Combinatorial metabolic engineering of Streptomyces sp. CB03234-S for the enhanced production of anthraquinone-fused enediyne tiancimycins

BACKGROUND

Anthraquinone-fused enediynes (AFEs) are excellent payloads for antibody-drug conjugates (ADCs). The yields of AFEs in the original bacterial hosts are extremely low. Multiple traditional methods had been adopted to enhance the production of the AFEs. Despite these efforts, the production titers of these compounds are still low, presenting a practical challenge for their development. Tiancimycins (TNMs) are a class of AFEs produced by Streptomyces sp. CB03234. One of their salient features is that they exhibit rapid and complete cell killing ability against various cancer cell lines.

RESULTS

In this study, a combinatorial metabolic engineering strategy guided by the CB03234-S genome and transcriptome was employed to improve the titers of TNMs. First, re-sequencing of CB03234-S (Ribosome engineered mutant strains) genome revealed the deletion of a 583-kb DNA fragment, accounting for about 7.5% of its genome. Second, by individual or combined inactivation of seven potential precursor competitive biosynthetic gene clusters (BGCs) in CB03234-S, a double-BGC inactivation mutant, S1009, was identified with an improved TNMs titer of 28.2 ± 0.8 mg/L. Third, overexpression of five essential biosynthetic genes, including two post-modification genes, and three self-resistance auxiliary genes, was also conducted, through which we discovered that mutants carrying the core genes, tnmE or tnmE10, exhibited enhanced TNMs production. The average TNMs yield reached 43.5 ± 2.4 mg/L in a 30-L fermenter, representing an approximately 360% increase over CB03234-S and the highest titer among all AFEs to date. Moreover, the resulting mutant produced TNM-W, a unique TNM derivative with a double bond instead of a common ethylene oxide moiety. Preliminary studies suggested that TNM-W was probably converted from TNM-A by both TnmE and TnmE10.

CONCLUSIONS

Based on the genome and transcriptome analyses, we adopted a combined metabolic engineering strategy for precursor enrichment and biosynthetic pathway reorganization to construct a high-yield strain of TNMs based on CB03234-S. Our study establishes a solid basis for the clinical development of AFE-based ADCs.

Transcriptional regulators of secondary metabolite biosynthesis in Streptomyces

In the post-genome era, great progress has been made in metabolic engineering using recombinant DNA technology to enhance the production of high-value products by Streptomyces. With the development of microbial genome sequencing techniques and bioinformatic tools, a growing number of secondary metabolite (SM) biosynthetic gene clusters in Streptomyces and their biosynthetic logics have been uncovered and elucidated. In order to increase our knowledge about transcriptional regulators in SM of Streptomyces, this review firstly makes a comprehensive summary of the characterized factors involved in enhancing SM production and awakening SM biosynthesis. Future perspectives on transcriptional regulator engineering for new SM biosynthesis by Streptomyces are also provided.

FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

Metabolic flux phenotyping of secondary metabolism in cyanobacteria

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor

BACKGROUND

Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds.

RESULTS

Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study.

CONCLUSIONS

The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by 'silent' BGCs.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Deciphering mechanisms of production of natural compounds using inducer-producer microbial consortia

Living organisms produce a wide range of metabolites. Because of their potential antibacterial, antifungal, antiviral, or cytostatic properties, such natural molecules are of high interest to the pharmaceutical industry. In nature, these metabolites are often synthesized via secondary metabolic biosynthetic gene clusters that are silent under the typical culturing conditions. Among different techniques used to activate these silent gene clusters, co-culturing of "producer" species with specific "inducer" microbes is a particularly appealing approach due to its simplicity. Although several "inducer-producer" microbial consortia have been reported in the literature and hundreds of different secondary metabolites with attractive biopharmaceutical properties have been described as a result of co-cultivating inducer-producer consortia, less attention has been devoted to the understanding of the mechanisms and possible means of induction for production of secondary metabolites in co-cultures. This lack of understanding of fundamental biological functions and inter-species interactions significantly limits the diversity and yield of valuable compounds using biological engineering tools. In this review, we summarize and categorize the known physiological mechanisms of production of secondary metabolites in inducer-producer consortia, and then discuss approaches that could be exploited to optimize the discovery and production of secondary metabolites.

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.

Systems metabolic engineering of Streptomyces venezuelae for the enhanced production of pikromycin

Pikromycin is an important precursor of drugs, for example, erythromycin. Hence, systems metabolic engineering for the enhanced pikromycin production can contribute to the development of pikromycin-related drugs. In this study, metabolic genes in Streptomyces venezuelae were systematically engineered for the enhanced pikromycin production. For this, a genome-scale metabolic model of S. venezuelae was reconstructed and simulated, which led to the selection of 11 metabolic gene targets. These metabolic genes, including four overexpression targets and seven knockdown targets, were individually engineered first. Next, two overexpression targets and two knockdown targets were selected based on the 11 strains' production performances in order to engineer two to four of these genes together for the potential synergistic effects on the pikromycin production. As a result, the NM1 strain with AQF52_RS24510 (methenyltetrahydrofolate cyclohydrolase/methylenetetrahydrofolate dehydrogenase) overexpression and AQF52_RS30320 (sulfite reductase) knockdown showed the best production performance among all the 22 strains constructed in this study. Fed-batch fermentation of the NM1 strain produced 295.25 mg/L of pikromycin, by far the best production titer using the native producer S. venezuelae, to the best of our knowledge. The systems metabolic engineering strategy demonstrated herein can also be applied to the overproduction of other secondary metabolites using S. venezuelae. This article is protected by copyright. All rights reserved.

Bioproduction process of natural products and biopharmaceuticals: Biotechnological aspects

Decades of research have been put in place for developing sustainable routes of bioproduction of high commercial value natural products (NPs) on the global market. In the last few years alone, we have witnessed significant advances in the biotechnological production of NPs. The development of new methodologies has resulted in a better understanding of the metabolic flux within the organisms, which have driven manipulations to improve production of the target product. This was further realised due to the recent advances in the omics technologies such as genomics, transcriptomics, proteomics, metabolomics and secretomics, as well as systems and synthetic biology. Additionally, the combined application of novel engineering strategies has made possible avenues for enhancing the yield of these products in an efficient and economical way. Invention of high-throughput technologies such as next generation sequencing (NGS) and toolkits for genome editing Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated 9 (CRISPR/Cas9) have been the game changers and provided unprecedented opportunities to generate rationally designed synthetic circuits which can produce complex molecules. This review covers recent advances in the engineering of various hosts for the production of bioactive NPs and biopharmaceuticals. It also highlights general approaches and strategies to improve their biosynthesis with higher yields in a perspective of plants and microbes (bacteria, yeast and filamentous fungi). Although there are numerous reviews covering this topic on a selected species at a time, our approach herein is to give a comprehensive understanding about state-of-art technologies in different platforms of organisms.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Intersecting Xenobiology and Neometabolism To Bring Novel Chemistries to Life

The diversity of life relies on a handful of chemical elements (carbon, oxygen, hydrogen, nitrogen, sulfur and phosphorus) as part of essential building blocks; some other atoms are needed to a lesser extent, but most of the remaining elements are excluded from biology. This circumstance limits the scope of biochemical reactions in extant metabolism - yet it offers a phenomenal playground for synthetic biology. Xenobiology aims to bring novel bricks to life that could be exploited for (xeno)metabolite synthesis. In particular, the assembly of novel pathways engineered to handle nonbiological elements (neometabolism) will broaden chemical space beyond the reach of natural evolution. In this review, xeno-elements that could be blended into nature's biosynthetic portfolio are discussed together with their physicochemical properties and tools and strategies to incorporate them into biochemistry. We argue that current bioproduction methods can be revolutionized by bridging xenobiology and neometabolism for the synthesis of new-to-nature molecules, such as organohalides.

Newly discovered Synechococcus sp. PCC 11901 is a robust cyanobacterial strain for high biomass production

Cyanobacteria, which use solar energy to convert carbon dioxide into biomass, are potential solar biorefineries for the sustainable production of chemicals and biofuels. However, yields obtained with current strains are still uncompetitive compared to existing heterotrophic production systems. Here we report the discovery and characterization of a new cyanobacterial strain, Synechococcus sp. PCC 11901, with promising features for green biotechnology. It is naturally transformable, has a short doubling time of ≈2 hours, grows at high light intensities and in a wide range of salinities and accumulates up to ≈33 g dry cell weight per litre when cultured in a shake-flask system using a modified growth medium − 1.7 to 3 times more than other strains tested under similar conditions. As a proof of principle, PCC 11901 engineered to produce free fatty acids yielded over 6 mM (1.5 g L⁻¹), an amount comparable to that achieved by similarly engineered heterotrophic organisms.

Włodarczyk et al. discover that cyanobacterium Synechococcus sp. PCC 11901 accumulates three times more biomass than other cyanobacterial strains in the same conditions. An engineered version of this strain also produces as much free fatty acid as other commonly used heterotrophic microorganisms, suggesting its utility for the sustainable production of carbon-based molecules.

A Penicillium rubens platform strain for secondary metabolite production

We present a Penicillium rubens strain with an industrial background in which the four highly expressed biosynthetic gene clusters (BGC) required to produce penicillin, roquefortine, chrysogine and fungisporin were removed. This resulted in a minimal secondary metabolite background. Amino acid pools under steady-state growth conditions showed reduced levels of methionine and increased intracellular aromatic amino acids. Expression profiling of remaining BGC core genes and untargeted mass spectrometry did not identify products from uncharacterized BGCs. This platform strain was repurposed for expression of the recently identified polyketide calbistrin gene cluster and achieved high yields of decumbenone A, B and C. The penicillin BGC could be restored through in vivo assembly with eight DNA segments with short overlaps. Our study paves the way for fast combinatorial assembly and expression of biosynthetic pathways in a fungal strain with low endogenous secondary metabolite burden.

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

Recent Advances in Synthetic Biology Approaches to Optimize Production of Bioactive Natural Products in Actinobacteria

Actinobacteria represent one of the most fertile sources for the discovery and development of natural products (NPs) with medicinal and industrial importance. However, production titers of actinobacterial NPs are usually low and require optimization for compound characterization and/or industrial production. In recent years, a wide variety of novel enabling technologies for engineering actinobacteria have been developed, which have greatly facilitated the optimization of NPs biosynthesis. In this review, we summarize the recent advances of synthetic biology approaches for overproducing desired drugs, as well as for the discovery of novel NPs in actinobacteria, including dynamic metabolic regulation based on metabolite-responsive promoters or biosensors, multi-copy chromosomal integration of target biosynthetic gene clusters (BGCs), promoter engineering-mediated rational BGC refactoring, and construction of genome-minimized Streptomyces hosts. Integrated with metabolic engineering strategies developed previously, these novel enabling technologies promise to facilitate industrial strain improvement process and genome mining studies for years to come.

Harnessing biocompatible chemistry for developing improved and novel microbial cell factories

White biotechnology relies on the sophisticated chemical machinery inside living cells for producing a broad range of useful compounds in a sustainable and environmentally friendly way. However, despite the impressive repertoire of compounds that can be generated using white biotechnology, this approach cannot currently fully replace traditional chemical production, often relying on petroleum as a raw material. One challenge is the limited number of chemical transformations taking place in living organisms. Biocompatible chemistry, that is non‐enzymatic chemical reactions taking place under mild conditions compatible with living organisms, could provide a solution. Biocompatible chemistry is not a novel invention, and has since long been used by living organisms. Examples include Fenton chemistry, used by microorganisms for degrading plant materials, and manganese or ketoacids dependent chemistry used for detoxifying reactive oxygen species. However, harnessing biocompatible chemistry for expanding the chemical repertoire of living cells is a relatively novel approach within white biotechnology, and it could potentially be used for producing valuable compounds which living organisms otherwise are not able to generate. In this mini review, we discuss such applications of biocompatible chemistry, and clarify the potential that lies in using biocompatible chemistry in conjunction with metabolically engineered cell factories for cheap substrate utilization, improved cell physiology, efficient pathway construction and novel chemicals production.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.

New Applications of Synthetic Biology Tools for Cyanobacterial Metabolic Engineering

Cyanobacteria are promising microorganisms for sustainable biotechnologies, yet unlocking their potential requires radical re-engineering and application of cutting-edge synthetic biology techniques. In recent years, the available devices and strategies for modifying cyanobacteria have been increasing, including advances in the design of genetic promoters, ribosome binding sites, riboswitches, reporter proteins, modular vector systems, and markerless selection systems. Because of these new toolkits, cyanobacteria have been successfully engineered to express heterologous pathways for the production of a wide variety of valuable compounds. Cyanobacterial strains with the potential to be used in real-world applications will require the refinement of genetic circuits used to express the heterologous pathways and development of accurate models that predict how these pathways can be best integrated into the larger cellular metabolic network. Herein, we review advances that have been made to translate synthetic biology tools into cyanobacterial model organisms and summarize experimental and in silico strategies that have been employed to increase their bioproduction potential. Despite the advances in synthetic biology and metabolic engineering during the last years, it is clear that still further improvements are required if cyanobacteria are to be competitive with heterotrophic microorganisms for the bioproduction of added-value compounds.

High GC Content Cas9-Mediated Genome-Editing and Biosynthetic Gene Cluster Activation in Saccharopolyspora erythraea

The overexpression of bacterial secondary metabolite biosynthetic enzymes is the basis for industrial overproducing strains. Genome editing tools can be used to further improve gene expression and yield. Saccharopolyspora erythraea produces erythromycin, which has extensive clinical applications. In this study, the CRISPR-Cas9 system was used to edit genes in the S. erythraea genome. A temperature-sensitive plasmid containing the PermE promoter, to drive Cas9 expression, and the Pj23119 and PkasO promoters, to drive sgRNAs, was designed. Erythromycin esterase, encoded by S. erythraea SACE_1765, inactivates erythromycin by hydrolyzing the macrolactone ring. Sequencing and qRT-PCR confirmed that reporter genes were successfully inserted into the SACE_1765 gene. Deletion of SACE_1765 in a high-producing strain resulted in a 12.7% increase in erythromycin levels. Subsequent PermE- egfp knock-in at the SACE_0712 locus resulted in an 80.3% increase in erythromycin production compared with that of wild type. Further investigation showed that PermE promoter knock-in activated the erythromycin biosynthetic gene clusters at the SACE_0712 locus. Additionally, deletion of indA (SACE_1229) using dual sgRNA targeting without markers increased the editing efficiency to 65%. In summary, we have successfully applied Cas9-based genome editing to a bacterial strain, S. erythraea, with a high GC content. This system has potential application for both genome-editing and biosynthetic gene cluster activation in Actinobacteria.

Advancing biotechnology with CRISPR/Cas9: recent applications and patent landscape

Clustered regularly interspaced short palindromic repeats (CRISPR) is poised to become one of the key scientific discoveries of the twenty-first century. Originating from prokaryotic and archaeal immune systems to counter phage invasions, CRISPR-based applications have been tailored for manipulating a broad range of living organisms. From the different elucidated types of CRISPR mechanisms, the type II system adapted from Streptococcus pyogenes has been the most exploited as a tool for genome engineering and gene regulation. In this review, we describe the different applications of CRISPR/Cas9 technology in the industrial biotechnology field. Next, we detail the current status of the patent landscape, highlighting its exploitation through different companies, and conclude with future perspectives of this technology.

Production of chemicals and proteins using biomass-derived substrates from a Streptomyces host

Bioproduction using microbes from biomass feedstocks is of interest in regards to environmental problems and cost reduction. Streptomyces as an industrial microorganism plays an important role in the production of useful secondary metabolites for various applications. This strain also secretes a wide range of extracellular enzymes which degrade various biopolymers in nature, and it consumes these degrading substrates as nutrients. Hence, Streptomyces can be employed as a cell factory for the conversion of biomass-derived substrates into various products. This review focuses on the following two points: (1) Streptomyces as a producer of enzymes for degrading biomass-derived polysaccharides and polymers; and, (2) wild-type and engineered strains of Streptomyces as a host for chemical production from biomass-derived substrates.

Actinomycetes: still a source of novel antibiotics

Covering: 2006 to 2017Actinomycetes have been, for decades, one of the most important sources for the discovery of new antibiotics with an important number of drugs and analogs successfully introduced in the market and still used today in clinical practice. The intensive antibacterial discovery effort that generated the large number of highly potent broad-spectrum antibiotics, has seen a dramatic decline in the large pharma industry in the last two decades resulting in a lack of new classes of antibiotics with novel mechanisms of action reaching the clinic. Whereas the decline in the number of new chemical scaffolds and the rediscovery problem of old known molecules has become a hurdle for industrial natural products discovery programs, new actinomycetes compounds and leads have continued to be discovered and developed to the preclinical stages. Actinomycetes are still one of the most important sources of chemical diversity and a reservoir to mine for novel structures that is requiring the integration of diverse disciplines. These can range from novel strategies to isolate species previously not cultivated, innovative whole cell screening approaches and on-site analytical detection and dereplication tools for novel compounds, to in silico biosynthetic predictions from whole gene sequences and novel engineered heterologous expression, that have inspired the isolation of new NPs and shown their potential application in the discovery of novel antibiotics. This review will address the discovery of antibiotics from actinomycetes from two different perspectives including: (1) an update of the most important antibiotics that have only reached the clinical development in the recent years despite their early discovery, and (2) an overview of the most recent classes of antibiotics described from 2006 to 2017 in the framework of the different strategies employed to untap novel compounds previously overlooked with traditional approaches.

Intertidal marine sediment harbours Actinobacteria with promising bioactive and biosynthetic potential

Actinobacteria are the major source of bioactive natural products that find their value in research and drug discovery programmes. Antimicrobial resistance and the resulting high demand for novel antibiotics underscore the need for exploring novel sources of these bacteria endowed with biosynthetic potential. Intertidal ecosystems endure regular periods of immersion and emersion, and represent an untapped source of Actinobacteria. In this study, we studied the diversity and biosynthetic potential of cultivable Actinobacteria from intertidal sediments of Diu Island in the Arabian Sea. A total of 148 Actinobacteria were selectively isolated using a stamping method with eight isolation media. Isolates were grouped into OTUs based on their 16S rRNA gene sequence, and categorized within actinobacterial families such as Glycomycetaceae, Micromonosporaceae, Nocardiaceae, Nocardiopsaceae, Pseudonocardiaceae, Streptomycetaceae, and Thermomonosporaceae. The biosynthetic potential of the Actinobacteria, necessary for secondary metabolite biosynthesis, was screened and confirmed by extensive fingerprinting approaches based on genes coding for polyketide synthases and nonribosomal peptide synthetases. The observed biosynthetic potential was correlated with the antibacterial activity exhibited by these isolates in laboratory conditions. Ultimately, the results demonstrate that intertidal sediment is a rich source of diverse cultivable Actinobacteria with high potential to synthesize novel bioactive compounds in their genomes.

Perspectives and Challenges in Microbial Communities Metabolic Modeling

Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These "super-organisms" play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future.

Deciphering bioactive peptides and their action mechanisms through proteomics

INTRODUCTION

Bioactive peptides such as antimicrobial peptides (AMPs), ribosomally synthesized and post translationally modified peptides (RiPPs) and the non-ribosomal peptides (NRPs) have emerged with promising applications in medicine, agriculture and industry. However, their development has been limited by several difficulties making it necessary to search for novel discovery methods. In this context, proteomics has been considered a reliable tool. Areas covered: This review highlights recent developments in proteomic tools that facilitate the discovery of AMPs, RiPPs and NRPs as well as the elucidation of action mechanisms of AMPs and resistance mechanisms of pathogens to them. Expert commentary: Proteomic approaches have emerged as useful tools for the study of bioactive peptides, especially mass spectrometry-based peptidomics profiling, a promising strategy for AMP discovery. Furthermore, the rapidly expanding fields of genome mining and genome sequencing techniques, as well as mass spectrometry, have revolutionized the discovery of novel RiPPs and NRPs from complex biological samples.