Engineering Cellular Metabolism

Advances in stress-tolerance elements for microbial cell factories

Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.

Design-build-test of recombinant Bacillus subtilis chassis cell by lifespan engineering for robust bioprocesses

Microbial cell factories utilize renewable raw materials for industrial chemical production, providing a promising path for sustainable development. Bacillus subtilis is widely used in industry for its food safety properties, but challenges remain in the limitations of microbial fermentation. This study proposes a novel strategy based on lifespan engineering to design robust B. subtilis chassis cells to supplement traditional metabolic modification strategies that can alleviate cell autolysis, tolerate toxic substrates, and get a higher mass transfer efficiency. The modified chassis cells could produce high levels of l-glutaminase, and tolerate hydroquinone to produce α-arbutin efficiently. In a 5 L bioreactor, the l-glutaminase enzyme activity of the final strain CRE15TG was increased to 2817.4 ± 21.7 U mL-1, about 1.98-fold compared with that of the wild type. The α-arbutin yield of strain CRE15A was increased to 134.7 g L-1, about 1.34-fold compared with that of the WT. To our knowledge, both of the products in this study performed the highest yields reported so far. The chassis modification strategy described in this study can Improve the utilization efficiency of chassis cells, mitigate the possible adverse effects caused by excessive metabolic modification of engineered strains, and provide a new idea for the future design of microbial cell factories.

Facilitating stable gene integration expression and copy number amplification in Bacillus subtilis through a reversible homologous recombination switch

Strengthening the expression level of integrated genes on the genome is crucial for consistently expressing key enzymes in microbial cell factories for efficient bioproduction in synthetic biology. In comparison to plasmid-based multi-copy expression, the utilization of chromosomal multi-copy genes offers increased stability of expression level, diminishes the metabolic burden on host cells, and enhances overall genetic stability. In this study, we developed the "BacAmp", a stabilized gene integration expression and copy number amplification system for high-level expression in Bacillus subtilis, which was achieved by employing a combination of repressor and non-natural amino acids (ncAA)-dependent expression system to create a reversible switch to control the key gene recA for homologous recombination. When the reversible switch is turned on, genome editing and gene amplification can be achieved. Subsequently, the reversible switch was turned off therefore stabilizing the gene copy number. The stabilized gene amplification system marked by green fluorescent protein, achieved a 3-fold increase in gene expression by gene amplification and maintained the average gene copy number at 10 after 110 generations. When we implemented the gene amplification system for the regulation of N-acetylneuraminic acid (NeuAc) synthesis, the copy number of the critical gene increased to an average of 7.7, which yielded a 1.3-fold NeuAc titer. Our research provides a new avenue for gene expression in synthetic biology and can be applied in metabolic engineering in B. subtilis.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

Machine learning: an advancement in biochemical engineering

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

Reprogramming cellular metabolism to increase the efficiency of microbial cell factories

Recent studies are increasingly focusing on advanced biotechnological tools, self-adjusting smart microorganisms, and artificial intelligent networks, to engineer microorganisms with various functions. Microbial cell factories are a vital platform for improving the bioproduction of medicines, biofuels, and biomaterials from renewable carbon sources. However, these processes are significantly affected by cellular metabolism, and boosting the efficiency of microbial cell factories remains a challenge. In this review, we present a strategy for reprogramming cellular metabolism to enhance the efficiency of microbial cell factories for chemical biosynthesis, which improves our understanding of microbial physiology and metabolic control. Current methods are mainly focused on synthetic pathways, metabolic resources, and cell performance. This review highlights the potential biotechnological strategy to reprogram cellular metabolism and provide novel guidance for designing more intelligent industrial microbes with broader applications in this growing field.

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Modulating metabolism through synthetic biology: Opportunities for two-stage fermentation

Bio-based production of fuels, chemicals and materials is needed to replace current fossil fuel based production. However, bio-based production processes are very costly, so the process needs to be as efficient as possible. Developments in synthetic biology tools has made it possible to dynamically modulate cellular metabolism during a fermentation. This can be used towards two-stage fermentations, where the process is separated into a growth and a production phase, leading to more efficient feedstock utilization and thus potentially lower costs. This article reviews the current status and some recent results in application of synthetic biology tools towards two-stage fermentations, and compares this approach to pre-existing ones, such as nutrient limitation and addition of toxins/inhibitors.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

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.

Debottlenecking the L-DOPA 4,5-dioxygenase step with enhanced tyrosine supply boosts betalain production in Nicotiana benthamiana

Synthetic biology provides emerging tools to produce valuable compounds in plant hosts as sustainable chemical production platforms. However, little is known about how supply and utilization of precursors is coordinated at the interface of plant primary and specialized metabolism, limiting our ability to efficiently produce high levels of target specialized metabolites in plants. L-Tyrosine is an aromatic amino acid precursor of diverse plant natural products including betalain pigments, which are used as the major natural food red colorants and more recently a visual marker for plant transformation. Here, we studied the impact of enhanced L-tyrosine supply on the production of betalain pigments by expressing arogenate dehydrogenase (TyrA) from table beet (Beta vulgaris, BvTyrAα), which has relaxed feedback inhibition by L-tyrosine. Unexpectedly, betalain levels were reduced when BvTyrAα was coexpressed with the betalain pathway genes in Nicotiana benthamiana leaves; L-tyrosine and 3,4-dihydroxy-L-phenylalanine (L-DOPA) levels were drastically elevated but not efficiently converted to betalains. An additional expression of L-DOPA 4,5-dioxygenase (DODA), but not CYP76AD1 or cyclo-DOPA 5-O-glucosyltransferase, together with BvTyrAα and the betalain pathway, drastically enhanced betalain production, indicating that DODA is a major rate-limiting step of betalain biosynthesis in this system. Learning from this initial test and further debottlenecking the DODA step maximized betalain yield to an equivalent or higher level than that in table beet. Our data suggest that balancing between enhanced supply ("push") and effective utilization ("pull") of precursor by alleviating a bottleneck step is critical in successful plant synthetic biology to produce high levels of target compounds.

Multi-Stimuli-Responsive Network of Multicatalytic Reactions using a Single Palladium/Platinum Catalyst

Given her unrivalled proficiency in the synthesis of all molecules of life, nature has been an endless source of inspiration for developing new strategies in organic chemistry and catalysis. However, one feature that remains thus far beyond chemists' grasp is her unique ability to adapt the productivity of metabolic processes in response to triggers that indicate the temporary need for specific metabolites. To demonstrate the remarkable potential of such stimuli-responsive systems, we present a metabolism-inspired network of multicatalytic processes capable of selectively synthesising a range of products from simple starting materials. Specifically, the network is built of four classes of distinct catalytic reactions-cross-couplings, substitutions, additions, and reductions, involving three organic starting materials-terminal alkyne, aryl iodide, and hydrosilane. All starting materials are either introduced sequentially or added to the system at the same time, with no continuous influx of reagents or efflux of products. All processes in the system are catalysed by a multifunctional heteronuclear PdII/PtII complex, whose performance can be controlled by specific additives and external stimuli. The reaction network exhibits a substantial degree of orthogonality between different pathways, enabling the controllable synthesis of ten distinct products with high efficiency and selectivity through simultaneous triggering and suppression mechanisms.

Physiochemically and Genetically Engineered Bacteria: Instructive Design Principles and Diverse Applications

With the comprehensive understanding of microorganisms and the rapid advances of physiochemical engineering and bioengineering technologies, scientists are advancing rationally-engineered bacteria as emerging drugs for treating various diseases in clinical disease management. Engineered bacteria specifically refer to advanced physiochemical or genetic technologies in combination with cutting edge nanotechnology or physical technologies, which have been validated to play significant roles in lysing tumors, regulating immunity, influencing the metabolic pathways, etc. However, there has no specific reviews that concurrently cover physiochemically- and genetically-engineered bacteria and their derivatives yet, let alone their distinctive design principles and various functions and applications. Herein, the applications of physiochemically and genetically-engineered bacteria, and classify and discuss significant breakthroughs with an emphasis on their specific design principles and engineering methods objective to different specific uses and diseases beyond cancer is described. The combined strategies for developing in vivo biotherapeutic agents based on these physiochemically- and genetically-engineered bacteria or bacterial derivatives, and elucidated how they repress cancer and other diseases is also underlined. Additionally, the challenges faced by clinical translation and the future development directions are discussed. This review is expected to provide an overall impression on physiochemically- and genetically-engineered bacteria and enlighten more researchers.

Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia

Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.

Combining enzyme and metabolic engineering for microbial supply of therapeutic phytochemicals

The history of pharmacology is deeply intertwined with plant-derived compounds, which continue to be crucial in drug development. However, their complex structures and limited availability in plants challenge drug discovery, optimization, development, and industrial production via chemical synthesis or natural extraction. This review delves into the integration of metabolic and enzyme engineering to leverage micro-organisms as platforms for the sustainable and reliable production of therapeutic phytochemicals. We argue that engineered microbes can serve a triple role in this paradigm: facilitating pathway discovery, acting as cell factories for scalable manufacturing, and functioning as platforms for chemical derivatization. Analyzing recent progress and outlining future directions, the review highlights microbial biotechnology's transformative potential in expanding plant-derived human therapeutics' discovery and supply chains.

Concentration Recognition-Based Auto-Dynamic Regulation System (CRUISE) Enabling Efficient Production of Higher Alcohols

Microbial factories lacking the ability of dynamically regulating the pathway enzymes overexpression, according to in situ metabolite concentrations, are suboptimal, especially when the metabolic intermediates are competed by growth and chemical production. The production of higher alcohols (HAs), which hijacks the amino acids (AAs) from protein biosynthesis, minimizes the intracellular concentration of AAs and thus inhibits the host growth. To balance the resource allocation and maintain stable AA flux, this work utilizes AA-responsive transcriptional attenuator ivbL and HA-responsive transcriptional activator BmoR to establish a concentration recognition-based auto-dynamic regulation system (CRUISE). This system ultimately maintains the intracellular homeostasis of AA and maximizes the production of HA. It is demonstrated that ivbL-driven enzymes overexpression can dynamically regulate the AA-to-HA conversion while BmoR-driven enzymes overexpression can accelerate the AA biosynthesis during the HA production in a feedback activation mode. The AA flux in biosynthesis and conversion pathways is balanced via the intracellular AA concentration, which is vice versa stabilized by the competition between AA biosynthesis and conversion. The CRUISE, further aided by scaffold-based self-assembly, enables 40.4 g L-1 of isobutanol production in a bioreactor. Taken together, CRUISE realizes robust HA production and sheds new light on the dynamic flux control during the process of chemical production.

Can biotechnology lead the way toward a sustainable pharmaceutical industry?

The impact-intensive and rapidly growing pharmaceutical industry must ensure its sustainability. This study reveals that environmental sustainability assessments have been conducted for only around 0.2% of pharmaceuticals, environmental impacts have significant variations among the assessed products, and different impact categories have not been consistently studied. Highly varied impacts require assessing more products to understand the industry's sustainability status. Reporting all impact categories will be crucial, especially when comparing production technologies. Biological production of (semi)synthetic pharmaceuticals could reduce their environmental costs, though the high impacts of biologically produced monoclonal antibodies should also be optimized. Considering the sustainability potential of biopharmaceuticals from economic, environmental, and social perspectives, collaboratively guiding their immense market growth would lead to the industry's sustainability transition.

TRYing to evaluate production costs in microbial biotechnology

Microbial fermentations offer the opportunity to produce a wide range of chemicals in a sustainable fashion, but it is important to carefully evaluate the production costs. This can be done on the basis of evaluation of the titer, rate, and yield (TRY) of the fermentation process. Here we describe how the three TRY metrics impact the technoeconomics of a microbial fermentation process, and we illustrate the use of these for evaluation of different processes in the production of two commodity chemicals, 1,3-propanediol (PDO) and ethanol, as well as for the fine chemical penicillin. On the basis of our discussions, we provide some recommendations on how the TRY metrics should be reported when new processes are described.

β-Dicarbonyls Facilitate Engineered Microbial Bromoform Biosynthesis

Ruminant livestock produce around 24% of global anthropogenic methane emissions. Methanogenesis in the animal rumen is significantly inhibited by bromoform, which is abundant in seaweeds of the genus Asparagopsis. This has prompted the development of livestock feed additives based on Asparagopsis to mitigate methane emissions, although this approach alone is unlikely to satisfy global demand. Here we engineer a non-native biosynthesis pathway to produce bromoform in vivo with yeast as an alternative biological source that may enable sustainable, scalable production of bromoform by fermentation. β-dicarbonyl compounds with low pKa values were identified as essential substrates for bromoform production and enabled bromoform synthesis in engineered Saccharomyces cerevisiae expressing a vanadate-dependent haloperoxidase gene. In addition to providing a potential route to the sustainable biological production of bromoform at scale, this work advances the development of novel microbial biosynthetic pathways for halogenation.

Enzymatic synthesis of mono- and trifluorinated alanine enantiomers expands the scope of fluorine biocatalysis

Fluorinated amino acids serve as an entry point for establishing new-to-Nature chemistries in biological systems, and novel methods are needed for the selective synthesis of these building blocks. In this study, we focused on the enzymatic synthesis of fluorinated alanine enantiomers to expand fluorine biocatalysis. The alanine dehydrogenase from Vibrio proteolyticus and the diaminopimelate dehydrogenase from Symbiobacterium thermophilum were selected for in vitro production of (R)-3-fluoroalanine and (S)-3-fluoroalanine, respectively, using 3-fluoropyruvate as the substrate. Additionally, we discovered that an alanine racemase from Streptomyces lavendulae, originally selected for setting an alternative enzymatic cascade leading to the production of these non-canonical amino acids, had an unprecedented catalytic efficiency in β-elimination of fluorine from the monosubstituted fluoroalanine. The in vitro enzymatic cascade based on the dehydrogenases of V. proteolyticus and S. thermophilum included a cofactor recycling system, whereby a formate dehydrogenase from Pseudomonas sp. 101 (either native or engineered) coupled formate oxidation to NAD(P)H formation. Under these conditions, the reaction yields for (R)-3-fluoroalanine and (S)-3-fluoroalanine reached >85% on the fluorinated substrate and proceeded with complete enantiomeric excess. The selected dehydrogenases also catalyzed the conversion of trifluoropyruvate into trifluorinated alanine as a first-case example of fluorine biocatalysis with amino acids carrying a trifluoromethyl group.

Decolorization and detoxication of malachite green by engineered Saccharomyces cerevisiae expressing novel thermostable laccase from Trametes trogii

Malachite Green (MG) is a widely used industrial dye that is hazardous to health. Herein, the decolourisation and detoxification of MG were achieved using the engineered Saccharomyces cerevisiae expressing novel thermostable laccase lcc1 from Trametes trogii. The engineered strain RCL produced a high laccase activity of 121.83 U L-1. Lcc1 was stable at temperatures ranging from 20 ℃ to 60 ℃ and showed a high tolerance to organic solvents. Moreover, Lcc1 could decolorize different kinds of dyes (azo, anthraquinone and triphenylmethane), among which, the decolorization ability of MG is the highest, reaching 95.10 %, and the decolorization rate of other triphenylmethane dyes also over 50 %. The RCL decolorized about 95 % of 50 mg L-1 of MG dye in 10 h at 30 ℃. The MG degradation products were analyzed. The industrial application potential of the RCL was evaluated by treating industrial wastewater and the decolourisation rates were over 90 %.

Developing the E. coli platform for efficient production of UMP-derived chemicals

5-Methyluridine (5-MU) is a prominent intermediate for industrial synthesis of several antiviral-drugs, however, its availability over the past decades has overwhelmingly relied on chemical and enzymatic strategies. Here, we have realized efficient production of 5-MU in E. coli, for the first time, via a designer artificial pathway consisting of a two-enzyme cascade (UMP 5-methylase and phosphatase). More importantly, we have engineered the E. coli cell factory to boost 5-MU production by systematic evaluation of multiple strategies, and as a proof of concept, we have further developed an antibiotic-free fermentation strategy to realize 5-MU production (10.71 g/L) in E. coli MB229 (a ΔthyA strain). Remarkably, we have also established a versatile and robust platform with exploitation of the engineered E. coli for efficient production of diversified UMP-derived chemicals. This study paves the way for future engineering of E. coli as a synthetic biology platform for acceleratively accessing UMP-derived chemical diversities.

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.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

A model-driven approach to upcycling recalcitrant feedstocks in Pseudomonas putida by decoupling PHA production from nutrient limitation

Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.

Ogataea polymorpha as a next-generation chassis for industrial biotechnology

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

AI in cellular engineering and reprogramming

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.

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

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

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

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

Proteomic analysis reveals that the co-ordination of cytosolic and mitochondrial pathways is beneficial for sabinene biosynthesis in engineered Saccharomyces cerevisiae

Reconstruction and optimization of biosynthetic pathways can help to overproduce target chemicals in microbial cell factories based on genetic engineering. However, the perturbation of biosynthetic pathways on cellular metabolism is not well investigated and profiling the engineered microbes remains challenging. The rapid development of omics tools has the potential to characterize the engineered microbial cell factory. Here, we performed label-free quantitative proteomic analysis and metabolomic analysis of engineered sabinene overproducing Saccharomyces cerevisiae strains. Combined metabolic analysis andproteomic analysis of targeted mevalonate (MVA) pathway showed that co-ordination of cytosolic and mitochondrial pathways had balanced metabolism, and genome integration of biosynthetic genes had higher sabinene production with less MVA enzymes. Furthermore, comparative proteomic analysis showed that compartmentalized mitochondria pathway had perturbation on central cellular metabolism. This study provided an omics analysis example for characterizing engineered cell factory, which can guide future regulation of the cellular metabolism and maintaining optimal protein expression levels for the synthesis of target products.

Model-guided metabolic rewiring to bypass pyruvate oxidation for pyruvate derivative synthesis by minimizing carbon loss

Engineering microbial hosts to synthesize pyruvate derivatives depends on blocking pyruvate oxidation, thereby causing severe growth defects in aerobic glucose-based bioprocesses. To decouple pyruvate metabolism from cell growth to improve pyruvate availability, a genome-scale metabolic model combined with constraint-based flux balance analysis, geometric flux balance analysis, and flux variable analysis was used to identify genetic targets for strain design. Using translation elements from a ~3,000 cistronic library to modulate fxpK expression in a bicistronic cassette, a bifido shunt pathway was introduced to generate three molecules of non-pyruvate-derived acetyl-CoA from one molecule of glucose, bypassing pyruvate oxidation and carbon dioxide generation. The dynamic control of flux distribution by T7 RNAP-mediated synthetic small RNA decoupled pyruvate catabolism from cell growth. Adaptive laboratory evolution and multi-omics analysis revealed that a mutated isocitrate dehydrogenase functioned as a metabolic switch to activate the glyoxylate shunt as the only C4 anaplerotic pathway to generate malate from two molecules of acetyl-CoA input and bypass two decarboxylation reactions in the tricarboxylic acid cycle. A chassis strain for pyruvate derivative synthesis was constructed to reduce carbon loss by using the glyoxylate shunt as the only C4 anaplerotic pathway and the bifido shunt as a non-pyruvate-derived acetyl-CoA synthetic pathway and produced 22.46, 27.62, and 6.28 g/L of l-leucine, l-alanine, and l-valine by a controlled small RNA switch, respectively. Our study establishes a novel metabolic pattern of glucose-grown bacteria to minimize carbon loss under aerobic conditions and provides valuable insights into cell design for manufacturing pyruvate-derived products.IMPORTANCEBio-manufacturing from biomass-derived carbon sources using microbes as a cell factory provides an eco-friendly alternative to petrochemical-based processes. Pyruvate serves as a crucial building block for the biosynthesis of industrial chemicals; however, it is different to improve pyruvate availability in vivo due to the coupling of pyruvate-derived acetyl-CoA with microbial growth and energy metabolism via the oxidative tricarboxylic acid cycle. A genome-scale metabolic model combined with three algorithm analyses was used for strain design. Carbon metabolism was reprogrammed using two genetic control tools to fine-tune gene expression. Adaptive laboratory evolution and multi-omics analysis screened the growth-related regulatory targets beyond rational design. A novel metabolic pattern of glucose-grown bacteria is established to maintain growth fitness and minimize carbon loss under aerobic conditions for the synthesis of pyruvate-derived products. This study provides valuable insights into the design of a microbial cell factory for synthetic biology to produce industrial bio-products of interest.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges

Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Jensen M. teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Identification of crucial roles of transcription factor IhfA on high production of free fatty acids in Escherichia coli

Transcription factor engineering has unique advantages in improving the performance of microbial cell factories due to the global regulation of gene transcription. Omics analyses and reverse engineering enable learning and subsequent incorporation of novel design strategies for further engineering. Here, we identify the role of the global regulator IhfA for overproduction of free fatty acids (FFAs) using CRISPRi-facilitated reverse engineering and cellular physiological characterization. From the differentially expressed genes in the ihfAL- strain, a total of 14 beneficial targets that enhance FFAs production by above 20 % are identified, which involve membrane function, oxidative stress, and others. For membrane-related genes, the engineered strains obtain lower cell surface hydrophobicity and increased average length of membrane lipid tails. For oxidative stress-related genes, the engineered strains present decreased reactive oxygen species (ROS) levels. These gene modulations enhance cellular robustness and save cellular resources, contributing to FFAs production. This study provides novel targets and strategies for engineering microbial cell factories with improved FFAs bioproduction.

Microbial-Driven Synthesis and Hydrolysis of Neohesperidin Dihydrochalcone: Biotransformation Process and Feasibility Investigation

A novel yeast-mediated hydrogenation was developed for the synthesis of neohesperidin dihydrochalcone (NHDC) in high yields (over 83%). Moreover, whole-cell catalytic hydrolysis was also designed to hydrolyze NHDC into potential sweeteners, hesperetin dihydrochalcone-7-O-glucoside (HDC-G) and hesperetin dihydrochalcone (HDC). The biohydrogenation was further combined with whole-cell hydrolysis to achieve a one-pot two-step biosynthesis, utilizing yeast to hydrogenate C═C in the structure, while Aspergillus niger cells hydrolyze glycosides. The conversion of NHDC and the proportion of hydrolysis products could be controlled by adjusting the catalysts, the components of the reaction system, and the addition of glucose. Furthermore, yeast-mediated biotransformation demonstrated superior reaction stability and enhanced safety and employed more cost-effective catalysts compared to the traditional chemical hydrogenation of NHDC synthesis. This research not only provides a new route for NHDC production but also offers a safe and flexible one-pot cascade biosynthetic platform for the production of high-value compounds from citrus processing wastes.

Autotrophic growth of Escherichia coli is achieved by a small number of genetic changes

Synthetic autotrophy is a promising avenue to sustainable bioproduction from CO2. Here, we use iterative laboratory evolution to generate several distinct autotrophic strains. Utilising this genetic diversity, we identify that just three mutations are sufficient for Escherichia coli to grow autotrophically, when introduced alongside non-native energy (formate dehydrogenase) and carbon-fixing (RuBisCO, phosphoribulokinase, carbonic anhydrase) modules. The mutated genes are involved in glycolysis (pgi), central-carbon regulation (crp), and RNA transcription (rpoB). The pgi mutation reduces the enzyme's activity, thereby stabilising the carbon-fixing cycle by capping a major branching flux. For the other two mutations, we observe down-regulation of several metabolic pathways and increased expression of native genes associated with the carbon-fixing module (rpiB) and the energy module (fdoGH), as well as an increased ratio of NADH/NAD+ - the cycle's electron-donor. This study demonstrates the malleability of metabolism and its capacity to switch trophic modes using only a small number of genetic changes and could facilitate transforming other heterotrophic organisms into autotrophs.

Production of Biobased Ethylbenzene by Cascade Biocatalysis with an Engineered Photodecarboxylase

Production of commodity chemicals, such as benzene, toluene, ethylbenzene, and xylenes (BTEX), from renewable resources is key for a sustainable society. Biocatalysis enables one-pot multistep transformation of bioresources under mild conditions, yet it is often limited to biochemicals. Herein, we developed a non-natural three-enzyme cascade for one-pot conversion of biobased l-phenylalanine into ethylbenzene. The key rate-limiting photodecarboxylase was subjected to structure-guided semirational engineering, and a triple mutant CvFAP(Y466T/P460A/G462I) was obtained with a 6.3-fold higher productivity. With this improved photodecarboxylase, an optimized two-cell sequential process was developed to convert l-phenylalanine into ethylbenzene with 82 % conversion. The cascade reaction was integrated with fermentation to achieve the one-pot bioproduction of ethylbenzene from biobased glycerol, demonstrating the potential of cascade biocatalysis plus enzyme engineering for the production of biobased commodity chemicals.

Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast

Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.

Metabolomics-based development of bioproduction processes toward industrial-scale production

Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.

From mechanism to application: Decrypting light-regulated denitrifying microbiome through geometric deep learning

Regulation on denitrifying microbiomes is crucial for sustainable industrial biotechnology and ecological nitrogen cycling. The holistic genetic profiles of microbiomes can be provided by meta-omics. However, precise decryption and further applications of highly complex microbiomes and corresponding meta-omics data sets remain great challenges. Here, we combined optogenetics and geometric deep learning to form a discover-model-learn-advance (DMLA) cycle for denitrification microbiome encryption and regulation. Graph neural networks (GNNs) exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels, which could be utilized to predict unknown phenotypes, elucidate molecular biology mechanisms, and advance biotechnologies. Through the DMLA cycle, we discovered the wavelength-divergent secretion system and nitrate-superoxide coregulation, realizing increasing extracellular protein production by 83.8% and facilitating nitrate removal with 99.9% enhancement. Our study showcased the potential of GNNs-empowered optogenetic approaches for regulating denitrification and accelerating the mechanistic discovery of microbiomes for in-depth research and versatile applications.

Unlocking the potential of bacterial endophytes from medicinal plants for drug discovery

Among the plant-associated microorganisms, the so-called endophytes continue to attract much attention because of their ability not only to protect host plants from biotic and abiotic stress factors, but also the potential to produce bioactive secondary metabolites. The latter property can elicit growth-promoting effects on plants, as well as boost the production of plant-specific secondary metabolites with valuable pharmacological properties. In addition, endophyte-derived secondary metabolites may be a rich source for the discovery of drugs to treat various diseases, including infections and cancer. However, the full potential of endophytes to produce bioactive secondary metabolites is often not revealed upon conventional cultivation in the laboratory. New advances in genomics and metabolic engineering offer exciting opportunities for the exploration and exploitation of endophytes' biosynthetic potential. This review focuses on bacterial endophytes of medicinal plants, some of their secondary metabolites and recent advances in deciphering their biosynthesis. The latter may assist in genetic engineering efforts aimed at the discovery of novel bioactive compounds with the potential to be developed into drugs.

Engineering peroxisomal biosynthetic pathways for maximization of triterpene production in Yarrowia lipolytica

Constructing efficient cell factories for product synthesis is frequently hampered by competing pathways and/or insufficient precursor supply. This is particularly evident in the case of triterpenoid biosynthesis in Yarrowia lipolytica, where squalene biosynthesis is tightly coupled to cytosolic biosynthesis of sterols essential for cell viability. Here, we addressed this problem by reconstructing the complete squalene biosynthetic pathway, starting from acetyl-CoA, in the peroxisome, thus harnessing peroxisomal acetyl-CoA pool and sequestering squalene synthesis in this organelle from competing cytosolic reactions. This strategy led to increasing the squalene levels by 1,300-fold relatively to native cytosolic synthesis. Subsequent enhancement of the peroxisomal acetyl-CoA supply by two independent approaches, 1) converting cellular lipid pool to peroxisomal acetyl-CoA and 2) establishing an orthogonal acetyl-CoA shortcut from CO2-derived acetate in the peroxisome, further significantly improved local squalene accumulation. Using these approaches, we constructed squalene-producing strains capable of yielding 32.8 g/L from glucose, and 31.6 g/L from acetate by employing a cofeeding strategy, in bioreactor fermentations. Our findings provide a feasible strategy for protecting intermediate metabolites that can be claimed by multiple reactions by engineering peroxisomes in Y. lipolytica as microfactories for the production of such intermediates and in particular acetyl-CoA-derived metabolites.

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 .

Simple phenylpropanoids: recent advances in biological activities, biosynthetic pathways, and microbial production

Covering: 2000 to 2023Simple phenylpropanoids are a large group of natural products with primary C6-C3 skeletons. They are not only important biomolecules for plant growth but also crucial chemicals for high-value industries, including fragrances, nutraceuticals, biomaterials, and pharmaceuticals. However, with the growing global demand for simple phenylpropanoids, direct plant extraction or chemical synthesis often struggles to meet current needs in terms of yield, titre, cost, and environmental impact. Benefiting from the rapid development of metabolic engineering and synthetic biology, microbial production of natural products from inexpensive and renewable sources provides a feasible solution for sustainable supply. This review outlines the biological activities of simple phenylpropanoids, compares their biosynthetic pathways in different species (plants, bacteria, and fungi), and summarises key research on the microbial production of simple phenylpropanoids over the last decade, with a focus on engineering strategies that seem to hold most potential for further development. Moreover, constructive solutions to the current challenges and future perspectives for industrial production of phenylpropanoids are presented.

Coenzyme A Thioester Intermediates as Platform Molecules in Cell-Free Chemical Biomanufacturing

The in vitro synthesis of Coenzyme A (CoA)-thioester intermediates opens new avenues to transform simple molecules into more complex and multifunctional ones by assembling cell-free biosynthetic cascades. In this review, we have systematically cataloged known CoA-dependent enzyme reactions that have been successfully implemented in vitro. To faciliate their identification, we provide their UniProt ID when available. Based on this catalog, we have organized enzymes into three modules: activation, modification, and removal. i) The activation module includes enzymes capable of fusing CoA with organic molecules. ii) The modification module includes enzymes capable of catalyzing chemical modifications in the structure of acyl-CoA intermediates. And iii) the removal module includes enzymes able to remove the CoA and release an organic molecule different from the one activated in the upstream. Based on these reactions, we constructed a reaction network that summarizes the most relevant CoA-dependent biosynthetic pathways reported until today. From the information available in the articles, we have plotted the total turnover number of CoA as a function of the product titer, observing a positive correlation between both parameters. Therefore, the success of a CoA-dependent in vitro pathway depends on its ability to regenerate CoA, but also to regenerate other cofactors such as NAD(P)H and ATP.

Light-Driven Metabolic Pathways in Non-Photosynthetic Biohybrid Bacteria

Biomanufacturing via microorganisms relies on carbon substrates for molecular feedstocks and a source of energy to carry out enzymatic reactions. This creates metabolic bottlenecks and lowers the efficiency for substrate conversion. Nanoparticle biohybridization with proteins and whole cell surfaces can bypass the need for redox cofactor regeneration for improved secondary metabolite production in a non-specific manner. Here we propose using nanobiohybrid organisms (Nanorgs), intracellular protein-nanoparticle hybrids formed through the spontaneous coupling of core-shell quantum dots (QDs) with histidine-tagged enzymes in non-photosynthetic bacteria, for light-mediated control of bacterial metabolism. This proved to eliminate metabolic constrictions and replace glucose with light as the source of energy in Escherichia coli, with an increase in growth by 1.7-fold in 75 % reduced nutrient media. Metabolomic tracking through carbon isotope labeling confirmed flux shunting through targeted pathways, with accumulation of metabolites downstream of respective targets. Finally, application of Nanorgs with the Ehrlich pathway improved isobutanol titers/yield by 3.9-fold in 75 % less sugar from E. coli strains with no genetic alterations. These results demonstrate the promise of Nanorgs for metabolic engineering and low-cost biomanufacturing.