Adaptive laboratory evolution resolves energy depletion to maintain high aromatic metabolite phenotypes in Escherichia coli strains lacking the Phosphotransferase System

Engineering metabolic flux for the microbial synthesis of aromatic compounds

Microbial cell factories have emerged as a sustainable alternative to traditional chemical synthesis and plant extraction methods for producing aromatic compounds. However, achieving economically viable production of these compounds in microbial systems remains a significant challenge. This review summarizes the latest advancements in metabolic flux regulation during the microbial production of aromatic compounds, providing an overview of its applications and practical outcomes. Various strategies aimed at improving the utilization of extracellular substrates, enhancing the efficiency of synthetic pathways for target products, and rewiring intracellular metabolic networks to boost the titer, yield, and productivity of aromatic compounds are discussed. Additionally, the persistent challenges in this field and potential solutions are highlighted.

De Novo Synthesis of Tyramine in Engineered Escherichia coli Using Two-Stage Dissolved Oxygen-Controlled Fermentation

Metabolic regulation and fermentation strategies limit the synthesis of tyramine. Here, a de novo synthetic tyramine-producing strain, LAN 25, was constructed. First, tyramine-producing strain was obtained by modifying the key metabolic nodes of tyrosine synthesis and overexpressing the tyrosine decarboxylase gene from Lactobacillus brevis. Then, a two-stage dissolved oxygen (DO)-controlled fermentation process was established in a 5 L fed-batch bioreactor. In the first stage, sufficient DO was supplied to accumulate tyrosine precursors, while in the second stage, limited DO was provided to promote tyramine synthesis. Next, ldhA and adhE were deleted to improve the strain's robustness. To enhance the redox flux (ATP/ADP ratio) under limited DO conditions, the anaerobic promoter, Pvgb, was used to control the expression of a ppk-based ATP regeneration system in response to DO changes. Additionally, the tyrosine internal transport system was modified. Finally, a titer of 21.33 g/L of tyramine with a yield of 0.092 g/g glucose was obtained.

Biosynthesis of D-1,2,4-butanetriol promoted by a glucose-xylose dual metabolic channel system in engineered Escherichia coli

D-1,2,4-butanetriol (BT) is a widely used fine chemical that can be manufactured by engineered Escherichia coli expressing heterologous pathways and using xylose as a substrate. The current study developed a glucose-xylose dual metabolic channel system in an engineered E. coli and Combinatorially optimized it using multiple strategies to promote BT production. The carbon catabolite repression effects were alleviated by deleting the gene ptsG that encodes the major glucose transporter IICBGlc and mutating the gene crp that encodes the catabolite repressor protein, thereby allowing C-fluxes of both glucose and xylose into their respective metabolic channels separately and simultaneously, which increased BT production by 33% compared with that of the original MJ133K-1 strain. Then, the branch metabolic pathways of intermediates in the BT channel were investigated, the transaminase HisC, the ketoreductases DlD, OLD, and IlvC, and the aldolase MhpE and YfaU were identified as the enzymes for the branched metabolism of 2-keto-3-deoxy-xylonate, deletion of the gene hisC increased BT titer by 21.7%. Furthermore, the relationship between BT synthesis and the intracellular NADPH level was examined, and deletion of the gene pntAB that encodes a transhydrogenase resulted in an 18.1% increase in BT production. The combination of the above approaches to optimize the metabolic network increased BT production by 47.5%, resulting in 2.67 g/L BT in 24 deep-well plates. This study provides insights into the BT biosynthesis pathway and demonstrates effective strategies to increase BT production, which will promote the industrialization of the biosynthesis of BT.

Diagnosis and mitigation of the systemic impact of genome reduction in Escherichia coli DGF-298

Microorganisms with simplified genomes represent interesting cell chassis for systems and synthetic biology. However, genome reduction can lead to undesired traits, such as decreased growth rate and metabolic imbalances. To investigate the impact of genome reduction on Escherichia coli strain DGF-298, a strain in which ~ 36% of the genome has been removed, we reconstructed a strain-specific metabolic model (iAC1061), investigated the regulation of gene expression using iModulon-based transcriptome analysis, and performed adaptive laboratory evolution to let the strain correct potential imbalances that arose during its simplification. The model notably predicted that the removal of all three key pathways for glycolaldehyde disposal in this microorganism would lead to a metabolic bottleneck through folate starvation. Glycolaldehyde is also known to cause self-generation of reactive oxygen species, as evidenced by the increased expression of oxidative stress resistance genes in the SoxS iModulon. The reintroduction of the aldA gene, responsible for one native glycolaldehyde disposal route, alleviated the constitutive oxidative stress response. Our results suggest that systems-level approaches and adaptive laboratory evolution have additive benefits when trying to repair and optimize genome-engineered strains.

IMPORTANCE

Genomic streamlining can be employed in model organisms to reduce complexity and enhance strain predictability. One of the most striking examples is the bacterial strain Escherichia coli DGF-298, notable for having over one-third of its genome deleted. However, such extensive genome modifications raise the question of how similar this simplified cell remains when compared with its parent, and what are the possible unintended consequences of this simplification. In this study, we used metabolic modeling along with iModulon-based transcriptomic analysis in different growth conditions to assess the impact of genome reduction on metabolism and gene regulation. We observed little impact of genomic reduction on the regulatory network of E. coli DGF-298 and identified a potential metabolic bottleneck leading to the constitutive activity of the SoxS iModulon. We then leveraged the model's predictions to successfully restore SoxS activity to the basal level.

Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes

Unraveling metabolite-protein interactions is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational efforts to identify the regulatory roles of metabolites in interaction with proteins, it remains challenging to achieve a genome-scale coverage of these interactions. Here, we leverage established gold standards for metabolite-protein interactions to train supervised classifiers using features derived from genome-scale metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs. Through a comprehensive comparative study, we explore the impact of different features and assess the effect of gold standards for non-interacting pairs on the performance of the classifiers. Using data sets from Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the features constructed by integrating fluxomic and proteomic data with metabolic phenotypes predicted from genome-scale metabolic models can be effectively used to train classifiers, accurately predicting metabolite-protein interactions in the context of metabolism. Our results reveal that the high performance of classifiers trained on these features is unaffected by the method used to generate gold standards for non-interacting pairs. Overall, our study introduces valuable features that improve the performance of identifying metabolite-protein interactions in the context of metabolism.

Engineered E. coli for D-pantothenic acid production with an acetolactate isomeroreductase mutant

D-Pantothenic acid, as a momentous vitamin, is extensively applied to feed, medicine, cosmetics and other fields. However, there are still limitations to produce D-pantothenic acid by microbial fermentation at present. In this paper, we constructed a recombinant strain for D-pantothenic acid production by blocking the organic acid pathway, boosting pyruvate biosynthesis, relieving feedback inhibition of acetolactate synthase, improving glucose intake capacity, and modifying essential genes in the metabolic pathway. In addition, a new acetolactate isomeroreductase mutant V412A origin from Escherichia coli (EcAHAIR) encoded by ilvC was obtained to explore its substrate promiscuity. Compared with the wild type, the variant EcAHAIR-V412A has reduced steric hindrance and enhanced intermolecular forces, resulting in a high affinity for 2-acetolactate. Eventually, the fermentation production of the final strain DPAN19/trc-ilvCV412A reached 4.65 g/L, increased by 192.5% compared with strain DPA8 in shake flask cultivation and produced 62.82 g/L D-pantothenic acid in a 5 L bioreactor. The metabolic engineering strategies and enzyme modification approaches described in this paper provide a particular perspective for the bio-manufacturing of D-pantothenic acid, branched-chain amino acids and its derivates.

Microbial production of L-methionine and its precursors using systems metabolic engineering

L-methionine is an essential amino acid with versatile applications in food, feed, cosmetics and pharmaceuticals. At present, the production of L-methionine mainly relies on chemical synthesis, which conflicts with the concern over serious environmental problems and sustainable development goals. In recent years, microbial production of natural products has been amply rewarded with the emergence and rapid development of system metabolic engineering. However, efficient L-methionine production by microbial fermentation remains a great challenge due to its complicated biosynthetic pathway and strict regulatory mechanism. Additionally, the engineered production of L-methionine precursors, L-homoserine, O-succinyl-L-homoserine (OSH) and O-acetyl-L-homoserine (OAH), has also received widespread attention because they can be catalyzed to L-methionine via a high-efficiently enzymatic reaction in vitro, which is also a promising alternative to chemical route. This review provides a comprehensive overview on the recent advances in the microbial production of L-methionine and its precursors, highlighting the challenges and potential solutions for developing L-methionine microbial cell factories from the perspective of systems metabolic engineering, aiming to offer guidance for future engineering.

Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

High-yield production of β-arbutin by identifying and eliminating byproducts formation

β-Arbutin is a plant-derived glycoside and widely used in cosmetic and pharmaceutical industries because of its safe and effective skin-lightening property as well as anti-oxidant, anti-microbial, and anti-inflammatory activities. In recent years, microbial fermentation has become a highly promising method for the production of β-arbutin. However, this method suffers from low titer and low yield, which has become the bottleneck for its widely industrial application. In this study, we used β-arbutin to demonstrate methods for improving yields for industrial-scale production in Escherichia coli. First, the supply of precursors phosphoenolpyruvate and uridine diphosphate glucose was improved, leading to a 4.6-fold increase in β-arbutin production in shaking flasks. The engineered strain produced 36.12 g/L β-arbutin with a yield of 0.11 g/g glucose in a 3-L bioreactor. Next, based on the substrate and product's structural similarity, an endogenous O-acetyltransferase was identified as responsible for 6-O-acetylarbutin formation for the first time. Eliminating the formation of byproducts, including 6-O-acetylarbutin, tyrosine, and acetate, resulted in an engineered strain producing 43.79 g/L β-arbutin with a yield of 0.22 g/g glucose in fed-batch fermentation. Thus, the yield increased twofold by eliminating byproducts formation. To the best of our knowledge, this is the highest titer and yield of β-arbutin ever reported, paving the way for the industrial production of β-arbutin. This study demonstrated a systematic strategy to alleviate undesirable byproduct accumulation and improve the titer and yield of target products. KEY POINTS: • A systematic strategy to improve titer and yield was showed • Genes responsible for 6-O-acetylarbutin formation were firstly identified • 43.79 g/L β-arbutin was produced in bioreactor, which is the highest titer so far.

Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes.

Glucose Transport in Escherichia coli: From Basics to Transport Engineering

Escherichia coli is the best-known model for the biotechnological production of many biotechnological products, including housekeeping and heterologous primary and secondary metabolites and recombinant proteins, and is an efficient biofactory model to produce biofuels to nanomaterials. Glucose is the primary substrate used as the carbon source for laboratory and industrial cultivation of E. coli for production purposes. Efficient growth and associated production and yield of desired products depend on the efficient sugar transport capabilities, sugar catabolism through the central carbon catabolism, and the efficient carbon flux through specific biosynthetic pathways. The genome of E. coli MG1655 is 4,641,642 bp, corresponding to 4702 genes encoding 4328 proteins. The EcoCyc database describes 532 transport reactions, 480 transporters, and 97 proteins involved in sugar transport. Nevertheless, due to the high number of sugar transporters, E. coli uses preferentially few systems to grow in glucose as the sole carbon source. E. coli nonspecifically transports glucose from the extracellular medium into the periplasmic space through the outer membrane porins. Once in periplasmic space, glucose is transported into the cytoplasm by several systems, including the phosphoenolpyruvate-dependent phosphotransferase system (PTS), the ATP-dependent cassette (ABC) transporters, and the major facilitator (MFS) superfamily proton symporters. In this contribution, we review the structures and mechanisms of the E. coli central glucose transport systems, including the regulatory circuits recruiting the specific use of these transport systems under specific growing conditions. Finally, we describe several successful examples of transport engineering, including introducing heterologous and non-sugar transport systems for producing several valuable metabolites.

Exploring the effects of palm kernel meal feeding on the meat quality and rumen microorganisms of Qinghai Tibetan sheep

Palm kernel meal (PKM) has been shown to be a high-quality protein source in ruminant feeds. This study focused on the effects of feed, supplemented with different amounts of PKM (ZL-0 as blank group, and ZL-15, ZL-18, and ZL-21 as treatment group), on the quality and flavor profile of Tibetan sheep meat. Furthermore, the deposition of beneficial metabolites in Tibetan sheep and the composition of rumen microorganisms on underlying regulatory mechanisms of meat quality were studied based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry as well as 16S rDNA sequencing. The results of the study showed that Tibetan sheep in the ZL-18 group exhibited superior eating quality and flavor profile while depositing more protein and fat relative to the other groups. The ZL-18 group also changed significantly in terms of the concentration and metabolic pathways of meat metabolites, as revealed by metabolomics. Metabolomics and correlation analyses finally showed that PKM feed mainly affected carbohydrate metabolism in muscle, which in turn affects meat pH, tenderness, and flavor. In addition, 18% of PKM increased the abundance of Christensenellaceae R-7 group, Ruminococcaceae UCG-013, Lachnospiraceae UCG-002, and Family XIII AD3011 group in the rumen but decreased the abundance of Prevotella 1; the above bacteria groups regulate meat quality by regulating rumen metabolites (succinic acid, DL-glutamic acid, etc.). Overall, the addition of PKM may improve the quality and flavor of the meat by affecting muscle metabolism and microorganisms in the rumen.

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.

Horizontal Transfer of Bacteriocin Biosynthesis Genes Requires Metabolic Adaptation To Improve Compound Production and Cellular Fitness

Biosynthetic gene clusters (BGCs) encoding the production of bacteriocins are widespread among bacterial isolates and are important genetic determinants of competitive fitness within a given habitat. Staphylococci produce a tremendous diversity of compounds, and the corresponding BGCs are frequently associated with mobile genetic elements, suggesting gain and loss of biosynthetic capacity. Pharmaceutical biology has shown that compound production in heterologous hosts is often challenging, and many BGC recipients initially produce small amounts of compound or show reduced growth rates. To assess whether transfer of BGCs between closely related Staphylococcus aureus strains can be instantly effective or requires elaborate metabolic adaptation, we investigated the intraspecies transfer of a BGC encoding the ribosomally synthesized and posttranslationally modified peptide (RiPP) micrococcin P1 (MP1). We found that acquisition of the BGC by S. aureus RN4220 enabled immediate MP1 production but also imposed a metabolic burden, which was relieved after prolonged cultivation by adaptive mutation. We used a multiomics approach to study this phenomenon and found adaptive evolution to select for strains with increased activity of the tricarboxylic acid cycle (TCA), which enhanced metabolic fitness and levels of compound production. Metabolome analysis revealed increases of central metabolites, including citrate and α-ketoglutarate in the adapted strain, suggesting metabolic adaptation to overcome the BGC-associated growth defects. Our results indicate that BGC acquisition requires genetic and metabolic predispositions, allowing the integration of bacteriocin production into the cellular metabolism. Inappropriate metabolic characteristics of recipients can entail physiological burdens, negatively impacting the competitive fitness of recipients within natural bacterial communities. IMPORTANCE Human microbiomes are critically associated with human health and disease. Importantly, pathogenic bacteria can hide in human-associated communities and can cause disease when the composition of the community becomes unbalanced. Bacteriocin-producing commensals are able to displace pathogens from microbial communities, suggesting that their targeted introduction into human microbiomes might prevent pathogen colonization and infection. However, to develop probiotic approaches, strains are needed that produce high levels of bioactive compounds and retain cellular fitness within mixed bacterial communities. Our work offers insights into the metabolic burdens associated with the production of the bacteriocin micrococcin P1 and highlights evolutionary strategies that increase cellular fitness in the context of production. Metabolic adaptations are most likely broadly relevant for bacteriocin producers and need to be considered for the future development of effective microbiome editing strategies.

A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli

Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.

Contributions of Adaptive Laboratory Evolution towards the Enhancement of the Biotechnological Potential of Non-Conventional Yeast Species

Changes in biological properties over several generations, induced by controlling short-term evolutionary processes in the laboratory through selective pressure, and whole-genome re-sequencing, help determine the genetic basis of microorganism's adaptive laboratory evolution (ALE). Due to the versatility of this technique and the imminent urgency for alternatives to petroleum-based strategies, ALE has been actively conducted for several yeasts, primarily using the conventional species Saccharomyces cerevisiae, but also non-conventional yeasts. As a hot topic at the moment since genetically modified organisms are a debatable subject and a global consensus on their employment has not yet been attained, a panoply of new studies employing ALE approaches have emerged and many different applications have been exploited in this context. In the present review, we gathered, for the first time, relevant studies showing the ALE of non-conventional yeast species towards their biotechnological improvement, cataloging them according to the aim of the study, and comparing them considering the species used, the outcome of the experiment, and the employed methodology. This review sheds light on the applicability of ALE as a powerful tool to enhance species features and improve their performance in biotechnology, with emphasis on the non-conventional yeast species, as an alternative or in combination with genome editing approaches.

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k_cat, termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k_cat values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate kmaxvivo (k_app) for k_cat and developed a high throughput method to acquire kmaxvivo from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology.

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli

MOTIVATION

Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms.

RESULTS

Here we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes.

AVAILABILITY

https://github.com/Rudan-X/NIDLE-flux-code.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Regulatory molecule cAMP changes cell fitness of the engineered Escherichia coli for terpenoids production

Terpenoids are a class of natural compounds with many important functions and applications. They are synthesized from a long synthetic pathway of isoprenyl unit coupling with the myriads of terpene synthases. Owing to the catalytic divergence of terpenoids synthesis, microbial production of terpenoids is compromised to the complexity of pathway engineering and suffers from the metabolic engineering burden. In this work, the adaptive Escherichia coli HP variant exhibited a general cell fitness in terpenoid synthesis. Especially, it could yield taxadiene of 193.2 mg/L in a test tube culture, which is a five-fold increase over the production in the wild type E. coli DH5α. Mutational analyses indicated that IS10 insertion in adenylate cyclase CyaA (CyaA_HP) resulted in lowering intracellular cyclic AMP (cAMP), which could regulate its receptor protein CRP to rewire cell metabolism and contributed to the improved cell fitness. Our results suggested a way to manipulate cell fitness for terpenoids production and other products.

The mannose phosphotransferase system (Man-PTS) - Mannose transporter and receptor for bacteriocins and bacteriophages

Mannose transporters constitute a superfamily (Man-PTS) of the Phosphoenolpyruvate Carbohydrate Phosphotransferase System (PTS). The membrane complexes are homotrimers of protomers consisting of two subunits, IIC and IID. The two subunits without recognizable sequence similarity assume the same fold, and in the protomer are structurally related by a two fold pseudosymmetry axis parallel to membrane-plane (Liu et al. (2019) Cell Research 29 680). Two reentrant loops and two transmembrane helices of each subunit together form the N-terminal transport domain. Two three-helix bundles, one of each subunit, form the scaffold domain. The protomer is stabilized by a helix swap between these bundles. The two C-terminal helices of IIC mediate the interprotomer contacts. PTS occur in bacteria and archaea but not in eukaryotes. Man-PTS are abundant in Gram-positive bacteria living on carbohydrate rich mucosal surfaces. A subgroup of IICIID complexes serve as receptors for class IIa bacteriocins and as channel for the penetration of bacteriophage lambda DNA across the inner membrane. Some Man-PTS are associated with host-pathogen and -symbiont processes.

Multiplex Design of the Metabolic Network for Production of l-Homoserine in Escherichia coli

In this study, the bottlenecks that sequentially limit l-homoserine biosynthesis were identified and resolved, based on rational and efficient metabolic-engineering strategies, coupled with CRISPR interference (CRISPRi)-based systematic analysis. The metabolomics data largely expanded our understanding of metabolic effects and revealed relevant targets for further modification to achieve better performance. The systematic analysis strategy, as well as metabolomics analysis, can be used to rationally design cell factories for the production of highly valuable chemicals.

l-Homoserine, which is one of the few amino acids that is not produced on a large scale by microbial fermentation, plays a significant role in the synthesis of a series of valuable chemicals. In this study, systematic metabolic engineering was applied to target Escherichia coli W3110 for the production of l-homoserine. Initially, a basic l-homoserine producer was engineered through the strategies of overexpressing thrA (encoding homoserine dehydrogenase), removing the degradative and competitive pathways by knocking out metA (encoding homoserine O-succinyltransferase) and thrB (encoding homoserine kinase), reinforcing the transport system, and redirecting the carbon flux by deleting iclR (encoding the isocitrate lyase regulator). The resulting strain constructed by these strategies yielded 3.21 g/liter of l-homoserine in batch cultures. Moreover, based on CRISPR-Cas9/dCas9 (nuclease-dead Cas9)-mediated gene repression for 50 genes, the iterative genetic modifications of biosynthesis pathways improved the l-homoserine yield in a stepwise manner. The rational integration of glucose uptake and recovery of l-glutamate increased l-homoserine production to 7.25 g/liter in shake flask cultivation. Furthermore, the intracellular metabolic analysis further provided targets for strain modification by introducing the anaplerotic route afforded by pyruvate carboxylase to oxaloacetate formation, which resulted in accumulating 8.54 g/liter l-homoserine (0.33 g/g glucose, 62.4% of the maximum theoretical yield) in shake flask cultivation. Finally, a rationally designed strain gave 37.57 g/liter l-homoserine under fed-batch fermentation, with a yield of 0.31 g/g glucose.

IMPORTANCE In this study, the bottlenecks that sequentially limit l-homoserine biosynthesis were identified and resolved, based on rational and efficient metabolic-engineering strategies, coupled with CRISPR interference (CRISPRi)-based systematic analysis. The metabolomics data largely expanded our understanding of metabolic effects and revealed relevant targets for further modification to achieve better performance. The systematic analysis strategy, as well as metabolomics analysis, can be used to rationally design cell factories for the production of highly valuable chemicals.

Evolution of an Escherichia coli PTS- strain: a study of reproducibility and dynamics of an adaptive evolutive process

Adaptive laboratory evolution (ALE) has been used to study and solve pressing questions about evolution, especially for the study of the development of mutations that confer increased fitness during evolutionary processes. In this contribution, we investigated how the evolutionary process conducted with the PTS^- mutant of Escherichia coli PB11 in three parallel batch cultures allowed the restoration of rapid growth with glucose as the carbon source. The significant findings showed that genomic sequence analysis of a set of newly evolved mutants isolated from ALE experiments 2-3 developed some essential mutations, which efficiently improved the fast-growing phenotypes throughout different fitness landscapes. Regulator galR was the target of several mutations such as SNPs, partial and total deletions, and insertion of an IS1 element and thus indicated the relevance of a null mutation of this gene in the adaptation of the evolving population of PB11 during the parallel ALE experiments. These mutations resulted in the selection of MglB and GalP as the primary glucose transporters by the evolving population, but further selection of at least a second adaptive mutation was also necessary. We found that mutations in the yfeO, rppH, and rng genes improved the fitness advantage of evolving PTS^- mutants and resulted in amplification of leaky activity in Glk for glucose phosphorylation and upregulation of glycolytic and other growth-related genes. Notably, we determined that these mutations appeared and were fixed in the evolving populations between 48 and 72 h of cultivation, which resulted in the selection of fast-growing mutants during one ALE experiments in batch cultures of 80 h duration.Key points• ALE experiments selected evolved mutants through different fitness landscapes in which galR was the target of different mutations: SNPs, deletions, and insertion of IS.• Key mutations in evolving mutants appeared and fixed at 48-72 h of cultivation.• ALE experiments led to increased understanding of the genetics of cellular adaptation to carbon source limitation.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (k _cats) are essential for a quantitative understanding of cells. Because k _cats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k _cats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k _cats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k _cats predict unseen proteomics data with much higher precision than in vitro k _cats. The results demonstrate that in vivo k _cats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

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.

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity

BACKGROUND

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.

RESULTS

Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.

CONCLUSIONS

The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Transporters of glucose and other carbohydrates in bacteria

Glucose arguably is the most important energy carrier, carbon source for metabolites and building block for biopolymers in all kingdoms of life. The proper function of animal organs and tissues depends on the continuous supply of glucose from the bloodstream. Most animals can resorb only a small number of monosaccharides, mostly glucose, galactose and fructose, while all other sugars oligosaccharides and dietary fibers are degraded and metabolized by the microbiota of the lower intestine. Bacteria, in contrast, are omnivorous. They can import and metabolize structurally different sugars and, as a consortium of different species, utilize almost any sugar, sugar derivative and oligosaccharide occurring in nature. Bacteria have membrane transport systems for the uptake of sugars against steep concentration gradients energized by ATP, the proton motive force and the high energy glycolytic intermediate phosphoenolpyruvate (PEP). Different uptake mechanisms and the broad range of overlapping substrate specificities allow bacteria to quickly adapt to and colonize changing environments. Here, we review the structures and mechanisms of bacterial representatives of (i) ATP-dependent cassette (ABC) transporters, (ii) major facilitator (MFS) superfamily proton symporters, (iii) sodium solute symporters (SSS) and (iv) enzyme II integral membrane subunits of the bacterial PEP-dependent phosphotransferase system (PTS). We give a short overview on the distribution of transporter genes and their phylogenetic relationship in different bacterial species. Some sugar transporters are hijacked for import of bacteriophage DNA and antibacterial toxins (bacteriocins) and they facilitate the penetration of polar antibiotics. Finally, we describe how the expression and activity of certain sugar transporters are controlled in response to the availability of sugars and how the presence and uptake of sugars may affect pathogenicity and host-microbiota interactions.

A toxicological study on photo-degradation products of environmental ibuprofen: Ecological and human health implications

Increasing quantities of pharmaceutical waste in the environment have disrupted the balance of ecosystems, and may have subsequent effects on human health. Although a handful of previous studies have shown the impacts of pharmaceutically active compounds on the environment, the toxicological effects of their degradation products remain largely unknown. In the current study, the photo-degradation products of environmental ibuprofen were assessed for both ecotoxicological and human health effects using a series of in vitro assays. Here, six of the major degradation products are synthesized with high purity (>98%) and characterized with ¹HNMR, ¹³CNMR, FT-IR and HRMS. To evaluate human health effects, three gut microbiota species, Lactobacillus acidophilus, Enterococcus faecalis and Escherichia coli, and two human cell lines, HEK293T and HepG2, are exposed to various concentrations of ibuprofen and its degradation products. On L. acidophilus, the ibuprofen degradation product (±)-(2R,3R)-2-(4-isobutylphenyl)-5-methylhexan-3-ol shows a greater toxic effect while ibuprofen enhances its growth at lower concentrations. At higher concentrations, ibuprofen shows at least a 2-fold higher toxicity compared to that of its degradation products. However, E. faecalis shows little or no effect upon exposure to these compounds. An induction of the SOS response in E. coli is observed but limited to only ibuprofen and 4-acetylbenzoic acid. In human cell line studies, survival of both HEK293T and HepG2 cell lines is profoundly impaired by the photo-degradation products of (±)- (2R,3R)-2-(4-isobutylphenyl)-5-methylhexan-3-ol, (±)-(2R,3S)-2-(4-isobutylphenyl)-5-methylhexan-3-ol, and (±)-1-(4-(1-hydroxy-2methylpropyl)phenyl)ethan-1-one. In this work, the bioluminescence bacterium, Aliivibrio fischeri, is used as a model to assess environmental impact. Both ibuprofen and its degradation products inhibit the growth of this gram-negative bacteria with the primary compound showing the most significant impact. Overall, our results highlight that some of the degradation products of ibuprofen can be more toxic to human kidney cell line and liver cell line than the parent compound while ibuprofen can be more toxic to human gut microbiota and A. fischeri than ibuprofen degradation products.

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.

New insights into transport capability of sugars and its impact on growth from novel mutants of Escherichia coli

The fast-growing capability of Escherichia coli strains used to produce industrially relevant metabolites relies on their capability to transport efficiently glucose or potential industrial feedstocks such as sucrose or xylose as carbon sources. E. coli imports extracellular glucose into the periplasmic space across the outer membrane porins: OmpC, OmpF, and LamB. As the internal membrane is an impermeable barrier for sugars, the cell employs several primary and secondary active transport systems, and the phosphoenolpyruvate (PEP)-sugar phosphotransferase (PTS) system for glucose transport. PTS:glucose is the preferred system by E. coli to transport and phosphorylate the periplasmic glucose; nevertheless, PTS imposes a strict metabolic control mechanism on the preferential consumption of glucose over other carbon sources in sugar mixtures such as glucose and xylose resulting from the hydrolysis of lignocellulosic biomass, by the carbon catabolite repression. In this contribution, we summarize the major sugar transport systems for glucose and disaccharide transport, the exhibited substrate plasticity, and their impact on the growth of E. coli, highlighting the relevance of PTS in the control of the expression of genes for the transport and catabolism of other sugars as xylose. We discuss the strategies developed by evolved mutants of E. coli during adaptive laboratory evolution experiments to overcome the nutritional stress condition imposed by inactivation of PTS as a strategy for the selection of fast-growing derivatives in glucose, xylose, or mixtures of glucose:xylose. This approach results in the recruitment of other primary and secondary active transporters, demonstrating relevant sugar plasticity in derivative-evolved mutants. Elucidation of the molecular and biochemical basis of sugar-transport substrate plasticity represents a consistent approach for sugar-transport system engineering for the design of efficient E. coli derivative strains with improved substrate assimilation for biotechnological purposes.

Development and characterization of acidic-pH-tolerant mutants of Zymomonas mobilis through adaptation and next-generation sequencing-based genome resequencing and RNA-Seq

BACKGROUND

Acid pretreatment is a common strategy used to break down the hemicellulose component of the lignocellulosic biomass to release pentoses, and a subsequent enzymatic hydrolysis step is usually applied to release hexoses from the cellulose. The hydrolysate after pretreatment and enzymatic hydrolysis containing both hexoses and pentoses can then be used as substrates for biochemical production. However, the acid-pretreated liquor can also be directly used as the substrate for microbial fermentation, which has an acidic pH and contains inhibitory compounds generated during pretreatment. Although the natural ethanologenic bacterium Zymomonas mobilis can grow in a broad range of pH 3.5 ~ 7.5, cell growth and ethanol fermentation are still affected under acidic-pH conditions below pH 4.0.

RESULTS

In this study, adaptive laboratory evolution (ALE) strategy was applied to adapt Z. mobilis under acidic-pH conditions. Two mutant strains named 3.6M and 3.5M with enhanced acidic pH tolerance were selected and confirmed, of which 3.5M grew better than ZM4 but worse than 3.6M in acidic-pH conditions that is served as a reference strain between 3.6M and ZM4 to help unravel the acidic-pH tolerance mechanism. Mutant strains 3.5M and 3.6M exhibited 50 ~ 130% enhancement on growth rate, 4 ~ 9 h reduction on fermentation time to consume glucose, and 20 ~ 63% improvement on ethanol productivity than wild-type ZM4 at pH 3.8. Next-generation sequencing (NGS)-based whole-genome resequencing (WGR) and RNA-Seq technologies were applied to unravel the acidic-pH tolerance mechanism of mutant strains. WGR result indicated that compared to wild-type ZM4, 3.5M and 3.6M have seven and five single nucleotide polymorphisms (SNPs), respectively, among which four are shared in common. Additionally, RNA-Seq result showed that the upregulation of genes involved in glycolysis and the downregulation of flagellar and mobility related genes would help generate and redistribute cellular energy to resist acidic pH while keeping normal biological processes in Z. mobilis. Moreover, genes involved in RND efflux pump, ATP-binding cassette (ABC) transporter, proton consumption, and alkaline metabolite production were significantly upregulated in mutants under the acidic-pH condition compared with ZM4, which could help maintain the pH homeostasis in mutant strains for acidic-pH resistance. Furthermore, our results demonstrated that in mutant 3.6M, genes encoding F₁F₀ ATPase to pump excess protons out of cells were upregulated under pH 3.8 compared to pH 6.2. This difference might help mutant 3.6M manage acidic conditions better than ZM4 and 3.5M. A few gene targets were then selected for genetics study to explore their role in acidic pH tolerance, and our results demonstrated that the expression of two operons in the shuttle plasmids, ZMO0956-ZMO0958 encoding cytochrome bc1 complex and ZMO1428-ZMO1432 encoding RND efflux pump, could help Z. mobilis tolerate acidic-pH conditions.

CONCLUSION

An acidic-pH-tolerant mutant 3.6M obtained through this study can be used for commercial bioethanol production under acidic fermentation conditions. In addition, the molecular mechanism of acidic pH tolerance of Z. mobilis was further proposed, which can facilitate future research on rational design of synthetic microorganisms with enhanced tolerance against acidic-pH conditions. Moreover, the strategy developed in this study combining approaches of ALE, genome resequencing, RNA-Seq, and classical genetics study for mutant evolution and characterization can be applied in other industrial microorganisms.

Adaptation to mutational inactivation of an essential gene converges to an accessible suboptimal fitness peak

The mechanisms of adaptation to inactivation of essential genes remain unknown. Here we inactivate E. coli dihydrofolate reductase (DHFR) by introducing D27G,N,F chromosomal mutations in a key catalytic residue with subsequent adaptation by an automated serial transfer protocol. The partial reversal G27- > C occurred in three evolutionary trajectories. Conversely, in one trajectory for D27G and in all trajectories for D27F,N strains adapted to grow at very low metabolic supplement (folAmix) concentrations but did not escape entirely from supplement auxotrophy. Major global shifts in metabolome and proteome occurred upon DHFR inactivation, which were partially reversed in adapted strains. Loss-of-function mutations in two genes, thyA and deoB, ensured adaptation to low folAmix by rerouting the 2-Deoxy-D-ribose-phosphate metabolism from glycolysis towards synthesis of dTMP. Multiple evolutionary pathways of adaptation converged to a suboptimal solution due to the high accessibility to loss-of-function mutations that block the path to the highest, yet least accessible, fitness peak.

Predicting how viruses and bacteria evolve remains a challenge. The ability to anticipate when and how bacteria might develop drug resistance would make treating life-threatening diseases easier and could potentially help prevent drug resistance altogether. Studying bacterial evolution under different conditions and cataloguing all possible DNA mutations that allow these bacteria to survive are crucial steps in predicting the appearance of drug resistance.

Studies have revealed that bacteria can adapt to sources of stress, such as antibiotics, in different ways – each involving mutations in distinct genes. However, not all the mutations provide the same benefits to the organism, and the factors that influence how bacteria will adapt are unclear.

Now, Rodrigues and Shakhnovich have used a new approach to push the adaptation ability of the bacterium Escherichia coli to the limit. First, they genetically engineered different E. coli strains by introducing distinct mutations to an enzyme the bacterium needs to make DNA. These mutations make the resulting strains dependent on external molecules to synthesize new DNA. Next, the cells were grown in conditions where the supply of these DNA precursors was progressively decreased, forcing them to adapt. The obvious way for bacteria to adapt to these conditions would be to ‘revert’ the mutations that Rodrigues and Shakhnovich introduced in the first place. By using this approach, Rodrigues and Shakhnovich were able to test how often the obvious evolutionary solution happens compared with presumably less-preferred alternative routes.

In rare cases, a specific mutation did restore the activity of the enzyme that enabled DNA synthesis. However, in most cases the bacteria found a different evolutionary solution whereby they all adapt to the decrease in external DNA precursors in the same way, but not by reverting the original mutation. Instead, further mutations disrupt the activity of two metabolic genes, allowing the cells to use the external DNA precursors more efficiently, and keep building DNA. These cells are therefore able to survive even when the levels of the external DNA components are very low, but they will die in the complete absence of these precursor molecules.

This evolutionary solution leads to a non-optimal effect: mutations that restore the activity of the original enzyme, which are the best solution when the two metabolic genes are intact, are no longer as effective. This finding represents a clear example of interactions between genes determining evolutionary outcomes. Rodrigues and Shakhnovich showed that, since it is more likely for a random mutation to disrupt a gene than to revert a previous mutation, adaptations that are less-than-optimal but still work might predominate simply because they happen faster.

Understanding why certain evolutionary adaptations prevail is an important step in predicting evolution and may lead to breakthroughs in many areas. For example, if scientists can identify mutations likely to make bacteria resistant to drugs, it may be possible to act proactively against the bacterial strains that carry those mutations. Eventually, if the factors that lead to specific adaptations are known, it may be possible to exploit this knowledge to create weaknesses in the bacteria’s own defences.

Carbohydrate Transport by Group Translocation: The Bacterial Phosphoenolpyruvate: Sugar Phosphotransferase System

The Bacterial Phosphoenolpyruvate (PEP) : Sugar Phosphotransferase System (PTS) mediates the uptake and phosphorylation of carbohydrates, and controls the carbon- and nitrogen metabolism in response to the availability of sugars. PTS occur in eubacteria and in a few archaebacteria but not in animals and plants. All PTS comprise two cytoplasmic phosphotransferase proteins (EI and HPr) and a species-dependent, variable number of sugar-specific enzyme II complexes (IIA, IIB, IIC, IID). EI and HPr transfer phosphorylgroups from PEP to the IIA units. Cytoplasmic IIA and IIB units sequentially transfer phosphates to the sugar, which is transported by the IIC and IICIID integral membrane protein complexes. Phosphorylation by IIB and translocation by IIC(IID) are tightly coupled. The IIC(IID) sugar transporters of the PTS are in the focus of this review. There are four structurally different PTS transporter superfamilies (glucose, glucitol, ascorbate, mannose) . Crystal structures are available for transporters of two superfamilies: bcIIC^mal (MalT, 5IWS, 6BVG) and bcIIC^chb (ChbC, 3QNQ) of B. subtilis from the glucose family, and IIC^asc (UlaA, 4RP9, 5ZOV) of E. coli from the ascorbate superfamily . They are homodimers and each protomer has an independent transport pathway which functions by an elevator-type alternating-access mechanism. bcIIC^mal and bcIIC^chb have the same fold, IIC^asc has a completely different fold. Biochemical and biophysical data accumulated in the past with the transporters for mannitol (IICBA^mtl) and glucose (IICB^glc) are reviewed and discussed in the context of the bcIIC^mal crystal structures. The transporters of the mannose superfamily are dimers of protomers consisting of a IIC and a IID protein chain. The crystal structure is not known and the topology difficult to predict. Biochemical data indicate that the IICIID complex employs a different transport mechanism . Species specific IICIID serve as a gateway for the penetration of bacteriophage lambda DNA across, and insertion of class IIa bacteriocins into the inner membrane. PTS transporters are inserted into the membrane by SecYEG translocon and have specific lipid requirements. Immunoelectron- and fluorescence microscopy indicate a non-random distribution and supramolecular complexes of PTS proteins.

Reframing gene essentiality in terms of adaptive flexibility

BACKGROUND

Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality.

RESULTS

In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed.

CONCLUSIONS

Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events.

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

Adaptive laboratory evolution (ALE) has emerged as a powerful tool in basic microbial research and strain development. In the context of metabolic science and engineering, it has been applied to study gene knockout responses, expand substrate ranges, improve tolerance to process conditions, and to improve productivity via designed growth coupling. In recent years, advancements in ALE methods and systems biology measurement technologies, particularly genome sequencing and ¹³C metabolic flux analysis (¹³C-MFA), have enabled detailed study of the mechanisms and dynamics of evolving metabolism. In this review, we discuss a range of studies that have applied flux analysis to adaptively evolved strains, as well as modeling frameworks developed to predict and interpret evolved fluxes. These efforts link mutations to fitness-enhanced phenotypes, identify bottlenecks and approaches to resolve them, and address systems concepts such as optimality.

Analysis of differentially upregulated proteins in ptsHIcrr- and rppH- mutants in Escherichia coli during an adaptive laboratory evolution experiment

The previous deletion of the cytoplasmic components of the phosphotransferase system (PTS) in Escherichia coli JM101 resulted in the PTS^- derivative strain PB11 with severely impaired growth capability in glucose as the sole carbon source. Previous adaptive laboratory evolution (ALE) experiment led to select a fast-growing strain named PB12 from PB11. Comparative genome analysis of PB12 showed a chromosomal deletion, which result in the loss of several genes including rppH which codes for the RNA pyrophosphohydrolase RppH, involved in the preparation of hundreds of mRNAs for further degradation by RNase E. Previous inactivation of rppH in PB11 (PB11rppH^-) improved significantly its growing capabilities and increased several mRNAs respect its parental strain PB11. These previous results led to propose to the PB11rppH^- mutant as an intermediate between PB11 and PB12 strains merged during the early ALE experiment. In this contribution, we report the metabolic response to the PTS^- and rppH^- mutations in the deep of a proteomic approach to understanding the relevance of rppH^- phenotype during an ALE experiment. Differentially upregulated proteins between the wild-type JM101/PB11, PB11/PB11rppH^-, and PB11/PB12 comparisons led to identifying 45 proteins between strain comparisons. Downregulated or upregulated proteins in PB11rppH^- were found expressed at an intermediate level with respect to PB11 and PB12. Many of these proteins were found involved in non-previously metabolic traits reported in the study of the PTS^- strains, including glucose, amino acids, ribose transport; amino acid biosynthesis; NAD biosynthesis/salvage pathway, biosynthesis of Ac-CoA precursors; detoxification and degradation pathways; stress response; protein synthesis; and possible mutator activities between comparisons. No changes were found in the expression of galactose permease GalP, previously proposed as the primary glucose transporter in the absence of PTS selected by the PTS^- derivatives during the ALE experiment. This result suggests that the evolving PTS^- population selected other transporters such as LamB, MglB, and ManX instead of GalP for glucose uptake during the early ALE experiment. Analysis of the biological relevance of the metabolic traits developed by the studied strains provided valuable information to understand the relevance of the rppH^- mutation in the PTS^- background during an ALE experiment as a strategy for the selection of valuable phenotypes for metabolic engineering purposes.

Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism

Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution.

The function of metabolic genes in the context of regulatory networks is not well understood. Here, the authors investigate the adaptive responses of E. coli after knockout of metabolic genes and highlight the influence of metabolite levels in the evolution of regulatory function.