Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes

Beyond CEN.PK - parallel engineering of selected S. cerevisiae strains reveals that superior chassis strains require different engineering approaches for limonene production

Genetically engineered microbes are increasingly utilized to produce a broad range of high-value compounds. However, most studies start with only a very narrow group of genetically tractable type strains that have not been selected for maximum titers or industrial robustness. In this study, we used high-throughput screening and parallel metabolic engineering to identify and optimize Saccharomyces cerevisiae chassis strains for the production of limonene, a monoterpene with applications in flavors, fragrances, and biofuels. We screened 921 genetically and phenotypically distinct S. cerevisiae strains for limonene tolerance and lipid content to identify optimal chassis strains for precision fermentation of limonene. In parallel, we also evaluated 16 different plant limonene synthases. Our results revealed that two of the selected strains showed approximately a 2-fold increase in titers compared to CEN.PK2-1C, the type strain that is often used as a chassis for limonene production, with the same genetic modifications in the mevalonate pathway. Intriguingly, the most effective engineering strategy proved strain-specific. Metabolic profiling revealed that this difference is likely explained by differences in native mevalonate production. Ultimately, by using strain-specific engineering strategies, we achieved 844 mg/L in a new strain, 40 % higher than the titer (605 mg/L) achieved by CEN.PK2-1C. Our findings demonstrate the potential of leveraging genetic diversity in S. cerevisiae for monoterpene bioproduction and highlight the necessity for tailoring metabolic engineering strategies to specific strains.

D-CAPS: an efficient CRISPR-Cas9-based phage defense system for E. coli

Escherichia coli is widely used in industrial chemical synthesis but faces significant challenges due to bacteriophage contamination, which reduces product quality and yield. Therefore, developing an efficient antiphage system is essential. In this study, we develop a CRISPR-Cas9-based antiphage system (CAPS) targeting essential genes of the T7 phage (gene 5 and gene 19) with single gRNAs transformed into MG1655 strains expressing Cas9. While CAPS provides limited resistance, with plating efficiencies ranging from 10 -5 to 10 -1, further optimization is needed. To enhance efficacy, we design a double-site-targeting CRISPR-Cas9-based antiphage system (D-CAPS). D-CAPS demonstrates complete resistance, with no plaques observed even at a high multiplicity of infection (MOI of 2), and growth curve analysis reveals that antiphage E. coli strains grow normally, similar to the wild-type strain, even at a high multiplicity of infection. Furthermore, D-CAPS is effective against BL21(DE3) strains, showing strong resistance and demonstrating its versatility across different E . coli strains. Protein expression analysis via green fluorescent protein confirms that E. coli carrying D-CAPS could maintain normal protein expression levels even in the presence of phages, comparable to wild-type strains. Overall, D-CAPS offers a robust and versatile approach to enhancing E. coli resistance to phages, providing a practical solution for protecting industrial E. coli strains and improving fermentation processes.

Improvements in large-scale production of tobacco etch virus protease

Tobacco-etch-virus (TEV) protease is the workhorse of many laboratories in which protein expression is the linchpin of downstream experiments. TEV protease is remarkable in its sequence specificity as the cleavage sequence rarely appears in higher organisms and its ability to cleave fusion tag proteins from proteins of interest. Herein we report work done on large-scale production of TEV protease using different promotors, media, fusion tags, and expression platforms. During our work we detected post-translational modification (gluconoylation and phosphogluconoylation) of TEV protease and the subsequent effects this has on the purity of the protein. Subsequently we made our pgl plus bacteria that negates these modifications and their effects. We also introduce a GFP-based assay for measurement of activity and ultimately a new set of protocols for producing 400-500 mg/L TEV protease.

Comprehensive evaluation of the capacities of microbial cell factories

Systems metabolic engineering is facilitating the development of high-performing microbial cell factories for producing chemicals and materials. However, constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here, we comprehensively evaluate the capacities of various microbial cell factories and propose strategies for systems metabolic engineering steps, including host strain selection, metabolic pathway reconstruction, and metabolic flux optimization. We analyze the metabolic capacities of five representative industrial microorganisms as cell factories for the production of 235 different bio-based chemicals and suggest the most suitable host strain for the corresponding chemical production. To improve the innate metabolic capacity by constructing more efficient metabolic pathways, heterologous metabolic reactions, and cofactor exchanges are systematically analyzed. Additionally, we present metabolic engineering strategies, which include up- and down-regulation target reactions, for the improved production of chemicals. Altogether, this study will serve as a comprehensive resource for the systems metabolic engineering of microorganisms in the bio-based production of chemicals.

Tailoring Escherichia coli BL21 (DE3) for preferential xylose utilization via metabolic and regulatory engineering

Xylose is the most abundant pentose in nature. However, it is usually obtained in mixtures with glucose, leading to carbon catabolite repression in many microorganisms. Among E. coli lineages, significant metabolic and regulatory differences exist, requiring distinct metabolic engineering strategies to develop a xylose-selective phenotype in the strains W, K-12, and C. In this study, strain ES02 was engineered from Escherichia coli BL21 (DE3) as a xylose-selective strain by deleting the glk, ptsG, and manZ genes. However, when grown in a mixture of xylose and glucose, this strain's specific growth rate and xylose consumption rate decreased by about 50% compared to cultures with only xylose. A modified version of the xylose-responsive transcriptional activator XylRQ31K was utilized to overcome this issue. The resulting strain ES04 (BL21 (DE3) Δglk, ΔmanZ, ΔptsG, xylR::Kmr, lacZ::xylRC91A-Gmr) efficiently used xylose as carbon source either alone or in a mixture with glucose, with a specific xylose consumption rate 75% higher than that of the wild-type strain BL21(DE3). Unexpectedly, strain ES04 partially recovers the ability to grow and consume glucose at a low rate, preferentially consuming xylose over glucose in sugar mixtures, revealing an altered carbon catabolite repression phenotype. Transcriptomics analysis suggested that glucose assimilation in this strain was related to the overexpression of the galactitol operon gatDCBAZY. Further inactivation of this operon confirmed its participation in glucose assimilation. KEY POINTS: • XylRQ31K alleviates carbon catabolite repression in the xylose-selective strain ES04. • Galactitol operon overexpression in ES04 links to partial glucose utilization. • ES04 strain preferentially uses xylose over glucose, revealing altered CCR.

Amyloid-like DNA bridging: a new mode of DNA shaping

All organisms depend on specific proteins to compact and organize their genomes. In eukaryotes, histones fulfil this role, while bacterial chromosomes are shaped by nucleoid-associated proteins (NAPs). Among its pleiotropic functions, the NAP Hfq plays a pivotal role in bacterial genome organization. In this study, we characterized the structure of the C-terminal extension of Hfq, which mediates chromosomal compaction, in its DNA-bound state. Using an integrative approach that combined transmission electron microscopy, neutron scattering, site-directed mutagenesis, and molecular modeling, we identified an amyloid module formed by the C-terminal region of Hfq. This module uniquely bridges and compacts six DNA molecules, marking the first documented instance of an amyloid structure with DNA-bridging properties. Our findings redefine the functional landscape of amyloids, linking them to genome architecture and gene regulation. This result suggests that amyloid-DNA interactions may represent a conserved mechanism across biological systems, with profound implications for understanding genome organization and the regulation of gene expression in both prokaryotes and eukaryotes.

Revealing systematic changes in the transcriptome during the transition from exponential growth to stationary phase

The composition of bacterial transcriptomes is determined by the transcriptional regulatory network (TRN). The TRN regulates the transition from one physiological state to another. Here, we use independent component analysis to monitor the composition of the transcriptome during the transition from the exponential growth phase to the stationary phase. With Escherichia coli K-12 MG1655 as a model strain, we trigger the transition using carbon, nitrogen, and sulfur starvation. We find that (i) the transition to the stationary phase accompanies common transcriptome changes, including increased stringent responses and reduced production of cellular building blocks and energy regardless of the limiting element; (ii) condition-specific changes are strongly associated with transcriptional regulators (e.g., Crp, NtrC, CysB, Cbl) responsible for metabolizing the limiting element; and (iii) the shortage of each limiting element differentially affects the production of amino acids and extracellular polymers. This study demonstrates how the combination of genome-scale datasets and new data analytics reveals the fundamental characteristics of a key transition in the life cycle of bacteria.

IMPORTANCE

Nutrient limitations are critical environmental perturbations in bacterial physiology. Despite its importance, a detailed understanding of how bacterial transcriptomes are adjusted has been limited. By utilizing independent component analysis (ICA) to decompose transcriptome data, this study reveals key regulatory events that enable bacteria to adapt to nutrient limitations. The findings not only highlight common responses, such as the stringent response, but also condition-specific regulatory shifts associated with carbon, nitrogen, and sulfur starvation. The insights gained from this work advance our knowledge of bacterial physiology, gene regulation, and metabolic adaptation.

Optimizing bioprocessing efficiency with OptFed: Dynamic nonlinear modeling improves product-to-biomass yield

Biotechnological production of recombinant molecules relies heavily on fed-batch processes. However, as the cells' growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. Process design is based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools (e.g., response surface methodology), which are unable to harvest the full potential of production. To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal one. In detail, we assume that cell-specific rates depend on several state variables and their derivatives. Using measurements of bioreactor volume, biomass, and product, we fit the kinetic constants of ordinary differential equations. A regression model avoids overfitting by reducing the number of parameters. Thereafter, OptFed predicts optimal process conditions by solving an optimal control problem using orthogonal collocation and nonlinear programming. In a case study, we apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19% and showcase OptFed's potential for enhancing process optimization in biotechnology.

A Study of the Community Relationships Between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field, which enables a guide for targeting genetic modifications and the optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Herein, we present a pipeline for community metabolic modeling with a user-friendly GUI, applying it to analyze interactions between Methylococcus capsulatus, a biotechnologically important methanotroph, and Escherichia coli W3110 under oxygen- and nitrogen-limited conditions. We constructed models with unmodified and homoserine-producing E. coli strains using the pipeline implemented in the original BioUML platform. The E. coli strain primarily utilized acetate from M. capsulatus under oxygen limitation. However, homoserine produced by E. coli significantly reduced acetate secretion and the community growth rate. This homoserine was taken up by M. capsulatus, converted to threonine, and further exchanged as amino acids. In nitrogen-limited modeling conditions, nitrate and ammonium exchanges supported the nitrogen needs, while carbon metabolism shifted to fumarate and malate, enhancing E. coli TCA cycle activity in both cases, with and without modifications. The presence of homoserine altered cross-feeding dynamics, boosting amino acid exchanges and increasing pyruvate availability for M. capsulatus. These findings suggest that homoserine production by E. coli optimizes resource use and has potential for enhancing microbial consortia productivity.

The evolutionary landscape of prokaryotic chromosome/plasmid balance

The balance between chromosomal and plasmid DNAs determines the genomic plasticity of prokaryotes. Natural selections, acting on the level of organisms or plasmids, shape the abundances of plasmid DNAs in prokaryotic genomes. Despite the importance of plasmids in health and engineering, there have been rare systematic attempts to quantitatively model and predict the determinants underlying the strength of different selection forces. Here, we develop a metabolic flux model that describes the intracellular resource competition between chromosomal and plasmid-encoded reactions. By coarse graining, this model predicts a landscape of natural selections on chromosome/plasmid balance, which is featured by the tradeoff between phenotypic and non-phenotypic selection pressures. This landscape is further validated by the observed pattern of plasmid distributions in the vast collection of prokaryotic genomes retrieved from the NCBI database. Our results establish a universal paradigm to understand the prokaryotic chromosome/plasmid interplay and provide insights into the evolutionary origin of plasmid diversity.

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.

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.

Understanding flux switching in metabolic networks through an analysis of synthetic lethals

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.

Use of acetate as substrate for sustainable production of homoserine and threonine by Escherichia coli W3110: A modular metabolic engineering approach

Acetate, a promising yet underutilized carbon source for biological production, was explored for the efficient production of homoserine and threonine in Escherichia coli W. A modular metabolic engineering approach revealed the crucial roles of both acetate assimilation pathways (AckA/Pta and Acs), optimized TCA cycle flux and glyoxylate shunt activity, and enhanced CoA availability, mediated by increased pantothenate kinase activity, for efficient homoserine production. The engineered strain W-H22/pM2/pR1P exhibited a high acetate assimilation rate (5.47 mmol/g cell/h) and produced 44.1 g/L homoserine in 52 h with a 53% theoretical yield (0.18 mol/mol) in fed-batch fermentation. Similarly, strain W-H31/pM2/pR1P achieved 45.8 g/L threonine in 52 h with a 65% yield (0.22 mol/mol). These results represent the highest reported levels of amino acid production using acetate, highlighting its potential as a valuable and sustainable feedstock for biomanufacturing.

Influence of CO2 and Dust on the Survival of Non-Resistant and Multi-Resistant Airborne E. coli Strains

The airborne transmission of bacterial pathogens poses a significant challenge to public health, especially with the emergence of antibiotic-resistant strains. This study investigated environmental factors influencing the survival of airborne bacteria, focusing on the effects of different carbon dioxide (CO2) and dust concentrations. The experiments were conducted in an atmospheric simulation chamber using the non-resistant wild-type E. coli K12 (JM109) and a multi-resistant variant (JM109-pEC958). Different CO2 (100 ppm, 800 ppm, 3000 ppm) and dust concentrations (250 µg m-3, 500 µg m-3, 2000 µg m-3) were tested to encompass a wide range of CO2 and dust levels. The results revealed that JM109-pEC958 exhibited greater resilience to high CO2 and dust concentrations compared to its non-resistant counterpart. At 3000 ppm CO2, the survival rate of JM109 was significantly reduced, while the survival rate of JM109-pEC958 remained unaffected. At the dust concentration of 250 µg m-3, JM109 exhibited significantly reduced survival, whereas JM109-pEC958 did not. When the dust concentration was increased to 500 and 2000 µg m-3, even the JM109-pEC958 experienced substantially reduced survival rates, which were still significantly higher than those of its non-resistant counterpart at these concentrations. These findings suggest that multi-resistant E. coli strains possess mechanisms enabling them to endure extreme environmental conditions better than non-resistant strains, potentially involving regulatory genes or efflux pumps. The study underscores the importance of understanding bacterial adaptation strategies to develop effective mitigation approaches against antibiotic-resistant bacteria in atmospheric environments. Overall, this study provides valuable insights into the interplay between environmental stressors and bacterial survival, serving as a foundational step towards elucidating the adaptation mechanisms of multi-resistant bacteria and informing strategies for combating antibiotic resistance in the atmosphere.

Identification of transporters involved in aromatic compounds tolerance through screening of transporter deletion libraries

Aromatic compounds are used in pharmaceutical, food, textile and other industries. Increased demand has sparked interest in exploring biotechnological approaches for their sustainable production as an alternative to chemical synthesis from petrochemicals or plant extraction. These aromatic products may be toxic to microorganisms, which complicates their production in cell factories. In this study, we analysed the toxicity of multiple aromatic compounds in common production hosts. Next, we screened a subset of toxic aromatics, namely 2-phenylethanol, 4-tyrosol, benzyl alcohol, berberine and vanillin, against transporter deletion libraries in Escherichia coli and Saccharomyces cerevisiae. We identified multiple transporter deletions that modulate the tolerance of the cells towards these compounds. Lastly, we engineered transporters responsible for 2-phenylethanol tolerance in yeast and showed improved 2-phenylethanol bioconversion from L-phenylalanine, with deletions of YIA6, PTR2 or MCH4 genes improving titre by 8-12% and specific yield by 38-57%. Our findings provide insights into transporters as targets for improving the production of aromatic compounds in microbial cell factories.

A validated pangenome-scale metabolic model for the Klebsiella pneumoniae species complex

The Klebsiella pneumoniae species complex (KpSC) is a major source of nosocomial infections globally with high rates of resistance to antimicrobials. Consequently, there is growing interest in understanding virulence factors and their association with cellular metabolic processes for developing novel anti-KpSC therapeutics. Phenotypic assays have revealed metabolic diversity within the KpSC, but metabolism research has been neglected due to experiments being difficult and cost-intensive. Genome-scale metabolic models (GSMMs) represent a rapid and scalable in silico approach for exploring metabolic diversity, which compile genomic and biochemical data to reconstruct the metabolic network of an organism. Here we use a diverse collection of 507 KpSC isolates, including representatives of globally distributed clinically relevant lineages, to construct the most comprehensive KpSC pan-metabolic model to date, KpSC pan v2. Candidate metabolic reactions were identified using gene orthology to known metabolic genes, prior to manual curation via extensive literature and database searches. The final model comprised a total of 3550 reactions, 2403 genes and can simulate growth on 360 unique substrates. We used KpSC pan v2 as a reference to derive strain-specific GSMMs for all 507 KpSC isolates, and compared these to GSMMs generated using a prior KpSC pan-reference (KpSC pan v1) and two single-strain references. We show that KpSC pan v2 includes a greater proportion of accessory reactions (8.8 %) than KpSC pan v1 (2.5 %). GSMMs derived from KpSC pan v2 also generate more accurate growth predictions, with high median accuracies of 95.4 % (aerobic, n=37 isolates) and 78.8 % (anaerobic, n=36 isolates) for 124 matched carbon substrates. KpSC pan v2 is freely available at https://github.com/kelwyres/KpSC-pan-metabolic-model, representing a valuable resource for the scientific community, both as a source of curated metabolic information and as a reference to derive accurate strain-specific GSMMs. The latter can be used to investigate the relationship between KpSC metabolism and traits of interest, such as reservoirs, epidemiology, drug resistance or virulence, and ultimately to inform novel KpSC control strategies.

Observation of a temperature dependent anomaly in the UV translucency of milk useful for UV-C preservation techniques

Milk fat globules and casein micelles are the dispersed particles of milk that are responsible for its typical white turbid appearance and usually make it difficult to treat with modern ultraviolet light (UV) preservation techniques. The translucency of milk depends largely on the refractive indices of the dispersed particles, which are directly affected by temperature changes, as incorporated triglycerides can crystallize, melt or transition into other polymorphs. These structural changes have a significant effect on the scattering properties and thus on the UV light propagation in milk, especially by milk fat globules. In this study, a temporary minimum in the optical density of milk was observed within UV wavelength at 14 °C when heating the milk from 6 to 40 °C. This anomaly is consistent with structural changes detected by a distinct endothermic peak at 14 °C using differential scanning calorimetry. Apparently, the optical density anomaly between 10 and 20 °C disappears when the polymorphic transition already has proceeded through previous isothermal equilibration. Thus, melting of equilibrated triglycerides may not affect the RI of milk fat globules at ca. 14 °C as much as melt-mediated polymorphic transitioning. An increased efficiency of UV-C preservation (254 nm) at the translucency optimum was demonstrated by temperature-dependent microbial inactivation experiments.

Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli

Temperature-sensitive (TS) mutants are a unique tool to perturb and engineer cellular systems. Here, we constructed a CRISPR library with 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. 1,269 of these mutants showed temperature-sensitive growth in a time-resolved competition assay. We reconstructed 94 TS mutants and measured their metabolism under growth arrest at 42°C using metabolomics. Metabolome changes were strong and mutant-specific, showing that metabolism of nongrowing E. coli is perturbation-dependent. For example, 24 TS mutants of metabolic enzymes overproduced the direct substrate metabolite due to a bottleneck in their associated pathway. A strain with TS homoserine kinase (ThrBF267D ) produced homoserine for 24 h, and production was tunable by temperature. Finally, we used a TS subunit of DNA polymerase III (DnaXL289Q ) to decouple growth from arginine overproduction in engineered E. coli. These results provide a strategy to identify TS mutants en masse and demonstrate their large potential to produce bacterial metabolites with nongrowing cells.

Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis

MOTIVATION

Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty.

RESULTS

In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes.

AVAILABILITY AND IMPLEMENTATION

The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

Bacterial-fungal interactions promote parallel evolution of global transcriptional regulators in a widespread Staphylococcus species

Experimental studies of microbial evolution have largely focused on monocultures of model organisms, but most microbes live in communities where interactions with other species may impact rates and modes of evolution. Using the cheese rind model microbial community, we determined how species interactions shape the evolution of the widespread food- and animal-associated bacterium Staphylococcus xylosus. We evolved S. xylosus for 450 generations alone or in co-culture with one of three microbes: the yeast Debaryomyces hansenii, the bacterium Brevibacterium aurantiacum, and the mold Penicillium solitum. We used the frequency of colony morphology mutants (pigment and colony texture phenotypes) and whole-genome sequencing of isolates to quantify phenotypic and genomic evolution. The yeast D. hansenii strongly promoted diversification of S. xylosus. By the end of the experiment, all populations co-cultured with the yeast were dominated by pigment and colony morphology mutant phenotypes. Populations of S. xylosus grown alone, with B. aurantiacum, or with P. solitum did not evolve novel phenotypic diversity. Whole-genome sequencing of individual mutant isolates across all four treatments identified numerous unique mutations in the operons for the SigB, Agr, and WalRK global regulators, but only in the D. hansenii treatment. Phenotyping and RNA-seq experiments highlighted altered pigment and biofilm production, spreading, stress tolerance, and metabolism of S. xylosus mutants. Fitness experiments revealed antagonistic pleiotropy, where beneficial mutations that evolved in the presence of the yeast had strong negative fitness effects in other biotic environments. This work demonstrates that bacterial-fungal interactions can have long-term evolutionary consequences within multispecies microbiomes by facilitating the evolution of strain diversity.

Conversion of methionine biosynthesis in Escherichia coli from trans- to direct-sulfurylation enhances extracellular methionine levels

Methionine is an essential amino acid in mammals and a precursor for vital metabolites required for the survival of all organisms. Consequently, its inclusion is required in diverse applications, such as food, feed, and pharmaceuticals. Although amino acids and other metabolites are commonly produced through microbial fermentation, high-yield biosynthesis of L-methionine remains a significant challenge due to the strict cellular regulation of the biosynthesis pathway. As a result, methionine is produced primarily synthetically, resulting in a racemic mixture of D,L-methionine. This study explores methionine bio-production in E. coli by replacing its native trans-sulfurylation pathway with the more common direct-sulfurylation pathway used by other bacteria. To this end, we generated a methionine auxotroph E. coli strain (MG1655) by simultaneously deleting metA and metB genes and complementing them with metX and metY from different bacteria. Complementation of the genetically modified E. coli with metX/metY from Cyclobacterium marinum or Deinococcus geothermalis, together with the deletion of the global repressor metJ and overexpression of the transporter yjeH, resulted in a substantial increase of up to 126 and 160-fold methionine relative to the wild-type strain, respectively, and accumulation of up to 700 mg/L using minimal MOPS medium and 2 ml culture. Our findings provide a method to study methionine biosynthesis and a chassis for enhancing L-methionine production by fermentation.

'Multi-omics' data integration: applications in probiotics studies

The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.

Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism

Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

BACKGROUND

Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools.

RESULTS

Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.

CONCLUSIONS

Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

Metabolic engineering of E. coli for β-alanine production using a multi-biosensor enabled approach

β-alanine is an important biomolecule used in nutraceuticals, pharmaceuticals, and chemical synthesis. The relatively eco-friendly bioproduction of β-alanine has recently attracted more interest than petroleum-based chemical synthesis. In this work, we developed two types of in vivo high-throughput screening platforms, wherein one was utilized to identify a novel target ribonuclease E (encoded by rne) as well as a redox-cofactor balancing module that can enhance de novo β-alanine biosynthesis from glucose, and the other was employed for screening fermentation conditions. When combining these approaches with rational upstream and downstream module engineering, an engineered E. coli producer was developed that exhibited 3.4- and 6.6-fold improvement in β-alanine yield (0.85 mol β-alanine/mole glucose) and specific β-alanine production (0.74 g/L/OD₆₀₀), respectively, compared to the parental strain in a minimal medium. Across all of the strains constructed, the best yielding strain exhibited 1.08 mol β-alanine/mole glucose (equivalent to 81.2% of theoretic yield). The final engineered strain produced 6.98 g/L β-alanine in a batch-mode bioreactor and 34.8 g/L through a whole-cell catalysis. This approach demonstrates the utility of biosensor-enabled high-throughput screening for the production of β-alanine.

A Kinetic Framework for Modeling Oleochemical Biosynthesis in E. coli

Microorganisms build fatty acids with biocatalytic assembly lines, or fatty acid synthases (FASs), that can be repurposed to produce a broad set of fuels and chemicals. Despite their versatility, the product profiles of FAS-based pathways are challenging to adjust without experimental iteration, and off-target products are common. This study uses a detailed kinetic model of the E. coli FAS as a foundation to model nine oleochemical pathways. These models provide good fits to experimental data and help explain unexpected results from in vivo studies. An analysis of pathways for alkanes and fatty acid ethyl esters, for example, suggests that reductions in titer caused by enzyme overexpression-an experimentally consistent phenomenon-can result from shifts in metabolite pools that are incompatible with the substrate specificities of downstream enzymes, and a focused examination of multiple alcohol pathways indicates that coordinated shifts in enzyme concentrations provide a general means of tuning the product profiles of pathways with promiscuous components. The study concludes by integrating all models into a graphical user interface. The models supplied by this work provide a versatile kinetic framework for studying oleochemical pathways in different biochemical contexts. This article is protected by copyright. All rights reserved.

Project-based learning course on metabolic network modelling in computational systems biology

Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL course on metabolic network modelling that has been running for several years within the Master in Integrated Systems Biology (MISB) at the University of Luxembourg. This 2-week full-time block course comprises an introduction into the core concepts and methods of constraint-based modelling (CBM), applied to toy models and large-scale networks alongside the preparation of individual student projects in week 1 and, in week 2, the presentation and execution of these projects. We describe in detail the schedule and content of the course, exemplary student projects, and reflect on outcomes and lessons learned. PBL requires the full engagement of students and teachers and gives a rewarding teaching experience. The presented course can serve as a role model and inspiration for other similar courses.

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

Adaptive laboratory evolution of Escherichia coli W enhances gamma-aminobutyric acid production using glycerol as the carbon source

The microbial conversion of glycerol into value-added commodity products has emerged as an attractive means to meet the demands of biosustainability. However, glycerol is a non-preferential carbon source for productive fermentation because of its low energy density. We employed evolutionary and metabolic engineering in tandem to construct an Escherichia coli strain with improved GABA production using glycerol as the feedstock carbon. Adaptive evolution of E. coli W under glycerol-limited conditions for 1300 generations harnessed an adapted strain with a metabolic system optimized for glycerol utilization. Mutation profiling, enzyme kinetic assays, and transcriptome analysis of the adapted strain allowed us to decipher the basis of glycerol adaptation at the molecular level. Importantly, increased substrate influx mediated by the mutant glpK and modulation of intracellular cAMP levels were the key drivers of improved fitness in the glycerol-limited condition. Leveraging the enhanced capability of glycerol utilization in the strain, we constructed a GABA-producing E. coli W-derivative with superior GABA production compared to the wild-type. Furthermore, rationally designed inactivation of the non-essential metabolic genes, including ackA, mgsA, and gabT, in the glycerol-adapted strain improved the final GABA titer and specific productivity by 3.9- and 4.3-fold, respectively, compared with the wild-type.

Tailoring Genetic Elements of the Plasmid-Driven T7 System for Stable and Robust One-Step Cloning and Protein Expression in Broad Escherichia coli

The plasmid-driven T7 system (PDT7) is a flexible approach to trigger protein overexpression; however, most of the reported PDT7 rely on many auxiliary elements or inducible systems to attenuate the toxicity from the orthogonality of the T7 system, which limits its application as the one-step cloning and protein expression system. In this study, we developed a stable and robust PDT7 via tailoring the genetic elements. By error-prone mutagenesis, a mutated T7RNAP with TTTT insertion conferred a trace but enough amount of T7RNAP for stable and efficient PDT7, denoted as PDT7m. The replication origin was kept at the same level, while the ribosome binding site (RBS) of the T7 promoter was the most contributing factor, thus enhancing the protein expression twofold using PDT7m. For application as a host-independent screening platform, both constitutive and IPTG-inducible PDT7m were constructed. It was found that each strain harnessed different IPTG inducibilities for tailor-made strain selection. Constitutive PDT7m was successfully used to express the homologous protein (i.e., lysine decarboxylase) or heterologous protein (i.e., carbonic anhydrase, CA) as a one-step cloning and protein expression tool to select the best strain for cadaverine (DAP) or CA production, respectively. Additionally, PDT7m is compatible with the pET system for coproduction of DAP and CA simultaneously. Finally, PDT7m was used for in vivo high-end chemical production of aminolevulinic acid (ALA), in which addition of the T7 terminator successfully enhanced 340% ALA titer, thus paving the way to rapidly and effectively screening the superior strain as a cell factory.

Automation assisted anaerobic phenotyping for metabolic engineering

BACKGROUND

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.

RESULTS

In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.

CONCLUSIONS

Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Virulent Drexlervirial Bacteriophage MSK, Morphological and Genome Resemblance With Rtp Bacteriophage Inhibits the Multidrug-Resistant Bacteria

Phage-host interactions are likely to have the most critical aspect of phage biology. Phages are the most abundant and ubiquitous infectious acellular entities in the biosphere, where their presence remains elusive. Here, the novel Escherichia coli lytic bacteriophage, named MSK, was isolated from the lysed culture of E. coli C (phix174 host). The genome of phage MSK was sequenced, comprising 45,053 bp with 44.8% G + C composition. In total, 73 open reading frames (ORFs) were predicted, out of which 24 showed a close homology with known functional proteins, including one tRNA-arg; however, the other 49 proteins with no proven function in the genome database were called hypothetical. Electron Microscopy and genome characterization have revealed that MSK phage has a rosette-like tail tip. There were, in total, 46 ORFs which were homologous to the Rtp genome. Among these ORFs, the tail fiber protein with a locus tag of MSK_000019 was homologous to Rtp 43 protein, which determines the host specificity. The other protein, MSK_000046, encodes lipoprotein (cor gene); that protein resembles Rtp 45, responsible for preventing adsorption during cell lysis. Thirteen MSK structural proteins were identified by SDS-PAGE analysis. Out of these, 12 were vital structural proteins, and one was a hypothetical protein. Among these, the protein terminase large (MSK_000072) subunit, which may be involved in DNA packaging and proposed packaging strategy of MSK bacteriophage genome, takes place through headful packaging using the pac-sites. Biosafety assessment of highly stable phage MSK genome analysis has revealed that the phage did not possess virulence genes, which indicates proper phage therapy. MSK phage potentially could be used to inhibit the multidrug-resistant bacteria, including AMP, TCN, and Colistin. Further, a comparative genome and lifestyle study of MSK phage confirmed the highest similarity level (87.18% ANI). These findings suggest it to be a new lytic isolated phage species. Finally, Blast and phylogenetic analysis of the large terminase subunit and tail fiber protein put it in Rtp viruses' genus of family Drexlerviridae.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.

In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

Inactivation of E. coli and L. innocua in milk by a thin film UV-C reactor modified with flow guiding elements (FGE)

In this study the suitability of a thin-film reactor (TFR) equipped with special flow guiding elements (FGE) was examined to analyse its capability to inactivate microorganisms in milk. Experiments were carried out with UHT-milk inoculated with Escherichia coli (E. coli), DH5α and Listeria innocua (L. innocua) WS 2258. Furthermore, the inactivation of microorganisms originally occurring in raw milk was investigated. E. coli, DH5α and L. innocua serving as biodosimeter were reduced by 4.58-log and 3.19-log, respectively. In milk, the original microorganisms showed a 4-log reduction. Without FGE the reduction was below 0.13-log. Thus, it can be derived that the efficacy of a UV-C thin-film reactor processing absorptive media like milk can be highly improved using FGE.

A One Health Perspective for Defining and Deciphering Escherichia coli Pathogenic Potential in Multiple Hosts

E. coli is one of the most common species of bacteria colonizing humans and animals. The singularity of E. coli 's genus and species underestimates its multifaceted nature, which is represented by different strains, each with different combinations of distinct virulence factors. In fact, several E. coli pathotypes, or hybrid strains, may be associated with both subclinical infection and a range of clinical conditions, including enteric, urinary, and systemic infections. E. coli may also express DNA-damaging toxins that could impact cancer development. This review summarizes the different E. coli pathotypes in the context of their history, hosts, clinical signs, epidemiology, and control. The pathotypic characterization of E. coli in the context of disease in different animals, including humans, provides comparative and One Health perspectives that will guide future clinical and research investigations of E. coli infections.

The impact of technical failures on recombinant production of soluble proteins in Escherichia coli: a case study on process and protein robustness

Technical failures lead to deviations in process parameters that can exceed studied process boundaries. The impact on cell and target protein is often unknown. However, investigations on common technical failures might yield interesting insights into process and protein robustness. Recently, we published a study on the impact of technical failures on an inclusion body process that showed high robustness due to the inherent stability of IBs. In this follow-up study, we investigated the influence of technical failures during production of two soluble, cytosolic proteins in E. coli BL21(DE3). Cell physiology, productivity and protein quality were analyzed, after technical failures in aeration, substrate supply, temperature and pH control had been triggered. In most cases, cell physiology and productivity recovered during a subsequent regeneration phase. However, our results highlight that some technical failures lead to persistent deviations and affect the quality of purified protein.

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

Short and long-read ultra-deep sequencing profiles emerging heterogeneity across five platform Escherichia coli strains

Reprogramming organisms for large-scale bioproduction counters their evolutionary objectives of fast growth and often leads to mutational collapse of the engineered production pathways during cultivation. Yet, the mutational susceptibility of academic and industrial Escherichia coli bioproduction host strains are poorly understood. In this study, we apply 2^nd and 3^rd generation deep sequencing to profile simultaneous modes of genetic heterogeneity that decimate engineered biosynthetic production in five popular E. coli hosts BL21(DE3), TOP10, MG1655, W, and W3110 producing 2,3-butanediol and mevalonic acid. Combining short-read and long-read sequencing, we detect strain and sequence-specific mutational modes including single nucleotide polymorphism, inversion, and mobile element transposition, as well as complex structural variations that disrupt the integrity of the engineered biosynthetic pathway. Our analysis suggests that organism engineers should avoid chassis strains hosting active insertion sequence (IS) subfamilies such as IS1 and IS10 present in popular E. coli TOP10. We also recommend monitoring for increased mutagenicity in the pathway transcription initiation regions and recombinogenic repeats. Together, short and long sequencing reads identified latent low-frequency mutation events such as a short detrimental inversion within a pathway gene, driven by 8-bp short inverted repeats. This demonstrates the power of combining ultra-deep DNA sequencing technologies to profile genetic heterogeneities of engineered constructs and explore the markedly different mutational landscapes of common E. coli host strains. The observed multitude of evolving variants underlines the usefulness of early mutational profiling for new synthetic pathways designed to sustain in organisms over long cultivation scales.

Physiological Response of Escherichia coli W3110 and BL21 to the Aerobic Expression of Vitreoscilla Hemoglobin

The aerobic growth and metabolic performance of Escherichia coli strains BL21 and W3110 were studied when the Vitreoscilla hemoglobin (VHb) was constitutively expressed in the chromosome. When VHb was expressed, acetate production decreased in both strains and was nearly eliminated in BL21. Transcriptional levels of the glyoxylate shunt genes decreased in both strains when VHb was expressed. However, higher transcription of the α-ketoglutarate dehydrogenase genes were observed for W3110, while for BL21 transcription levels decreased. VHb expression reduced the transcription of the cytochrome bo₃ genes only in BL21. These results are useful for better selecting a production host.

Converting Escherichia coli MG1655 into a chemical overproducer through inactivating defense system against exogenous DNA

Escherichia coli strain K-12 MG1655 has been proposed as an appropriate host strain for industrial production. However, the direct application of this strain suffers from the transformation inefficiency and plasmid instability. Herein, we conducted genetic modifications at a serial of loci of MG1655 genome, generating a robust and universal host strain JW128 with higher transformation efficiency and plasmid stability that can be used to efficiently produce desired chemicals after introducing the corresponding synthetic pathways. Using JW128 as the host, the titer of isobutanol reached 5.76 g/L in shake-flask fermentation, and the titer of lycopene reached 1.91 g/L in test-tube fermentation, 40-fold and 5-fold higher than that of original MG1655, respectively. These results demonstrated JW128 is a promising chassis for high-level production of value-added chemicals.

A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin-diadinoxanthin pathway over violaxanthin-neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.