The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes

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.

Streptomyces sungeiensis SD3 as a Microbial Chassis for the Heterologous Production of Secondary Metabolites

We present the newly isolated Streptomyces sungeiensis SD3 strain as a promising microbial chassis for heterologous production of secondary metabolites. S. sungeiensis SD3 exhibits several advantageous traits as a microbial chassis, including genetic tractability, rapid growth, susceptibility to antibiotics, and metabolic capability supporting secondary metabolism. Genomic and transcriptomic sequencing unveiled the primary metabolic capabilities and secondary biosynthetic pathways of S. sungeiensis SD3, including a previously unknown pathway responsible for the biosynthesis of streptazone B1. The unique placement of S. sungeiensis SD3 in the phylogenetic tree designates it as a type strain, setting it apart from other frequently employed Streptomyces chassis. This distinction makes it the preferred chassis for expressing biosynthetic gene clusters (BGCs) derived from strains within the same phylogenetic or neighboring phylogenetic clade. The successful expression of secondary biosynthetic pathways from a closely related yet slow-growing strain underscores the utility of S. sungeiensis SD3 as a heterologous expression chassis. Validation of CRISPR/Cas9-assisted genetic tools for chromosomal deletion and insertion paved the way for further strain improvement and BGC refactoring through rational genome editing. The addition of S. sungeiensis SD3 to the heterologous chassis toolkit will facilitate the discovery and production of secondary metabolites.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Mining natural products for advanced biofuels and sustainable bioproducts

Recently, there has been growing interest in the sustainable production of biofuels and bioproducts derived from renewable sources. Natural products, the largest and more structurally diverse group of metabolites, hold significant promise as sources for such bio-based products. However, there are two primary challenges in harnessing natural products' potential: precise mining of biosynthetic gene clusters (BGCs) that can be used as scaffolds or bioparts and their functional expression for biofuel and bioproduct manufacture. In this review, we explore recent advances in the development of bioinformatic tools for BGC mining and the manipulation of various hosts for natural product-based biofuels and bioproducts manufacture. Moreover, we discuss potential strategies for expanding the chemical diversity of biofuels and bioproducts and enhancing their overall yield.

Systems Analysis of Highly Multiplexed CRISPR-Base Editing in Streptomycetes

CRISPR tools, especially Cas9n-sgRNA guided cytidine deaminase base editors such as CRISPR-BEST, have dramatically simplified genetic manipulation of streptomycetes. One major advantage of CRISPR base editing technology is the possibility to multiplex experiments in genomically instable species. Here, we demonstrate scaled up Csy4 based multiplexed genome editing using CRISPR-mcBEST in Streptomyces coelicolor. We evaluated the system by simultaneously targeting 9, 18, and finally all 28 predicted specialized metabolite biosynthetic gene clusters in a single experiment. We present important insights into the performance of Csy4 based multiplexed genome editing at different scales. Using multiomics analysis, we investigated the systems wide effects of such extensive editing experiments and revealed great potentials and important bottlenecks of CRISPR-mcBEST. The presented analysis provides crucial data and insights toward the development of multiplexed base editing as a novel paradigm for high throughput engineering of Streptomyces chassis and beyond.

Synthetic Biology Tools for Engineering Microbial Cells to Fight Superbugs

With the increase in clinical cases of bacterial infections with multiple antibiotic resistance, the world has entered a health crisis. Overuse, inappropriate prescribing, and lack of innovation of antibiotics have contributed to the surge of microorganisms that can overcome traditional antimicrobial treatments. In 2017, the World Health Organization published a list of pathogenic bacteria, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE). These bacteria can adapt to multiple antibiotics and transfer their resistance to other organisms; therefore, studies to find new therapeutic strategies are needed. One of these strategies is synthetic biology geared toward developing new antimicrobial therapies. Synthetic biology is founded on a solid and well-established theoretical framework that provides tools for conceptualizing, designing, and constructing synthetic biological systems. Recent developments in synthetic biology provide tools for engineering synthetic control systems in microbial cells. Applying protein engineering, DNA synthesis, and in silico design allows building metabolic pathways and biological circuits to control cellular behavior. Thus, synthetic biology advances have permitted the construction of communication systems between microorganisms where exogenous molecules can control specific population behaviors, induce intracellular signaling, and establish co-dependent networks of microorganisms.

Regulatory Part Engineering for High-Yield Protein Synthesis in an All-Streptomyces-Based Cell-Free Expression System

Streptomyces-based cell-free expression systems have been developed to meet the demand for synthetic biology applications. However, protein yields from the previous Streptomyces systems are relatively low, and there is a serious limitation of available genetic tools such as plasmids for gene (co)expression. Here, we sought to expand the plasmid toolkit with a focus on the enhancement of protein production. By screening native promoters and ribosome binding sites, we were able to construct a panel of plasmids with different abilities for protein synthesis, which covered a nearly 3-fold range of protein yields. Using the most efficient plasmid, the protein yield reached up to a maximum value of 515.7 ± 25.3 μg/mL. With the plasmid toolkit, we anticipate that our Streptomyces cell-free system will offer great opportunities for cell-free synthetic biology applications such as in vitro biosynthesis of valuable natural products when cell-based systems remain difficult or not amenable.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Evolutionary Genome Mining for the Discovery and Engineering of Natural Product Biosynthesis

Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.

Accurate prediction of mutation-induced frequency shifts in chlorophyll proteins with a simple electrostatic model

Photosynthetic pigment-protein complexes control local chlorophyll (Chl) transition frequencies through a variety of electrostatic and steric forces. Site-directed mutations can modify this local spectroscopic tuning, providing critical insight into native photosynthetic functions and offering the tantalizing prospect of creating rationally designed Chl proteins with customized optical properties. Unfortunately, at present, no proven methods exist for reliably predicting mutation-induced frequency shifts in advance, limiting the method's utility for quantitative applications. Here, we address this challenge by constructing a series of point mutants in the water-soluble chlorophyll protein of Lepidium virginicum and using them to test the reliability of a simple computational protocol for mutation-induced site energy shifts. The protocol uses molecular dynamics to prepare mutant protein structures and the charge density coupling model of Adolphs et al. [Photosynth. Res. 95, 197-209 (2008)] for site energy prediction; a graphical interface that implements the protocol automatically is published online at http://nanohub.org/tools/pigmenthunter. With the exception of a single outlier (presumably due to unexpected structural changes), we find that the calculated frequency shifts match the experiment remarkably well, with an average error of 1.6 nm over a 9 nm spread in wavelengths. We anticipate that the accuracy of the method can be improved in the future with more advanced sampling of mutant protein structures.

A High-Yield Streptomyces Transcription-Translation Toolkit for Synthetic Biology and Natural Product Applications

Streptomyces spp. are a major source of clinical antibiotics and industrial chemicals. Streptomyces venezuelae ATCC 10712 is a fast-growing strain and a natural producer of chloramphenicol, jadomycin, and pikromycin, which makes it an attractive candidate as a next-generation synthetic biology chassis. Therefore, genetic tools that accelerate the development of S. venezuelae ATCC 10712, as well as other Streptomyces spp. models, are highly desirable for natural product engineering and discovery. To this end, a dedicated S. venezuelae ATCC 10712 cell-free system is provided in this protocol to enable high-yield heterologous expression of high G+C (%) genes. This protocol is suitable for small-scale (10-100 μL) batch reactions in either 96-well or 384-well plate format, while reactions are potentially scalable. The cell-free system is robust and can achieve high yields (~5-10 μM) for a range of recombinant proteins in a minimal setup. This work also incorporates a broad plasmid toolset for real-time measurement of mRNA and protein synthesis, as well as in-gel fluorescence staining of tagged proteins. This protocol can also be integrated with high-throughput gene expression characterization workflows or the study of enzyme pathways from high G+C (%) genes present in Actinomycetes genomes.

Microbial cell factories: a biotechnology journey across species

An increasingly large number of microbial species with potential for synthetic biology and metabolic engineering has been introduced over the last few years, adding huge variety to the opportunities of biotechnology. Historically, however, only a handful of microbes have attained the acceptance and widespread use that are needed to fulfil the needs of industrial bioproduction. Synthetic biology is setting out to standardise the methods, parts and platform organisms for bioproduction. These platform organisms, or chassis cells, derive from what has been termed microbial cell factories since the 1990s. In this collection of reviews, 18 microbial cell factories are featured, which belong to one of these three groups: (i) microbes already used before modern biotechnology was introduced; (ii) the first generation of engineered microbes; and (iii) promising new host organisms. The reviews are intended to provide readers with an overview of the current state of methodology and application of these cell factories, and with guidelines of how to use them for bioproduction.

Biosynthetic Potential of Streptomyces Rationalizes Genome-Based Bioprospecting

Streptomyces are the most prolific source of structurally diverse microbial natural products. Advancing genome-based analysis reveals the previously unseen potential of Streptomyces to produce numerous novel secondary metabolites, which allows us to take natural product discovery to the next phase. However, at present there is a huge disproportion between the rate of genome reports and discovery of new compounds. From this perspective of harnessing the enduring importance of Streptomyces, we discuss the recent genome-directed advancements inspired by hidden biosynthetic wealth that provide hope for future antibiotics.