Design Of Microbial Cell Factories Research Articles

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

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

A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities.

Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.

Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering

BackgroundIntegrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells.ResultsUsing constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae.ConclusionsThis study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.

Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

High-quality genome-scale metabolic models (GEMs) could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies. Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae, high-quality GEMs for non-model industrial microorganisms are still scarce. Zymomonas mobilis subsp. mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production. Although five GEMs of Z. mobilis have been constructed, these models are either generating ATP incorrectly, or lacking information of plasmid genes, or not providing standard format file. In this study, a high-quality GEM iZM516 of Z. mobilis ZM4 was constructed. The information from the improved genome annotation, literature, datasets of Biolog Phenotype Microarray studies, and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516. Finally, 516 genes, 1389 reactions, 1437 metabolites, and 3 cell compartments are included in iZM516, which also had the highest MEMOTE score of 91% among all published GEMs of Z. mobilis. Cell growth was then predicted by iZM516, which had 79.4% agreement with the experimental results of the substrate utilization. In addition, the potential endogenous succinate synthesis pathway of Z. mobilis ZM4 was proposed through simulation and analysis using iZM516. Furthermore, metabolic engineering strategies to produce succinate and 1,4-butanediol (1,4-BDO) were designed and then simulated under anaerobic condition using iZM516. The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies, which was comparable to that of the model species E. coli. Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z. mobilis as the chassis, but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.

EtiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis.

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

EcBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Enzyme annotation for orphan reactions and its applications in biomanufacturing

There are lots of biochemical reactions in the biosynthetic pathway without associated enzymes. Reactions predicted by retro-biosynthetic tools are not assigned gene sequences. Besides, non-natural reactions designed with novel functions also lack suitable enzymes. All these reactions can be categorized as orphan reactions. The absence of protein-encoding genes in these orphan reactions limits their direct experimental implementation. Computational tools have been developed to find candidate enzymes for these orphan reactions. Herein, we discuss recent advances in these computational tools, including reaction similarity-based methods for calculating the substructural similarity between orphan reactions and known enzymatic reactions; sequence-based tools combine metabolic knowledge base and phenotypic information with genomic, transcriptomic, and metabolomic data to mine appropriate enzymes for orphan reactions; and approaches based on the creation of enzyme variants for orphan reactions as enzyme engineering modifications and de novo design of enzymes. We believe that our review will greatly facilitate the design of microbial cell factories and contribute to the development of the biomanufacturing field.

Perspectives on the design of microbial cell factories to produce prenylflavonoids

Prenylflavonoids are flavonoid-derived compounds characterized by the presence of a lipophilic prenylated side-chain in the flavonoid skeleton. These compounds are present in several food supplements and food products, and have a wide variety of recognized biological activities, namely estrogenic, antioxidant, anti-inflammatory and anticancer. Since these compounds are present in nature in very low amounts, their extraction from plants is not enough to fulfill the current demand, besides being an inefficient and environmentally unfriendly process. For these reasons, the use of microorganisms as microbial cell factories represents an interesting alternative to produce prenylflavonoids in a faster and cheaper way. Saccharomyces cerevisiae has been used as chassis to produce prenylflavonoids. Moreover, Escherichia coli can also emerge as an alternative chassis to produce these compounds. However, there is still a long way before prenylflavonoids can be produced by heterologous organisms with relevant yields and titers. In this review, we highlight the biosynthetic pathways involved in the production of prenylflavonoids. Additionally, we review the advances, challenges and strategies on the heterologous production of these compounds in a competitive way.

Model-guided development of an evolutionarily stable yeast chassis.

First‐principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model‐based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome‐scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product‐specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi‐omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re‐routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer‐aided design of microbial cell factories.

Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms

Computational modelling of metabolic networks has become an established procedure in the metabolic engineering of production strains. One key principle that is frequently used to guide the rational design of microbial cell factories is the stoichiometric coupling of growth and product synthesis, which makes production of the desired compound obligatory for growth. Here we show that the coupling of growth and production is feasible under appropriate conditions for almost all metabolites in genome-scale metabolic models of five major production organisms. These organisms comprise eukaryotes and prokaryotes as well as heterotrophic and photoautotrophic organisms, which shows that growth coupling as a strain design principle has a wide applicability. The feasibility of coupling is proven by calculating appropriate reaction knockouts, which enforce the coupling behaviour. The study presented here is the most comprehensive computational investigation of growth-coupled production so far and its results are of fundamental importance for rational metabolic engineering.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

Metabolic flux analysis for Escherichia coli by flux balance analysis.

Conventional metabolic flux analysis (MFA) of Escherichia coli wild type and of pathway gene knockout mutants cultivated under anaerobic condition is explained in detail in this chapter. To place the MFA results into the context of the literature, the regulation of central carbon metabolism in terms of catabolite regulation by the phosphotransferase system (PTS) and the response to oxygen limitations via global regulators is reviewed. The effects of gene deletions such as pflA, pta, ppc, pykF, adhE, and ldhA on the metabolic network are presented. Moreover, for the pflA mutant the effects of various carbon sources were quantified. The chapter thereby contributes to the discussion of metabolic network function and the design of microbial cell factories.

The Role of Synthetic Biology in the Design of Microbial Cell Factories for Biofuel Production

Insecurity in the supply of fossil fuels, volatile fuel prices, and major concerns regarding climate change have sparked renewed interest in the production of fuels from renewable resources. Because of this, the use of biodiesel has grown dramatically during the last few years and is expected to increase even further in the future. Biodiesel production through the use of microbial systems has marked a turning point in the field of biofuels since it is emerging as an attractive alternative to conventional technology. Recent progress in synthetic biology has accelerated the ability to analyze, construct, and/or redesign microbial metabolic pathways with unprecedented precision, in order to permit biofuel production that is amenable to industrial applications. The review presented here focuses specifically on the role of synthetic biology in the design of microbial cell factories for efficient production of biodiesel.