Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida.

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

Advances in Engineering Nucleotide Sugar Metabolism for Natural Product Glycosylation in Saccharomyces cerevisiae.

Glycosylation is a ubiquitous modification present across all of biology, affecting many things such as physicochemical properties, cellular recognition, subcellular localization, and immunogenicity. Nucleotide sugars are important precursors needed to study glycosylation and produce glycosylated products. Saccharomyces cerevisiae is a potentially powerful platform for producing glycosylated biomolecules, but it lacks nucleotide sugar diversity. Nucleotide sugar metabolism is complex, and understanding how to engineer it will be necessary to both access and study heterologous glycosylations found across biology. This review overviews the potential challenges with engineering nucleotide sugar metabolism in yeast from the salvage pathways that convert free sugars to their associated UDP-sugars to de novo synthesis where nucleotide sugars are interconverted through a complex metabolic network with governing feedback mechanisms. Finally, recent examples of engineering complex glycosylation of small molecules in S. cerevisiae are explored and assessed.

Complete biosynthesis of QS-21 in engineered yeast.

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans1,2. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis3,4. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host's native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families-a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases-from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure-activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era.

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification, biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

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.

Elucidation of genes enhancing natural product biosynthesis through co-evolution analysis.

Streptomyces has the largest repertoire of natural product biosynthetic gene clusters (BGCs), yet developing a universal engineering strategy for each Streptomyces species is challenging. Given that some Streptomyces species have larger BGC repertoires than others, we proposed that a set of genes co-evolved with BGCs to support biosynthetic proficiency must exist in those strains, and that their identification may provide universal strategies to improve the productivity of other strains. We show here that genes co-evolved with natural product BGCs in Streptomyces can be identified by phylogenomics analysis. Among the 597 genes that co-evolved with polyketide BGCs, 11 genes in the 'coenzyme' category have been examined, including a gene cluster encoding for the cofactor pyrroloquinoline quinone. When the pqq gene cluster was engineered into 11 Streptomyces strains, it enhanced production of 16,385 metabolites, including 36 known natural products with up to 40-fold improvement and several activated silent gene clusters. This study provides an innovative engineering strategy for improving polyketide production and finding previously unidentified BGCs.

Engineered Saccharomyces cerevisiae as a Biosynthetic Platform of Nucleotide Sugars.

Glycosylation of biomolecules can greatly alter their physicochemical properties, cellular recognition, subcellular localization, and immunogenicity. Glycosylation reactions rely on the stepwise addition of sugars using nucleotide diphosphate (NDP)-sugars. Making these substrates readily available will greatly accelerate the characterization of new glycosylation reactions, elucidation of their underlying regulation mechanisms, and production of glycosylated molecules. In this work, we engineered Saccharomyces cerevisiae to heterologously express nucleotide sugar synthases to access a wide variety of uridine diphosphate (UDP)-sugars from simple starting materials (i.e., glucose and galactose). Specifically, activated glucose, uridine diphosphate d-glucose (UDP-d-Glc), can be converted to UDP-d-glucuronic acid (UDP-d-GlcA), UDP-d-xylose (UDP-d-Xyl), UDP-d-apiose (UDP-d-Api), UDP-d-fucose (UDP-d-Fuc), UDP-l-rhamnose (UDP-l-Rha), UDP-l-arabinopyranose (UDP-l-Arap), and UDP-l-arabinofuranose (UDP-l-Araf) using the corresponding nucleotide sugar synthases of plant and microbial origins. We also expressed genes encoding the salvage pathway to directly activate free sugars to achieve the biosynthesis of UDP-l-Arap and UDP-l-Araf. We observed strong inhibition of UDP-d-Glc 6-dehydrogenase (UGD) by the downstream product UDP-d-Xyl, which we circumvented using an induction system (Tet-On) to delay the production of UDP-d-Xyl to maintain the upstream UDP-sugar pool. Finally, we performed a time-course study using strains containing the biosynthetic pathways to produce five non-native UDP-sugars to elucidate their time-dependent interconversion and the role of UDP-d-Xyl in regulating UDP-sugar metabolism. These engineered yeast strains are a robust platform to (i) functionally characterize sugar synthases in vivo, (ii) biosynthesize a diverse selection of UDP-sugars, (iii) examine the regulation of intracellular UDP-sugar interconversions, and (iv) produce glycosylated secondary metabolites and proteins.

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.

Edible mycelium bioengineered for enhanced nutritional value and sensory appeal using a modular synthetic biology toolkit.

Filamentous fungi are critical in the transition to a more sustainable food system. While genetic modification of these organisms has promise for enhancing the nutritional value, sensory appeal, and scalability of fungal foods, genetic tools and demonstrated use cases for bioengineered food production by edible strains are lacking. Here, we develop a modular synthetic biology toolkit for Aspergillus oryzae, an edible fungus used in fermented foods, protein production, and meat alternatives. Our toolkit includes a CRISPR-Cas9 method for gene integration, neutral loci, and tunable promoters. We use these tools to elevate intracellular levels of the nutraceutical ergothioneine and the flavor-and color molecule heme in the edible biomass. The strain overproducing heme is red in color and is readily formulated into imitation meat patties with minimal processing. These findings highlight the promise of synthetic biology to enhance fungal foods and provide useful genetic tools for applications in food production and beyond.

Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing.

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

Foldy: An open-source web application for interactive protein structure analysis.

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy's interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy's interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

Complete biosynthesis of the potent vaccine adjuvant QS-21.

QS-21 is a potent vaccine adjuvant currently sourced by extraction from the Chilean soapbark tree. It is a key component of human vaccines for shingles, malaria, coronavirus disease 2019 and others under development. The structure of QS-21 consists of a glycosylated triterpene scaffold coupled to a complex glycosylated 18-carbon acyl chain that is critical for immunostimulant activity. We previously identified the early pathway steps needed to make the triterpene glycoside scaffold; however, the biosynthetic route to the acyl chain, which is needed for stimulation of T cell proliferation, was unknown. Here, we report the biogenic origin of the acyl chain, characterize the series of enzymes required for its synthesis and addition and reconstitute the entire 20-step pathway in tobacco, thereby demonstrating the production of QS-21 in a heterologous expression system. This advance opens up unprecedented opportunities for bioengineering of vaccine adjuvants, investigating structure-activity relationships and understanding the mechanisms by which these compounds promote the human immune response.

Expanding the structural diversity of terpenes by synthetic biology approaches.

Terpenoids display chemical and structural diversities as well as important biological activities. Despite their extreme variability, the range of these structures is limited by the scope of natural products that canonically derive from interconvertible five-carbon (C5) isoprene units. New approaches have recently been developed to expand their structural diversity. This review systematically explores the combinatorial biosynthesis of noncanonical building blocks via the coexpression of the canonical mevalonate (MVA) pathway and C-methyltransferases (C-MTs), or by using the lepidopteran mevalonate (LMVA) pathway. Unnatural terpenoids can be created from farnesyl diphosphate (FPP) analogs by chemobiological synthesis and terpene cyclopropanation by artificial metalloenzymes (ArMs). Advanced technologies to accelerate terpene biosynthesis are discussed. This review provides a valuable reference for increasing the diversity of valuable terpenoids and their derivatives, as well as for expanding their potential applications.

Development of Corynebacterium glutamicum as a monoterpene production platform.

Monoterpenes are commonly known for their role in the flavors and fragrances industry and are also gaining attention for other uses like insect repellant and as potential renewable fuels for aviation. Corynebacterium glutamicum, a Generally Recognized as Safe microbe, has been a choice organism in industry for the annual million ton-scale bioproduction of amino acids for more than 50 years; however, efforts to produce monoterpenes in C. glutamicum have remained relatively limited. In this study, we report a further expansion of the C. glutamicum biosynthetic repertoire through the development and optimization of a mevalonate-based monoterpene platform. In the course of our plasmid design iterations, we increased flux through the mevalonate-based bypass pathway, measuring isoprenol production as a proxy for monoterpene precursor abundance and demonstrating the highest reported titers in C. glutamicum to date at 1504.6 mg/L. Our designs also evaluated the effects of backbone, promoter, and GPP synthase homolog origin on monoterpene product titers. Monoterpene production was further improved by disrupting competing pathways for isoprenoid precursor supply and by implementing a biphasic production system to prevent volatilization. With this platform, we achieved 321.1 mg/L of geranoids, 723.6 mg/L of 1,8-cineole, and 227.8 mg/L of linalool. Furthermore, we determined that C. glutamicum first oxidizes geraniol through an aldehyde intermediate before it is asymmetrically reduced to citronellol. Additionally, we demonstrate that the aldehyde reductase, AdhC, possesses additional substrate promiscuity for acyclic monoterpene aldehydes.

A General and Convenient Peptide Self-Assembling Mechanism for Developing Supramolecular Versatile Nanomaterials Based on The Biosynthetic Hybrid Amyloid-Resilin Protein.

Self-assembling peptides are valuable building blocks to fabricate supramolecular biomaterials, which have broad applications from biomedicine to biotechnology. However, limited choices to induce different globular proteins into hydrogels hinder these designs. Here, an easy-to-implement and tunable self-assembling strategy, which employs Ure2 amyloidogenic peptide, are described to induce any target proteins to assemble into supramolecular hydrogels alone or in combination with notable compositional control. Furthermore, the collective effect of nanoscale interactions among amyloid nanofibrils and partially disordered elastomeric polypeptides are investigated. This led to many useful macroscopic material properties simultaneously emerging from one pure protein material, i.e. strong adhesion to any substrates under wet conditions, rapidly self--assembling into robust and porous hydrogels, adaptation to remodeling processes, strongly promoting cell adhesion, proliferation and differentiation. Moreover, he demonstrated this supramolecular material's robust performance in vitro and vivo for tissue engineering, cosmetic and hemostasis applications and exhibited superior performance compared to corresponding commercial counterparts. To the best of his knowledge, few pure protein-based materials could meet such seemingly mutually exclusive properties simultaneously. Such versatility renders this novel supramolecular nanomaterial as next-generation functional protein-based materials, and he demonstrated the sequence level modulation of structural order and disorder as an untapped principle to design new proteins.

One-pot selective biosynthesis of Tyrian purple in Escherichia coli.

Tyrian purple (6,6'-Dibromoindigo) is an ancient precious dye, which possesses remarkable properties as a biocompatible semiconductor material. Recently, biosynthesis has emerged as an alternative for the sustainable production of Tyrian purple from a natural substrate. However, the selectivity issue in enzymatic tryptophan (Trp) and bromotryptophan (6-Br-Trp) degradation was an obstacle for obtaining high-purity Tyrian purple in a single cell biosynthesis. In this study, we present a simplified one-pot process for the production of Tyrian purple from Trp in Escherichia coli (E. coli) using Trp 6-halogenase from Streptomyces toxytricini (SttH), tryptophanase from E. coli (TnaA) and a two-component indole oxygenase from Providencia Rettgeri GS-2 (GS-C and GS-D). To enhance the in vivo solubility and activity of SttH and flavin reductase (Fre) fusion enzyme (Fre-L3-SttH), a chaperone system of GroEL/GroES (pGro7) was introduced in addition to the implementation of a set of optimization strategies, including fine-tuning the expression vector, medium, concentration of bromide salt and inducer. To overcome the selectivity issue and achieve a higher conversion yield of Tyrian purple with minimal indigo formation, we applied the λpL/pR-cI857 thermoinducible system to temporally control the bifunctional fusion enzyme of TnaA and monooxygenase GS-C (TnaA-L3-GS-C). Through optimization of the fermentation process, we were able to achieve a Tyrian purple titer of 44.5 mg L-1 with minimal indigo byproduct from 500 µM Trp. To the best of our knowledge, this is the first report of the selective production of Tyrian purple in E. colivia a one-pot process.

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing.

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.

UniKP: a unified framework for the prediction of enzyme kinetic parameters.

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.