to −7.8; p<0.0001), with severely depressed values of the basal segment −9.3 (4.2) %. Treatment with metoprolol improved LS of the apical segment (−2.8%; 95% CI: −4.2 to −1.3; p<0.001) and mid segment (−1.1%; 95% CI: −2.0 to −0.3; p=0.007). The segmental improvement with metoprolol treatment was reflected in an increase in resting global LS (LV GLS) (−1.4%; 95% CI: −2.3 to −0.6; p<0.001). Peak exercise was associated with a reduction in LV GLS, yet the improvement in LV GLS during metoprolol was consistent upon exercise (−1.3%; 95% CI: −2.6 to −0.1; p=0.03).

Conclusion

Segmental LS of the LV in obstructive HCM showed an abnormal apical-basal difference. Exercise was associated with a reduction in LV GLS, illustrating an impaired myocardial functional reserve capacity. Metoprolol significantly improved LS of the apical and mid segments of the LV at rest and LV GLS during exercise.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Novo nordic foundation

Temporal trends, characteristics and comorbidities of patients with hypertrophic cardiomyopathy in Denmark from 2005 to 2018: a nationwide cohort study

Background

The majority of patients with Hypertrophic cardiomyopathy (HCM) are mildly symptomatic or unaware of their condition, but some develop serious complications such as heart failure, atrial fibrillation (AF) as well as sudden cardiac death. Previous studies have suggested that HCM detection-rates in women are significantly lower than in men. This leads to diagnosis often being delayed to later stages of the disease in women, where symptoms are more severe. Further characterization of HCM patients to improve early detection of adverse outcomes and warning signs might improve long-term outcomes.

Purpose

To describe the characteristics of Danish patients diagnosed with HCM between 2005–2018 and determine trends and changes in these factors over time.

Methods

All patients aged 16 years or older with a diagnosis of HCM between the 1st of January 2005 and the 31st of December 2018 were identified in Danish nationwide administrative registers and included in the study.

Time trends were calculated, and differences analyzed using the Cochran-Armitage trend test and linear regression.

Results

A total of 3856 patients were diagnosed with HCM in the study period and included in the study. The median age at diagnosis was 68 years (IQR 56 and 78 years), 53% were male, and 44% were diagnosed with obstructive HCM, while the number of patients diagnosed with HCM each year overall increased. At the time of diagnosis, 22,3% patients were previously diagnosed with ischemic heart disease, 17% with AF, 7,5% with ischemic stroke, 13,6% with heart failure. Median age and gender distribution remained stable over time, while the proportion of obstructive HCM among the newly diagnosed decreased (p = <0.001) (Figure1). During the study period, there was a significant decrease in the prevalence of heart failure (p = <0.001), ischemic heart disease (p = <0.001), and chronic obstructive pulmonary syndrome (p = <0.001), while the prevalence of AF, hypertension and ischemic stroke remained stable (Figure 2).

Conclusion

Despite previous studies describing a gender gap in patients diagnosed with HCM, gender distribution was near equal in this cohort. The number of patients diagnosed with non-obstructive HCM is increasing and the prevalence of comorbidities such as heart failure, ischemic heart disease, and COPD decreased over time.

Whether these findings are the result of improved early detection of HCM warrants further research and examination.

Funding Acknowledgement

Type of funding sources: Public hospital(s). Main funding source(s): Herlev-Gentofte Hospital, Denmark

Serotonin G Protein-Coupled Receptor-Based Biosensing Modalities in Yeast

Serotonin is a key neurotransmitter involved in numerous physiological processes and serves as an important precursor for manufacturing bioactive indoleamines and alkaloids used in the treatment of human pathologies. In humans, serotonin sensing and signaling can occur by 12 G protein-coupled receptors (GPCRs) coupled to Gα proteins. In yeast, human serotonin GPCRs coupled to Gα proteins have previously been shown to function as whole-cell biosensors of serotonin. However, systematic characterization of serotonin biosensing modalities between variant serotonin GPCRs and application thereof for high-resolution serotonin quantification is still awaiting. To systematically assess GPCR signaling in response to serotonin, we characterized reporter gene expression at two different pHs of a 144-sized library encoding all 12 human serotonin GPCRs in combination with 12 different Gα proteins engineered in yeast. From this screen, we observed changes in the biosensor sensitivities of >4 orders of magnitude. Furthermore, adopting optimal biosensing designs and pH conditions enabled high-resolution high-performance liquid chromatography-validated sensing of serotonin produced in yeast. Lastly, we used the yeast platform to characterize 19 serotonin GPCR polymorphisms found in human populations. While major differences in signaling were observed among the individual polymorphisms when studied in yeast, a cross-comparison of selected variants in mammalian cells showed both similar and disparate results. Taken together, our study highlights serotonin biosensing modalities of relevance to both biotechnological and potential human health applications.

Atrial fibrillation and stroke risk of patients with hypertrophic cardiomyopathy in denmark from 2005-2018: a nationwide cohort study

Funding Acknowledgements

Type of funding sources: Public hospital(s). Main funding source(s): Internal funding: Herlev-Gentofte Cardiovascular Research Department.

Background

Hypertrophic cardiomyopathy (HCM) can be associated with serious complications such as heart failure, atrial fibrillation (AF) and sudden cardiac death. The treatment of AF in HCM patients can be challenging since AF often aggravates symptoms. Previous studies have suggested that HCM patients with AF have an elevated risk of thromboembolism and stroke compared to AF patients without HCM regardless of their CHA2DS2VASc score.

Purpose

To determine the risk of AF and stroke in HCM patients.

Methods

Through the Danish National Registers all patients aged 16 or older diagnosed with HCM between the 1st of January 2005, and the 31st of December 2018 were included in the analysis. The association between HCM, AF, and stroke was investigated using multivariable Cox proportional-hazard analysis adjusted for gender, age, atrial fibrillation, ischemic heart disease, chronic obstructive pulmonary disease, chronic kidney disease and hypertension. Cumulative incidence of AF and stroke was calculated using the Aalen-Johansen estimator, taking death as a competing risk into account.

Results

A total of 3856 patients were included, 2060 (53,4%) were male and median age of 67,8 (IQR 56 and 77,8) years. During the study period, 384 (10%) patients were diagnosed with AF. The risk of AF was significantly lower in males (HR 0,72 (0,59–0,90), p-value = 0.003) and for patients below 60 years (HR 0,18 (0,12–0,27), p-value = <0.001). (Figure 1)

157 (4,1%) of the HCM patients developed stroke. The risk of developing stroke was significantly decreased for patients aged under 60 (HR 0,32 (0,2–0,52), p-value = <0.001). There was no increased risk of stroke comparing genders.

Stroke risk was further analyzed in patients with known AF at time of inclusion, 656 (17%) in total. Compared to patients without AF and adjusted for age, gender and co-morbidities, there was no significant difference in stroke risk between these groups (HR 1.2 (0,81-1,76), p-value = 0.36). (Figure 2)

Conclusion

AF and stroke are common complications in HCM. Women are more susceptible to developing AF than men, and age over 60 at the time of diagnosis was associated with significantly higher risk of AF and stroke. HCM patients with previously known AF did not have a significantly elevated risk of stroke. More research will be needed to further explore these connections.

RNA-mediated in vivo Directed Evolution in Yeast

Directed evolution is a powerful approach to obtain genetically-encoded sought-for traits. Compared to the prolonged adaptation regimes to mutations occurring under natural selection, directed evolution unlocks rapid screening and selection of mutants with improved traits from vast mutated sequence spaces. Many systems have been developed to search variant landscapes based on ex vivo or in vivo mutagenesis, to identify and select new-to-nature and optimized properties in biomolecules. Yet, the majority of such systems rely on tedious iterations of library preparation, propagation, and selection steps. Furthermore, among the relatively few in vivo directed evolution systems developed to mitigate handling of repetitive ex vivo steps, directed evolution of DNA is the standard approach. Here, we present the protocol for designing the transfer of genetic information from evolving RNA donors to DNA in baker's yeast, using CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE). We use mutant T7 RNA polymerase to introduce mutations in RNA donors, while incorporation into DNA is directed by CRISPR/Cas9. As such, CRAIDE offers an opportunity to study fundamental questions, such as RNA's contribution to the evolution of DNA-based life on Earth. Graphic abstract: CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE).

CasPER: A CRISPR/Cas9-Based Method for Directed Evolution in Genomic Loci in Saccharomyces cerevisiae

Here, in this chapter, we describe a detailed protocol for the method named Cas9-mediated protein evolution reaction or short CasPER. CasPER is based on the generation of large 300-600-bp mutagenized linear DNA fragments by error-prone PCR which are used as a donor for repair of double-strand break mediated by Cas9 and subsequently integrated to the genome. This method can be efficiently used for directed evolution of desired essential or nonessential genes in the genome and most importantly can be multiplexed. Altogether, the described method allows for heterogeneous DNA integration with successful transformation efficiencies of 98-100% for both single and multiplex targeting.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Clinical effects of metoprolol in obstructive hypertrophic cardiomyopathy (TEMPO). A randomized, double-blinded, placebo-controlled crossover trial

Background

Treatment with beta blockers (BB) has been used for symptomatic relief in patients with obstructive hypertrophic cardiomyopathy (HCM) for decades. Even so, the guideline recommendation for the use of BB rests on expert opinions and observational cohort studies. Providing comprehensive high-quality data on the effects of BB in obstructive HCM is essential, especially in the context of newly developed pharmacological treatment strategies specifically targeting this disease (1).

Purpose

The study aimed to investigate the effects of metoprolol on left ventricular outflow tract (LVOT) obstruction, symptoms, and exercise capacity in patients with obstructive HCM.

Methods

This double-blinded, placebo-controlled, randomized crossover trial enrolled 30 patients with obstructive HCM and New York Heart Association (NYHA) class ≥ II symptoms from 1 May 2018 to 1 September 2020. Patients received metoprolol or placebo for two consecutive two-week periods in random order. The effect parameters were LVOT gradients, NYHA class, Canadian Cardiovascular Society (CCS) grading angina class, Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS), and cardiopulmonary exercise testing.

Results

Compared with placebo, the LVOT gradient during metoprolol was lower at rest (25 [15–58] mmHg versus 72 [28–87] mmHg; p=0.007), at peak exercise (28 [18–40] mmHg versus 62 [31–113] mmHg; p<0.001), and post-exercise (45 [24–100] mmHg versus 115 [55–171] mmHg; p<0.0001) (figure 1). During metoprolol treatment, 14% of patients were in NYHA class III compared with 38% on placebo (p<0.01). Likewise, no patients were in CCS class ≥ III during metoprolol compared with 10% during placebo (p<0.01). These findings were confirmed by a higher KCCQ-OSS score during metoprolol (76.2 (16.2) versus 73.8 (19.5), p=0.039) (figure 2). Peak oxygen consumption did not differ between study arms.

Conclusion

Compared with placebo, metoprolol reduced LVOT obstruction at rest and during exercise, provided symptom relief, and improved quality of life in patients with obstructive HCM. However, exercise capacity remained unchanged. Findings from the present study support the guideline recommendations that BB should be the first drug of choice in patients with obstructive HCM who develop symptoms.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Novo nordic foundation, Skibsreder Per Henriksen, R. og hustrus Foundation

Metabolic engineering for plant natural products biosynthesis: new procedures, concrete achievements and remaining limits

Microorganisms and plants represent major sources of natural compounds with a plethora of bioactive properties. Among these, plant natural products (PNPs) remain indispensable to human health. With few exceptions, PNP-based pharmaceuticals come from plant specialized metabolisms and display a structure far too complex for a profitable production by total chemical synthesis. Accordingly, their industrial processes of supply are still mostly based on the extraction of final products or precursors directly from plant materials. This implies that particular contexts (e.g. pandemics, climate changes) and natural resource overexploitation are main drivers for the high production cost and recurrent supply shortages. Recently, biotechnological manufacturing alternatives gave rise to a multitude of benchmark studies implementing the production of important PNPs in various heterologous hosts. Here, we spotlight unprecedented advancements in the field of metabolic engineering dedicated to the heterologous production of a prominent series of PNPs that were achieved during the year 2020. We also discuss how the knowledge accumulated in recent years could pave the way for a broader manufacturing palette of natural products from a wide range of natural resources.

Engineering G protein-coupled receptor signalling in yeast for biotechnological and medical purposes

G protein-coupled receptors (GPCRs) comprise the largest class of membrane proteins in the human genome, with a common denominator of seven-transmembrane domains largely conserved among eukaryotes. Yeast is naturally armoured with three different GPCRs for pheromone and sugar sensing, with the pheromone pathway being extensively hijacked for characterising heterologous GPCR signalling in a model eukaryote. This review focusses on functional GPCR studies performed in yeast and on the elucidated hotspots for engineering, and discusses both endogenous and heterologous GPCR signalling. Key emphasis will be devoted to studies describing important engineering parameters to consider for successful coupling of GPCRs to the yeast mating pathway. We also review the various means of applying yeast for studying GPCRs, including the use of yeast armed with heterologous GPCRs as a platform for (i) deorphanisation of orphan receptors, (ii) metabolic engineering of yeast for production of bioactive products and (iii) medical applications related to pathogen detection and drug discovery. Finally, this review summarises the current challenges related to expression of functional membrane-bound GPCRs in yeast and discusses the opportunities to continue capitalising on yeast as a model chassis for functional GPCR signalling studies.

A Reporter System for Cytosolic Protein Aggregates in Yeast

Protein misfolding and aggregation are linked to neurodegenerative diseases of mammals and suboptimal protein expression within biotechnology. Tools for monitoring protein aggregates are therefore useful for studying disease-related aggregation and for improving soluble protein expression in heterologous hosts for biotechnology purposes. In this work, we developed a promoter-reporter system for aggregated protein on the basis of the yeast native response to misfolded protein. To this end, we first studied the proteome of yeast in response to the expression of folded soluble and aggregation-prone protein baits and identified genes encoding proteins related to protein folding and the response to heat stress as well as the ubiquitin-proteasome system that are over-represented in cells expressing an aggregation-prone protein. From these data, we created and validated promoter-reporter constructs and further engineered the best performing promoters by increasing the copy number of upstream activating sequences and optimization of culture conditions. Our best promoter-reporter has an output dynamic range of approximately 12-fold upon expression of the aggregation-prone protein and responded to increasing levels of aggregated protein. Finally, we demonstrate that the system can discriminate between yeast cells expressing different prion precursor proteins and select the cells expressing folded soluble protein from mixed populations. Our reporter system is thus a simple tool for diagnosing protein aggregates in living cells and should be applicable for the health and biotechnology industries.

Dietary Change Enables Robust Growth-Coupling of Heterologous Methyltransferase Activity in Yeast

Genetic modifications of living organisms and proteins are made possible by a catalogue of molecular and synthetic biology tools, yet proper screening assays for genetic variants of interest continue to lag behind. Synthetic growth-coupling (GC) of enzyme activities offers a simple, inexpensive way to track such improvements. In this follow-up study we present the optimization of a recently established GC design for screening of heterologous methyltransferases (MTases) and related pathways in the yeast Saccharomyces cerevisiae. Specifically, upon testing different media compositions and genetic backgrounds, improved GC of different heterologous MTase activities is obtained. Furthermore, we demonstrate the strength of the system by screening a library of catechol O-MTase variants converting protocatechuic acid into vanillic acid. We demonstrated high correlation (R² = 0.775) between vanillic acid and cell density as a proxy for MTase activity. We envision that the improved MTase GC can aid evolution-guided optimization of biobased production processes for methylated compounds with yeast in the future.

Deploying Microbial Synthesis for Halogenating and Diversifying Medicinal Alkaloid Scaffolds

Plants produce some of the most potent therapeutics and have been used for thousands of years to treat human diseases. Today, many medicinal natural products are still extracted from source plants at scale as their complexity precludes total synthesis from bulk chemicals. However, extraction from plants can be an unreliable and low-yielding source for human therapeutics, making the supply chain for some of these life-saving medicines expensive and unstable. There has therefore been significant interest in refactoring these plant pathways in genetically tractable microbes, which grow more reliably and where the plant pathways can be more easily engineered to improve the titer, rate and yield of medicinal natural products. In addition, refactoring plant biosynthetic pathways in microbes also offers the possibility to explore new-to-nature chemistry more systematically, and thereby help expand the chemical space that can be probed for drugs as well as enable the study of pharmacological properties of such new-to-nature chemistry. This perspective will review the recent progress toward heterologous production of plant medicinal alkaloids in microbial systems. In particular, we focus on the refactoring of halogenated alkaloids in yeast, which has created an unprecedented opportunity for biosynthesis of previously inaccessible new-to-nature variants of the natural alkaloid scaffolds.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Improvement of cis,cis-Muconic Acid Production in Saccharomyces cerevisiae through Biosensor-Aided Genome Engineering

Muconic acid is a potential platform chemical for the production of nylon, polyurethanes, and terephthalic acid. It is also an attractive functional copolymer in plastics due to its two double bonds. At this time, no economically viable process for the production of muconic acid exists. To harness novel genetic targets for improved production of cis,cis-muconic acid (CCM) in the yeast Saccharomyces cerevisiae, we employed a CCM-biosensor coupled to GFP expression with a broad dynamic response to screen UV-mutagenesis libraries of CCM-producing yeast. Via fluorescence activated cell sorting we identified a clone Mut131 with a 49.7% higher CCM titer and 164% higher titer of biosynthetic intermediate-protocatechuic acid (PCA). Genome resequencing of the Mut131 and reverse engineering identified seven causal missense mutations of the native genes (PWP2, EST2, ATG1, DIT1, CDC15, CTS2, and MNE1) and a duplication of two CCM biosynthetic genes, encoding dehydroshikimate dehydratase and catechol 1,2-dioxygenase, which were not recognized as flux controlling before. The Mut131 strain was further rationally engineered by overexpression of the genes encoding for PCA decarboxylase and AROM protein without shikimate dehydrogenase domain (Aro1p^ΔE), and by restoring URA3 prototrophy. The resulting engineered strain produced 20.8 g/L CCM in controlled fed-batch fermentation, with a yield of 66.2 mg/g glucose and a productivity of 139 mg/L/h, representing the highest reported performance metrics in a yeast for de novo CCM production to date and the highest production of an aromatic compound in yeast. The study illustrates the benefit of biosensor-based selection and brings closer the prospect of biobased muconic acid.

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.