Dynamic control in metabolic engineering: Theories, tools, and applications

Role of small molecules as drug candidates for reprogramming somatic cells into induced pluripotent stem cells: A comprehensive review

With the use of specific genetic factors and recent developments in cellular reprogramming, it is now possible to generate lineage-committed cells or induced pluripotent stem cells (iPSCs) from readily available and common somatic cell types. However, there are still significant doubts regarding the safety and effectiveness of the current genetic methods for reprogramming cells, as well as the conventional culture methods for maintaining stem cells. Small molecules that target specific epigenetic processes, signaling pathways, and other cellular processes can be used as a complementary approach to manipulate cell fate to achieve a desired objective. It has been discovered that a growing number of small molecules can support lineage differentiation, maintain stem cell self-renewal potential, and facilitate reprogramming by either increasing the efficiency of reprogramming or acting as a genetic reprogramming factor substitute. However, ongoing challenges include improving reprogramming efficiency, ensuring the safety of small molecules, and addressing issues with incomplete epigenetic resetting. Small molecule iPSCs have significant clinical applications in regenerative medicine and personalized therapies. This review emphasizes the versatility and potential safety benefits of small molecules in overcoming challenges associated with the iPSCs reprogramming process.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Promoter engineering enables precise metabolic regulation towards efficient β-elemene production in Ogataea polymorpha

Precisely controlling gene expression is beneficial for optimizing biosynthetic pathways for improving the production. However, promoters in nonconventional yeasts such as Ogataea polymorpha are always limited, which results in incompatible gene modulation. Here, we expanded the promoter library in O. polymorpha based on transcriptional data, among which 13 constitutive promoters had the strengths ranging from 0-55% of PGAP, the commonly used strong constitutive promoter, and 2 were growth phase-dependent promoters. Subsequently, 2 hybrid growth phase-dependent promoters were constructed and characterized, which had 2-fold higher activities. Finally, promoter engineering was applied to precisely regulate cellular metabolism for efficient production of β-elemene. The glyceraldehyde-3-phosphate dehydrogenase gene GAP was downregulated to drive more flux into pentose phosphate pathway (PPP) and then to enhance the supply of acetyl-CoA by using phosphoketolase-phosphotransacetylase (PK-PTA) pathway. Coupled with the phase-dependent expression of synthase module (ERG20∼LsLTC2 fusion), the highest titer of 5.24 g/L with a yield of 0.037 g/(g glucose) was achieved in strain YY150U under fed-batch fermentation in shake flasks. This work characterized and engineered a series of promoters, that can be used to fine-tune genes for constructing efficient yeast cell factories.

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.

LowTempGAL: a highly responsive low temperature-inducible GAL system in Saccharomyces cerevisiae

Temperature is an important control factor for biologics biomanufacturing in precision fermentation. Here, we explored a highly responsive low temperature-inducible genetic system (LowTempGAL) in the model yeast Saccharomyces cerevisiae. Two temperature biosensors, a heat-inducible degron and a heat-inducible protein aggregation domain, were used to regulate the GAL activator Gal4p, rendering the leaky LowTempGAL systems. Boolean-type induction was achieved by implementing a second-layer control through low-temperature-mediated repression on GAL repressor gene GAL80, but suffered delayed response to low-temperature triggers and a weak response at 30°C. Application potentials were validated for protein and small molecule production. Proteomics analysis suggested that residual Gal80p and Gal4p insufficiency caused suboptimal induction. 'Turbo' mechanisms were engineered through incorporating a basal Gal4p expression and a galactose-independent Gal80p-supressing Gal3p mutant (Gal3Cp). Varying Gal3Cp configurations, we deployed the LowTempGAL systems capable for a rapid stringent high-level induction upon the shift from a high temperature (37-33°C) to a low temperature (≤30°C). Overall, we present a synthetic biology procedure that leverages 'leaky' biosensors to deploy highly responsive Boolean-type genetic circuits. The key lies in optimisation of the intricate layout of the multi-factor system. The LowTempGAL systems may be applicable in non-conventional yeast platforms for precision biomanufacturing.

Metabolic engineering of Escherichia coli for high-level production of the biodegradable polyester monomer 2-pyrone-4,6-dicarboxylic acid

2-Pyrone-4,6-dicarboxylic acid (PDC), a chemically stable pseudo-aromatic dicarboxylic acid, is a promising building block compound for manufacturing biodegradable polyesters. This study aimed to construct high-performance cell factories enabling the efficient production of PDC from glucose. Firstly, the effective enzymes of the PDC biosynthetic pathway were overexpressed on the chromosome of the 3-dehydroshikimate overproducing strain. Consequently, the one-step biosynthesis of PDC from glucose was achieved. Further, the PDC production was enhanced by multi-copy integration of the key gene PsligC encoding 4-carboxy-2-hydroxymuconate-6-semialdehyde dehydrogenase and co-expression of Vitreoscilla hemoglobin. Subsequently, the PDC production was substantially improved by redistributing the metabolic flux for cell growth and PDC biosynthesis based on dynamically downregulating the expression of pyruvate kinase. The resultant strain PDC50 produced 129.37 g/L PDC from glucose within 78 h under fed-batch fermentation conditions, with a yield of 0.528 mol/mol and an average productivity of 1.65 g/L/h. The findings of this study lay the foundation for the potential industrial production of PDC.

Programming Dynamic Division of Labor Using Horizontal Gene Transfer

The metabolic engineering of microbes has broad applications, including biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multistep pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is the division of labor (DOL) in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted interstrain population dynamics. Through modeling, we show that dynamic division of labor (DDOL), which we define as the DOL between indiscrete populations capable of dynamic and reversible interchange, can overcome these limitations and enable the robust maintenance of burdensome, multistep pathways. We propose that DDOL can be mediated by horizontal gene transfer (HGT) and use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

Transcriptome analysis of Kluyveromyces marxianus under succinic acid stress and development of robust strains

Kluyveromyces marxianus has become an attractive non-conventional yeast cell factory due to its advantageous properties such as high thermal tolerance and rapid growth. Succinic acid (SA) is an important platform molecule that has been applied in various industries such as food, material, cosmetics, and pharmaceuticals. SA bioproduction may be compromised by its toxicity. Besides, metabolite-responsive promoters are known to be important for dynamic control of gene transcription. Therefore, studies on global gene transcription under various SA concentrations are of great importance. Here, comparative transcriptome changes of K. marxianus exposed to various concentrations of SA were analyzed. Enrichment and analysis of gene clusters revealed repression of the tricarboxylic acid cycle and glyoxylate cycle, also activation of the glycolysis pathway and genes related to ergosterol synthesis. Based on the analyses, potential SA-responsive promoters were investigated, among which the promoter strength of IMTCP2 and KLMA_50231 increased 43.4% and 154.7% in response to 15 g/L SA. In addition, overexpression of the transcription factors Gcr1, Upc2, and Ndt80 significantly increased growth under SA stress. Our results benefit understanding SA toxicity mechanisms and the development of robust yeast for organic acid production. KEY POINTS: • Global gene transcription of K. marxianus is changed by succinic acid (SA) • Promoter activities of IMTCP2 and KLMA_50123 are regulated by SA • Overexpression of Gcr1, Upc2, and Ndt80 enhanced SA tolerance.

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules ("functional modules"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.

IMPORTANCE

This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

Re-discovering Prunus fruit varieties as antiangiogenic agents by metabolomic and bioinformatic approach

In this work, a comparative chemical-biological study of nine plum varieties (Prunus domestica L. and Prunus salicina Lindl.) with two commercial ones was carried out to improve their cultivation and use in the agri-food chain. The chemical quali-quantitative fingerprint by HR-Orbitrap/ESI-MS showed similar profiles, being 'Rossa Casa Velasco' the richest in phenols and anthocyanins. All the extracts were investigated for their in vitro antioxidant as well as antiangiogenic activity by two in vivo models, chick chorioallantoic membrane and zebrafish embryos. Among investigated varieties 'Scarrafona', 'Rusticano', 'Marisa', 'Rossa Casa Velasco', 'Verdone', and 'Sangue di Drago' showed the best antiangiogenic activities (30-50 % inhibition). Finally, the chemical/biological datasets processed with a bioinformatic approach revealed that a large group of flavonoids, procyanidins, and anthocyanins significantly correlated with all the three antioxidant tests (DPPH, FRAP, and ABTS), while quinic acid and icariside F2 resulted positively correlated with CAM at both 100 and 200 μg/egg.

Building a highly efficient Streptomyces super-chassis for secondary metabolite production by reprogramming naturally-evolved multifaceted shifts

Streptomyces has an extensive array of bioactive secondary metabolites (SMs). Nevertheless, devising a framework for the heterologous production of these SMs remains challenging. We here reprogrammed a versatile plug-and-play Streptomyces super-chassis and established a universal pipeline for production of diverse SMs via understanding of the inherent pleiotropic effects of ethanol shock on jadomycin production in Streptomyces venezuelae. We initially identified and characterized a set of multiplex targets (afsQ1, bldD, bldA, and miaA) that contribute to SM (jadomycin) production when subjected to ethanol shock. Subsequently, we developed an ethanol-induced orthogonal amplification system (EOAS), enabling dynamic and precise control over targets. Ultimately, we integrated these multiplex targets into functional units governed by the EOAS, generating a universal and plug-and-play Streptomyces super-chassis. In addition to achieving the unprecedented titer and yield of jadomycin B, we also evidenced the potential of this super-chassis for production of diverse heterologous SMs, including antibiotic oxytetracycline, anticancer drug doxorubicins, agricultural herbicide thaxtomin A, and plant growth regulator guvermectin, all with the yields of >10 mg/g glucose in a simple mineral medium. Given that the production of SMs all required complexed medium and the cognate yields were usually much lower, our achievement of using a universal super-chassis and engineering pipeline in a simple mineral medium is promising for convenient heterologous production of SMs.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Engineered co-culture for consolidated production of phenylpropanoids directly from aromatic-rich biomass

Consolidated bioprocesses for the in situ hydrolysis and conversion of biomass feedstocks into value-added products offers great potential for both process and cost reduction. However, to date few consolidated bioprocesses have been developed that target aromatic rich feedstock fractions. Reported here is the development of synthetic co-cultivation for the consolidated hydrolysis and valorisation of corncob hydroxycinnamic acids. Biomass hydrolysis was achieved via a secretion module developed in B. subtilis using a genetically encoded biosensor-actuator to secrete hydrolytic enzymes. Conversion was achieved via a biotransformation module developed in E. coli using a suite of plug-and-play encoded enzymes to convert the released hydroxycinnamic acids into high-value phenylpropanoid target compounds. Finally, employing cellulolytic pre-treatment, extractive fermentation and in situ product recovery multiple aromatic products, coniferol and chavicol, were isolated from the same process in high purity.

The expanded CRISPR toolbox for constructing microbial cell factories

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

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes

Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield. In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium. We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 33% and an increase of supercoiled pDNA volumetric productivity by 13 % upon limitation of sulfate. In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.

Modular bioengineering of whole-cell catalysis for sialo-oligosaccharide production: coordinated co-expression of CMP-sialic acid synthetase and sialyltransferase

BACKGROUND

In whole-cell bio-catalysis, the biosystems engineering paradigm shifts from the global reconfiguration of cellular metabolism as in fermentation to a more focused, and more easily modularized, optimization of comparably short cascade reactions. Human milk oligosaccharides (HMO) constitute an important field for the synthetic application of cascade bio-catalysis in resting or non-living cells. Here, we analyzed the central catalytic module for synthesis of HMO-type sialo-oligosaccharides, comprised of CMP-sialic acid synthetase (CSS) and sialyltransferase (SiaT), with the specific aim of coordinated enzyme co-expression in E. coli for reaction flux optimization in whole cell conversions producing 3'-sialyllactose (3SL).

RESULTS

Difference in enzyme specific activity (CSS from Neisseria meningitidis: 36 U/mg; α2,3-SiaT from Pasteurella dagmatis: 5.7 U/mg) was compensated by differential protein co-expression from tailored plasmid constructs, giving balance between the individual activities at a high level of both (α2,3-SiaT: 9.4 × 102 U/g cell dry mass; CSS: 3.4 × 102 U/g cell dry mass). Finally, plasmid selection was guided by kinetic modeling of the coupled CSS-SiaT reactions in combination with comprehensive analytical tracking of the multistep conversion (lactose, N-acetyl neuraminic acid (Neu5Ac), cytidine 5'-triphosphate; each up to 100 mM). The half-life of SiaT in permeabilized cells (≤ 4 h) determined the efficiency of 3SL production at 37 °C. Reaction at 25 °C gave 3SL (40 ± 4 g/L) in ∼ 70% yield within 3 h, reaching a cell dry mass-specific productivity of ∼ 3 g/(g h) and avoiding intermediary CMP-Neu5Ac accumulation.

CONCLUSIONS

Collectively, balanced co-expression of CSS and SiaT yields an efficient (high-flux) sialylation module to support flexible development of E. coli whole-cell catalysts for sialo-oligosaccharide production.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

Programming dynamic division of labor using horizontal gene transfer

The metabolic engineering of microbes has broad applications, including in biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multi-step pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is division of labor (DOL), in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted inter-strain population dynamics. Through modeling, we show that dynamic division of labor (DDOL) mediated by horizontal gene transfer (HGT) can overcome these limitations and enable the robust maintenance of burdensome, multi-step pathways. We also use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.

Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast

Dynamic metabolic engineering is a strategy to switch key metabolic pathways in microbial cell factories from biomass generation to accumulation of target products. Here, we demonstrate that optogenetic intervention in the cell cycle of budding yeast can be used to increase production of valuable chemicals, such as the terpenoid β-carotene or the nucleoside analog cordycepin. We achieved optogenetic cell-cycle arrest in the G2/M phase by controlling activity of the ubiquitin-proteasome system hub Cdc48. To analyze the metabolic capacities in the cell cycle arrested yeast strain, we studied their proteomes by timsTOF mass spectrometry. This revealed widespread, but highly distinct abundance changes of metabolic key enzymes. Integration of the proteomics data in protein-constrained metabolic models demonstrated modulation of fluxes directly associated with terpenoid production as well as metabolic subsystems involved in protein biosynthesis, cell wall synthesis, and cofactor biosynthesis. These results demonstrate that optogenetically triggered cell cycle intervention is an option to increase the yields of compounds synthesized in a cellular factory by reallocation of metabolic resources.

Towards universal synthetic heterotrophy using a metabolic coordinator

Engineering the utilization of non-native substrates, or synthetic heterotrophy, in proven industrial microbes such as Saccharomyces cerevisiae represents an opportunity to valorize plentiful and renewable sources of carbon and energy as inputs to bioprocesses. We previously demonstrated that activation of the galactose (GAL) regulon, a regulatory structure used by this yeast to coordinate substrate utilization with biomass formation during growth on galactose, during growth on the non-native substrate xylose results in a vastly altered gene expression profile and faster growth compared with constitutive overexpression of the same heterologous catabolic pathway. However, this effort involved the creation of a xylose-inducible variant of Gal3p (Gal3pSyn4.1), the sensor protein of the GAL regulon, preventing this semi-synthetic regulon approach from being easily adapted to additional non-native substrates. Here, we report the construction of a variant Gal3pMC (metabolic coordinator) that exhibits robust GAL regulon activation in the presence of structurally diverse substrates and recapitulates the dynamics of the native system. Multiple molecular modeling studies suggest that Gal3pMC occupies conformational states corresponding to galactose-bound Gal3p in an inducer-independent manner. Using Gal3pMC to test a regulon approach to the assimilation of the non-native lignocellulosic sugars xylose, arabinose, and cellobiose yields higher growth rates and final cell densities when compared with a constitutive overexpression of the same set of catabolic genes. The subsequent demonstration of rapid and complete co-utilization of all three non-native substrates suggests that Gal3pMC-mediated dynamic global gene expression changes by GAL regulon activation may be universally beneficial for engineering synthetic heterotrophy.

Using a synthetic machinery to improve carbon yield with acetylphosphate as the core

In microbial cell factory, CO2 release during acetyl-CoA production from pyruvate significantly decreases the carbon atom economy. Here, we construct and optimize a synthetic carbon conserving pathway named as Sedoheptulose-1,7-bisphosphatase Cycle with Trifunctional PhosphoKetolase (SCTPK) in Escherichia coli. This cycle relies on a generalist phosphoketolase Xfspk and converts glucose into the stoichiometric amounts of acetylphosphate (AcP). Furthermore, genetic circuits responding to AcP positively or negatively are created. Together with SCTPK, they constitute a gene-metabolic oscillator that regulates Xfspk and enzymes converting AcP into valuable chemicals in response to intracellular AcP level autonomously, allocating metabolic flux rationally and improving the carbon atom economy of bioconversion process. Using this synthetic machinery, mevalonate is produced with a yield higher than its native theoretical yield, and the highest titer and yield of 3-hydroxypropionate via malonyl-CoA pathway are achieved. This study provides a strategy for improving the carbon yield of microbial cell factories.

In vitro allosteric transcription factor-based biosensing

A biosensor is an analytical device that converts a biological response into a measurable output signal. Bacterial allosteric transcription factors (aTFs) have been utilized as a novel class of recognition elements for in vitro biosensing, which circumvents the limitations of aTF-based whole-cell biosensors (WCBs) and helps to meet the increasing requirement of small-molecule biosensors for diverse applications. In this review, we summarize the recent advances related to the configuration of aTF-based biosensors in vitro. Particularly, we evaluate the advantages of aTFs for in vitro biosensing and highlight their great potential for the establishment of robust and easy-to-implement biosensing strategies. We argue that key technical innovations and generalizable workflows will enhance the pipeline for facile construction of diverse aTF-based small-molecule biosensors.

Recent Progress in Metabolic Engineering of Escherichia coli for the Production of Various C4 and C5-Dicarboxylic Acids

As an alternative to petrochemical synthesis, well-established industrial microbes, such as Escherichia coli, are employed to produce a wide range of chemicals, including dicarboxylic acids (DCAs), which have significant potential in diverse areas including biodegradable polymers. The demand for biodegradable polymers has been steadily rising, prompting the development of efficient production pathways on four- (C4) and five-carbon (C5) DCAs derived from central carbon metabolism to meet the increased demand via the biosynthesis. In this context, E. coli is utilized to produce these DCAs through various metabolic engineering strategies, including the design or selection of metabolic pathways, pathway optimization, and enhancement of catalytic activity. This review aims to highlight the recent advancements in metabolic engineering techniques for the production of C4 and C5 DCAs in E. coli.

Bayesian Optimization for Design of Multiscale Biological Circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

Gene expression dynamics in input-responsive engineered living materials programmed for bioproduction

Engineered living materials (ELMs) fabricated by encapsulating microbes in hydrogels have great potential as bioreactors for sustained bioproduction. While long-term metabolic activity has been demonstrated in these systems, the capacity and dynamics of gene expression over time is not well understood. Thus, we investigate the long-term gene expression dynamics in microbial ELMs constructed using different microbes and hydrogel matrices. Through direct gene expression measurements of engineered E. coli in F127-bisurethane methacrylate (F127-BUM) hydrogels, we show that inducible, input-responsive genetic programs in ELMs can be activated multiple times and maintained for multiple weeks. Interestingly, the encapsulated bacteria sustain inducible gene expression almost 10 times longer than free-floating, planktonic cells. These ELMs exhibit dynamic responsiveness to repeated induction cycles, with up to 97% of the initial gene expression capacity retained following a subsequent induction event. We demonstrate multi-week bioproduction cycling by implementing inducible CRISPR transcriptional activation (CRISPRa) programs that regulate the expression of enzymes in a pteridine biosynthesis pathway. ELMs fabricated from engineered S. cerevisiae in bovine serum albumin (BSA) - polyethylene glycol diacrylate (PEGDA) hydrogels were programmed to express two different proteins, each under the control of a different chemical inducer. We observed scheduled bioproduction switching between betaxanthin pigment molecules and proteinase A in S. cerevisiae ELMs over the course of 27 days under continuous cultivation. Overall, these results suggest that the capacity for long-term genetic expression may be a general property of microbial ELMs. This work establishes approaches for implementing dynamic, input-responsive genetic programs to tailor ELM functions for a wide range of advanced applications.

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

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

Designing artificial pathways for improving chemical production

Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Advances in biosynthesis of higher alcohols in Escherichia coli

In recent years, the development of green energy to replace fossil fuels has been the focus of research. Higher alcohols are important biofuels and chemicals. The production of higher alcohols in microbes has gained attention due to its environmentally friendly character. Higher alcohols have been synthesized in model microorganism Escherichia coli, and the production has reached the gram level through enhancement of metabolic flow, the balance of reducing power and the optimization of fermentation processes. Sustainable bio-higher alcohols production is expected to replace fossil fuels as a green and renewable energy source. Therefore, this review summarizes the latest developments in producing higher alcohols (C3-C6) by E. coli, elucidate the main bottlenecks limiting the biosynthesis of higher alcohols, and proposes potential engineering strategies of improving the production of biological higher alcohols. This review would provide a theoretical basis for further research on higher alcohols production by E. coli.

Metabolic Engineering of Yarrowia lipolytica for Terpenoid Production: Tools and Strategies

Terpenoids are a diverse group of compounds with isoprene units as basic building blocks. They are widely used in the food, feed, pharmaceutical, and cosmetic industries due to their diverse biological functions such as antioxidant, anticancer, and immune enhancement. With an increase in understanding the biosynthetic pathways of terpenoids and advances in synthetic biology techniques, microbial cell factories have been built for the heterologous production of terpenoids, with the oleaginous yeast Yarrowia lipolytica emerging as an outstanding chassis. In this paper, recent progress in the development of Y. lipolytica cell factories for terpenoid production with a focus on the advances in novel synbio tools and metabolic engineering strategies toward enhanced terpenoid biosynthesis is reviewed.

An optogenetic toolkit for light-inducible antibiotic resistance

Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.

Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies

Microbial bioproduction often faces challenges related to populational heterogeneity, where cells exhibit varying biosynthesis capabilities. Bioproduction heterogeneity can stem from genetic and non-genetic factors, resulting in decreased titer, yield, stability, and reproducibility. Consequently, understanding and controlling bioproduction heterogeneity are crucial for enhancing the economic competitiveness of large-scale biomanufacturing. In this review, we provide a comprehensive overview of current understandings of the various mechanisms underlying bioproduction heterogeneity. Additionally, we examine common strategies for controlling bioproduction heterogeneity based on these mechanisms. By implementing more robust measures to mitigate heterogeneity, we anticipate substantial enhancements in the scalability and stability of bioproduction processes.

ONE-SENTENCE SUMMARY

This review summarizes current understandings of different mechanisms of bioproduction heterogeneity and common control strategies based on these mechanisms.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Engineering of non-model eukaryotes for bioenergy and biochemical production

The prospect of leveraging naturally occurring phenotypes to overcome bottlenecks constraining the bioeconomy has marshalled increased exploration of nonconventional organisms. This review discusses the status of non-model eukaryotic species in bioproduction, the evaluation criteria for effectively matching a candidate host to a biosynthetic process, and the genetic engineering tools needed for host domestication. We present breakthroughs in genome editing and heterologous pathway design, delving into innovative spatiotemporal modulation strategies that potentiate more refined engineering capabilities. We cover current understanding of genetic instability and its ramifications for industrial scale-up, highlighting key factors and possible remedies. Finally, we propose future opportunities to expand the current collection of available hosts and provide guidance to benefit the broader bioeconomy.

Stochastic simulation algorithm for isotope-based dynamic flux analysis

Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.

A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida

Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.

Advances in riboswitch-based biosensor as food samples detection tool

Food safety has always been a hot issue of social concern, and biosensing has been widely used in the field of food safety detection. Compared with traditional aptamer-based biosensors, aptamer-based riboswitch biosensing represents higher precision and programmability. A riboswitch is an elegant example of controlling gene expression, where the target is coupled to the aptamer domain, resulting in a conformational change in the downstream expression domain and determining the signal output. Riboswitch-based biosensing can be extensively applied to the portable real-time detection of food samples. The numerous key features of riboswitch-based biosensing emphasize their sustainability, renewable, and testing, which promises to transform engineering applications in the field of food safety. This review covers recent developments in riboswitch-based biosensors. The brief history, definition, and modular design (regulatory mode, reporter, and expression platform) of riboswitch-based biosensors are explained for better insight into the design and construction. We summarize recent advances in various riboswitch-based biosensors involving theophylline, malachite green, tetracycline, neomycin, fluoride, thrombin, naringenin, ciprofloxacin, and paromomycin, aiming to provide general guidance for the design of riboswitch-based biosensors. Finally, the challenges and prospects are also summarized as a way forward stratagem and signs of progress.

Recent progress on health effects and biosynthesis of two key sialylated human milk oligosaccharides, 3'-sialyllactose and 6'-sialyllactose

Human milk oligosaccharides (HMOs), the third major solid component in breast milk, are recognized as the first prebiotics for health benefits in infants. Sialylated HMOs are an important type of HMOs, accounting for approximately 13% of total HMOs. 3'-Sialyllactose (3'-SL) and 6'-sialyllactose (6'-SL) are two simplest sialylated HMOs. Both SLs display promising prebiotic effects, especially in promoting the proliferation of bifidobacteria and shaping the gut microbiota. SLs exhibit several health effects, including antiadhesive antimicrobial ability, antiviral activity, prevention of necrotizing enterocolitis, immunomodulatory activity, regulation of intestinal epithelial cell response, promotion of brain development, and cognition improvement. Both SLs have been approved as "Generally Recognized as Safe" by the American Food and Drug Administration and are commercially added to infant formula. The biosynthesis of SLs using enzymatic or microbial approaches has been widely studied. The enzymatic synthesis of SLs can be realized by two types of enzymes, sialidases with trans-sialidase activity and sialyltransferases. Microbial synthesis can be achieved by the multiple recombinant bacteria in one-pot reaction, which express the enzymes involved in SL synthesis pathways separately or in combination, or by metabolically engineered strains in a fermentation process. In this article, the physiological properties of 3'-SL and 6'-SL are summarized in detail and the biosynthesis of these SLs via enzymatic and microbial synthesis is comprehensively reviewed.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

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.

Spatial-temporal regulation of fatty alcohol biosynthesis in yeast

BACKGROUND

Construction of efficient microbial cell factories is one of the core steps for establishing green bio-manufacturing processes. However, the complex metabolic regulation makes it challenging in driving the metabolic flux toward the product biosynthesis. Dynamically coupling the biosynthetic pathways with the cellular metabolism at spatial-temporal manner should be helpful for improving the production with alleviating the cellular stresses.

RESULTS

In this study, we observed the mismatch between fatty alcohol biosynthesis and cellular metabolism, which compromised the fatty alcohol production in Saccharomyces cerevisiae. To enhance the fatty alcohol production, we spatial-temporally regulated fatty alcohol biosynthetic pathway by peroxisomal compartmentalization (spatial) and dynamic regulation of gene expression (temporal). In particular, fatty acid/acyl-CoA responsive promoters were identified by comparative transcriptional analysis, which helped to dynamically regulate the expression of acyl-CoA reductase gene MaFAR1 and improved fatty alcohol biosynthesis by 1.62-fold. Furthermore, enhancing the peroxisomal supply of acyl-CoA and NADPH further improved fatty alcohol production to 282 mg/L, 2.52 times higher than the starting strain.

CONCLUSIONS

This spatial-temporal regulation strategy partially coordinated fatty alcohol biosynthesis with cellular metabolism including peroxisome biogenesis and precursor supply, which should be applied for production of other products in microbes.

A Toolkit for Precise, Multigene Control in Saccharomyces cerevisiae

Systems that allow researchers to precisely control the expression of genes are fundamental to biological research, biotechnology, and synthetic biology. However, few inducible gene expression systems exist that can enable simultaneous multigene control under common nutritionally favorable conditions in the important model organism and chassis Saccharomyces cerevisiae. Here we repurposed ligand binding domains from mammalian type I nuclear receptors to establish a family of up to five orthogonal synthetic gene expression systems in yeast. Our systems enable tight, independent, multigene control through addition of inert hormones and are capable of driving robust and rapid gene expression outputs, in some cases achieving up to 600-fold induction. As a proof of principle, we placed expression of four enzymes from the violacein biosynthetic pathway under independent expression control to selectively route pathway flux by addition of specific inducer combinations. Our results establish a modular, versatile, and potentially expandable toolkit for multidimensional control of gene expression in yeast that can be used to construct and control naturally occurring and synthetic gene networks.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

Olfactory Receptors as an Emerging Chemical Sensing Scaffold

Chemical biosensors are an increasingly ubiquitous part of our lives. Beyond enzyme-coupled assays, recent synthetic biology advances now allow us to hijack more complex biosensing systems to respond to difficult to detect analytes, such as chemical small molecules. Here, we briefly overview recent advances in the biosensing of small molecules, including nucleic acid aptamers, allosteric transcription factors, and two-component systems. We then look more closely at a recently developed chemical sensing system, G protein-coupled receptor (GPCR)-based sensors. Finally, we consider the chemical sensing capabilities of the largest GPCR subfamily, olfactory receptors (ORs). We examine ORs' role in nature, their potential as a biomedical target, and their ability to detect compounds not amenable for detection using other biological scaffolds. We conclude by evaluating the current challenges, opportunities, and future applications of GPCR- and OR-based sensors.

Combining metabolite doping and metabolic engineering to improve 2-phenylethanol production by engineered cyanobacteria

2-Phenylethanol (2-PE) is a rose-scented aromatic compound, with broad application in cosmetic, pharmaceutical, food and beverage industries. Many plants naturally synthesize 2-PE via Shikimate Pathway, but its extraction is expensive and low-yielding. Consequently, most 2-PE derives from chemical synthesis, which employs petroleum as feedstock and generates unwanted by products and health issues. The need for “green” processes and the increasing public demand for natural products are pushing biotechnological production systems as promising alternatives. So far, several microorganisms have been investigated and engineered for 2-PE biosynthesis, but a few studies have focused on autotrophic microorganisms. Among them, the prokaryotic cyanobacteria can represent ideal microbial factories thanks to their ability to photosynthetically convert CO2 into valuable compounds, their minimal nutritional requirements, high photosynthetic rate and the availability of genetic and bioinformatics tools. An engineered strain of Synechococcus elongatus PCC 7942 for 2-PE production, i.e., p120, was previously published elsewhere. The strain p120 expresses four heterologous genes for the complete 2-PE synthesis pathway. Here, we developed a combined approach of metabolite doping and metabolic engineering to improve the 2-PE production kinetics of the Synechococcus elongatus PCC 7942 p120 strain. Firstly, the growth and 2-PE productivity performances of the p120 recombinant strain were analyzed to highlight potential metabolic constraints. By implementing a BG11 medium doped with L-phenylalanine, we covered the metabolic burden to which the p120 strain is strongly subjected, when the 2-PE pathway expression is induced. Additionally, we further boosted the carbon flow into the Shikimate Pathway by overexpressing the native Shikimate Kinase in the Synechococcus elongatus PCC 7942 p120 strain (i.e., 2PE_aroK). The combination of these different approaches led to a 2-PE yield of 300 mg/gDW and a maximum 2-PE titer of 285 mg/L, 2.4-fold higher than that reported in literature for the p120 recombinant strain and, to our knowledge, the highest recorded for photosynthetic microorganisms, in photoautotrophic growth condition. Finally, this work provides the basis for further optimization of the process aimed at increasing 2-PE productivity and concentration, and could offer new insights about the use of cyanobacteria as appealing microbial cell factories for the synthesis of aromatic compounds.

Engineering Escherichia coli asymmetry distribution-based synthetic consortium for shikimate production

Microbial consortia constitute a promising tool for achieving high-value chemical bio-production. However, customizing the consortium ratio remains challenging. Herein, an asymmetry distribution-based synthetic consortium (ADSC) was developed to switch cell phenotypes using shikimate synthesis for proof of concept. First, the cell pole-organizing protein PopZ was screened as a mediator of asymmetric protein distribution in Escherichia coli. The ADSC was then constructed to incorporate PopZ-mediated asymmetry distribution and a TetR-based transcription repression switch to achieve the dynamical control of microbial population ratio. Finally, the ADSC was used to decouple cell growth from shikimate synthesis by effectively coordinating the ratio of growing cells and production cells at the consortium level, thereby increasing shikimate titer to 30.1 g/L in the 7.5-L bioreactor with a minimal medium. This titer was further improved to 82.5 g/L when using rich medium fermentation. Our results illustrate a novel approach to control consortium structure through ADSC-mediated regulation, highlighting its potential as an efficient strategy for controlling metabolic state in microbes. This article is protected by copyright. All rights reserved.