How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook

Transforming acid whey into a resource by selective removal of lactic acid and galactose using optimized food-grade microorganisms

The presence of lactic acid and galactose makes spray drying of acid whey (AW) a significant challenge for the dairy industry. In this study, a novel approach is explored to remove these compounds, utilizing food-grade microorganisms. For removing lactic acid, Corynebacterium glutamicum was selected, which has an inherent ability to metabolize lactic acid but does so slowly. To accelerate lactic acid metabolism, a mutant strain G6006 was isolated through adaptive laboratory evolution, which metabolized all lactic acid from AW two times faster than its parent strain. To eliminate galactose, a lactose-negative mutant of Lactococcus lactis that cannot produce lactate was generated. This strain was then co-cultured with G6006 to maximize the removal of both lactic acid and galactose. The microbially "filtered" AW could readily be spray dried into a stable lactose powder. This study highlights the potential of utilizing food-grade microorganisms to process AW, which currently constitutes a global challenge.

Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive evolution

The development of a method for identifying latent reprogramming in gene expression data resulting from adaptive laboratory evolution (ALE) in response to genetic or environmental perturbations has been a challenge. In this study, a method called Metabolic Reprogramming Identifier (MRI), based on the integration of expression data to a genome-scale metabolic model has been developed. To identify key genes playing the main role in reprogramming, a MILP problem is presented and maximization of an adaptation score as a criterion indicating a pattern of using metabolism with maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression differences between wild-type and evolved strains were selected as key genes for reprogramming. This score is also applied to evaluate the compatibility of expression patterns with maximal use of key genes. The method was implemented to investigate the reprogramming of Escherichia coli during adaptive evolution caused by changing carbon sources. cyoC and cydB responsible for establishing proton gradient across the inner membrane were identified to be vital in the E. coli reprogramming when switching from glucose to lactate. These results indicate the importance of the inner membrane in reprogramming of E. coli to adapt to the new environment. The method predicts no reprogramming occurs during the evolution for growth on glycerol.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

Metabolic changes of the acetogen Clostridium sp. AWRP through adaptation to acetate challenge

In this study, we report the phenotypic changes that occurred in the acetogenic bacterium Clostridium sp. AWRP as a result of an adaptive laboratory evolution (ALE) under the acetate challenge. Acetate-adapted strain 46 T-a displayed acetate tolerance to acetate up to 10 g L^-1 and increased ethanol production in small-scale cultures. The adapted strain showed a higher cell density than AWRP even without exogenous acetate supplementation. 46 T-a was shown to have reduced gas consumption rate and metabolite production. It was intriguing to note that 46 T-a, unlike AWRP, continued to consume H₂ at low CO₂ levels. Genome sequencing revealed that the adapted strain harbored three point mutations in the genes encoding an electron-bifurcating hydrogenase (Hyt) crucial for autotrophic growth in CO₂ + H₂, in addition to one in the dnaK gene. Transcriptome analysis revealed that most genes involved in the CO₂-fixation Wood-Ljungdahl pathway and auxiliary pathways for energy conservation (e.g., Rnf complex, Nfn, etc.) were significantly down-regulated in 46 T-a. Several metabolic pathways involved in dissimilation of nucleosides and carbohydrates were significantly up-regulated in 46 T-a, indicating that 46 T-a evolved to utilize organic substrates rather than CO₂ + H₂. Further investigation into degeneration in carbon fixation of the acetate-adapted strain will provide practical implications for CO₂ + H₂ fermentation using acetogenic bacteria for long-term continuous fermentation.

Recent progress in adaptive laboratory evolution of industrial microorganisms

Adaptive laboratory evolution (ALE) is a technique for the selection of strains with better phenotypes by long-term culture under a specific selection pressure or growth environment. Because ALE does not require detailed knowledge of a variety of complex and interactive metabolic networks, and only needs to simulate natural environmental conditions in the laboratory to design a selection pressure, it has the advantages of broad adaptability, strong practicability, and more convenient transformation of strains. In addition, ALE provides a powerful method for studying the evolutionary forces that change the phenotype, performance, and stability of strains, resulting in more productive industrial strains with beneficial mutations. In recent years, ALE has been widely used in the activation of specific microbial metabolic pathways and phenotypic optimization, the efficient utilization of specific substrates, the optimization of tolerance to toxic substance, and the biosynthesis of target products, which is more conducive to the production of industrial strains with excellent phenotypic characteristics. In this paper, typical examples of ALE applications in the development of industrial strains and the research progress of this technology are reviewed, followed by a discussion of its development prospects.

Generation of an Escherichia coli strain growing on methanol via the ribulose monophosphate cycle

Methanol is a liquid with high energy storage capacity that holds promise as an alternative substrate to replace sugars in the biotechnology industry. It can be produced from CO₂ or methane and its use does not compete with food and animal feed production. However, there are currently only limited biotechnological options for the valorization of methanol, which hinders its widespread adoption. Here, we report the conversion of the industrial platform organism Escherichia coli into a synthetic methylotroph that assimilates methanol via the energy efficient ribulose monophosphate cycle. Methylotrophy is achieved after evolution of a methanol-dependent E. coli strain over 250 generations in continuous chemostat culture. We demonstrate growth on methanol and biomass formation exclusively from the one-carbon source by ¹³C isotopic tracer analysis. In line with computational modeling, the methylotrophic E. coli strain optimizes methanol oxidation by upregulation of an improved methanol dehydrogenase, increasing ribulose monophosphate cycle activity, channeling carbon flux through the Entner-Doudoroff pathway and downregulating tricarboxylic acid cycle enzymes. En route towards sustainable bioproduction processes, our work lays the foundation for the efficient utilization of methanol as the dominant carbon and energy resource.

Using one carbon compounds as feedstock is a promising approach in abating climate change. Here, the authors report the conversion of E. coli into a synthetic methylotroph that assimilates methanol via the ribulose monophosphate cycle and a set of distinctive mutations.

CAM Models: Lessons and Implications for CAM Evolution

The evolution of Crassulacean acid metabolism (CAM) by plants has been one of the most successful strategies in response to aridity. On the onset of climate change, expanding the use of water efficient crops and engineering higher water use efficiency into C3 and C4 crops constitute a plausible solution for the problems of agriculture in hotter and drier environments. A firm understanding of CAM is thus crucial for the development of agricultural responses to climate change. Computational models on CAM can contribute significantly to this understanding. Two types of models have been used so far. Early CAM models based on ordinary differential equations (ODE) reproduced the typical diel CAM features with a minimal set of components and investigated endogenous day/night rhythmicity. This line of research brought to light the preponderant role of vacuolar malate accumulation in diel rhythms. A second wave of CAM models used flux balance analysis (FBA) to better understand the role of CO₂ uptake in flux distribution. They showed that flux distributions resembling CAM metabolism emerge upon constraining CO₂ uptake by the system. We discuss the evolutionary implications of this and also how CAM components from unrelated pathways could have integrated along evolution.

Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications

Pyruvate dehydrogenase complex (PDC) functions as the main determinant of the respiro-fermentative balance because it converts pyruvate to acetyl-coenzyme A (CoA), which then enters the TCA (tricarboxylic acid cycle). PDC is repressed by the pyruvate dehydrogenase complex regulator (PdhR) in Escherichia coli. The deletion of the pdhR gene compromises fitness in aerobic environments. We evolve the E. coli pdhR deletion strain to examine its achievable growth rate and the underlying adaptive strategies. We find that (1) optimal proteome allocation to PDC is critical in achieving optimal growth rate; (2) expression of PDC in evolved strains is reduced through mutations in the Shine-Dalgarno sequence; (3) rewiring of the TCA flux and increased reactive oxygen species (ROS) defense occur in the evolved strains; and (4) the evolved strains adapt to an efficient biomass yield. Together, these results show how adaptation can find alternative regulatory mechanisms for a key cellular process if the primary regulatory mode fails.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Adaptive laboratory evolution of methylotrophic Escherichia coli enables synthesis of all amino acids from methanol-derived carbon

Recent attempts to create synthetic Escherichia coli methylotrophs identified that de novo biosynthesis of amino acids, in the presence of methanol, presents significant challenges in achieving autonomous methylotrophic growth. Previously engineered methanol-dependent strains required co-utilization of stoichiometric amounts of co-substrates and methanol. As such, these strains could not be evolved to grow on methanol alone. In this work, we have explored an alternative approach to enable biosynthesis of all amino acids from methanol-derived carbon in minimal media without stoichiometric coupling. First, we identified that biosynthesis of threonine was limiting the growth of our methylotrophic E. coli. To address this, we performed adaptive laboratory evolution to generate a strain that grew efficiently in minimal medium with methanol and threonine. Methanol assimilation and growth of the evolved strain were analyzed, and, interestingly, we found that the evolved strain synthesized all amino acids, including threonine, from methanol-derived carbon. The evolved strain was then further engineered through overexpression of an optimized threonine biosynthetic pathway. We show that the resulting methylotrophic E. coli strain has a methanol-dependent growth phenotype with homoserine as co-substrate. In contrast to previous methanol-dependent strains, co-utilization of homoserine is not stoichiometrically linked to methanol assimilation. As such, future engineering of this strain and successive adaptive evolution could enable autonomous growth on methanol as the sole carbon source. KEY POINTS: • Adaptive evolution of E. coli enables biosynthesis of all amino acids from methanol. • Overexpression of threonine biosynthesis pathway improves methanol assimilation. • Methanol-dependent growth is seen in minimal media with homoserine as co-substrate.

Prokaryotic life finds a way: insights from evolutionary experimentation in bacteria

While evolution proceeds through the generation of random variant alleles, the application of selective pressures can select for subsets of mutations that confer fitness-improving physiological benefits. This, in essence, defines the process of adaptive evolution. The rapid replication rate of bacteria has allowed for the design of experiments to study these processes over a reasonable timeframe within a laboratory setting. This has been greatly assisted by advances in tractability of diverse microorganisms, next generation sequencing technologies and bioinformatic analysis pipelines. Examining the processes by which organisms adapt their genetic code to cope with sub-optimal growth conditions has yielded a wealth of molecular insight into diverse biological processes. Here we discuss how the study of adaptive evolutionary trajectories in bacteria has allowed for improved understanding of stress responses, revealed important insight into microbial physiology, allowed for the production of highly optimised strains for use in biotechnology and increased our knowledge of the role of genomic plasticity in chronic infections.

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.

Regulatory interventions improve the biosynthesis of limiting amino acids from methanol carbon to improve synthetic methylotrophy in Escherichia coli

Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, that is, the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. We targeted global and local amino acid regulatory networks. Those include removal of amino acid allosteric feedback inhibition (argA^H15Y , ilvA^L447F , hisG^E271K , leuA^G462D , proB^D107N , thrA^S345F , trpE^S40F ), knockouts of transcriptional repressors (ihfA, metJ); and overexpression of amino acid biosynthetic operons (hisGDCBHAFI, leuABCD, thrABC, trpEDCBA) and transcriptional regulators (crp, purR). Compared to the parent methylotrophic E. coli strain that was unable to synthesize these amino acids from methanol carbon, these strategies resulted in improved biosynthesis of limiting proteinogenic amino acids (histidine, leucine, lysine, methionine, phenylalanine, threonine, tyrosine) from methanol carbon. In several cases, improved amino acid biosynthesis from methanol carbon led to improvements in methylotrophic growth in methanol minimal medium supplemented with a small amount of yeast extract. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via global and local regulatory modifications.

Novel xylose transporter Cs4130 expands the sugar uptake repertoire in recombinant Saccharomyces cerevisiae strains at high xylose concentrations

BACKGROUND

The need to restructure the world's energy matrix based on fossil fuels and mitigate greenhouse gas emissions stimulated the development of new biobased technologies for renewable energy. One promising and cleaner alternative is the use of second-generation (2G) fuels, produced from lignocellulosic biomass sugars. A major challenge on 2G technologies establishment is the inefficient assimilation of the five-carbon sugar xylose by engineered Saccharomyces cerevisiae strains, increasing fermentation time. The uptake of xylose across the plasma membrane is a critical limiting step and the budding yeast S. cerevisiae is not designed with a broad transport system and regulatory mechanisms to assimilate xylose in a wide range of concentrations present in 2G processes.

RESULTS

Assessing diverse microbiomes such as the digestive tract of plague insects and several decayed lignocellulosic biomasses, we isolated several yeast species capable of using xylose. Comparative fermentations selected the yeast Candida sojae as a potential source of high-affinity transporters. Comparative genomic analysis elects four potential xylose transporters whose properties were evaluated in the transporter null EBY.VW4000 strain carrying the xylose-utilizing pathway integrated into the genome. While the traditional xylose transporter Gxf1 allows an improved growth at lower concentrations (10 g/L), strains containing Cs3894 and Cs4130 show opposite responses with superior xylose uptake at higher concentrations (up to 50 g/L). Docking and normal mode analysis of Cs4130 and Gxf1 variants pointed out important residues related to xylose transport, identifying key differences regarding substrate translocation comparing both transporters.

CONCLUSIONS

Considering that xylose concentrations in second-generation hydrolysates can reach high values in several designed processes, Cs4130 is a promising novel candidate for xylose uptake. Here, we demonstrate a novel eukaryotic molecular transporter protein that improves growth at high xylose concentrations and can be used as a promising target towards engineering efficient pentose utilization in yeast.

Modeling microbial metabolic trade-offs in a chemostat

Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to species-environment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.

Unlocking nature's biosynthetic potential by directed genome evolution

Microorganisms have been increasingly explored as microbial cell factories for production of fuels, chemicals, drugs, and materials. Among the various metabolic engineering strategies, directed genome evolution has emerged as one of the most powerful tools to unlock the full biosynthetic potential of microorganisms. Here we summarize the directed genome evolution strategies that have been developed in recent years, including adaptive laboratory evolution and various targeted genome-scale engineering strategies, and discuss their applications in basic and applied biological research.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities

Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.

Triggering the stringent response enhances synthetic methanol utilization in Escherichia coli

Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, i.e. the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. By activating the stringent/stress response via ppGpp overproduction, or DksA and RpoS overexpression, we demonstrate improved biosynthesis of proteinogenic amino acids via endogenous upregulation of amino acid synthesis pathway genes. Thus, we were able to achieve biosynthesis of several limiting amino acids from methanol-derived carbon, in contrast to the control methylotrophic E. coli strain. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via stringent/stress response activation.

Synthetic methylotrophy: Strategies to assimilate methanol for growth and chemicals production

Methanol is an attractive and broadly available substrate for large-scale bioproduction of fuels and chemicals. It contains more energy and electrons per carbon than carbohydrates and can be cheaply produced from natural gas. Synthetic methylotrophy refers to the development of non-native methylotrophs such as Escherichia coli and Corynebacterium glutamicum to utilize methanol as a carbon source. Here, we discuss recent advances in engineering these industrial hosts to assimilate methanol for growth and chemicals production through the introduction of the ribulose monophosphate (RuMP) cycle. In addition, we present novel strategies based on flux coupling and adaptive laboratory evolution to engineer new strains that can grow exclusively on methanol.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

GREACE-assisted adaptive laboratory evolution in endpoint fermentation broth enhances lysine production by Escherichia coli

BACKGROUND

Late-stage fermentation broth contains high concentrations of target chemicals. Additionally, it contains various cellular metabolites which have leaked from lysed cells, which would exert multifactorial stress to industrial hyperproducers and perturb both cellular metabolism and product formation. Although adaptive laboratory evolution (ALE) has been wildly used to improve stress tolerance of microbial cell factories, single-factor stress condition (i.e. target product or sodium chloride at a high concentration) is currently provided. In order to enhance bacterial stress tolerance to actual industrial production conditions, ALE in late-stage fermentation broth is desired. Genome replication engineering assisted continuous evolution (GREACE) employs mutants of the proofreading element of DNA polymerase complex (DnaQ) to facilitate mutagenesis. Application of GREACE coupled-with selection under stress conditions is expected to accelerate the ALE process.

RESULTS

In this study, GREACE was first modified by expressing a DnaQ mutant KR5-2 using an arabinose inducible promoter on a temperature-sensitive plasmid, which resulted in timed mutagenesis introduction. Using this method, tolerance of a lysine hyperproducer E. coli MU-1 was improved by enriching mutants in a lysine endpoint fermentation broth. Afterwards, the KR5-2 expressing plasmid was cured to stabilize acquired genotypes. By subsequent fermentation evaluation, a mutant RS3 with significantly improved lysine production capacity was selected. The final titer, yield and total amount of lysine produced by RS3 in a 5-L batch fermentation reached 155.0 ± 1.4 g/L, 0.59 ± 0.02 g lysine/g glucose, and 605.6 ± 23.5 g, with improvements of 14.8%, 9.3%, and 16.7%, respectively. Further metabolomics and genomics analyses, coupled with molecular biology studies revealed that mutations SpeB^A302V, AtpB^S165N and SecY^M145V mainly contributed both to improved cell integrity under stress conditions and enhanced metabolic flux into lysine synthesis.

CONCLUSIONS

Our present study indicates that improving a lysine hyperproducer by GREACE-assisted ALE in its stressful living environment is efficient and effective. Accordingly, this is a promising method for improving other valuable chemical hyperproducers.