Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale

Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

A versatile microbial platform as a tunable whole-cell chemical sensor

Biosensors are used to detect and quantify chemicals produced in industrial microbiology with high specificity, sensitivity, and portability. Most biosensors, however, are limited by the need for transcription factors engineered to recognize specific molecules. In this study, we overcome the limitations typically associated with traditional biosensors by engineering Pseudomonas putida for whole-cell sensing of a variety of chemicals. Our approach integrates fluorescent reporters with synthetic auxotrophies within central metabolism that can be complemented by target analytes in growth-coupled setups. This platform enables the detection of a wide array of structurally diverse chemicals under various conditions, including co-cultures of producer cell factories and sensor strains. We also demonstrate the applicability of this versatile biosensor platform for monitoring complex biochemical processes, including plastic degradation by either purified hydrolytic enzymes or engineered bacteria. This microbial system provides a rapid, sensitive, and readily adaptable tool for monitoring cell factory performance and for environmental analyzes.

Optimized CRISPR Interference System for Investigating Pseudomonas alloputida Genes Involved in Rhizosphere Microbiome Assembly

Pseudomonas alloputida KT2440 (formerly P. putida) has become both a well-known chassis organism for synthetic biology and a model organism for rhizosphere colonization. Here, we describe a CRISPR interference (CRISPRi) system in KT2440 for exploring microbe-microbe interactions in the rhizosphere and for use in industrial systems. Our CRISPRi system features three different promoter systems (XylS/Pm, LacI/Plac, and AraC/PBAD) and a dCas9 codon-optimized for Pseudomonads, all located on a mini-Tn7-based transposon that inserts into a neutral site in the genome. It also includes a suite of pSEVA-derived sgRNA expression vectors, where the expression is driven by synthetic promoters varying in strength. We compare the three promoter systems in terms of how well they can precisely modulate gene expression, and we discuss the impact of environmental factors, such as media choice, on the success of CRISPRi. We demonstrate that CRISPRi is functional in bacteria colonizing the rhizosphere, with repression of essential genes leading to a 10-100-fold reduction in P. alloputida cells per root. Finally, we show that CRISPRi can be used to modulate microbe-microbe interactions. When the gene pvdH is repressed and P. alloputida is unable to produce pyoverdine, it loses its ability to inhibit other microbes in vitro. Moreover, our design is amendable for future CRISPRi-seq studies and in multispecies microbial communities, with the different promoter systems providing a means to control the level of gene expression in many different environments.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Pigments from pathogenic bacteria: a comprehensive update on recent advances

Bacterial pigments stand out as exceptional natural bioactive compounds with versatile functionalities. The pigments represent molecules from distinct chemical categories including terpenes, terpenoids, carotenoids, pyridine, pyrrole, indole, and phenazines, which are synthesized by diverse groups of bacteria. Their spectrum of physiological activities encompasses bioactive potentials that often confer fitness advantages to facilitate the survival of bacteria amid challenging environmental conditions. A large proportion of such pigments are produced by bacterial pathogens mostly as secondary metabolites. Their multifaceted properties augment potential applications in biomedical, food, pharmaceutical, textile, paint industries, bioremediation, and in biosensor development. Apart from possessing a less detrimental impact on health with environmentally beneficial attributes, tractable and scalable production strategies render bacterial pigments a sustainable option for novel biotechnological exploration for untapped discoveries. The review offers a comprehensive account of physiological role of pigments from bacterial pathogens, production strategies, and potential applications in various biomedical and biotechnological fields. Alongside, the prospect of combining bacterial pigment research with cutting-edge approaches like nanotechnology has been discussed to highlight future endeavours.

CRISPR Tools for Engineering Prokaryotic Systems: Recent Advances and New Applications

In the past decades, the broad selection of CRISPR-Cas systems has revolutionized biotechnology by enabling multimodal genetic manipulation in diverse organisms. Rooted in a molecular engineering perspective, we recapitulate the different CRISPR components and how they can be designed for specific genetic engineering applications. We first introduce the repertoire of Cas proteins and tethered effectors used to program new biological functions through gene editing and gene regulation. We review current guide RNA (gRNA) design strategies and computational tools and how CRISPR-based genetic circuits can be constructed through regulated gRNA expression. Then, we present recent advances in CRISPR-based biosensing, bioproduction, and biotherapeutics across in vitro and in vivo prokaryotic systems. Finally, we discuss forthcoming applications in prokaryotic CRISPR technology that will transform synthetic biology principles in the near future.

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

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

A critical assessment on scalable technologies using high solids loadings in lignocellulose biorefinery: challenges and solutions

The pretreatment and the enzymatic saccharification are the key steps in the extraction of fermentable sugars for further valorization of lignocellulosic biomass (LCB) to biofuels and value-added products via biochemical and/or chemical conversion routes. Due to low density and high-water absorption capacity of LCB, the large volume of water is required for its processing. Integration of pretreatment, saccharification, and co-fermentation has succeeded and well-reported in the literature. However, there are only few reports on extraction of fermentable sugars from LCB with high biomass loading (>10% Total solids-TS) feasible to industrial reality. Furthermore, the development of enzymatic cocktails can overcome technology hurdles with high biomass loading. Hence, a better understanding of constraints involved in the development of technology with high biomass loading can result in an economical and efficient yield of fermentable sugars for the production of biofuels and bio-chemicals with viable titer, rate, and yield (TRY) at industrial scale. The present review aims to provide a critical assessment on the production of fermentable sugars from lignocelluloses with high solid biomass loading. The impact of inhibitors produced during both pretreatment and saccharification has been elucidated. Moreover, the limitations imposed by high solid loading on efficient mass transfer during saccharification process have been elaborated.

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 .

Establishment of an Efficient Expression and Regulation System in Streptomyces for Economical and High-Level Production of the Natural Blue Pigment Indigoidine

Indigoidine, as a kind of natural blue pigment, is widely used in textiles, food, and pharmaceuticals and is mainly synthesized from l-glutamine via a condensation reaction by indigoidine synthetases, most of which originates from Streptomyces species. However, due to the complex metabolic switches of Streptomyces, most of the researchers choose to overexpress indigoidine synthetases in the heterologous host to achieve high-level production of indigoidine. Considering the advantages of low-cost culture medium and simple culture conditions during the large-scale culture of Streptomyces, here, an updated regulation system derived from the Streptomyces self-sustaining system, constructed in our previous study, was established for the highly efficient production of indigoidine in Streptomyces lividans TK24. The updated system was constructed via promoter mining and σhrdB expression optimization, and this system was applied to precisely and continuously regulate the expression of indigoidine synthetase IndC derived from Streptomyces albus J1704. Finally, the engineered strain was cultured with cheap industrial glycerol as a supplementary carbon source, and 14.3 and 46.27 g/L indigoidine could be achieved in a flask and a 4 L fermentor, respectively, reaching the highest level of microbial synthesis of indigoidine. This study will lay a foundation for the industrial application of Streptomyces cell factories to produce indigoidine.

Application of functional genomics for domestication of novel non-model microbes

With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications.

ONE-SENTENCE SUMMARY

The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

The expanded CRISPR toolbox for constructing microbial cell factories

Microbial cell factories (MCFs) convert low-cost carbon sources into valuable compounds. The CRISPR/Cas9 system has revolutionized MCF construction as a remarkable genome editing tool with unprecedented programmability. Recently, the CRISPR toolbox has been significantly expanded through the exploration of new CRISPR systems, the engineering of Cas effectors, and the incorporation of other effectors, enabling multi-level regulation and gene editing free of double-strand breaks. This expanded CRISPR toolbox powerfully promotes MCF construction by facilitating pathway construction, enzyme engineering, flux redistribution, and metabolic burden control. In this article, we summarize different CRISPR tool designs and their applications in MCF construction for gene editing, transcriptional regulation, and enzyme modulation. Finally, we also discuss future perspectives for the development and application of the CRISPR toolbox.

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.

Improving microbial bioproduction under low-oxygen conditions

Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.

Discovery and remodeling of Vibrio natriegens as a microbial platform for efficient formic acid biorefinery

Formic acid (FA) has emerged as a promising one-carbon feedstock for biorefinery. However, developing efficient microbial hosts for economically competitive FA utilization remains a grand challenge. Here, we discover that the bacterium Vibrio natriegens has exceptional FA tolerance and metabolic capacity natively. This bacterium is remodeled by rewiring the serine cycle and the TCA cycle, resulting in a non-native closed loop (S-TCA) which as a powerful metabolic sink, in combination with laboratory evolution, enables rapid emergence of synthetic strains with significantly improved FA-utilizing ability. Further introduction of a foreign indigoidine-forming pathway into the synthetic V. natriegens strain leads to the production of 29.0 g · L-1 indigoidine and consumption of 165.3 g · L-1 formate within 72 h, achieving a formate consumption rate of 2.3 g · L-1 · h-1. This work provides an important microbial chassis as well as design rules to develop industrially viable microorganisms for FA biorefinery.

Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering

Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.

Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.

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.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Recent progress in the synthesis of advanced biofuel and bioproducts

Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass toward a broader consideration of how to reduce carbon dioxide to fuels and chemical products.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

Trimming Gene Deletion Strategies for Growth-Coupled Production in Constraint-Based Metabolic Networks: TrimGdel

When simulating genome-scale metabolite production using constraint-based metabolic networks, it is often necessary to find gene deletion strategies which lead to growth-coupled production, which means that target metabolites are produced when cell growth is maximized. Existing methods are effective when the number of gene deletions is relatively small, but when the number of required gene deletions exceeds approximately 1% of whole genes, the time required for the calculation is often unfeasible. Therefore, a complementing algorithm that is effective even when the required number of gene deletions is approximately 1% to 5% of whole genes would be helpful because the number of deletable genes in a strain is increasing with advances in genetic engineering technology. In this study, the author developed an algorithm, TrimGdel, which first computes a strategy with many gene deletions that results in growth-coupled production and then gradually reduces the number of gene deletions while ensuring the original production rate and growth rate. The results of the computer experiments showed that TrimGdel can calculate stoichiometrically feasible gene deletion strategies, especially those whose sizes are 1 to 5% of whole genes, which lead to growth-coupled production of many target metabolites, which include useful vitamins such as biotin and pantothenate, for which existing methods could not.

Revealing oxidative pentose metabolism in new Pseudomonas putida isolates

The Pseudomonas putida group in the Gammaproteobacteria has been intensively studied for bioremediation and plant growth promotion. Members of this group have recently emerged as promising hosts to convert intermediates derived from plant biomass to biofuels and biochemicals. However, most strains of P. putida cannot metabolize pentose sugars derived from hemicellulose. Here, we describe three isolates that provide a broader view of the pentose sugar catabolism in the P. putida group. One of these isolates clusters with the well-characterized P. alloputida KT2440 (Strain BP6); the second isolate clustered with plant growth-promoting strain P. putida W619 (Strain M2), while the third isolate represents a new species in the group (Strain BP8). Each of these isolates possessed homologous genes for oxidative xylose catabolism (xylDXA) and a potential xylonate transporter. Strain M2 grew on arabinose and had genes for oxidative arabinose catabolism (araDXA). A CRISPR interference (CRISPRi) system was developed for strain M2 and identified conditionally essential genes for xylose growth. A glucose dehydrogenase was found to be responsible for initial oxidation of xylose and arabinose in strain M2. These isolates have illuminated inherent diversity in pentose catabolism in the P. putida group and may provide alternative hosts for biomass conversion.

Rapid quantification of alcohol production in microorganisms based on nanostructure-initiator mass spectrometry (NIMS)

We described a mass spectrometry-based assay to rapidly quantify the production of primary alcohols directly from cell cultures. This novel assay used the combination of TEMPO-based oxidation chemistry and oxime ligation, followed by product analysis based on Nanostructure-Initiator Mass Spectrometry. This assay enables quantitative monitor both C5 to C18 alcohols as well as glucose and gluconate in the growth medium to support strain characterization and optimization. We find that this assay yields similar results to gas chromatography for isoprenol production but required much less acquisition time per sample. We applied this assay to gain new insights into P. Putida's utilization of alcohols and find that this strain largely could not grow on heptanol and octanol.

Biosystem design of Corynebacterium glutamicum for bioproduction

Corynebacterium glutamicum, a natural glutamate-producing bacterium adopted for industrial production of amino acids, has been extensively explored recently for high-level biosynthesis of amino acid derivatives, bulk chemicals such as organic acids and short-chain alcohols, aromatics, and natural products, including polyphenols and terpenoids. Here, we review the recent advances with a focus on biosystem design principles, metabolic characterization and modeling, omics analysis, utilization of nonmodel feedstock, emerging CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) tools for Corynebacterium strain engineering, biosensors, and novel strains of C. glutamicum. Future research directions for developing C. glutamicum cell factories are also discussed.

Biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening for over-production phenotypes

With rapid advances in the development of metabolic pathways and synthetic biology toolkits, a persisting challenge in microbial bioproduction is how to optimally rewire metabolic fluxes and accelerate the concomitant high-throughput phenotype screening. Here we developed a biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening approach that combines a titratable mismatch CRISPR interference and a biosensor mediated screening for high-production phenotypes in Escherichia coli. We first developed a programmable mismatch CRISPRi that could afford multiple levels of interference efficacy with a one-pot sgRNA pool (a total of 16 variants for each target gene) harboring two consecutive random mismatches in the seed region of sgRNA spacers. The mismatch CRISPRi was demonstrated to enable almost a full range of gene knockdown when targeting different positions on genes. As a proof-of-principle demonstration of the BATCH screening system, we designed doubly mismatched sgRNA pools targeting 20 relevant genes in E. coli and optimized a PadR-based p-coumaric acid biosensor with broad dynamic range for the eGFP fluorescence guided high-production screening. Using sgRNA variants for the combinatorial knockdown of pfkA and ptsI, the p-coumaric acid titer was increased by 40.6% to o 1308.6 mg/l from glycerol in shake flasks. To further demonstrate the general applicability of the BATCH screening system, we recruited a HpdR-based butyrate biosensor that facilitated the screening of E. coli strains achieving 19.0% and 25.2% increase of butyrate titer in shake flasks with sgRNA variants targeting sucA and ldhA, respectively. This work reported the establishment of a plug-and-play approach that enables multilevel modulation of metabolic fluxes and high-throughput screening of high-production phenotypes.

Photobiological production of high-value pigments via compartmentalized co-cultures using Ca-alginate hydrogels

Engineered cyanobacterium Synechococcus elongatus can use light and CO₂ to produce sucrose, making it a promising candidate for use in co-cultures with heterotrophic workhorses. However, this process is challenged by the mutual stresses generated from the multispecies microbial culture. Here we demonstrate an ecosystem where S. elongatus is freely grown in a photo-bioreactor (PBR) containing an engineered heterotrophic workhorse (either β-carotene-producing Yarrowia lipolytica or indigoidine-producing Pseudomonas putida) encapsulated in calcium-alginate hydrogel beads. The encapsulation prevents growth interference, allowing the cyanobacterial culture to produce high sucrose concentrations enabling the production of indigoidine and β-carotene in the heterotroph. Our experimental PBRs yielded an indigoidine titer of 7.5 g/L hydrogel and a β-carotene titer of 1.3 g/L hydrogel, amounts 15-22-fold higher than in a comparable co-culture without encapsulation. Moreover, ¹³C-metabolite analysis and protein overexpression tests indicated that the hydrogel beads provided a favorable microenvironment where the cell metabolism inside the hydrogel was comparable to that in a free culture. Finally, the heterotroph-containing hydrogels were easily harvested and dissolved by EDTA for product recovery, while the cyanobacterial culture itself could be reused for the next batch of immobilized heterotrophs. This co-cultivation and hydrogel encapsulation system is a successful demonstration of bioprocess optimization under photobioreactor conditions.

Engineering isoprenoids production in metabolically versatile microbial host Pseudomonas putida

With the increasing need for microbial bioproduction to replace petrochemicals, it is critical to develop a new industrial microbial workhorse that improves the conversion of lignocellulosic carbon to biofuels and bioproducts in an economically feasible manner. Pseudomonas putida KT2440 is a promising microbial host due to its capability to grow on a broad range of carbon sources and its high tolerance to xenobiotics. In this study, we engineered P. putida KT2440 to produce isoprenoids, a vast category of compounds that provide routes to many petrochemical replacements. A heterologous mevalonate (MVA) pathway was engineered to produce potential biofuels isoprenol (C₅) and epi-isozizaene (C₁₅) for the first time in P. putida. We compared the difference between three different isoprenoid pathways in P. putida on isoprenol production and achieved 104 mg/L of isoprenol production in a batch flask experiment through optimization of the strain. As P. putida can natively consume isoprenol, we investigated how to prevent this self-consumption. We discovered that supplementing L-glutamate in the medium can effectively prevent isoprenol consumption in P. putida and metabolomics analysis showed an insufficient energy availability and an imbalanced redox status during isoprenol degradation. We also showed that the engineered P. putida strain can produce isoprenol using aromatic substrates such as p-coumarate as the sole carbon source, and this result demonstrates that P. putida is a valuable microbial chassis for isoprenoids to achieve sustainable biofuel production from lignocellulosic biomass.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Translating advances in microbial bioproduction to sustainable biotechnology

Advances in synthetic biology have radically changed our ability to rewire microorganisms and significantly improved the scalable production of a vast array of drop-in biopolymers and biofuels. The success of a drop-in bioproduct is contingent on market competition with petrochemical analogues and weighted upon relative economic and environmental metrics. While the quantification of comparative trade-offs is critical for accurate process-level decision making, the translation of industrial ecology to synthetic biology is often ambiguous and assessment accuracy has proven challenging. In this review, we explore strategies for evaluating industrial biotechnology through life cycle and techno-economic assessment, then contextualize how recent developments in synthetic biology have improved process viability by expanding feedstock availability and the productivity of microbes. By juxtaposing biological and industrial constraints, we highlight major obstacles between the disparate disciplines that hinder accurate process evaluation. The convergence of these disciplines is crucial in shifting towards carbon neutrality and a circular bioeconomy.

CRISPR-Based Approaches for Gene Regulation in Non-Model Bacteria

CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) have become ubiquitous approaches to control gene expression in bacteria due to their simple design and effectiveness. By regulating transcription of a target gene(s), CRISPRi/a can dynamically engineer cellular metabolism, implement transcriptional regulation circuitry, or elucidate genotype-phenotype relationships from smaller targeted libraries up to whole genome-wide libraries. While CRISPRi/a has been primarily established in the model bacteria Escherichia coli and Bacillus subtilis, a growing numbering of studies have demonstrated the extension of these tools to other species of bacteria (here broadly referred to as non-model bacteria). In this mini-review, we discuss the challenges that contribute to the slower creation of CRISPRi/a tools in diverse, non-model bacteria and summarize the current state of these approaches across bacterial phyla. We find that despite the potential difficulties in establishing novel CRISPRi/a in non-model microbes, over 190 recent examples across eight bacterial phyla have been reported in the literature. Most studies have focused on tool development or used these CRISPRi/a approaches to interrogate gene function, with fewer examples applying CRISPRi/a gene regulation for metabolic engineering or high-throughput screens and selections. To date, most CRISPRi/a reports have been developed for common strains of non-model bacterial species, suggesting barriers remain to establish these genetic tools in undomesticated bacteria. More efficient and generalizable methods will help realize the immense potential of programmable CRISPR-based transcriptional control in diverse bacteria.

Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems

CRISPR-Cas transcriptional circuits hold great promise as platforms for engineering metabolic networks and information processing circuits. Historically, prokaryotic CRISPR control systems have been limited to CRISPRi. Creating approaches to integrate CRISPRa for transcriptional activation with existing CRISPRi-based systems would greatly expand CRISPR circuit design space. Here, we develop design principles for engineering prokaryotic CRISPRa/i genetic circuits with network topologies specified by guide RNAs. We demonstrate that multi-layer CRISPRa/i cascades and feedforward loops can operate through the regulated expression of guide RNAs in cell-free expression systems and E. coli. We show that CRISPRa/i circuits can program complex functions by designing type 1 incoherent feedforward loops acting as fold-change detectors and tunable pulse-generators. By investigating how component characteristics relate to network properties such as depth, width, and speed, this work establishes a framework for building scalable CRISPRa/i circuits as regulatory programs in cell-free expression systems and bacterial hosts. A record of this paper's transparent peer review process is included in the supplemental information.

Nitrogen Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop-out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. Furthermore, we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers. IMPORTANCE Understanding the genetic basis of P. putida's diverse metabolism is imperative for us to reach its full potential as a host for metabolic engineering. Many target molecules of the bioeconomy and their precursors contain nitrogen. This study provides functional evidence linking hundreds of genes to their roles in the metabolism of nitrogenous compounds, and provides an interactive tool for visualizing these data. We further characterize several aminotransferases, lactamases, and regulators, which are of particular interest for metabolic engineering.

CRISPRi enables fast growth followed by stable aerobic pyruvate formation in Escherichia coli without auxotrophy

CRISPR interference (CRISPRi) was applied to enable the aerobic production of pyruvate in Escherichia coli MG1655 under glucose excess conditions by targeting the promoter regions of aceE or pdhR. Knockdown strains were cultivated in aerobic shaking flasks and the influence of inducer concentration and different sgRNA binding sites on the production of pyruvate was measured. Targeting the promoter regions of aceE or pdhR triggered pyruvate production during the exponential phase and reduced expression of aceE. In lab-scale bioreactor fermentations, an aceE silenced strain successfully produced pyruvate under fully aerobic conditions during the exponential phase, but loss of productivity occurred during a subsequent nitrogen-limited phase. Targeting the promoter region of pdhR enabled pyruvate production during the growth phase of cultivations, and a continued low-level accumulation during the nitrogen-limited production phase. Combinatorial targeting of the promoter regions of both aceE and pdhR in E. coli MG1655 pdCas9 psgRNA_aceE_234_pdhR_329 resulted in the stable aerobic production of pyruvate with non-growing cells at YP/S  =  0.36 ± 0.029 gPyruvate/gGlucose in lab-scale bioreactors throughout an extended nitrogen-limited production phase.

A Self-Sustained System Spanning the Primary and Secondary Metabolism Stages to Boost the Productivity of Streptomyces

Streptomyces species possess strong secondary metabolism, the switches of which from the primary metabolism are complex and thus a challenge to holistically optimize their productivities. To avoid the complex switches and to reduce the limitations of different metabolic stages on the synthesis of metabolites, we designed a Streptomyces self-sustained system (StSS) that contains two functional modules, the primary metabolism module (PM) and the secondary metabolism module (SM). The PM includes endogenous housekeeping sigma factor σ^hrdB and σ^hrdB-dependent promoters, which are used to express target genes in the primary metabolism phase. SM consists of the expression cassette of σ^hrdB under the control of a secondary metabolism promoter, which maintains continuous activity of the σ^hrdB-dependent promoters in the secondary metabolism phase. As a proof-of-principle, the StSS was used to boost the production of some non-toxic metabolites, including indigoidine, undecylprodigiosin (UDP), ergothioneine, and avermectin, in Streptomyces. All these metabolites can undergo a continuous production process spanning the primary and secondary metabolism stages instead of being limited to a specific stage. Scale-up of UDP fermentation in a 4 L fermentor indicated that the StSS is a stable and robust system, the titer of which was enhanced to 1.1 g/L, the highest at present. This study demonstrated that the StSS is a simple but powerful strategy to rationally engineer Streptomyces cell factories for the efficient production of non-toxic metabolites via reconstructing the relationships between primary and secondary metabolism.

Next-Generation Genetic and Fermentation Technologies for Safe and Sustainable Production of Food Ingredients: Colors and Flavorings

A growing human population is a significant issue in food security owing to the limited land and resources available for agricultural food production. To solve these problems, sustainable food manufacturing processes and the development of alternative foods and ingredients are needed. Metabolic engineering and synthetic biology can help solve the food security issue and satisfy the demand for alternative food production. Bioproduction of food ingredients by microbial fermentation is a promising method to replace current manufacturing processes, such as extraction from natural materials and chemical synthesis, with more ecofriendly and sustainable operations. This review highlights successful examples of bioproduction for food additives by engineered microorganisms, with an emphasis on colorants and flavors that are extensively used in the food industry. Recent strain engineering developments and fermentation strategies for producing selected food colorants and flavors are introduced with discussions on the current status and future perspectives.

Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genomics Characterization of an Engineered Corynebacterium glutamicum in Bioreactor Cultivation Under Ionic Liquid Stress

Corynebacterium glutamicum is an ideal microbial chassis for production of valuable bioproducts including amino acids and next generation biofuels. Here we resequence engineered isopentenol (IP) producing C. glutamicum BRC-JBEI 1.1.2 strain and assess differential transcriptional profiles using RNA sequencing under industrially relevant conditions including scale transition and compare the presence vs absence of an ionic liquid, cholinium lysinate ([Ch][Lys]). Analysis of the scale transition from shake flask to bioreactor with transcriptomics identified a distinct pattern of metabolic and regulatory responses needed for growth in this industrial format. These differential changes in gene expression corroborate altered accumulation of organic acids and bioproducts, including succinate, acetate, and acetoin that occur when cells are grown in the presence of 50 mM [Ch][Lys] in the stirred-tank reactor. This new genome assembly and differential expression analysis of cells grown in a stirred tank bioreactor clarify the cell response of an C. glutamicum strain engineered to produce IP.

Synthetic Biology Advanced Natural Product Discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

Development of dual-inducible duet-expression vectors for tunable gene expression control and CRISPR interference-based gene repression in Pseudomonas putida KT2440

The development of P. putida as an industrial host requires a sophisticated molecular toolbox for strain improvement, including vectors for gene expression and repression. To augment existing expression plasmids for metabolic engineering, we developed a series of dual-inducible duet-expression vectors for P. putida KT2440. A number of inducible promoters (Plac , Ptac , PtetR/tetA and Pbad ) were used in different combinations to differentially regulate the expression of individual genes. Protein expression was evaluated by measuring the fluorescence of reporter proteins (GFP and RFP). Our experiments demonstrated the use of compatible plasmids, a useful approach to coexpress multiple genes in P. putida KT2440. These duet vectors were modified to generate a fully inducible CRISPR interference system using two catalytically inactive Cas9 variants from S. pasteurianus (dCas9) and S. pyogenes (spdCas9). The utility of developed CRISPRi system(s) was demonstrated by repressing the expression of nine conditionally essential genes, resulting in growth impairment and prolonged lag phase for P. putida KT2440 growth on glucose. Furthermore, the system was shown to be tightly regulated, tunable and to provide a simple way to identify essential genes with an observable phenotype.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli

We report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. Deregulated networks are less sensitive to environmental conditions improving process robustness. Process robustness in turn leads to predictable scalability, minimizing the need for traditional process optimization. We validate process robustness and scalability of strains and bioprocesses synthesizing the important industrial chemicals alanine, citramalate and xylitol. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

Saccharomyces cerevisiae as host for the recombinant production of polyketides and nonribosomal peptides

As a robust, fast growing and genetically tractable organism, the budding yeast Saccharomyces cerevisiae is one of the most widely used hosts in biotechnology. Its applications range from the manufacturing of vaccines and hormones to bulk chemicals and biofuels. In recent years, major efforts have been undertaken to expand this portfolio to include structurally complex natural products, such as polyketides and nonribosomally synthesized peptides. These compounds often have useful pharmacological properties, which make them valuable drugs for the treatment of infectious diseases, cancer, or autoimmune disorders. In nature, polyketides and nonribosomal peptides are generated by consecutive condensation reactions of short chain acyl-CoAs or amino acids, respectively, with the substrates and reaction intermediates being bound to large, multidomain enzymes. For the reconstitution of these multistep catalytic processes, the enzymatic assembly lines need to be functionally expressed and the required substrates must be supplied in reasonable quantities. Furthermore, the production hosts need to be protected from the toxicity of the biosynthetic products. In this review, we will summarize and evaluate the status quo regarding the heterologous production of polyketides and nonribosomal peptides in S. cerevisiae. Based on a comprehensive literature analysis, prerequisites for a successful pathway reconstitution could be deduced, as well as recurring bottlenecks in this microbial host.

Model-guided dynamic control of essential metabolic nodes boosts acetyl-coenzyme A-dependent bioproduction in rewired Pseudomonas putida

Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.

Production of Rainbow Colorants by Metabolically Engineered Escherichia coli

There has been much interest in producing natural colorants to replace synthetic colorants of health concerns. Escherichia coli has been employed to produce natural colorants including carotenoids, indigo, anthocyanins, and violacein. However, production of natural green and navy colorants has not been reported. Many natural products are hydrophobic, which are accumulated inside or on the cell membrane. This causes cell growth limitation and consequently reduces production of target chemicals. Here, integrated membrane engineering strategies are reported for the enhanced production of rainbow colorants-three carotenoids and four violacein derivatives-as representative hydrophobic natural products in E. coli. By integration of systems metabolic engineering, cell morphology engineering, inner- and outer-membrane vesicle formation, and fermentation optimization, production of rainbow colorants are significantly enhanced to 322 mg L-1 of astaxanthin (red), 343 mg L-1 of β-carotene (orange), 218 mg L-1 of zeaxanthin (yellow), 1.42 g L-1 of proviolacein (green), 0.844 g L-1 of prodeoxyviolacein (blue), 6.19 g L-1 of violacein (navy), and 11.26 g L-1 of deoxyviolacein (purple). The membrane engineering strategies reported here are generally applicable to microbial production of a broader range of hydrophobic natural products, contributing to food, cosmetic, chemical, and pharmaceutical industries.

Stepwise genetic engineering of Pseudomonas putida enables robust heterologous production of prodigiosin and glidobactin A

Polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS) comprise biosynthetic pathways that provide access to diverse, often bioactive natural products. Metabolic engineering can improve production metrics to support characterization and drug-development studies, but often native hosts are difficult to genetically manipulate and/or culture. For this reason, heterologous expression is a common strategy for natural product discovery and characterization. Many bacteria have been developed to express heterologous biosynthetic gene clusters (BGCs) for producing polyketides and nonribosomal peptides. In this article, we describe tools for using Pseudomonas putida, a Gram-negative soil bacterium, as a heterologous host for producing natural products. Pseudomonads are known to produce many natural products, but P. putida production titers have been inconsistent in the literature and often low compared to other hosts. In recent years, synthetic biology tools for engineering P. putida have greatly improved, but their application towards production of natural products is limited. To demonstrate the potential of P. putida as a heterologous host, we introduced BGCs encoding the synthesis of prodigiosin and glidobactin A, two bioactive natural products synthesized from a combination of PKS and NRPS enzymology. Engineered strains exhibited robust production of both compounds after a single chromosomal integration of the corresponding BGC. Next, we took advantage of a set of genome-editing tools to increase titers by modifying transcription and translation of the BGCs and increasing the availability of auxiliary proteins required for PKS and NRPS activity. Lastly, we discovered genetic modifications to P. putida that affect natural product synthesis, including a strategy for removing a carbon sink that improves product titers. These efforts resulted in production strains capable of producing 1.1 g/L prodigiosin and 470 mg/L glidobactin A.