Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.

A temperature-induced metabolic shift in the emerging human pathogen Photorhabdus asymbiotica

Photorhabdus is a bacterial genus containing both insect and emerging human pathogens. Most insect-restricted species display temperature restriction, unable to grow above 34°C, while Photorhabdus asymbiotica can grow at 37°C to infect mammalian hosts and cause Photorhabdosis. Metabolic adaptations have been proposed to facilitate the survival of this pathogen at higher temperatures, yet the biological mechanisms underlying these are poorly understood. We have reconstructed an extensively manually curated genome-scale metabolic model of P. asymbiotica (iEC1073, BioModels ID MODEL2309110001), validated through in silico gene knockout and nutrient utilization experiments with an excellent agreement between experimental data and model predictions. Integration of iEC1073 with transcriptomics data obtained for P. asymbiotica at temperatures of 28°C and 37°C allowed the development of temperature-specific reconstructions representing metabolic adaptations the pathogen undergoes when shifting to a higher temperature in a mammalian compared to insect host. Analysis of these temperature-specific reconstructions reveals that nucleotide metabolism is enriched with predicted upregulated and downregulated reactions. iEC1073 could be used as a powerful tool to study the metabolism of P. asymbiotica, in different genetic or environmental conditions.

IMPORTANCE

Photorhabdus bacterial species contain both human and insect pathogens, and most of these species cannot grow in higher temperatures. However, Photorhabdus asymbiotica, which infects both humans and insects, can grow in higher temperatures and undergoes metabolic adaptations at a temperature of 37°C compared to that of insect body temperature. Therefore, it is important to examine how this bacterial species can metabolically adapt to survive in higher temperatures. In this work, using a mathematical model, we have examined the metabolic shift that takes place when the bacteria switch from growth conditions in 28°C to 37°C. We show that P. asymbiotica potentially experiences predicted temperature-induced metabolic adaptations at 37°C predominantly clustered within the nucleotide metabolism pathway.

Genomic insights into fish pathogenic bacteria: A systems biology perspective for sustainable aquaculture

Fish diseases significantly challenge global aquaculture, causing substantial financial losses and impacting sustainability, trade, and socioeconomic conditions. Understanding microbial pathogenesis and virulence at the molecular level is crucial for disease prevention in commercial fish. This review provides genomic insights into fish pathogenic bacteria from a systems biology perspective, aiming to promote sustainable aquaculture. It covers the genomic characteristics of various fish pathogens and their industry impact. The review also explores the systems biology of zebrafish, fish bacterial pathogens, and probiotic bacteria, offering insights into fish production, potential vaccines, and therapeutic drugs. Genome-scale metabolic models aid in studying pathogenic bacteria, contributing to disease management and antimicrobial development. Researchers have also investigated probiotic strains to improve aquaculture health. Additionally, the review highlights bioinformatics resources for fish and fish pathogens, which are essential for researchers. Systems biology approaches enhance understanding of bacterial fish pathogens by revealing virulence factors and host interactions. Despite challenges from the adaptability and pathogenicity of bacterial infections, sustainable alternatives are necessary to meet seafood demand. This review underscores the potential of systems biology in understanding fish pathogen biology, improving production, and promoting sustainable aquaculture.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups

Bacillus subtilis is an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes of B. subtilis with 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divide B. subtilis strains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights into B. subtilis' metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats.

IMPORTANCE

As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. Bacillus subtilis strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 B. subtilis strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.

A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search

Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.

Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism

Enzymes of the central metabolism tend to assemble into transient supramolecular complexes. However, the functional significance of the interactions, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from Bacillus subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in phase separated droplets we show that MDH-ICD interaction leads to enzyme agglomeration with a concomitant enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory demonstrates that MDH-mediated clustering of ICD molecules explains the observed phenomena. In vivo analyses reveal that MDH overexpression leads to accumulation of 2-oxoglutarate and reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings suggest that the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities

Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Metabolic rewiring enables ammonium assimilation via a non-canonical fumarate-based pathway

Glutamate serves as the major cellular amino group donor. In Bacillus subtilis, glutamate is synthesized by the combined action of the glutamine synthetase and the glutamate synthase (GOGAT). The glutamate dehydrogenases are devoted to glutamate degradation in vivo. To keep the cellular glutamate concentration high, the genes and the encoded enzymes involved in glutamate biosynthesis and degradation need to be tightly regulated depending on the available carbon and nitrogen sources. Serendipitously, we found that the inactivation of the ansR and citG genes encoding the repressor of the ansAB genes and the fumarase, respectively, enables the GOGAT-deficient B. subtilis mutant to synthesize glutamate via a non-canonical fumarate-based ammonium assimilation pathway. We also show that the de-repression of the ansAB genes is sufficient to restore aspartate prototrophy of an aspB aspartate transaminase mutant. Moreover, in the presence of arginine, B. subtilis mutants lacking fumarase activity show a growth defect that can be relieved by aspB overexpression, by reducing arginine uptake and by decreasing the metabolic flux through the TCA cycle.

Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration

Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.

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 by 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 (with a predefined maximum number of reaction deletions) 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 various 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 beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank 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 .

Design of a sorbitol-activated nitrogen metabolism-dependent regulatory system for redirection of carbon metabolism flow in Bacillus licheniformis

Synthetic regulation of metabolic fluxes has emerged as a common strategy to improve the performance of microbial cell factories. The present regulatory toolboxes predominantly rely on the control and manipulation of carbon pathways. Nitrogen is an essential nutrient that plays a vital role in growth and metabolism. However, the availability of broadly applicable tools based on nitrogen pathways for metabolic regulation remains limited. In this work, we present a novel regulatory system that harnesses signals associated with nitrogen metabolism to redirect excess carbon flux in Bacillus licheniformis. By engineering the native transcription factor GlnR and incorporating a sorbitol-responsive element, we achieved a remarkable 99% inhibition of the expression of the green fluorescent protein reporter gene. Leveraging this system, we identified the optimal redirection point for the overflow carbon flux, resulting in a substantial 79.5% reduction in acetoin accumulation and a 2.6-fold increase in acetate production. This work highlight the significance of nitrogen metabolism in synthetic biology and its valuable contribution to metabolic engineering. Furthermore, our work paves the way for multidimensional metabolic regulation in future synthetic biology endeavors.

Bacillus subtilis extracellular protease production incurs a context-dependent cost

Microbes encounter a wide range of polymeric nutrient sources in various environmental settings, which require processing to facilitate growth. Bacillus subtilis, a bacterium found in the rhizosphere and broader soil environment, is highly adaptable and resilient due to its ability to utilise diverse sources of carbon and nitrogen. Here, we explore the role of extracellular proteases in supporting growth and assess the cost associated with their production. We provide evidence of the essentiality of extracellular proteases when B. subtilis is provided with an abundant, but polymeric nutrient source and demonstrate the extracellular proteases as a shared public good that can operate over a distance. We show that B. subtilis is subjected to a public good dilemma, specifically in the context of growth sustained by the digestion of a polymeric food source. Furthermore, using mathematical simulations, we uncover that this selectively enforced dilemma is driven by the relative cost of producing the public good. Collectively, our findings reveal how bacteria can survive in environments that vary in terms of immediate nutrient accessibility and the consequent impact on the population composition. These findings enhance our fundamental understanding of how bacteria respond to diverse environments, which has importance to contexts ranging from survival in the soil to infection and pathogenesis scenarios.

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.

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PP_i) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PP_i sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PP_i synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PP_i-generating cycles. In all cases, the metabolic cost of PP_i synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PP_i could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PP_i sources likely account for most PP_i production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PP_i levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

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.

A model industrial workhorse: Bacillus subtilis strain 168 and its genome after a quarter of a century

The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).

Phenotype-specific estimation of metabolic fluxes using gene expression data

A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.

Biotechnological and food synthetic biology potential of platform strain: Bacillus licheniformis

Bacillus licheniformis is one of the most characteristic Gram-positive bacteria. Its unique genetic background and safety characteristics make it have important biologic applications in the food industry, including, the biosynthesis of high value-added bioproducts, probiotic functions, biological treatment of wastes derived from food production, etc. In this review, these recent advances are summarized and presented systematically for the first time. In addition, we highlight synthetic biology strategies as a potential driver of developing this strain for wider and more efficient application in the food industry. Finally, we present the current challenges faced and provide our unique perspective on relevant future research directions. In summary, this review will provide an illuminating and comprehensive perspective that will allow an in-depth understanding of B. licheniformis and promote its more effective development in the food industry.

Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment

Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV's high prevalence and associated chronic and acute women's health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA, fas, suhB, and psd, were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women's health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment.

etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

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

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

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

Investigation of two metabolic engineering approaches for (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis

BACKGROUND

Flux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in Bacillus subtilis to enhance (R,R)-2,3-butanediol (2,3-BD) production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis.

RESULTS

Single-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings.

CONCLUSIONS

We showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.

Metabolic Engineering of Bacillus subtilis for Riboflavin Production: A Review

Riboflavin (vitamin B₂) is one of the essential vitamins that the human body needs to maintain normal metabolism. Its biosynthesis has become one of the successful models for gradual replacement of traditional chemical production routes. B. subtilis is characterized by its short fermentation time and high yield, which shows a huge competitive advantage in microbial fermentation for production of riboflavin. This review summarized the advancements of regulation on riboflavin production as well as the synthesis of two precursors of ribulose-5-phosphate riboflavin (Ru5P) and guanosine 5'-triphosphate (GTP) in B. subtilis. The different strategies to improve production of riboflavin by metabolic engineering were also reviewed.

Predicting stress response and improved protein overproduction in Bacillus subtilis

Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.

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.

Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions

In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific "drug" transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol-key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the "one transporter-one organ" concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal "drug" transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

Understanding and application of Bacillus nitrogen regulation: A synthetic biology perspective

BACKGROUND

Nitrogen sources play an essential role in maintaining the physiological and biochemical activity of bacteria. Nitrogen metabolism, which is the core of microorganism metabolism, makes bacteria able to autonomously respond to different external nitrogen environments by exercising complex internal regulatory networks to help them stay in an ideal state. Although various studies have been put forth to better understand this regulation in Bacillus, and many valuable viewpoints have been obtained, these views need to be presented systematically and their possible applications need to be specified.

AIM OF REVIEW

The intention is to provide a deep and comprehensive understanding of nitrogen metabolism in Bacillus, an important industrial microorganism, and thereby apply this regulatory logic to synthetic biology to improve biosynthesis competitiveness. In addition, the potential researches in the future are also discussed.

KEY SCIENTIFIC CONCEPT OF REVIEW

Understanding the meticulous regulation process of nitrogen metabolism in Bacillus not only could facilitate research on metabolic engineering but also could provide constructive insights and inspiration for studies of other microorganisms.

Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids

Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

In silico-guided metabolic engineering of Bacillus subtilis for efficient biosynthesis of purine nucleosides by blocking the key backflow nodes

Background

Purine nucleosides play essential roles in cellular physiological processes and have a wide range of applications in the fields of antitumor/antiviral drugs and food. However, microbial overproduction of purine nucleosides by de novo metabolic engineering remains a great challenge due to their strict and complex regulatory machinery involved in biosynthetic pathways.

Results

In this study, we designed an in silico-guided strategy for overproducing purine nucleosides based on a genome-scale metabolic network model in Bacillus subtilis. The metabolic flux was analyzed to predict two key backflow nodes, Drm (purine nucleotides toward PPP) and YwjH (PPP–EMP), to resolve the competitive relationship between biomass and purine nucleotide synthesis. In terms of the purine synthesis pathway, the first backflow node Drm was inactivated to block the degradation of purine nucleotides, which greatly increased the inosine production to 13.98–14.47 g/L without affecting cell growth. Furthermore, releasing feedback inhibition of the purine operon by promoter replacement enhanced the accumulation of purine nucleotides. In terms of the central carbon metabolic pathways, the deletion of the second backflow node YwjH and overexpression of Zwf were combined to increase inosine production to 22.01 ± 1.18 g/L by enhancing the metabolic flow of PPP. By switching on the flux node of the glucose-6-phosphate to PPP or EMP, the final inosine engineered strain produced up to 25.81 ± 1.23 g/L inosine by a pgi-based metabolic switch with a yield of 0.126 mol/mol glucose, a productivity of 0.358 g/L/h and a synthesis rate of 0.088 mmol/gDW/h, representing the highest yield in de novo engineered inosine bacteria. Under the guidance of this in silico-designed strategy, a general chassis bacterium was generated, for the first time, to efficiently synthesize inosine, adenosine, guanosine, IMP and GMP, which provides sufficient precursors for the synthesis of various purine intermediates.

Conclusions

Our study reveals that in silico-guided metabolic engineering successfully optimized the purine synthesis pathway by exploring efficient targets, which could be applied as a superior strategy for efficient biosynthesis of biotechnological products.

Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

Background

Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed.

Results

In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis.

Conclusion

The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

In silico target-based strain engineering of Saccharomyces cerevisiae for terpene precursor improvement

Systems-based metabolic engineering enables cells to enhance product formation by predicting gene knockout and overexpression targets using modeling tools. FOCuS, a novel metaheuristic tool, was used to predict flux improvement targets in terpenoid pathway using the genome-scale model of Saccharomyces cerevisiae, iMM904. Some of the key knockout target predicted includes LYS1, GAP1, AAT1, AAT2, TH17, KGD-m, MET14, PDC1 and ACO1. It was also observed that the knockout reactions belonged either to fatty acid biosynthesis, amino acid synthesis pathways or nucleotide biosynthesis pathways. Similarly, overexpression targets such as PFK1, FBA1, ZWF1, TDH1, PYC1, ALD6, TPI1, PDX1 and ENO1 were established using three different existing gene amplification algorithms. Most of the overexpression targets belonged to glycolytic and pentose phosphate pathways. Each of these targets had plausible role for improving flux toward sterol pathway and were seemingly not artifacts. Moreover, an in vitro study as validation was carried with overexpression of ALD6 and TPI1. It was found that there was an increase in squalene synthesis by 2.23- and 4.24- folds, respectively, when compared with control. In general, the rationale for predicting these in silico targets was attributed to either increasing the acetyl-CoA precursor pool or regeneration of NADPH, which increase the sterol pathway flux.

Network Reconstruction and Modelling Made Reproducible with moped

Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses.

Functional analysis of H+-pumping membrane-bound pyrophosphatase, ADP-glucose synthase, and pyruvate phosphate dikinase as pyrophosphate sources in Clostridium thermocellum

The atypical glycolysis of Clostridium thermocellum is characterized by the use of pyrophosphate (PP_i) as phosphoryl donor for phosphofructokinase (Pfk) and pyruvate phosphate dikinase (Ppdk) reactions. Previously, biosynthetic PP_i was calculated to be stoichiometrically insufficient to drive glycolysis. This study investigates the role of a H⁺-pumping membrane-bound pyrophosphatase, glycogen cycling, a predicted Ppdk-malate shunt cycle and acetate cycling in generating PP_i. Knockout studies and enzyme assays confirmed that clo1313_0823 encodes a membrane-bound pyrophosphatase. Additionally, clo1313_0717-0718 was confirmed to encode ADP-glucose synthase by knockouts, glycogen measurements in C. thermocellum and heterologous expression in E. coli. Unexpectedly, individually-targeted gene deletions of the four putative PP_i sources did not have a significant phenotypic effect. Although combinatorial deletion of all four putative PP_i sources reduced the growth rate by 22% (0.30±0.01 h^-1) and the biomass yield by 38% (0.18±0.00 g_biomass g_substrate^-1), this change was much smaller than what would be expected for stoichiometrically essential PP_i-supplying mechanisms. Growth-arrested cells of the quadruple knockout readily fermented cellobiose indicating that the unknown PP_i-supplying mechanisms are independent of biosynthesis. An alternative hypothesis that ATP-dependent Pfk activity circumvents a need for PP_i altogether, was falsified by enzyme assays, heterologous expression of candidate genes and whole-genome sequencing. As a secondary outcome, enzymatic assays confirmed functional annotation of clo1313_1832 as ATP- and GTP-dependent fructokinase. These results indicate that the four investigated PP_i sources individually and combined play no significant PP_i-supplying role and the true source(s) of PP_i, or alternative phosphorylating mechanisms, that drive glycolysis in C. thermocellum remain(s) elusive. IMPORTANCE Increased understanding of the central metabolism of C. thermocellum is important from a fundamental as well as from a sustainability and industrial perspective. In addition to showing that H⁺-pumping membrane-bound PPase, glycogen cycling, a Ppdk-malate shunt cycle, and acetate cycling are not significant sources of PP_i supply, this study adds functional annotation of four genes and availability of an updated PP_i stoichiometry from biosynthesis to the scientific domain. Together, this aids future metabolic engineering attempts aimed to improve C. thermocellum as a cell factory for sustainable and efficient production of ethanol from lignocellulosic material through consolidated bioprocessing with minimal pretreatment. Getting closer to elucidating the elusive source of PP_i, or alternative phosphorylating mechanisms, for the atypical glycolysis is itself of fundamental importance. Additionally, the findings of this study directly contribute to investigations into trade-offs between thermodynamic driving force versus energy yield of PP_i- and ATP-dependent glycolysis.

Modeling Drosophila gut microbe interactions reveals metabolic interconnectivity

We know a lot about varying gut microbiome compositions. Yet, how the bacteria affect each other remains elusive. In mammals, this is largely based on the sheer complexity of the microbiome with at least hundreds of different species. Thus, model organisms such as Drosophila melanogaster are commonly used to investigate mechanistic questions as the microbiome consists of only about 10 leading bacterial species. Here, we isolated gut bacteria from laboratory-reared Drosophila, sequenced their respective genomes, and used this information to reconstruct genome-scale metabolic models. With these, we simulated growth in mono- and co-culture conditions and different media including a synthetic diet designed to grow Drosophila melanogaster. Our simulations reveal a synergistic growth of some but not all gut microbiome members, which stems on the exchange of distinct metabolites including tricarboxylic acid cycle intermediates. Culturing experiments confirmed our predictions. Our study thus demonstrates the possibility to predict microbiome-derived growth-promoting cross-feeding.

Metabolic engineering design to enhance (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis based on flux balance analysis

Background

Glycerol is a desirable alternative substrate for 2,3-butanediol (2,3-BD) production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis.

Results

B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study.

Conclusions

The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01688-y.

Metabolic engineering of Bacillus subtilis based on genome-scale metabolic model to promote fengycin production

Fengycin is an important lipopeptide antibiotic that can be produced by Bacillus subtilis. However, the production capacity of the unmodified wild strain is very low. Therefore, a computationally guided engineering method was proposed to improve the fengycin production capacity. First, based on the annotated genome and biochemical information, a genome-scale metabolic model of Bacillus subtilis 168 was constructed. Subsequently, several potential genetic targets were identified through the flux balance analysis and minimization of metabolic adjustment algorithm that can ensure an increase in the production of fengycin. In addition, according to the results predicted by the model, the target genes accA (encoding acetyl-CoA carboxylase), cypC (encoding fatty acid beta-hydroxylating cytochrome P450) and gapA (encoding glyceraldehyde-3-phosphate dehydrogenase) were overexpressed in the parent strain Bacillus subtilis 168. The yield of fengycin was increased by 56.4, 46.6, and 20.5% by means of the overexpression of accA, cypC, and gapA, respectively, compared with the yield from the parent strain. The relationship between the model prediction and experimental results proves the effectiveness and rationality of this method for target recognition and improving fengycin production.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02990-7.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Reconstruction of Litopenaeus vannamei Genome-Scale Metabolic Network Model and Nutritional Requirements Analysis of Different Shrimp Commercial Varieties

As an important tool for systematic analysis, genome-scale metabolic network (GSMN) model has been widely used in various organisms. However, there are few reports on the GSMNs of aquatic crustaceans. Litopenaeus vannamei is the largest and most productive shrimp species. Feed improvement is one of the important methods to improve the yield of L. vannamei and control water pollution caused by the inadequate absorption of feed. In this work, the first L. vannamei GSMN named iGH3005 was reconstructed and applied to the optimization of feed. iGH3005 was reconstructed based on the genomic data. The model includes 2,292 reactions and 3,005 genes. iGH3005 was used to analyze the nutritional requirements of five different L. vannamei commercial varieties and the genes influencing the metabolism of the nutrients. Based on the simulation, we found that tyrosine-protein kinase src64b like may catalyze different reactions in different commercial varieties. The preference of carbohydrate utilization is different in various commercial varieties, which may due to the different expressions of some genes. In addition, this investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate. The feed for different commercial varieties should be adjusted accordingly, and possible adjustment schemes were provided. The results of this work provided important information for physiological research and optimization of the components in feed of L. vannamei.

Dissecting industrial fermentations of fine flavour cocoa through metagenomic analysis

The global demand for fine-flavour cocoa has increased worldwide during the last years. Fine-flavour cocoa offers exceptional quality and unique fruity and floral flavour attributes of high demand by the world's elite chocolatiers. Several studies have highlighted the relevance of cocoa fermentation to produce such attributes. Nevertheless, little is known regarding the microbial interactions and biochemistry that lead to the production of these attributes on farms of industrial relevance, where traditional fermentation methods have been pre-standardized and scaled up. In this study, we have used metagenomic approaches to dissect on-farm industrial fermentations of fine-flavour cocoa. Our results revealed the presence of a shared core of nine dominant microorganisms (i.e. Limosilactobacillus fermentum, Saccharomyces cerevisiae, Pestalotiopsis rhododendri, Acetobacter aceti group, Bacillus subtilis group, Weissella ghanensis group, Lactobacillus_uc, Malassezia restricta and Malassezia globosa) between two farms located at completely different agro-ecological zones. Moreover, a community metabolic model was reconstructed and proposed as a tool to further elucidate the interactions among microorganisms and flavour biochemistry. Our work is the first to reveal a core of microorganisms shared among industrial farms, which is an essential step to process engineering aimed to design starter cultures, reducing fermentation times, and controlling the expression of undesirable phenotypes.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.