iML1515, a knowledgebase that computes Escherichia coli traits

Engineering a synthetic energy-efficient formaldehyde assimilation cycle in Escherichia coli

One-carbon (C1) substrates, such as methanol or formate, are attractive feedstocks for circular bioeconomy. These substrates are typically converted into formaldehyde, serving as the entry point into metabolism. Here, we design an erythrulose monophosphate (EuMP) cycle for formaldehyde assimilation, leveraging a promiscuous dihydroxyacetone phosphate dependent aldolase as key enzyme. In silico modeling reveals that the cycle is highly energy-efficient, holding the potential for high bioproduct yields. Dissecting the EuMP into four modules, we use a stepwise strategy to demonstrate in vivo feasibility of the modules in E. coli sensor strains with sarcosine as formaldehyde source. From adaptive laboratory evolution for module integration, we identify key mutations enabling the accommodation of the EuMP reactions with endogenous metabolism. Overall, our study demonstrates the proof-of-concept for a highly efficient, new-to-nature formaldehyde assimilation pathway, opening a way for the development of a methylotrophic platform for a C1-fueled bioeconomy in the future.

A Fermentation State Marker Rule Design Task in Metabolic Engineering

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO

IMPORTANCE

The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.

Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data

The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

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

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

Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan

Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.

Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow

IMPORTANCE

Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.

Systems and synthetic biology-driven engineering of live bacterial therapeutics

The past decade has seen growing interest in bacterial engineering for therapeutically relevant applications. While early efforts focused on repurposing genetically tractable model strains, such as Escherichia coli, engineering gut commensals is gaining traction owing to their innate capacity to survive and stably propagate in the intestine for an extended duration. Although limited genetic tractability has been a major roadblock, recent advances in systems and synthetic biology have unlocked our ability to effectively harness native gut commensals for therapeutic and diagnostic purposes, ranging from the rational design of synthetic microbial consortia to the construction of synthetic cells that execute "sense-and-respond" logic operations that allow real-time detection and therapeutic payload delivery in response to specific signals in the intestine. In this review, we outline the current progress and latest updates on microbial therapeutics, with particular emphasis on gut commensal engineering driven by synthetic biology and systems understanding of their molecular phenotypes. Finally, the challenges and prospects of engineering gut commensals for therapeutic applications are discussed.

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.

Microbial thermogenesis is dependent on ATP concentrations and the protein kinases ArcB, GlnL, and YccC

Organisms necessarily release heat energy in their pursuit of survival. This process is known as cellular thermogenesis and is implicated in many processes from cancer metabolism to spontaneous farm fires. However, the molecular basis for this fundamental phenomenon is yet to be elucidated. Here, we show that the major players involved in the cellular thermogenesis of Escherichia coli are the protein kinases ArcB, GlnL, and YccC. We also reveal the substrate-level control of adenosine triphosphate (ATP)-driven autophosphorylation that governs cellular thermogenesis. Specifically, through live cell microcalorimetry, we find these regulatory proteins, when knocked out in a model E. coli strain, dysregulate cellular thermogenesis. This dysregulation can be seen in an average 25% or greater increase in heat output by these cells. We also discover that both heat output and intracellular ATP levels are maximal during the late log phase of growth. Additionally, we show that microbial thermogenesis can be engineered through overexpressing glnL. Our results demonstrate a correlation between ATP concentrations in the cell and a cell's ability to generate excess heat. We expect this work to be the foundation for engineering thermogenically tuned organisms for a variety of applications.

Genome-scale metabolic modeling of the human gut bacterium Bacteroides fragilis strain 638R

Bacteroides fragilis is a universal member of the dominant commensal gut phylum Bacteroidetes. Its fermentation products and abundance have been linked to obesity, inflammatory bowel disease, and other disorders through its effects on host metabolic regulation and the immune system. As of yet, there has been no curated systems-level characterization of B. fragilis' metabolism that provides a comprehensive analysis of the link between human diet and B. fragilis' metabolic products. To address this, we developed a genome-scale metabolic model of B. fragilis strain 638R. The model iMN674 contains 1,634 reactions, 1,362 metabolites, three compartments, and reflects the strain's ability to utilize 142 metabolites. Predictions made with this model include its growth rate and efficiency on these substrates, the amounts of each fermentation product it produces under different conditions, and gene essentiality for each biomass component. The model highlights and resolves gaps in knowledge of B. fragilis' carbohydrate metabolism and its corresponding transport proteins. This high quality model provides the basis for rational prediction of B. fragilis' metabolic interactions with its environment and its host.

Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

Pichia pastoris growth-coupled heme biosynthesis analysis using metabolic modelling

Soy leghemoglobin is one of the most important and key ingredients in plant-based meat substitutes that can imitate the colour and flavour of the meat. To improve the high-yield production of leghemoglobin protein and its main component-heme in the yeast Pichia pastoris, glycerol and methanol cultivation conditions were studied. Additionally, in-silico metabolic modelling analysis of growth-coupled enzyme quantity, suggests metabolic gene up/down-regulation strategies for heme production. First, cultivations and metabolic modelling analysis of P. pastoris were performed on glycerol and methanol in different growth media. Glycerol cultivation uptake and production rates can be increased by 50% according to metabolic modelling results, but methanol cultivation-is near the theoretical maximum. Growth-coupled metabolic optimisation results revealed the best feasible upregulation (33 reactions) (1.47% of total reactions) and 66 downregulation/deletion (2.98% of total) reaction suggestions. Finally, we describe reaction regulation suggestions with the highest potential to increase heme production yields.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

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

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

Elucidating the CodY regulon in Staphylococcus aureus USA300 substrains TCH1516 and LAC

CodY is a conserved broad-acting transcription factor that regulates the expression of genes related to amino acid metabolism and virulence in Gram-positive bacteria. Here, we performed the first in vivo determination of CodY target genes using a novel CodY monoclonal antibody in methicillin-resistant Staphylococcus aureus (MRSA) USA300. Our results showed (i) the same 135 CodY promoter binding sites regulating the 165 target genes identified in two closely related virulent S. aureus USA300 TCH1516 and LAC strains; (ii) the differential binding intensity for the same target genes under the same conditions was due to sequence differences in the same CodY-binding site in the two strains; (iii) a CodY regulon comprising 72 target genes that are differentially regulated relative to a CodY deletion strain, representing genes that are mainly involved in amino acid transport and metabolism, inorganic ion transport and metabolism, transcription and translation, and virulence, all based on transcriptomic data; and (iv) CodY systematically regulated central metabolic flux to generate branched-chain amino acids (BCAAs) by mapping the CodY regulon onto a genome-scale metabolic model of S. aureus. Our study performed the first system-level analysis of CodY in two closely related USA300 TCH1516 and LAC strains, revealing new insights into the similarities and differences of CodY regulatory roles between the closely related strains. IMPORTANCE With the increasing availability of whole-genome sequences for many strains within the same pathogenic species, a comparative analysis of key regulators is needed to understand how the different strains uniquely coordinate metabolism and expression of virulence. To successfully infect the human host, Staphylococcus aureus USA300 relies on the transcription factor CodY to reorganize metabolism and express virulence factors. While CodY is a known key transcription factor, its target genes are not characterized on a genome-wide basis. We performed a comparative analysis to describe the transcriptional regulation of CodY between two dominant USA300 strains. This study motivates the characterization of common pathogenic strains and an evaluation of the possibility of developing specialized treatments for major strains circulating in the population.

Insights into the metabolic specificities of pathogenic strains from the Ralstonia solanacearum species complex

All the strains grouped under the species Ralstonia solanacearum represent a species complex responsible for many diseases on agricultural crops throughout the world. The strains have different lifestyles and host range. Here, we investigated whether specific metabolic pathways contribute to strain diversification. To this end, we carried out systematic comparisons on 11 strains representing the diversity of the species complex. We reconstructed the metabolic network of each strain from its genome sequence and looked for the metabolic pathways differentiating the different reconstructed networks and, by extension, the different strains. Finally, we conducted an experimental validation by determining the metabolic profile of each strain with the Biolog technology. Results revealed that the metabolism is conserved between strains, with a core metabolism composed of 82% of the pan-reactome. The three species composing the species complex could be distinguished according to the presence/absence of some metabolic pathways, in particular, one involving salicylic acid degradation. Phenotypic assays revealed that the trophic preferences on organic acids and several amino acids such as glutamine, glutamate, aspartate, and asparagine are conserved between strains. Finally, we generated mutants lacking the quorum-sensing-dependent regulator PhcA in four diverse strains, and we showed that the phcA-dependent trade-off between growth and production of virulence factors is conserved across the R. solanacearum species complex. IMPORTANCE Ralstonia solanacearum is one of the most important threats to plant health worldwide, causing disease on a very large range of agricultural crops such as tomato or potato. Behind the R. solanacearum name are hundreds of strains with different host range and lifestyle, classified into three species. Studying the differences between strains allows to better apprehend the biology of the pathogens and the specificity of some strains. None of the published genomic comparative studies have focused on the metabolism of the strains so far. We developed a new bioinformatic pipeline to build high-quality metabolic networks and used a combination of metabolic modeling and high-throughput phenotypic Biolog microplates to look for the metabolic differences between 11 strains across the three species. Our study revealed that genes encoding enzymes are overall conserved, with few variations between strains. However, more variations were observed when considering substrate usage. These variations probably result from regulation rather than the presence or absence of enzymes in the genome.

Protein engineering and iterative multimodule optimization for vitamin B6 production in Escherichia coli

Vitamin B6 is an essential nutrient with extensive applications in the medicine, food, animal feed, and cosmetics industries. Pyridoxine (PN), the most common commercial form of vitamin B6, is currently chemically synthesized using expensive and toxic chemicals. However, the low catalytic efficiencies of natural enzymes and the tight regulation of the metabolic pathway have hindered PN production by the microbial fermentation process. Here, we report an engineered Escherichia coli strain for PN production. Parallel pathway engineering is performed to decouple PN production and cell growth. Further, protein engineering is rationally designed including the inefficient enzymes PdxA, PdxJ, and the initial enzymes Epd and Dxs. By the iterative multimodule optimization strategy, the final strain produces 1.4 g/L of PN with productivity of 29.16 mg/L/h by fed-batch fermentation. The strategies reported here will be useful for developing microbial strains for the production of vitamins and other bioproducts having inherently low metabolic fluxes.

Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli. As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models

Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

TranSyT, an innovative framework for identifying transport systems

MOTIVATION

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE's release, software development for the automatic integration of transport reactions into models has stalled.

RESULTS

Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

AVAILABILITY AND IMPLEMENTATION

TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt_paper.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Metabolic Modeling of Hermetia illucens Larvae Resource Allocation for High-Value Fatty Acid Production

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation, and growth functions. While the biochemical network details are well known, the understanding of the intense regulation principles is still limited. We chose to investigate the Hermetia illucens fly at the larval stage because this stage is a crucial period for the successful accumulation and allocation of resources for the subsequent organism's developmental stages. We combined iterative wet lab experiments and innovative metabolic modeling design approaches to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments on larvae and the Gainesville diet composition. We built and validated the first H. illucens medium-size, stoichiometric metabolic model to predict the effects of diet-based alterations on fatty acid allocation potential. Using optimization methods such as flux balance and flux variability analysis on the novel insect metabolic model, we predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework for researching the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable, and directed high-value chemicals.

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.

Engineering a new-to-nature cascade for phosphate-dependent formate to formaldehyde conversion in vitro and in vivo

Formate can be envisioned at the core of a carbon-neutral bioeconomy, where it is produced from CO₂ by (electro-)chemical means and converted into value-added products by enzymatic cascades or engineered microbes. A key step in expanding synthetic formate assimilation is its thermodynamically challenging reduction to formaldehyde. Here, we develop a two-enzyme route in which formate is activated to formyl phosphate and subsequently reduced to formaldehyde. Exploiting the promiscuity of acetate kinase and N-acetyl-γ-glutamyl phosphate reductase, we demonstrate this phosphate (P_i)-based route in vitro and in vivo. We further engineer a formyl phosphate reductase variant with improved formyl phosphate conversion in vivo by suppressing cross-talk with native metabolism and interface the P_i route with a recently developed formaldehyde assimilation pathway to enable C2 compound formation from formate as the sole carbon source in Escherichia coli. The P_i route therefore offers a potent tool in expanding the landscape of synthetic formate assimilation.

Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem. Our culture media formulations, atlas of metabolomics data, and genome-scale metabolic reconstructions form a freely available collection of resources to support further study of the biology of this prevalent gut bacterium.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

New Insights on Metabolic Features of Bacillus subtilis Based on Multistrain Genome-Scale Metabolic Modeling

Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level.

Engineering Escherichia coli for Poly-β-hydroxybutyrate Production from Methanol

The naturally occurring one-carbon assimilation pathways for the production of acetyl-CoA and its derivatives often have low product yields because of carbon loss as CO2. We constructed a methanol assimilation pathway to produce poly-3-hydroxybutyrate (P3HB) using the MCC pathway, which included the ribulose monophosphate (RuMP) pathway for methanol assimilation and non-oxidative glycolysis (NOG) for acetyl-CoA (precursor for PHB synthesis) production. The theoretical product carbon yield of the new pathway is 100%, hence no carbon loss. We constructed this pathway in E. coli JM109 by introducing methanol dehydrogenase (Mdh), a fused Hps–phi (hexulose-6-phosphate synthase and 3-phospho-6-hexuloisomerase), phosphoketolase, and the genes for PHB synthesis. We also knocked out the frmA gene (encoding formaldehyde dehydrogenase) to prevent the dehydrogenation of formaldehyde to formate. Mdh is the primary rate-limiting enzyme in methanol uptake; thus, we compared the activities of three Mdhs in vitro and in vivo and then selected the one from Bacillus methanolicus MGA3 for further study. Experimental results indicate that, in agreement with the computational analysis results, the introduction of the NOG pathway is essential for improving PHB production (65% increase in PHB concentration, up to 6.19% of dry cell weight). We demonstrated that PHB can be produced from methanol via metabolic engineering, which provides the foundation for the future large-scale use of one-carbon compounds for biopolymer production.

Data integration across conditions improves turnover number estimates and metabolic predictions

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

MOTIVATION

While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.

RESULTS

This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways.

AVAILABILITY AND IMPLEMENTATION

A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Trimming Gene Deletion Strategies for Growth-Coupled Production in Constraint-Based Metabolic Networks: TrimGdel

When simulating genome-scale metabolite production using constraint-based metabolic networks, it is often necessary to find gene deletion strategies which lead to growth-coupled production, which means that target metabolites are produced when cell growth is maximized. Existing methods are effective when the number of gene deletions is relatively small, but when the number of required gene deletions exceeds approximately 1% of whole genes, the time required for the calculation is often unfeasible. Therefore, a complementing algorithm that is effective even when the required number of gene deletions is approximately 1% to 5% of whole genes would be helpful because the number of deletable genes in a strain is increasing with advances in genetic engineering technology. In this study, the author developed an algorithm, TrimGdel, which first computes a strategy with many gene deletions that results in growth-coupled production and then gradually reduces the number of gene deletions while ensuring the original production rate and growth rate. The results of the computer experiments showed that TrimGdel can calculate stoichiometrically feasible gene deletion strategies, especially those whose sizes are 1 to 5% of whole genes, which lead to growth-coupled production of many target metabolites, which include useful vitamins such as biotin and pantothenate, for which existing methods could not.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli

The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01

While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described. IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.

Vibrio natriegens genome-scale modeling reveals insights into halophilic adaptations and resource allocation

Vibrio natriegens is a Gram-negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism-specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome-scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry-based proteomics data confirmed the translation of at least 76% of the enzyme-encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP-generating system and the discovery of a role for a sodium-dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens.

Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN

Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.