Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle

Enhancing erythromycin production in Saccharopolyspora erythraea through rational engineering and fermentation refinement: A Design-Build-Test-Learn approach

Industrial production of bioactive compounds from actinobacteria, such as erythromycin and its derivatives, faces challenges in achieving optimal yields. To this end, the Design-Build-Test-Learn (DBTL) framework, a systematic metabolic engineering approach, was employed to enhance erythromycin production in Saccharopolyspora erythraea (S. erythraea) E3 strain. A genetically modified strain, S. erythraea E3-CymRP21-dcas9-sucC (S. erythraea CS), was developed by suppressing the sucC gene using an inducible promoter and dcas9 protein. The strain exhibited improved erythromycin synthesis, attributed to enhanced precursor synthesis and increased NADPH availability. Transcriptomic and metabolomic analyses revealed altered central carbon metabolism, amino acid metabolism, energy metabolism, and co-factor/vitamin metabolism in CS. Augmented amino acid metabolism led to nitrogen depletion, potentially causing cellular autolysis during later fermentation stages. By refining the fermentation process through ammonium sulfate supplementation, erythromycin yield reached 1125.66 mg L-1, a 43.5% increase. The results demonstrate the power of the DBTL methodology in optimizing erythromycin production, shedding light on its potential for revolutionizing antibiotic manufacturing in response to the global challenge of antibiotic resistance.

Methanol Dehydrogenases as a Key Biocatalysts for Synthetic Methylotrophy

One-carbon (C1) chemicals are potential building blocks for cheap and sustainable re-sources such as methane, methanol, formaldehyde, formate, carbon monoxide, and more. These resources have the potential to be made into raw materials for various products used in our daily life or precursors for pharmaceuticals through biological and chemical processes. Among the soluble C1 substrates, methanol is regarded as a biorenewable platform feedstock because nearly all bioresources can be converted into methanol through syngas. Synthetic methylotrophy can be exploited to produce fuels and chemicals using methanol as a feedstock that integrates natural or artificial methanol assimilation pathways in platform microorganisms. In the methanol utilization in methylotrophy, methanol dehydrogenase (Mdh) is a primary enzyme that converts methanol to formaldehyde. The discovery of new Mdhs and engineering of present Mdhs have been attempted to develop synthetic methylotrophic bacteria. In this review, we describe Mdhs, including in terms of their enzyme properties and engineering for desired activity. In addition, we specifically focus on the application of various Mdhs for synthetic methylotrophy.

Dietary natural products as a potential inhibitor towards advanced glycation end products and hyperglycemic complications: A phytotherapy approaches

Natural products exist in various natural foods such as plants, herbs, fruits, and vegetables. Furthermore, marine life offers potential natural products with significant biological activity. The biochemical reaction is known as advanced glycation end products (AGEs) occurs in the human body. On the other hand, foods are capable of a wide range of processing conditions resulting in the generation of exogenous AGEs adducts. Protein glycation and the formation of advanced glycation end products both contribute to the pathogenesis of hyperglycemic complications. AGEs also play a pivotal role in microvascular and macrovascular complications progression by receptors for advanced glycation end products (RAGE). RAGE activate by AGEs leads to up-regulation of transcriptional factor NF-kB and inflammatory genes. Around the globe, researchers are working in various approaches for therapeutical implications on controlling AGEs mediated disease complications. In this regard, one of the potential promising agents observed with a wide range of AGEs inhibition by food-derived natural products. Current biotechnological tools have been turned to natural products or phytochemicals to manufacture the molecules without compromising their functionality. Metabolic engineering and bioinformatics perspectives have recently enabled the generation of a few potent metabolites with anti-diabetic activity. As the primary focus, this review article will also discuss multidisciplinary approaches that emphasize current advances in anti-diabetic therapeutic action and future perspectives of natural products.

BioParts-A Biological Parts Search Portal and Updates to the ICE Parts Registry Software Platform

Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database. This distributed database allows users from one registry to query and access plasmid, strain, (DNA) part, plant seed, and protein entry types in other connected registries. Users can also transfer entries from one ICE registry to another or make them publicly accessible. Bioparts, the new search portal, combines the ease and convenience of modern web search engines with the capabilities of bioinformatics search tools such as BLAST. This portal, available at bioparts.org, allows anyone to search for publicly accessible biological part information (e.g., NCBI, iGEM, SynBioHub, Addgene), including parts publicly accessible through ICE Registries. Additionally, the portal offers a REST API that enables third-party applications and tools to access the portal's functionality programmatically.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

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

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications

Genome-scale metabolic modeling is and will continue to play a central role in computational systems metabolic engineering and synthetic biology applications for the productions of chemicals and antibiotics. To that end, a survey and workflows of methods used for the development of high-quality genome-scale metabolic models (GEMs) and chassis design for synthetic biology are described here. The chapter consists of two parts (a) the methods of development of GEMs (Escherichia coli as a case study) and (b) E. coli chassis design for synthetic production of 1,4-butanediol (BDO). The methods described here can guide existing and future development of GEMs coupled with host chassis design for synthetic productions of novel antibiotics.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host

Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream ¹³C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

Rapid SERS Detection of Thiol-Containing Natural Products in Culturing Complex

Thiol-containing natural products possess a wide range of bioactivities. The burst of synthetic biology technology facilitates the discovery of new thiol-containing active ingredients. Herein, we report a sensitive, quick, and robust surface-enhanced Raman scattering technology for specific and multiplex detection of thiol-containing compounds without purification requirements and also indicating the thiols with different chemical environments. Using this platform, we successfully demonstrated the simultaneous detection of thiol-containing compounds from as low as 1 μM of analytes spiked in complex culture matrices.

novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/.

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a "digital bioconverter"-a tool to connect the digital aspects of the design-build-learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

MiR-16-5p regulates postmenopausal osteoporosis by directly targeting VEGFA

In this study, we used bioinformatics tools, and experiments with patient tissues and human mesenchymal stem cells (hMSCs) to identify differentially regulated genes (DEGs) and microRNAs (miRNAs) that promote postmenopausal osteoporosis. By analyzing the GSE56815 dataset from the NCBI GEO database, we identified 638 DEGs, including 371 upregulated and 267 downregulated genes, in postmenopausal women with low bone density. Enrichment and protein-protein interaction network analyses showed that TP53, RPS27A, and VEGFA were the top three hub genes with the highest degree of betweenness and closeness centrality. TargetScanHuman and DIANA software analyses and dual luciferase reporter assays confirmed that miR-16a-5p directly targets the 3'UTR of VEGFA. Postmenopausal patients with osteoporosis showed higher miR-16-5p and lower VEGFA levels than those without osteoporosis (n=10 each). VEGFA levels were higher in miR-16-5p knockdown hMSCs and were reduced in miR-16-5p-overexpressing hMSCs. mRNA expression of osteogenic markers, ALP, OCN, and RUNX2, as well as calcium deposition based on Alizarin red staining, correlated inversely with miR-16-5p levels and correlated positively with VEGFA levels. These findings suggest that miR-16-5p suppresses osteogenesis by inhibiting VEGFA expression and is a promising target for postmenopausal osteoporosis therapy.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

Antimicrobial biosynthetic potential and diversity of culturable soil actinobacteria from forest ecosystems of Northeast India

Actinobacteria is a goldmine for the discovery of abundant secondary metabolites with diverse biological activities. This study explores antimicrobial biosynthetic potential and diversity of actinobacteria from Pobitora Wildlife Sanctuary and Kaziranga National Park of Assam, India, lying in the Indo-Burma mega-biodiversity hotspot. A total of 107 actinobacteria were isolated, of which 77 exhibited significant antagonistic activity. 24 isolates tested positive for at least one of the polyketide synthase type I, polyketide synthase type II or non-ribosomal peptide synthase genes within their genome. Their secondary metabolite pathway products were predicted to be involved in the production of ansamycin, benzoisochromanequinone, streptogramin using DoBISCUIT database. Molecular identification indicated that these actinobacteria predominantly belonged to genus Streptomyces, followed by Nocardia and Kribbella. 4 strains, viz. Streptomyces sp. PB-79 (GenBank accession no. KU901725; 1313 bp), Streptomyces sp. Kz-28 (GenBank accession no. KY000534; 1378 bp), Streptomyces sp. Kz-32 (GenBank accession no. KY000536; 1377 bp) and Streptomyces sp. Kz-67 (GenBank accession no. KY000540; 1383 bp) showed ~89.5% similarity to the nearest type strain in EzTaxon database and may be considered novel. Streptomyces sp. Kz-24 (GenBank accession no. KY000533; 1367 bp) showed only 96.2% sequence similarity to S. malaysiensis and exhibited minimum inhibitory concentration of 0.024 µg/mL against methicilin resistant Staphylococcus aureus ATCC 43300 and Candida albicans MTCC 227. This study establishes that actinobacteria isolated from the poorly explored Indo-Burma mega-biodiversity hotspot may be an extremely rich reservoir for production of biologically active compounds for human welfare.

Immediate, multiplexed and sequential genome engineering facilitated by CRISPR/Cas9 in Saccharomyces cerevisiae

A method called Cas-3P allowing for immediate, multiplexed and sequential genome engineering was developed using one plasmid expressing Cas9 and three marked plasmid backbones (P1, P2 and P3) for guide RNA (gRNA) expression. The three marked gRNA plasmid backbones were recurred in a P1-P2-P3 order for sequential gene targeting, without construction of any additional plasmid and elimination of gRNA plasmid by induction in each round. The efficiency of direct gRNA plasmid curing mediated by Cas-3P was more than 40% in sequential gene targeting. Besides, Cas-3P allowed single-, double- and triple-loci gene targeting with an efficiency of 75%, 36.8% and 8.2% within 3-4 days, respectively. Through three sequential rounds of gene targeting within 10 days, S. cerevisiae was optimized for the production of patchoulol by replacing one promoter, overexpressing three genes and disrupting four genes. The work is important for practical application in the cell factory engineering of S. cerevisiae.

Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries

Synthetic biology utilizes the Design-Build-Test-Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure's low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

CF-Targeter: A Rational Biological Cell Factory Targeting Platform for Biosynthetic Target Chemicals

Biosynthesis is a promising method for chemical synthesis. However, due to varieties between different microorganism hosts, yield and heterologous pathways needed for production of target chemical may also vary from different strains. One of the main challenges in metabolic engineering is to select an appropriate chassis host for specified target chemical production. However, with thousands of microorganisms existing in nature and extremely complicated metabolism within them, it is still time-consuming and error-prone work to achieve such a goal only through experimental methods, even with some existing computational methods. Hence, more efficient methods should be proposed to assist in selecting appropriate chassis hosts. In this article, based on symbolic reaction repositories and a pathway search algorithm which performed 1 400 000 searches for per target compound, we established a biological reasoning system for appropriate chassis host selection by coupling with various GEM-models. By using a supercomputer to calculate the biosynthetic pathways for more than 1 month, nearly 50 000 000 biosynthetic pathways are computed for production of 6026 compounds within 70 microorganisms. With retrieved organisms for specified target production, several heterologous biosynthetic pathways can be shown in length order, and then the maximum theoretical yields and thermodynamic feasibility can be calculated in real time under customized growth conditions and physiological states. From the computation results, the system not only identifies experimentally validated pathways but also outputs more efficient solutions with less heterologous steps or higher maximum possible theoretical yield by engineering other organism hosts. CF-targeter is available at http://www.rxnfinder.org/cf_targeter/.

Actinomycete-Derived Polyketides as a Source of Antibiotics and Lead Structures for the Development of New Antimicrobial Drugs

Actinomycetes are remarkable producers of compounds essential for human and veterinary medicine as well as for agriculture. The genomes of those microorganisms possess several sets of genes (biosynthetic gene cluster (BGC)) encoding pathways for the production of the valuable secondary metabolites. A significant proportion of the identified BGCs in actinomycetes encode pathways for the biosynthesis of polyketide compounds, nonribosomal peptides, or hybrid products resulting from the combination of both polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The potency of these molecules, in terms of bioactivity, was recognized in the 1940s, and started the "Golden Age" of antimicrobial drug discovery. Since then, several valuable polyketide drugs, such as erythromycin A, tylosin, monensin A, rifamycin, tetracyclines, amphotericin B, and many others were isolated from actinomycetes. This review covers the most relevant actinomycetes-derived polyketide drugs with antimicrobial activity, including anti-fungal agents. We provide an overview of the source of the compounds, structure of the molecules, the biosynthetic principle, bioactivity and mechanisms of action, and the current stage of development. This review emphasizes the importance of actinomycetes-derived antimicrobial polyketides and should serve as a "lexicon", not only to scientists from the Natural Products field, but also to clinicians and others interested in this topic.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

MOTIVATION

Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.

RESULTS

As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.

AVAILABILITY AND IMPLEMENTATION

The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines

Covering: 2014 to 2019Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) have been the subject of engineering efforts for multiple decades. Their modular assembly line architecture potentially allows unlocking vast chemical space for biosynthesis. However, attempts thus far are often met with mixed success, due to limited molecular compatibility of the parts used for engineering. Now, new engineering strategies, increases in genomic data, and improved computational tools provide more opportunities for major progress. In this review we highlight some of the challenges and progressive strategies for the re-design of NRPSs & type I PKSs and survey useful computational tools and approaches to attain the ultimate goal of semi-automated and design-based engineering of novel peptide and polyketide products.

Application of Cell-Free Protein Synthesis for Faster Biocatalyst Development

Cell-free protein synthesis (CFPS) has become an established tool for rapid protein synthesis in order to accelerate the discovery of new enzymes and the development of proteins with improved characteristics. Over the past years, progress in CFPS system preparation has been made towards simplification, and many applications have been developed with regard to tailor-made solutions for specific purposes. In this review, various preparation methods of CFPS systems are compared and the significance of individual supplements is assessed. The recent applications of CFPS are summarized and the potential for biocatalyst development discussed. One of the central features is the high-throughput synthesis of protein variants, which enables sophisticated approaches for rapid prototyping of enzymes. These applications demonstrate the contribution of CFPS to enhance enzyme functionalities and the complementation to in vivo protein synthesis. However, there are different issues to be addressed, such as the low predictability of CFPS performance and transferability to in vivo protein synthesis. Nevertheless, the usage of CFPS for high-throughput enzyme screening has been proven to be an efficient method to discover novel biocatalysts and improved enzyme variants.

Applied evolution: phylogeny-based approaches in natural products research

Here we highlight how phylogenetic analyses can be used to facilitate natural product discovery and structure elucidation.

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism

BACKGROUND

Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.

RESULTS

Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.

CONCLUSIONS

The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L⁻¹. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.

A living foundry for Synthetic Biological Materials: A synthetic biology roadmap to new advanced materials

Society is on the cusp of harnessing recent advances in synthetic biology to discover new bio-based products and routes to their affordable and sustainable manufacture. This is no more evident than in the discovery and manufacture of Synthetic Biological Materials, where synthetic biology has the capacity to usher in a new Materials from Biology era that will revolutionise the discovery and manufacture of innovative synthetic biological materials. These will encompass novel, smart, functionalised and hybrid materials for diverse applications whose discovery and routes to bio-production will be stimulated by the fusion of new technologies positioned across physical, digital and biological spheres. This article, which developed from an international workshop held in Manchester, United Kingdom, in 2017 [1], sets out to identify opportunities in the new materials from biology era. It considers requirements, early understanding and foresight of the challenges faced in delivering a Discovery to Manufacturing Pipeline for synthetic biological materials using synthetic biology approaches. This challenge spans the complete production cycle from intelligent and predictive design, fabrication, evaluation and production of synthetic biological materials to new ways of bringing these products to market. Pathway opportunities are identified that will help foster expertise sharing and infrastructure development to accelerate the delivery of a new generation of synthetic biological materials and the leveraging of existing investments in synthetic biology and advanced materials research to achieve this goal.

STRENDA DB: enabling the validation and sharing of enzyme kinetics data

Standards for reporting enzymology data (STRENDA) DB is a validation and storage system for enzyme function data that incorporates the STRENDA Guidelines. It provides authors who are preparing a manuscript with a user-friendly, web-based service that checks automatically enzymology data sets entered in the submission form that they are complete and valid before they are submitted as part of a publication to a journal.

Selenzyme: enzyme selection tool for pathway design

Summary

Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries.

Availability and implementation

The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enzyme Discovery: Enzyme Selection and Pathway Design

In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.

Directing evolution: the next revolution in drug discovery?

The strong biological rationale to pursue challenging drug targets such as protein-protein interactions has stimulated the development of novel screening strategies, such as DNA-encoded libraries, to allow broader areas of chemical space to be searched. There has also been renewed interest in screening natural products, which are the result of evolutionary selection for a function, such as interference with a key signalling pathway of a competing organism. However, recent advances in several areas, such as understanding of the biosynthetic pathways for natural products, synthetic biology and the development of biosensors to detect target molecules, are now providing new opportunities to directly harness evolutionary pressure to identify and optimize compounds with desired bioactivities. Here, we describe innovations in the key components of such strategies and highlight pioneering examples that indicate the potential of the directed-evolution concept. We also discuss the scientific gaps and challenges that remain to be addressed to realize this potential more broadly in drug discovery.

Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences

A mRNA's translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in Escherichia coli. We found that the mRNAs' translation rates were repressed, by up to 530-fold, when mRNA structures overlapped with the ribosome's footprint. In contrast, when the mRNA structure was located outside the ribosome's footprint, translation was repressed by <2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.

Evolutionary algorithms and synthetic biology for directed evolution: commentary on "on the mapping of genotype to phenotype in evolutionary algorithms" by Peter A. Whigham, Grant Dick, and James Maclaurin

I rehearse two issues around the commentary of Whigham and colleagues. (1) There really are many more reasons than those given as to why natural evolution cannot reasonably find or select the 'optimal' individual. (2) A series of experimental molecular biology programmes, known generically as directed evolution, can use operators and selection schemes that natural evolution cannot. When developed further using the methods of synthetic biology, there are no operators or schemes for in silico evolution that cannot be applied precisely to directed evolution. The issues raised apply only to natural evolution but not to directed evolution.

Application of theoretical methods to increase succinate production in engineered strains

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

SYNBIOCHEM Synthetic Biology Research Centre, Manchester - A UK foundry for fine and speciality chemicals production

The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.

Mapping the patent landscape of synthetic biology for fine chemical production pathways

A goal of synthetic biology bio‐foundries is to innovate through an iterative design/build/test/learn pipeline. In assessing the value of new chemical production routes, the intellectual property (IP) novelty of the pathway is important. Exploratory studies can be carried using knowledge of the patent/IP landscape for synthetic biology and metabolic engineering. In this paper, we perform an assessment of pathways as potential targets for chemical production across the full catalogue of reachable chemicals in the extended metabolic space of chassis organisms, as computed by the retrosynthesis‐based algorithm RetroPath. Our database for reactions processed by sequences in heterologous pathways was screened against the PatSeq database, a comprehensive collection of more than 150M sequences present in patent grants and applications. We also examine related patent families using Derwent Innovations. This large‐scale computational study provides useful insights into the IP landscape of synthetic biology for fine and specialty chemicals production.