Network rigidity and metabolic engineering in metabolite overproduction

Rewiring the methanol assimilation pathway in the methylotrophic yeast Pichia pastoris for high-level production of erythritol

Methanol, which is non-food competing, abundant, inexpensive, and potentially renewable, serves as an ideal alternative feedstock for biomanufacturing. Although engineered methylotrophic yeasts have successfully achieved gram-scale production of C2 (acetyl-CoA), C3 (pyruvate), and C6 (fructose-6-phosphate) building blocks from methanol, the production of C4-based (i.e. erythrose-4-phosphate) chemicals remains unexplored. This study demonstrates high-level methanol-to-erythritol production by rewiring the methanol assimilation pathway of Pichia pastoris, achieved through trimming and strengthening the carbon rearrangement network (CRN). Notably, we introduced a bacterial ribulose monophosphate (RuMP) pathway in addition to the native xylulose monophosphate (XuMP) pathway of P. pastoris, creating a hybrid network that significantly improved erythritol production and reduced pentitol byproduct formation. Combining these strategies generated a high-producing recombinant strain, achieving titers up to 31.5 g/L in fermentor culture. This study validates the feasibility of engineering P. pastoris for the efficient conversion of methanol to valuable erythrose-4-phosphate (E4P)-based chemicals. The CRN rewiring strategies employed here offer a valuable reference for engineering methylotrophic cell factories for the production of a wide range of chemicals from methanol.

Synthetic Engineering of Microbes for Production of Terpenoid Food Ingredients

Terpenoids are a class of chemicals comprising many food ingredient chemicals. Synthetic biology and metabolic engineering have been performed to produce microbial cell factories for their production. For improved production of various terpenoid ingredients, heterologous synthetic pathways can be optimized at multiple dimensions. Optimizing chassis precursor supply and overcoming the host's inherent metabolic rigidity are crucial for enhancing overall efficiency of heterologous terpenoid production. Integrating synthetic regulatory circuits can facilitate the staged programming and precise optimization of heterologous and endogenous metabolism. Engineering long-term genetic and metabolic stability is essential for the successful scale-up of commercial production. Maximizing efficiency in food terpenoid production will rely on interdisciplinary synthetic and engineering biology tools to advance state-of-the-art capabilities for the streamlined design and construction of complex genotypes in microbial chassis.

The EcoCyc database (2025)

EcoCyc is a bioinformatics database (DB) available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project was to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc is also available. This review outlines the data content of EcoCyc and the procedures by which this content is generated.

The Strategy and Application of Gene Attenuation in Metabolic Engineering

Metabolic engineering has a wide range of applications, spanning key sectors such as energy, pharmaceuticals, agriculture, chemicals, and environmental sustainability. Its core focus is on precisely modulating metabolic pathways to achieve efficient, sustainable, and environmentally friendly biomanufacturing processes, offering new possibilities for societal sustainable development. Gene attenuation is a critical technique within metabolic engineering, pivotal in optimizing metabolic fluxes and improving target metabolite yields. This review article discusses gene attenuation mechanisms, the applications across various biological systems, and implementation strategies. Additionally, we address potential future challenges and explore its potential to drive further advancements in the field.

Production of aromatic amino acids and their derivatives by Escherichia coli and Corynebacterium glutamicum

Demand for aromatic amino acids (AAAs), such as L-phenylalanine, L-tyrosine, and L-tryptophan, has been increasing as they are used in animal feed and as precursors in the synthesis of industrial and pharmaceutical compounds. These AAAs are biosynthesized through the shikimate pathway in microorganisms and plants, and the reactions in the AAA biosynthesis pathways are strictly regulated at the levels of both gene expression and enzyme activity. Various attempts have been made to produce AAAs and their derivatives using microbial cells and to optimize production. In this review, we summarize the metabolic pathways involved in the biosynthesis of AAAs and their regulation and review recent research on AAA production using industrial bacteria, such as Escherichia coli and Corynebacterium glutamicum. Studies on fermentative production of AAA derivatives, including L-3,4-dihydroxyphenylalanine, tyrosol, and 3-hydroxytyrosol, are also discussed.

Metabolic analyses of Yarrowia lipolytica for biopolymer production reveals roadblocks and strategies for microbial utilizing volatile fatty acids as sustainable feedstocks

This study quantifies metabolic features of engineered Yarrowia lipolytica strains for converting volatile fatty acids (VFAs) into poly-3-hydroxybutyrate (PHB) via 13C-metabolic flux analysis and RNA-Seq. Yarrowia lipolytica is unable to grow with C4 ∼ C6 VFAs due to substrate toxicity, while propionate (C3) metabolism leads to slow growth and minimal PHB production due to enzymatic limitations in substrate assimilation pathways. Acetate is a viable but challenging VFA feedstock. Comparing to glucose, acetate catabolism results in low ATP/ADP ratios, high enzyme usage, substantial CO2 release (>50 % of input carbon), and limited NADPH. Several strategies may overcome these roadblocks: 1) glucose-VFA co-catabolism improves energy charge, alleviates metabolic imbalances, reduces flux rigidity, and lowers the enzyme expression burden; 2) overexpressing acetyl-CoA synthetase and nitrogen limitation increase acetate uptake and PHB synthesis during glucose-acetate co-utilization; and 3) repression of oxidase facilitates fluxes towards PHB synthesis. The results provide insights into efficient utilization of acetate as feedstock.

Marine cosmetics and the blue bioeconomy: From sourcing to success stories

As the global population continues to grow, so does the demand for longer, healthier lives and environmentally responsible choices. Consumers are increasingly drawn to naturally sourced products with proven health and wellbeing benefits. The marine environment presents a promising yet underexplored resource for the cosmetics industry, offering bioactive compounds with the potential for safe and biocompatible ingredients. This manuscript provides a comprehensive overview of the potential of marine organisms for cosmetics production, highlighting marine-derived compounds and their applications in skin/hair/oral-care products, cosmeceuticals and more. It also lays down critical safety considerations and addresses the methodologies for sourcing marine compounds, including harvesting, the biorefinery concept, use of systems biology for enhanced product development, and the relevant regulatory landscape. The review is enriched by three case studies: design of macroalgal skincare products in Iceland, establishment of a microalgal cosmetics spin-off in Italy, and the utilization of marine proteins for cosmeceutical applications.

Stepwise Flux Optimization for Enhanced GABA Production from Acetate in Escherichia coli

Strategic allocation of metabolic flux is essential for achieving a higher production performance in genetically engineered organisms. Flux optimization between cell growth and chemical production has led to the establishment of cost-effective chemical production methods in microbial cell factories. This effect is amplified when utilizing a low-cost carbon source. γ-Aminobutyric acid (GABA), crucial in pharmaceuticals and biodegradable polymers, can be efficiently produced from acetate, a cost-effective substrate. However, a balanced distribution of acetate-derived flux is essential for optimizing the production without hindering growth. In this study, we demonstrated GABA production from acetate using Escherichia coli by focusing on optimizing the metabolic flux at isocitrate and α-ketoglutarate nodes. Through a series of flux optimizations, the final strain produced 2.54 g/L GABA from 5.91 g/L acetate in 24 h (0.43 g/g yield). These findings suggest that delicate flux balancing with the application of a cheap substrate can contribute to cost-effective production of GABA.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Engineering a Synthetic Escherichia coli Coculture for Compartmentalized de novo Biosynthesis of Isobutyl Butyrate from Mixed Sugars

Short-chain esters are versatile chemicals that can be used as flavors, fragrances, solvents, and fuels. The de novo ester biosynthesis consists of diverging and converging pathway submodules, which is challenging to engineer to achieve optimal metabolic fluxes and selective product synthesis. Compartmentalizing the pathway submodules into specialist cells that facilitate pathway modularization and labor division is a promising solution. Here, we engineered a synthetic Escherichia coli coculture with the compartmentalized sugar utilization and ester biosynthesis pathways to produce isobutyl butyrate from a mixture of glucose and xylose. To compartmentalize the sugar-utilizing pathway submodules, we engineered a xylose-utilizing E. coli specialist that selectively consumes xylose over glucose and bypasses carbon catabolite repression (CCR) while leveraging the native CCR machinery to activate a glucose-utilizing E. coli specialist. We found that the compartmentalization of sugar catabolism enabled simultaneous co-utilization of glucose and xylose by a coculture of the two E. coli specialists, improving the stability of the coculture population. Next, we modularized the isobutyl butyrate pathway into the isobutanol, butyl-CoA, and ester condensation submodules, where we distributed the isobutanol submodule to the glucose-utilizing specialist and the other submodules to the xylose-utilizing specialist. Upon compartmentalization of the isobutyl butyrate pathway submodules into these sugar-utilizing specialist cells, a robust synthetic coculture was engineered to selectively produce isobutyl butyrate, reduce the biosynthesis of unwanted ester byproducts, and improve the production titer as compared to the monoculture.

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Advances in the optimization of central carbon metabolism in metabolic engineering

Central carbon metabolism (CCM), including glycolysis, tricarboxylic acid cycle and the pentose phosphate pathway, is the most fundamental metabolic process in the activities of living organisms that maintains normal cellular growth. CCM has been widely used in microbial metabolic engineering in recent years due to its unique regulatory role in cellular metabolism. Using yeast and Escherichia coli as the representative organisms, we summarized the metabolic engineering strategies on the optimization of CCM in eukaryotic and prokaryotic microbial chassis, such as the introduction of heterologous CCM metabolic pathways and the optimization of key enzymes or regulatory factors, to lay the groundwork for the future use of CCM optimization in metabolic engineering. Furthermore, the bottlenecks in the application of CCM optimization in metabolic engineering and future application prospects are summarized.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Silk fibroin production in Escherichia coli is limited by a positive feedback loop between metabolic burden and toxicity stress

To investigate the metabolic elasticity and production bottlenecks for recombinant silk proteins in Escherichia coli, we performed a comprehensive characterization of one elastin-like peptide strain (ELP) and two silk protein strains (A5 4mer, A5 16mer). Our approach included ¹³C metabolic flux analysis, genome-scale modeling, transcription analysis, and ¹³C-assisted media optimization experiments. Three engineered strains maintained their central flux network during growth, while measurable metabolic flux redistributions (such as the Entner-Doudoroff pathway) were detected. Under metabolic burdens, the reduced TCA fluxes forced the engineered strain to rely more on substrate-level phosphorylation for ATP production, which increased acetate overflow. Acetate (as low as 10 mM) in the media was highly toxic to silk-producing strains, which reduced 4mer production by 43% and 16mer by 84%, respectively. Due to the high toxicity of large-size silk proteins, 16mer's productivity was limited, particularly in the minimal medium. Therefore, metabolic burden, overflow acetate, and toxicity of silk proteins may form a vicious positive feedback loop that fractures the metabolic network. Three solutions could be applied: 1) addition of building block supplements (i.e., eight key amino acids: His, Ile, Phe, Pro, Tyr, Lys, Met, Glu) to reduce metabolic burden; 2) disengagement of growth and production; and 3) use of non-glucose based substrate to reduce acetate overflow. Other reported strategies were also discussed in light of decoupling this positive feedback loop.

Metabolic Engineering of Microorganisms to Produce Pyruvate and Derived Compounds

Pyruvate is a hub of various endogenous metabolic pathways, including glycolysis, TCA cycle, amino acid, and fatty acid biosynthesis. It has also been used as a precursor for pyruvate-derived compounds such as acetoin, 2,3-butanediol (2,3-BD), butanol, butyrate, and L-alanine biosynthesis. Pyruvate and derivatives are widely utilized in food, pharmaceuticals, pesticides, feed additives, and bioenergy industries. However, compounds such as pyruvate, acetoin, and butanol are often chemically synthesized from fossil feedstocks, resulting in declining fossil fuels and increasing environmental pollution. Metabolic engineering is a powerful tool for producing eco-friendly chemicals from renewable biomass resources through microbial fermentation. Here, we review and systematically summarize recent advances in the biosynthesis pathways, regulatory mechanisms, and metabolic engineering strategies for pyruvate and derivatives. Furthermore, the establishment of sustainable industrial synthesis platforms based on alternative substrates and new tools to produce these compounds is elaborated. Finally, we discuss the potential difficulties in the current metabolic engineering of pyruvate and derivatives and promising strategies for constructing efficient producers.

Engineering yeast for high-level production of diterpenoid sclareol

The diterpenoid sclareol is an industrially important precursor for alternative sustainable supply of ambergris. However, its current production from plant extraction is neither economical nor environmental-friendly, since it requires laborious and cost-intensive purification procedures and plants cultivation is susceptible to environmental factors. Engineering cell factories for bio-manufacturing can enable sustainable production of natural products. However, stringent metabolic regulation poses challenges to rewire cellular metabolism for overproduction of compounds of interest. Here we used a modular approach to globally rewire the cellular metabolism for improving sclareol production to 11.4 g/L in budding yeast Saccharomyces cerevisiae, the highest reported diterpenoid titer in microbes. Metabolic flux analysis showed that modular balanced metabolism drove the metabolic flux toward the biosynthesis of targeted molecules, and transcriptomic analysis revealed that the expression of central metabolism genes was shaped for a new balanced metabolism, which laid a foundation in extensive metabolic engineering of other microbial species for sustainable bio-production.

Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications

Metabolic engineering has evolved towards creating cell factories with increasingly complex pathways as economic criteria push biotechnology to higher value products to provide a sustainable source of speciality chemicals. Optimization of such pathways often requires high combinatory exploration of best pathway balance, and this has led to increasing use of high-throughput automated strain construction platforms or novel optimization techniques. In addition, the low catalytic efficiency of such pathways has shifted emphasis from gene expression strategies towards novel protein engineering to increase specific activity of the enzymes involved so as to limit the metabolic burden associated with excessively high pressure on ribosomal machinery when using massive overexpression systems. Metabolic burden is now generally recognized as a major hurdle to be overcome with consequences on genetic stability but also on the intensified performance needed industrially to attain the economic targets for successful product launch. Increasing awareness of the need to integrate novel genetic information into specific sites within the genome which not only enhance genetic stability (safe harbors) but also enable maximum expression profiles has led to genome-wide assessment of best integration sites, and bioinformatics will facilitate the identification of most probable landing pads within the genome.To facilitate the transfer of novel biotechnological potential to industrial-scale production, more attention, however, has to be paid to engineering metabolic fitness adapted to the specific stress conditions inherent to large-scale fermentation and the inevitable heterogeneity that will occur due to mass transfer limitations and the resulting deviation away from ideal conditions as seen in laboratory-scale validation of the engineered cells. To ensure smooth and rapid transfer of novel cell lines to industry with an accelerated passage through scale-up, better coordination is required form the onset between the biochemical engineers involved in process technology and the genetic engineers building the new strain so as to have an overall strategy able to maximize innovation at all levels. This should be one of our key objectives when building fermentation-friendly chassis organisms.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Metabolic reconfiguration enables synthetic reductive metabolism in yeast

Cell proliferation requires the integration of catabolic processes to provide energy, redox power and biosynthetic precursors. Here we show how the combination of rational design, metabolic rewiring and recombinant expression enables the establishment of a decarboxylation cycle in the yeast cytoplasm. This metabolic cycle can support growth by supplying energy and increased provision of NADPH or NADH in the cytosol, which can support the production of highly reduced chemicals such as glycerol, succinate and free fatty acids. With this approach, free fatty acid yield reached 40% of theoretical yield, which is the highest yield reported for Saccharomyces cerevisiae to our knowledge. This study reports the implementation of a synthetic decarboxylation cycle in the yeast cytosol, and its application in achieving high yields of valuable chemicals in cell factories. Our study also shows that, despite extensive regulation of catabolism in yeast, it is possible to rewire the energy metabolism, illustrating the power of biodesign.

Metabolic Fingerprint Analysis of Cytochrome b5-producing E. coli N4830-1 Using FT-IR Spectroscopy

Optimization of recombinant protein expression in bacteria is an important task in order to increase protein yield while maintaining the structural fidelity of the product. In this study, we employ Fourier transform infrared (FT-IR) spectroscopy as a high throughput metabolic fingerprinting approach to optimize and monitor cytochrome b₅ (CYT b₅) production in Escherichia coli N4830-1, as the heterologous host. Cyt b₅ was introduced as a plasmid with between 0 and 6 copies under a strong promoter. The FT-IR spectroscopy results combined with multivariate chemometric analysis illustrated discriminations among culture conditions as well as revealing features that correlated to the different cytb₅ gene copy numbers. The second derivative of the FT-IR spectral data allowed for the quantitative detection of Cyt b₅ directly inside the intact cells without the need for extraction, and highlighted changes in protein secondary structure that was directly correlated to the cytb₅ gene copy number and protein content, and was in complete agreement with quantitative findings of standard traditional techniques such as SDS–PAGE and western blot analysis.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

An in vivo gene amplification system for high level expression in Saccharomyces cerevisiae

Bottlenecks in metabolic pathways due to insufficient gene expression levels remain a significant problem for industrial bioproduction using microbial cell factories. Increasing gene dosage can overcome these bottlenecks, but current approaches suffer from numerous drawbacks. Here, we describe HapAmp, a method that uses haploinsufficiency as evolutionary force to drive in vivo gene amplification. HapAmp enables efficient, titratable, and stable integration of heterologous gene copies, delivering up to 47 copies onto the yeast genome. The method is exemplified in metabolic engineering to significantly improve production of the sesquiterpene nerolidol, the monoterpene limonene, and the tetraterpene lycopene. Limonene titre is improved by 20-fold in a single engineering step, delivering ∼1 g L-1 in the flask cultivation. We also show a significant increase in heterologous protein production in yeast. HapAmp is an efficient approach to unlock metabolic bottlenecks rapidly for development of microbial cell factories.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all of 161 production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

Bio-conversion of CO2 into biofuels and other value-added chemicals via metabolic engineering

Carbon dioxide (CO₂) occurs naturally in the atmosphere as a trace gas, which is produced naturally as well as by anthropogenic activities. CO₂ is a readily available source of carbon that in principle can be used as a raw material for the synthesis of valuable products. The autotrophic organisms are naturally equipped to convert CO₂ into biomass by obtaining energy from sunlight or inorganic electron donors. This autotrophic CO₂ fixation has been exploited in biotechnology, and microbial cell factories have been metabolically engineered to convert CO₂ into biofuels and other value-added bio-based chemicals. A variety of metabolic engineering efforts for CO₂ fixation ranging from basic copy, paste, and fine-tuning approaches to engineering and testing of novel synthetic CO₂ fixing pathways have been demonstrated. In this paper, we review the current advances and innovations in metabolic engineering for bio-conversion of CO₂ into bio biofuels and other value-added bio-based chemicals.

Conserved Metabolic and Evolutionary Themes in Microbial Degradation of Carbamate Pesticides

Carbamate pesticides are widely used as insecticides, nematicides, acaricides, herbicides and fungicides in the agriculture, food and public health sector. However, only a minor fraction of the applied quantity reaches the target organisms. The majority of it persists in the environment, impacting the non-target biota, leading to ecological disturbance. The toxicity of these compounds to biota is mediated through cholinergic and non-cholinergic routes, thereby making their clean-up cardinal. Microbes, specifically bacteria, have adapted to the presence of these compounds by evolving degradation pathways and thus play a major role in their removal from the biosphere. Over the past few decades, various genetic, metabolic and biochemical analyses exploring carbamate degradation in bacteria have revealed certain conserved themes in metabolic pathways like the enzymatic hydrolysis of the carbamate ester or amide linkage, funnelling of aryl carbamates into respective dihydroxy aromatic intermediates, C1 metabolism and nitrogen assimilation. Further, genomic and functional analyses have provided insights on mechanisms like horizontal gene transfer and enzyme promiscuity, which drive the evolution of degradation phenotype. Compartmentalisation of metabolic pathway enzymes serves as an additional strategy that further aids in optimising the degradation efficiency. This review highlights and discusses the conclusions drawn from various analyses over the past few decades; and provides a comprehensive view of the environmental fate, toxicity, metabolic routes, related genes and enzymes as well as evolutionary mechanisms associated with the degradation of widely employed carbamate pesticides. Additionally, various strategies like application of consortia for efficient degradation, metabolic engineering and adaptive laboratory evolution, which aid in improvising remediation efficiency and overcoming the challenges associated with in situ bioremediation are discussed.

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

Background

Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems.

Results

We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes.

Conclusions

We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04066-y.

Development of antisense RNA-mediated quantifiable inhibition for metabolic regulation

Trans-regulating elements such as noncoding RNAs are crucial in modifying cells, and has shown broad application in synthetic biology, metabolic engineering and RNA therapies. Although effective, titration of the regulatory levels of such elements is less explored. Encouraged by the need of fine-tuning cellular functions, we studied key parameters of the antisense RNA design including oligonucleotide length, targeting region and relative dosage to achieve differentiated inhibition. We determined a 30-nucleotide configuration that renders efficient and robust inhibition. We found that by targeting the core RBS region proportionally, quantifiable inhibition levels can be rationally obtained. A mathematic model was established accordingly with refined energy terms and successfully validated by depicting the inhibition levels for genomic targets. Additionally, we applied this fine-tuning approach for 4-hydroxycoumarin biosynthesis by simultaneous and quantifiable knockdown of multiple targets, resulting in a 3.58-fold increase in titer of the engineered strain comparing to that of the non-regulated. We believe the developed tool is broadly compatible and provides an extra layer of control in modifying living systems.

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Achieved quantifiable asRNA inhibition by varying core RBS coverage.

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Developed and validated a mathematical model for quantifiable inhibition.

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Improved 4-hydroxycoumarin biosynthesis by 3.58 folds with multiplexed inhibition.

Engineering of xylose metabolism in Escherichia coli for the production of valuable compounds

The lignocellulosic sugar d-xylose has recently gained prominence as an inexpensive alternative substrate for the production of value-added compounds using genetically modified organisms. Among the prokaryotes, Escherichia coli has become the de facto host for the development of engineered microbial cell factories. The favored status of E. coli resulted from a century of scientific explorations leading to a deep understanding of its systems. However, there are limited literature reviews that discuss engineered E. coli as a platform for the conversion of d-xylose to any target compounds. Additionally, available critical review articles tend to focus on products rather than the host itself. This review aims to provide relevant and current information about significant advances in the metabolic engineering of d-xylose metabolism in E. coli. This focusses on unconventional and synthetic d-xylose metabolic pathways as several review articles have already discussed the engineering of native d-xylose metabolism. This paper, in particular, is essential to those who are working on engineering of d-xylose metabolism using E. coli as the host.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

Toward fine-tuned metabolic networks in industrial microorganisms

There are numerous microorganisms in nature capable of synthesizing diverse useful compounds; however, these natural microorganisms are generally inefficient in the production of target products on an industrial scale, relative to either chemical synthesis or extraction methods. To achieve industrial production of useful compounds, these natural microorganisms must undergo a certain degree of mutation or effective fine-tuning strategies. This review describes how to achieve an ideal metabolic fine-tuned process, including static control strategies and dynamic control strategies. The static control strategies mainly focus on various matabolic engineering strategies, including protein engineering, upregulation/downregulation, and combinatrorial control of these metabolic engineering strategies, to enhance the flexibility of their application in fine-tuned metabolic metworks. Then, we focus on the dynamic control strategies for fine-tuned metabolic metworks. The design principles derived would guide us to construct microbial cell factories for various useful compounds.

Effects of overexpression of STB5 in Saccharomyces cerevisiae on fatty acid biosynthesis, physiology and transcriptome

Microbial conversion of biomass to fatty acids (FA) and products derived thereof is an attractive alternative to the traditional oleochemical production route from animal and plant lipids. This study examined if NADPH-costly FA biosynthesis could be enhanced by overexpressing the transcription factor Stb5 in Saccharomyces cerevisiae. Stb5 activates expression of multiple genes encoding enzymes within the pentose phosphate pathway (PPP) and other NADPH-producing reactions. Overexpression of STB5 led to a decreased growth rate and an increased free fatty acid (FFA) production during growth on glucose. The improved FFA synthetic ability in the glucose phase was shown to be independent of flux through the oxidative PPP. RNAseq analysis revealed that STB5 overexpression had wide-ranging effects on the transcriptome in the batch phase, and appeared to cause a counterintuitive phenotype with reduced flux through the oxidative PPP. During glucose limitation, when an increased NADPH supply is likely less harmful, an overall induction of the proposed target genes of Stb5 (eg. GND1/2, TAL1, ALD6, YEF1) was observed. Taken together, the strategy of utilizing STB5 overexpression to increase NADPH supply for reductive biosynthesis is suggested to have potential in strains engineered to have strong ability to consume excess NADPH, alleviating a potential redox imbalance.

Cell Factory Engineering

Rational approaches to modifying cells to make molecules of interest are of substantial economic and scientific interest. Most of these efforts aim at the production of native metabolites, expression of heterologous biosynthetic pathways, or protein expression. Reviews of these topics have largely focused on individual strategies or cell types, but collectively they fall under the broad umbrella of a growing field known as cell factory engineering. Here we condense >130 reviews and key studies in the art into a meta-review of cell factory engineering. We identified 33 generic strategies in the field, all applicable to multiple types of cells and products, and proven successful in multiple major cell types. These apply to three major categories: production of native metabolites and/or bioactives, heterologous expression of biosynthetic pathways, and protein expression. This meta-review provides general strategy guides for the broad range of applications of rational engineering of cell factories.

Turnover Dependent Phenotypic Simulation: A Quantitative Constraint-Based Simulation Method That Accommodates All Main Strain Design Strategies

The uncertain relationship between genotype and phenotype can make strain engineering an arduous trial and error process. To identify promising gene targets faster, constraint-based modeling methodologies are often used, although they remain limited in their predictive power. Even though the search for gene knockouts is fairly established in constraint-based modeling, most strain design methods still model gene up/down-regulations by forcing the corresponding flux values to fixed levels without taking in consideration the availability of resources. Here, we present a constraint-based algorithm, the turnover dependent phenotypic simulation (TDPS) that quantitatively simulates phenotypes in a resource conscious manner. Unlike other available algorithms, TDPS does not force flux values and considers resource availability, using metabolite production turnovers as an indicator of metabolite abundance. TDPS can simulate up-regulation of metabolic reactions as well as the introduction of heterologous genes, alongside gene deletion and down-regulation scenarios. TDPS simulations were validated using engineered Saccharomyces cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite. For many of the strains evaluated, the experimental production yields were within the simulated intervals and the relative strain performance could be predicted with TDPS. However, the algorithm failed to predict some of the production changes observed experimentally, suggesting that further improvements are necessary. The results also showed that TDPS may be helpful in finding metabolic bottlenecks, but further experiments would be required to confirm these findings.

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.

The EcoCyc Database

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.