Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.Extensive microdiversity within  Resolving the nano-niche: Simulations of metabolism and physiology across pangenomes link the molecular and ecosystem scales. Prochlorococcus is the most abundant photosynthetic organism in the ocean, contributing most of the primary production in vast regions of the nutrient-starved subtropical gyres  . The PanGEM consists of 1117 orthologous gene clusters, 1484 reactions, and 1282 metabolites and was manually curated and annotated with multiple bioinformatics, cheminformatics, and systems biology databases and tools . On the basis of a power law regression  and quality tested using a community standard  for each sequenced organism (referred to as in silico strains) are generated using a compressed sensing (CS)–based approach  on the Atlantic Meridional Transect cruise (AMT-13) in late summer of 2003. A suite of 66 physical, chemical, and biological measurements from satellite optics, shipboard sensors, and discrete samples were interpolated at 10-m depth intervals from 10 to 200 m at 32 stations from 48°N to 45°S, passing through the Senegalo-Mauritanian upwelling region  than a linear form (R2 = 0.40), suggesting a nonlinear density dependence of mortality. Direct taxon-specific in situ phytoplankton growth rate measurements are scarce . Hence, potentially important light history dynamics associated with deep convective mixing may be improperly captured by our steady-state model.At high latitudes, the temperatures, nutrient concentrations, and light levels near the surface correspond to those typical of the deep chlorophyll maximum in the central gyres, resulting in an outcropping of low light–adapted ecotypes. In the northern hemisphere, LLI was the dominant ecotype at the surface, both in our simulations of growth rates and in observed abundance. Breaking the general trend of correspondence, we predicted high growth rates of low light–adapted ecotypes in the southernmost stations of the transect where the observed population density was low. This might reflect a dominant top-down control of populations in that region  followed an expected trend, contributing less (15 ± 14%) in high-chlorophyll regions (>15 mg chlorophyll a m−2) and more (52 ± 31%) in the oligotrophic regions, in line with previous reports (f-ratio (the proportion of nitrogen uptake supplied by nitrate) , with higher values associated with deeper or higher-latitude samples. This range and pattern is consistent with previous measurements of Prochlorococcus elemental stoichiometry in the Sargasso Sea (5.4 to 7.4) (−1 near the surface to 30.3 KJ gDW−1 below the euphotic depth (fig. S10). Population elemental stoichiometry and energy content reflect both the strain composition of each population and the phenotypic states of those strains that, in turn, reflect a great many underlying physiological processes, including alterations to macromolecular compositions and photoacclimation.The elemental composition and energy content of phytoplankton constrain marine food webs and the cycles of carbon, nitrogen, phosphorus, and other bioelements . Especially under low-light conditions, physiological acclimation was necessary to acquire sufficient reductant to exceed nongrowth associated maintenance adenosine triphosphate (ATP) demands. For example, the threshold for net positive growth of a high light–adapted strain (MIT 9312) acclimated to ideal laboratory conditions required a minimum flux of 17.7 μmol photons mS*, uptake rates are held at the level demanded by some other limiting resource . As expected, diffusion limitation was prevalent for nitrite and nitrate transport in the lower latitudes above the nutricline, where concentrations were routinely in the low-nanomolar range. Observed ammonium concentrations were predominantly just below the predicted S* (fig. S13), and transport was often limited by membrane surface area. It is possible that additional nitrogen sources that were not resolved in our simulation, especially urea and amino acids, supply the remaining deficit in nitrogen demanded by maximal growth rates; however, the competition for membrane space among multiple transporters would be even more prevalent. Phosphate concentrations were mostly above S* throughout the transect, with the notable exception of the surface layer between 20°N and 30°N where concentrations approached a diffusion-limited regime, a region known to exhibit seasonal phosphorus limitation  are regulated by a trade-off between synthesis costs, available space on the membrane, and uptake rates. OptTrans explicitly accounts for these trade-offs using a mechanistic model that combines quantitative proteomics, molecular modeling, and FBA to predict the optimal abundance of each transporter and the optimal cell size  (+)–dependent glutamate dehydrogenase . The auxiliary role of GDH in nitrogen assimilation under low-light, high-nitrogen conditions has been recognized in the model freshwater cyanobacterium Synechocystis  indicated energy limitation, although there is also a small effect (in the same order) on encounter rates due to differences in molecular diffusivity. Several low light–adapted strains acclimated to growth in these low-light, high-inorganic nitrogen environments were further able to reduce the energy demands of nitrogen assimilation by redirecting a substantial portion (up to 48%) of the flux from the ATP-dependent glutamine synthetase–glutamate synthase complex (glutamate aminating) to the reduced form of nicotinamide adenine dinucleotide phosphate , protein content was reduced, leaving the remaining cellular mass to be partitioned predominantly between either carbohydrate or lipid stores. Because the energy requirements for de novo synthesis of lipids is considerably higher than of carbohydrates, allocating C to lipids was favored near the surface, while carbohydrates were relatively enriched just above the deep chlorophyll maximum depth interval. This solution points to lipid storage as an auxiliary electron sink to cope with high-light, low-nitrogen conditions. The resulting changes in the energy content of biomass can therefore be decoupled from synoptic changes in cellular C:N ratios, a prediction with implications for food web dynamics and trophic transfer efficiency . These predictions are consistent with observations of Ek) was remarkably consistent with both in situ observations , assimilation number (rvations  (17). Theratures . Subsurf+ reductase. In some strains, excess reductant is dissipated by the plastoquinone terminal oxidase (PTOX) reaction, while in other strains lacking PTOX, the generation of singlet oxygen and other free oxygen radicals is unavoidable, requiring glutathione or thioredoxin peroxidases. Partial recovery of the photorespiratory by-product 2-phosphoglycolate was only possible for a subset of the LLIV strains, while other ecotypes are known to excrete substantial amounts of the hydroxy acid glycolate . For example, accessory genes associated with the LLIV clade included an enrichment in chitin-degrading and other polysaccharide-degrading enzymes and transporters, resources that are expected to supplement their energy-limited niche below the deep chlorophyll maximum depth. While our metabolic networks resolve the diversity of mixotrophic potential across the Prochlorococcus pangenome, comprehensive determinations of compound-specific dissolved organic matter concentrations in the marine environment remains an analytical challenge. Although exogenous organic substrate uptake was not included in our AMT simulations, ecotype-specific patterns were evident from an analysis of growth capabilities on various sole N, P, and S sources. For example, LLI and LLIV strains were universally able to grow on nitrite as a sole N source, while only four strains from other ecotypes shared that capability (fig. S16).A survey of growth capabilities and pathway enrichment analysis among the core and accessory reaction subsets of the Prochlorococcus  and are provided as a gridded dataset (data S2). To compute downwelling irradiance, we combined in situ optical profiler diffuse spectral attenuation coefficients with synoptic measurements of satellite cloud cover as a mask over a model of clear-sky direct and diffuse spectral radiation. Simulations conducted in this study were restricted to a subset of 32 stations  for which ecotype abundance data , MIT 9312 (HLII), NATL2A (LLI), MIT 1304 (LLII/III), and MIT 9313 (LLIV). Strain MED4 was analyzed for macromolecular compositions  as well as elemental composition  and dry weight as in . The current pangenome consists of 77 sequenced isolates, seven metagenome-assembled genomes, and 564 single-cell amplified genomes, comprising 866,894 unique open reading frames. Translated coding sequences were mapped to KEGG orthologs (KOs) using a bidirectional blast with KEGG’s kofamKOALA, pairing the number of unique KOs to 2084. A database containing nucleotide sequences, translated amino acid sequences, header strings, database identifiers , metadata, and descriptive statistics for each genome was compiled; this database was the basis for draft reconstruction of the PanGEM network. The draft reconstruction was then manually curated following a standard process (section S1.3) .S is the stoichiometric matrix, v is the vector of fluxes and its respective lower and upper bounds (lbv and ubv), and c is a predefined coefficient vector whose entries define the weighted set of reactions that determine fitness. Here, the objective function is defined as growth rate, the rate of synthesis of 1 g of ash-free dry biomass of specified composition. Several variations of FBA and sensitivity analyses are used throughout this article, as described in the Supplementary Materials (section S1.2).A chemical flux balance can be derived from the law of mass action (section S1.2). Assuming a steady state, a set of optimal flux distributions can be predicted that maximizes an objective function by solving the linear programTwo sequential acclimation steps, OptTrans and PhysOpt, are performed on each strain GEM at each environmental grid point. Physiological data specific to each strain, or to its closest phylogenetic relative, are initially assigned to the corresponding GEM to represent its phenotype when grown under optimal laboratory conditions. Physiological data, collected as a part of this study (data S4 to S7) and from the literature (data S8), include cell size spectra, biochemical compositions , maximum carbon fixation rates, and maintenance requirements. Boundary constraints for all parameters  are assigned to each strain GEM by compiling experimental data collected under a wide range of growth conditions. The first bilevel optimization, OptTrans, searches for the optimal distribution of metabolic fluxes, transporter abundances, and cell size that maximizes fitness, given the external concentration of each nutrient . Quantitative proteomics data and molecular modeling are used to derive catalytic rates and transmembrane domain dimensions for each transporter, necessary to predict external substrate concentration–dependent uptake rates . The OGT dataset from TOME 1.0 .Growth rates and metabolic fluxes were adjusted for temperature effects by comparing in situ growth temperatures to a model of temperature-dependent growth, parameterized for each strain. Our approach provides a strain-specific parameterization while avoiding the effects of adaptive laboratory evolution, which can be substantial (


Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the “mass-flow” through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host–pathogen interactions, with a specific focus on oomycete plant pathogens and in particular  The metabolism of an organism defines its capabilities to take up nutrients from the environment and to convert these into essential building blocks such as nucleic acids and amino acids . CellulaPhytophthora infestans. We provide an overview of recent developments in this field and discuss challenges in reconstructing GEMs in these organisms. Finally, we propose a workflow for reconstructing high-quality GEMs and lay out a number of challenges that need to be addressed for systems biology to provide its full potential to study the intimate interactions between plants and pathogens.In this review, we first provide a broad overview of the field of systems biology, with a focus on genome-scale metabolic models (GEMs). We then discuss the relevance of GEMs to study pathogens and host–pathogen interactions, particularly oomycete plant pathogens such as The rise of computational biology, high-throughput analyses tools, and advanced measurement technologies in the early twenty-first century enabled biologists to analyze the presence of basically all molecules in an organism and to measure their quantities and interactions at cell or tissue level . Today, in silico identification of essential genes, reactions, or metabolites by predicting the effect of enzyme knockouts on the functioning of the system -based models for enzyme kinetics, Bayesian, Boolean, or rule-based models for signaling and regulatory networks, or constraint-based steady-state models of metabolism such as GEMs; e system . Since me system .BOX 1Metabolism is a complex system of thousands of biochemical reactions, most of which are catalyzed by metabolic enzymes. GEMs have been developed as an effective way to generate hypotheses about cellular metabolism . A GEM iGEMs are assumed to be in steady state, which means the net uptake of nutrient mass is equal to the net production of biomass, implying there is no net accumulation of metabolites. This simplifies the model to a system of linear equations. Each reaction in the GEM is considered to have a flux, i.e., a steady-state reaction rate. The most popular method to model metabolism in this framework is called flux balance analysis FBA; . An assuThe integration of omics data can be used to impose additional model constraints or objective functions . Since mThe linear program of a GEM can often be solved in a matter of seconds on modern computers, making it a powerful computational tool to predict the effect of perturbations to GEMs . For ins(A) GEMs are reconstructed from a genome sequence and connect enzymes to reactions. (B) A GEM is a network that simulates fluxes from nutrient uptake to the production of biomass precursors. (C) The mathematical representation of a GEM. Reactions are stored in a stoichiometric matrix that is multiplied by a vector of fluxes. The solution space is limited by constraints. Here, only two fluxes are shown, but the optimization is in n dimensions.Basic principles of GEMs. Because of their integrative nature, by relating genes with reactions and metabolites, GEMs can serve as a knowledge base for species-specific information on the biochemical capacity of an organism, as deduced from its genome and from prior knowledge. Prior knowledge typically integrated in a GEM includes, for instance, the nutrients taken up and the metabolites produced in the form of biomass or secondary metabolites. Importantly, prior knowledge should be used to correct the model where automated methods are limited, such as the inference of species-specific enzymatic substrates, gene–protein–reaction associations , subcellular localization of enzymes, directionality of reactions, and substrates of transporters . Next toThe main purpose of GEMs is enabling flux simulations that can be used to investigate system complexity and dynamics . For insSystems biology offers a powerful toolbox to study pathogens and their relation with their hosts . PathogeHaemophilus influenzae that causes disease in humans  we breathe, for sequestering CO2 and maintaining the balance in the global ecosystem, and for the production of food and feed. However, plants are under constant threat of pathogens, such as fungi, oomycetes, bacteria, and viruses. In agriculture, the resulting yield losses can be substantial, reaching up to 30%  and destruction (phthora). Its arrival in Europe marked the birth of plant pathology as a discipline and ever since P. infestans has been a favorite subject of investigation  and modeled the metabolic changes in the plant when challenged by P. infestans, based on transcriptome data.Although the GEM of a plant pathogen is in principle suitable to predict essential metabolic genes and reactions, and to simulate growth (biomass production) tans GEM  with a teraction . We used vitamin . In this vitamin . Botero Phytophthora pathogen and a plant is when a germ tube emerging from a sporangium or cyst senses a suitable surface and starts swelling at the tip. Host entry by Phytophthora is facilitated by a mechanical slicing mechanism that breaches the epidermal cells  and 2009 (P. infestans) revealed that these species profoundly differ in genome size and content  within the assembled genome sequences , which ince tags . Many sunce tags . Howevernce tags . This woSaccharomyces cerevisiae, about 20% of all genes lack any functional association , a system to cluster protein orthologs with validated functions , and thiociation . Lineageociation . KOs areociation . In our In omics-based bioinformatics studies, it is common practice to search for overrepresentation (enrichment) of functional annotations in differentially abundant molecules such as mRNA or proteins . Over thP. infestans) pathosystem was subject of several systems biology studies complete and ideally gapless genome assemblies of pathogens as well as their hosts are required. The genomes need to be sequenced and assembled using novel technologies and advanced assembly methods to attain the complete coding information . Gene prSecond, the identified enzymes are then used as input for reconstructing a draft metabolic network. Functional annotation of (subunits of) metabolic enzymes and the associated reactions and metabolites should be curated according to the literature and according to the established protocol , pinpoinin vitro assays can be performed to characterize the pathogen’s growth behavior under different conditions. The ideal medium is similar in composition to the pathogen’s natural hosts to mimic natural growth. The medium needs to be analyzed by untargeted metabolomics (mass spectrometry and/or nuclear magnetic resonance) over multiple time points to provide insight into the presence and abundance of specific metabolites. Metabolites that strongly change in abundance during pathogen growth and between subsequent sampling stages are likely assimilated or secreted. This can be indicative of a nutrient transporter on the plasma membrane that is capable of transporting the respective metabolite. In addition, isotope-labeled metabolites can be added to the medium to test the assimilation of specific nutrients (Third, utrients , such asPhaeodactylum tricornutum to quantify the percentages of carbohydrate, protein, DNA/RNA, and fatty acids per gram of cellular dry weight (Fourth, the relative pathogen biomass composition and growth rates should be measured. A promising approach for this is Fourier-transform infrared spectroscopy FTIR; . This mey weight . Once thin vitro, there are likely alternative enzymes or metabolic routes that compensate for this mutation. The model should be inspected on incorrect annotations or missing reactions accordingly.Fifth, predicted phenotypes, e.g., induced by specific nutrient starvation, should be validated by growth experiments and gene/reaction essentiality should be validated in knockout or knockdown mutants, for instance, as was done for the nitrate assimilation cluster . As discSixth, the refined pathogen GEM should be integrated with a similarly refined GEM for its host. This necessitates deploying more sophisticated constraints and objective functions to simulate a more realistic symbiosis for this pathosystem, such as multi-objective simulations to address the competition for nutrients .In addition to the here proposed steps, there is a large and rapidly increasing number of methods and algorithms that can be applied to GEMs to gain further insights into the complex system of pathogen–host interactions . For insSystems biology, in particular GEMs, offers a unique approach to study oomycetes and their intricate interactions with their hosts. GEMs not only offer a holistic overview of metabolism, but also constitute a foundation on which to incorporate omics measurements at various levels, allowing integrated analyses of key processes in pathogenesis and pathogen–host interaction. Although there are still a number of remaining technical and methodological challenges, GEMs hold great promise for providing mechanistic insight into strategies exploited by oomycetes to proliferate and infect their hosts, ultimately allowing us to develop new means of controlling these highly relevant pathogens.SR wrote the first draft of the manuscript. FG edited sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This work was funded by the Food-for-Thought campaign from Wageningen University Fund.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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In this Commentary, we discuss the recent advances in the field that suggest that the kinetome may be more conserved than expected. A conserved kinetome will accelerate the development of future kinetic models of integrated cellular functions and expand their scope and usability in many fields of biology and biomedicine.Computational biologists have labored for decades to produce kinetic models to mechanistically explain complex metabolic phenomena. The estimation of numerical values for the large number of kinetic parameters required for constructing large‐scale models has been a major challenge. This collection of kinetic constants has recently been termed the  This Commentary highlights recent discoveries suggesting that the kinetome (a collection of kinetic parameters) can be conserved and discusses how a conserved kinetome will accelerate the development of future kinetic models. Once whole‐genome sequences became available in the mid‐1990s, the reconstruction of metabolic networks reached the genome‐scale. Over the past two decades, genome‐scale metabolic models (GEMs) built from these network reconstructions have contributed to our understanding of systems biology of metabolism, with applications ranging from metabolic engineering to bacterial evolution to infectious disease. Genetic variation can now be incorporated into genome‐scale models of metabolism, transcription, translation, proteostasis, and cellular stresses. GEMs are often simulated using flux balance analysis that requires a minimal number of parameters estimated from empirical data.Kinetic models, on the other hand, have traditionally been limited by the need for extensive parameterization. If the kinetome can be estimated from high‐throughput data, then we can develop large‐scale—even genome‐scale—kinetic models. Since GEMs have a direct genetic basis, a new generation of kinetic models can be directly rooted in protein structures and sequence variation. The kinetome of such models would be large, requiring the estimation of many unmeasured parameters. However, if segments of the kinetome are conserved, then parameterization will be simplified. Reference kinetomes can be estimated for well‐characterized strains and applied to less well‐known strains.in vivo turnover rates of bacterial enzymes have been characterized using ratios of proteomic and fluxomic data  studies. ALE allows for the generation of strains that have adapted to high growth rates following the deletion of genes that encode specific metabolic enzymes. This approach results in the generation of “metabolic specialist” strains whose pathway usage has been rewired by ALE following the loss of a key metabolic enzyme. A large study resulted in an estimation of turnover rates for the same enzyme in multiple metabolic specialists Fig . These eE. coli with orthogenes from a diverse range of other species, from hyperthermophilic archaea to humans  models), or they specify the enzyme abundances based on measurement (enzyme‐constrained models). Ultimately, these models enable the study of the principles that underlie how the limited number of protein molecules that a cell can carry are optimally allocated to metabolic enzymes that have an inherent set of kinetic constants , providing insight into whether different omic data types can be used together to infer kinetic parameter values.et al, ex vivo erythropoietic models might be used in conjunction with gene editing of the adult hematopoietic stem cells to engineer specialist RBCs.The RBC is the most abundant cell in the human body, thus representing an important starting point for the development of personalized medicine in human systems. In a recent study, personalized RBC kinetic models were constructed through parameterization based on metabolomic, fluxomic, and genotyping data  from individual tumor samples to constrain maximum flux rates for cancer patients  and protein concentration (protein molecules per unit volume) through the well‐known equation, in vivo flux (iv), determined via fluxomics, is given by iv = app,ik × ie, where app,ik is the apparent catalytic rate for enzyme i in vivo, and ie is the in vivo enzyme concentration as determined by proteomic measurements. The term app,maxk (the maximum appk observed across multiple conditions) is a proxy for kcatinvivo and can be used in simulation models. When working with a large number of enzymes, ie becomes constrained by limited proteome size (∑i=1nei), and thus proteome allocation becomes important (Nilsson et al, The


Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases. Cystic fibrosis (CF) is the most common form of the autosomal recessive disease in Caucasians and affects 1 in every 3000–3500 births in Europe and United States . Many riBurkholderia cepacia complex (Bcc) emerged as important pathogens in the airways of immunocompromised humans, especially those with CF. The treatment of Bcc infections is challenging because of the natural resistance to various classes of antibiotics and long-term colonization, and the ability to adapt to nutrient-deficient biofilm microenvironment [ironment . There iironment . The eraRevealing the pathogen and microenvironment interactions requires an analysis of large reaction networks, which can be identified through the use of systems biology approaches. Systems biology is an integrative discipline that assembles experimental data with computational methods to describe the sophisticated biological processes of cells, tissues, and microorganisms . Genome-B. cenocepacia with its microenvironment based on the metabolic modeling and flux balance analysis (FBA) approach. Next, we examined the influence of uptake reactions, which involve substrate exchange with the extracellular space, not only on bacterial growth rates but also on flux distribution across intracellular pathways based on synthetic lethality analysis. Afterward, gene expression data from two different conditions  were integrated with the GEM of B. cenocepacia to investigate the effect of environmental conditions on bacterial metabolism.In this study, we investigated the metabolic interactions between Burkholderia cenocepacia J2315 (iPY1537) was downloaded in SBML format from the authors’ website  [The GEM of er 2019) . Detailser 2019) . BiomassWe performed the simulation of single and double synthetic lethal reactions by constraining upper and lower bounds of the fluxes to zero and selecting the constraint that would make the objective function unfeasible. Individual reactions that inhibit the objective function considered as single lethal reactions. Constraining the model by removing one pair of reactions at a time , which resulted in no cell growth, was determined as double lethal reactions. Exchange reactions were settled by available COBRA functions and were constrained individually, and were paired by setting these fluxes to zero. Biomass formation was used as the objective function. Essential exchange reactions were defined as those in which the absence of nutrients by inhibition of these reactions eventually leads to cell death. Cellular metabolic pathways were determined from enclosed subsystems of the model, and unassigned pathways were disregarded.B. cenocepacia J2315 isolates under exponential and biofilm conditions were downloaded from ArrayExpress  [B. cenocepacia. Sample accession numbers of the transcripts are accessible in The raw RNA sequencing data of wild type EB19669) . Six RNAB. cenocepacia J2315 [index function. Then, estimation of the transcripts’ abundances was performed with quant function. The abundance estimates were reported in transcripts per million (TPM).We quantified gene expression levels with the alignment-free pipeline Kallisto (v0.46.1) , which dGene level differential expression data were analyzed with the DESeq2 package (v1.22.2)  in the RB. cenocepacia by the Metabolic Adjustment for Differential Expression (MADE) algorithm using Toolbox for Integrating Genome-Scale Metabolic Models, Expression Data, and Transcriptional Regulatory Networks (TIGER) package [B. cenocepacia. To understand how the transition from exponential phase to biofilm phase impacts the intracellular metabolic fluxes and growth of B. cenocepacia, we performed the flux variability analysis of all fluxes reaching the optimal solution. Constraints were defined according to synthetic cystic fibrosis sputum medium (SCFM), a medium designed to replicate the environment of the cystic fibrosis lung [We integrated transcriptomics data with the GEM of  package ,18. Thissis lung . MinimumB. cenocepacia (iPY1537), which contained 1667 reactions and 1513 genes. We evaluated the influence of a reaction without flux on the entire known metabolic network through the synthetic lethality analysis approach  of the single lethal reactions; among these lipid metabolism reactions, fatty acid biosynthesis  was most frequently found. In remaining 71 single lethal reactions, glycan metabolism reactions were the most common   . n = 162) of the model. Notably, as the exchange and transport reactions in the model of Burkholderia cenocepacia J2315 have been extensively curated based on medium information, we grouped them together in our essentiality analysis to investigate the potentially important metabolites in the environment for the growth of bacteria. Exchange of oxygen and O2 transport via diffusion were identified as single lethal exchange/transport reactions  were most commonly responsible for pairs becoming lethal and were found in 26.1% of the pairs  were most common in lethal pairs with at least one exchange/transport reaction, and paired with tryptophan supply of the cell  were in the same directionality, and 287 reactions (48.6%) were altered between conditions. We found that a total of 176 unique exchange/transport reactions, 79 (44.9%) reactions were common, and 97 (55.1%) reactions varied across two different conditions . ReactioMetabolism of amino acids with 59 (31%) altered reactions scored as the most varied pathway when we excluded exchange/transport reactions. This was followed by carbohydrate metabolism reactions with 52 (27%) and purine and pyrimidine metabolism with 29 (15%) reactions that introduced changes. Vitamin metabolism was the fourth most altered pathway with 20 (11%) changing reactions in total for ascorbate and aldarate metabolism, folate metabolism, and pantothenate and CoA biosynthesis .In detail, pathways in arginine and proline metabolism (100%) and glycine, serine, and threonine metabolism (100%) and ascorbate and aldarate metabolism (100%) were the pathways, in which all the reactions are altered between two conditions . This isWe also examined the regulation of vitamin metabolism and the subsequent effect on various physiological functions in the system during biofilm conditions. We found that both arginine proline metabolism and the ascorbate aldarate pathway are connected to the citrate cycle through 2,5-dioxopentanoate synthesis.Condition-specific models had fluxes on 119 and 157 exchange/transport reactions for exponential and biofilm conditions, respectively. In both phases, a total of 97 fluxes were unchanged. According to the altered exchange/transport reactions, more amino acids were consumed when the cells were at the biofilm phase: 3 and 14 additional amino acid intakes were predicted in addition to common exchange/transport reactions in exponential and biofilm conditions, respectively . As the B. cenocepacia J2315. Altered metabolic fluxes were identified from the synthetic lethality analysis as well as condition-specific models based on gene expression profile. The models predicted tendencies to exclusive pathways in the biofilm development when comparing exponential conditions.In this study, the systems biology approach for model perturbation and data integration allowed us to evaluate how environmental conditions affect the metabolic pathways of B. cenocepacia under exponential conditions. The influence of different conditions on synthetic lethal interactions was adjusted by perturbing the model based on reaction constraints. According to the synthetic lethality analysis, reactions involved in fatty acid biosynthesis were most frequently found in single lethal reactions. Control at the level of fatty acid biosynthesis is crucial for membrane homeostasis, because the biophysical properties of membranes are determined by the composition of the fatty acids that are produced by de novo biosynthesis. Since lipids are the cell’s primary structural components, the enzymes of bacterial fatty acid biosynthesis are attractive targets for antimicrobial drug discovery [First, we provided a comprehensive analysis of essential reactions and affected pathways in iscovery . To thisiscovery . We claiReactions involved in phenylalanine, tyrosine, and tryptophan biosynthesis were most frequently found in lethal pairs with at least one exchange/transport reaction when we excluded single lethal reactions. Tryptophan exchange was responsible for 36% of those reaction pairs becoming lethal. In this manner, Zhang et al. showed that the absence of exogenous tryptophan caused an immediate killing on auxotrophic mycobacterium species, which is consistent with our findings . Since tB. cenocepacia with its environment and how the metabolic network is regulated as a result of biofilm development, we integrated the transcriptomics data of exponential and biofilm phases into the iPY1537. Our results highlighted that glycerolipid synthesis, lipopolysaccharide biosynthesis, fatty acid biosynthesis, and glycerophospholipid metabolism were least affected when the conditions are altered. Bacterial lipid metabolism is responsible for producing structural molecules and biological energy storage, and the biosynthesis of the extracellular polymeric substances that are crucial for biofilm formation [Second, to obtain a holistic understanding of the interactions between ormation . Phosphoormation . The encB. cenocepacia allowed us to investigate these features in the context of the metabolic modeling. Integration of transcriptomic data of the biofilm conditions revealed that bacteria tend to uptake more amino acids from the environment for growth, and reactions of the pentose-phosphate pathway are activated. These in silico predictions are compatible with the fact that throughout exponential growth, carbohydrates are first consumed as the main supply of carbon and energy but are promptly exhausted, and additional fuel molecules such as lipids and proteins are utilized to maintain survival [While earlier studies have prospected the organization of amino acid and carbohydrate metabolism of microorganisms under different environmental conditions ,27, the survival .Bacillus subtilis entered the stationary phase [Escherichia coli and almost complete depletion of serine in the stationary phase [B. cenocepacia. It can be assumed that serine is first converted to pyruvate, then used for gluconeogenesis or channeled into the Krebs cycle for energy production. Therefore, antimetabolite for serine can be potentially used to block these serine related metabolic pathways and inhibit the growth or biofilm formation, which should be evaluated in future studies.Pathway-based analysis also unveiled which fluxes in amino acid metabolism were most affected by biofilm conditions. Glycine, serine, and threonine metabolism and arginine and proline metabolism were the two subsystems in which all of the reactions were altered between conditions. In particular, the glycine, serine, and threonine pathway is closely related to central metabolism. The amino acid serine is an indispensable biological molecule, either a basic constituent for protein synthesis or as a precursor of other amino acids, nucleotides, and phospholipids . In parary phase . They alry phase ,32,33,34Several reactions in ascorbate and aldarate metabolism, folate metabolism, and pantothenate biosynthesis were altered, based on our metabolic analysis. These reactions are involved in various biological processes as the energy supply for growth and survival. In this manner, the role of the 2,5-dioxopentanoate and related pathways offer targets for understanding the aspects of microbial biofilm metabolism.B. cenocepacia with its microenvironment and predicted the physiology during adaptation to the biofilm condition, which allowed us prior knowledge to provide testable hypotheses regarding the functionality of metabolic pathways. We pointed out considerable properties in metabolic response to extracellular nutrient availability and pathway susceptibility to altered growth conditions. These predictions can guide further experimental validation in the context of characterizing essential linkages of the bacteria with their microenvironment and discovery of potential targets for biofilm-based infections. As the metabolic models only focused on metabolic genes (i. e. regardless of genes involved in regulatory processes), future studies may integrate the metabolic models together with other biological systems, such as regulatory, signaling, and protein–protein interaction networks to enable more comprehensive understanding of the biofilm formation process and pathogenic role of the bacteria.In this study, we performed computational analysis to reveal the interactions between the global properties of


The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite that supports the interactive exploration of metabolic networks. Any metabolic network visualization tool must by necessity show only a subset of all possible metabolite connections, or the results will be visually overwhelming. Existing tools, even those that purport to show an organism’s full metabolic network, limit the set of displayed connections based on predefined pathways or other preselected criteria. We sought instead to provide a tool that would give the user dynamic control over which connections to follow.The Metabolic Network Explorer is an easy-to-use, web-based software tool that allows the user to specify a starting metabolite of interest and interactively explore its immediate metabolic neighborhood in either or both directions to any desired depth, letting the user select from the full set of connected reactions. Although, as for other tools, only a small portion of the metabolic network is visible at a time, that portion is selected by the user, based on the full reaction complement, and it is easy to switch among alternate paths of interest. The display is intuitive, customizable, and provides copious links to more detailed information pages.The Metabolic Network Explorer fills a gap in the set of metabolic network visualization tools and complements other modes of exploration. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a BioCyc Pathway/Genome Database. Metabolic networks are large and complex graphs that can be daunting to comprehend. Past research has provided multiple modes for exploring metabolic networks. Highly localized modes include viewing single reactions, single metabolites and the reactions they participate in , 4, 6, aThe Metabolic Network Explorer fills a gap in the existing modes of exploration, enabling the user to begin at a starting metabolite and interactively explore the metabolic neighborhood around that starting point by moving forward and backward in the network and building up a linear path of arbitrary length. The Explorer enables exploration of connected sets of reactions without the use of pre-defined pathways.E. coli K-12 MG1655, and MetaCyc, a database of metabolic reactions and enzymes from all branches of life. The BioCyc website is powered by Pathway Tools [https://BioCyc.org (or any other Pathway Tools website) and select Metabolic Network Explorer from the Metabolism menu. When an organism PGDB is selected, this tool will access the metabolic network of that organism. When MetaCyc is the selected PGDB, this tool accesses metabolites and reactions from all domains of life for applications such as metabolic engineering. Access to EcoCyc and MetaCyc is free; access to other PGDBs beyond a limited number of free monthly pageviews requires a paid subscription. Alternatively, users can install Pathway Tools locally, build a PGDB for any organism of interest from its annotated genome, and use the Metabolic Network Explorer to interactively explore its predicted metabolic network.The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite. BioCyc  is a colay Tools , a softwWe are not aware of other tools that permit the sort of neighborhood-based exploration provided by the Metabolic Network Explorer. Metabolic databases such as BioCyc , KEGG 66, ReactoM is a JavaScript object that includes a near-complete list of possible precursor and successor metabolites based on the set of reactions in the database.  A precursor metabolite is any metabolite P such that a reaction exists with P as a reactant and M as a product. A successor metabolite is any metabolite S such that a reaction exists with M as a reactant and S as a product. Some reactions are reversible or of unspecified directionality; in such cases, a metabolite could be either a predecessor or a successor, so is included in both categories. For every neighbor metabolite, we record the list of reactions that link the neighbor to M. The data for each reaction includes its full reaction equation, its EC number, the list of enzymes and genes that catalyze the reaction, and any pathways in which the reaction participates.The Metabolic Network Explorer is a web application with its display generated entirely using HTML, JavaScript and CSS. The primary data structure is a simple linear path consisting of a list of steps, such that each step consists of a metabolite and an optional successor reaction that links the metabolite to the next step. A second data structure records the metabolic neighborhood of every metabolite that has been retrieved from the database. The metabolic neighborhood of a metabolite The user specifies a starting metabolite, and a query is issued to BioCyc or another Pathway Tools server to retrieve the immediate metabolic neighborhood of the selected starting metabolite. The web browser displays the starting metabolite with the list of possible precursors to the left, and the list of possible successors to the right. The user can mouse over any precursor or successor metabolite to see information about the linking reaction(s). The user can also click the plus icon next to any metabolite to add that metabolite to the path. If the metabolite to be added is a precursor to the first metabolite in the path or a successor to the last metabolite in the path, the path is extended in that direction. If the selected metabolite is connected to an intermediate metabolite on the current path, it will replace the existing portion of the path connected to the intermediate metabolite. Thus, only a single linear path  can be displayed at a time. When a portion of a path is replaced, the original path is saved to a list of previous paths. The user can easily switch back and forth among previous paths. A path containing more than one metabolite is displayed as a vertical linear pathway diagram, with each reaction optionally including side metabolites, enzymes and genes, EC numbers, compound structures, and links to pathways.l-homoserine (bottom). Other paths were explored along the way; they are listed in the Previous Paths section in the panel at right and can be restored by clicking on them. The display is information-rich, yet easy for a biologist to quickly comprehend. All the potential predecessor or successor metabolites of a given metabolite are collected into a single list  displayed to the left or right of each central metabolite, minimizing diagram clutter. Color-coding on the plus icons indicates whether the reaction to and/or from that metabolite is unidirectional, potentially bidirectional, spontaneous, or if there are multiple reactions. When the path includes two metabolites connected by multiple reactions, as in the case of the middle two metabolites in the figure, the number of reactions is listed, and the user can select which is displayed. The diagram is customizable, allowing the user to select which display elements should be visible. Figure Figure The result is a tool that enables easy, interactive browsing of a portion of the metabolic network. Unlike other more general network visualization tools, no prior preselection or preloading of a sub-network is required beyond the initial selection of the database of interest. Limiting the tool to showing only a single linear path at a time allows for a compact, easy-to-read, aesthetically appealing display, but full predecessor and successor metabolite lists visible for every metabolite in the selected path make it easy to identify and explore alternate paths. Ready access to detailed information pages for the reactions, pathways and enzymes associated with the current path provides important context.The Metabolic Network Explorer is a new web tool that enables interactive generation and display of small metabolic subnetworks of interest without being limited to predefined pathways. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a PGDB. It complements other tools for interacting with metabolic networks and fills an important gap in the set of metabolic network visualization tools.


Flavonoids are a structurally diverse class of natural products that have been found to have a range of beneficial activities in humans. However, the clinical utilisation of these molecules has been limited due to their low solubility, chemical stability, bioavailability and extensive intestinal metabolism in vivo. Recently, the view has been formed that site-specific modification of flavonoids by methylation and/or glycosylation, processes that occur in plants endogenously, can be used to improve and adapt their biophysical and pharmacokinetic properties. The traditional source of flavonoids and their modified forms is from plants and is limited due to the low amounts present in biomass, intrinsic to the nature of secondary metabolite biosynthesis. Access to greater amounts of flavonoids, and understanding of the impact of modifications, requires a rethink in terms of production, more specifically towards the adoption of plant biosynthetic pathways into ex planta synthesis approaches. Advances in synthetic biology and metabolic engineering, aided by protein engineering and machine learning methods, offer attractive and exciting avenues for ex planta flavonoid synthesis. This review seeks to explore the applications of synthetic biology towards the ex planta biosynthesis of flavonoids, and how the natural plant methylation and glycosylation pathways can be harnessed to produce modified flavonoids with more favourable biophysical and pharmacokinetic properties for clinical use. It is envisaged that the development of viable alternative production systems for the synthesis of flavonoids and their methylated and glycosylated forms will help facilitate their greater clinical application. Flavonoids are one the most abundant and broadly distributed families of biologically active plant natural products (PNP). Chemically, flavonoids have a C6-C3-C6 skeleton, with two phenyl aromatic rings (A and B) along with a heterocyclic ring (C-ring). Based on the substitution of the basic skeleton and B-ring attachment, flavonoids have been split into several subclasses, for example, flavanones, flavones, flavonols, flavan-3-ols, isoflavones, and isoflavanones  1]. Ove. Ove1]. The role of isoflavones as chemopreventive compounds has been well-established. A number of epidemiological studies, along with retrospective meta-analysis and prospective observational studies, have established that flavonoids possess anticancer activities ,3,4,5. ASeveral flavonoids have been analysed for their anticancer activities, both in vitro and in vivo, including daidzein, quercetin, silymarin, luteolin, kaempferol, and apigenin . These cInterest in flavonoids production is growing, which is reflected by their 16.5% projected CAGR, increasing the current market from USD 1.9 billion in 2019 to USD 3.5 billion by 2025 . AlthougMany pharmacological functions are associated with flavonoids; however, a number of problems are holding back their development as approved drugs for clinical use and, to some extent, further research studies . Some ofThe absorption and metabolism of flavonoids have been extensively studied in the last two decades. Generally, the PK profile  of flavonoids is not optimal and varies considerably across different classes . Flavono2 as well as excreted in the feces. On the other hand, the absorbed quercetin was also excreted out of the body within 48 h [Intestinal metabolism also affects the absorption of flavonoids via chemical reactions occurring in epithelial cells of the small intestine and/or mediated by the intestinal microbiota . Flavonohin 48 h . Quercethin 48 h . Furtherhin 48 h ,28,29,30Halia bara was optimum at a wavelength of 310 umolm−2s−1 and any variation in this wavelength results in reduced biosynthesis and bioactivity of flavonoids [Surprisingly, the PK profile of flavonoids is also compromised in planta due to environmental factors like light, temperature, oxygen exposure, pH and ultraviolet radiation. Light exposure, specifically UV light, can alter the biosynthesis of flavonoids in host plants. For example, the antioxidant activity of total flavonoids isolated from plant avonoids . As enviFlavonoids are present in very minute quantities (micro/milligram per kg of plant biomass) in plant hosts. Therefore, continuous extraction from plant sources, on the one hand, strains an already vulnerable agriculture sector and, on the other hand, might result in price hikes due to unstable supply and demand issues . FlavonoChemical stability, PK issues and low availability are significant obstacles to the clinical development of flavonoids as effective chemopreventive therapy, because the required in vivo level is almost impossible to achieve, even with high oral doses ,35. HoweThe chemical structure of flavonoids generally, and different substitutions of the basic skeleton specifically, define chemical stability and bioavailability. It is now widely accepted that the substitution of basic flavonoid skeleton has a strong influence on the absorption, distribution and metabolism of flavonoids ,37.S-adenosyl-L-methionine (SAM) through a biomolecular nuclear substitution (SN2) reaction [Methylation, the addition of a methyl group to a substrate, controls several important functions of cells, from gene regulation through epigenetics to maintaining cellular energy status . Dependireaction ,39. Catereaction .The role of methylation in improving the metabolic stability of flavonoids has been documented . O-methyBCRP-transduced human-leukemia K562 cells [Methylated derivatives of flavonoids usually show higher bioactivity, and the site as well as extent of methylation play an important role. B-ring methylation appears to have greater anti-cancer potential, where a study of over 30 different flavonoids showed that methylation of 4-C of the B-ring is linked with higher potency . For exa62 cells . This ph62 cells . Similar62 cells . The aut62 cells ,50. ThesGlycosylation affects physicochemical properties, immunogenicity, and PK/PD characteristics of chemical compounds ,52. The O-glucoside) were highly stable in their solutions under a range of storage conditions  [Flavonoid glycosides are generally soluble in water and alcohol; however, a few flavonoids, such as rutin and hesperidin, are sparingly soluble. Conversely, aglycans (non-glycosylated parent flavonoid) usually dissolve in non-polar solvents . As wellsolvent) . ImproveO-glucoside and the possible reason for this might be their better water-solubility and higher absorption [Anti-BCRP activity has also been observed for a few glycosylated flavonoids, for example, apigenin-7-glucoside and luteolin-4′-sorption . In anotsorption . Thus, gsorption ,58. On tO-glucoside having metabolites which are five times more available compared to the metabolites of quercetin-3-O-rutinoside [On the other hand, in some cases, glycosylated flavonoids tend to show more variable bioavailability than their aglycans ,60. Thistinoside . Similartinoside . TherefoGlycosylation reactions of flavonoids are carried out by enzymes like glycosyltransferases (GT) and glycosylhydrolases (GH), comprising GH13 and GH70 families . At presDue to the immense potential of flavonoid, chemical synthesis methods have been explored since the beginning of 20th century, aided by the rapid development of new protocols. In fact, the first synthetic flavonoid glycoside, anthocyanin, was synthesized in 1926 by Robertson and Robinson, which is a 3-O-Glycoside of anthocyanidins . Since tEscherichia coli and Saccharomyces cerevisiae [E. coli and S. cerevisiae was a huge breakthrough, paving the way to the use of microbial systems as an alternative synthetic platform for the synthesis of plant natural products [Flavonoid biosynthesis pathways, like other plant secondary metabolites, are very complex and operate under a tight multi-level regulation. The biosynthesis of particular flavonoids depends not only upon pathway-specific enzymes but also on their interaction with other, competing partners; therefore, the genetic engineering of native hosts is difficult ,67. Realrevisiae . The sucproducts . With reIn the following, recent developments in synthetic biology and metabolic engineering approaches are discussed, which will directly or indirectly help us to synthesize flavonoids (and derivatives) with an improved PK profile.Modern metabolic engineering approaches focus on the (re)construction of metabolic pathways in suitable microbial hosts . This caS. cerevisiae [E. coli [S. cerevisiae [Yarrowia lipolytica [Numerous impressive examples of microbial engineering to produce flavonoids in microbes have been reported in recent years . The de revisiae , and E. [E. coli  strains [E. coli , synthesrevisiae  and the polytica  are a fepolytica . ExpandiSynthetic biology efforts are mostly associated with living organisms; however, the rise in cell free systems as a new platform for synthetic biology has been seen in recent decades. Due to its inherent nature, cell-free metabolic engineering (CFME) provides an open reaction environment, can aid metabolic engineering in various ways, and has been adopted by the research community to probe metabolic pathways . Cell-frCell-free metabolic engineering is developing into a powerful approach to produce complex natural product biomolecules, and has successfully been used for the synthesis of flavonoids. Recently, Ying Zhang et al. demonstrated the in vitro biosynthesis of naringenin using a cell-free system, where they were able to produce 11.22 mg/L of product in a three-hour incubation . The repLeishmania cell-free lysate, is a significant progress towards the economic feasibility of CFME [Cell-free systems offer multiple advantages, and can sometimes can be used along with cell-based systems to synthesize complex molecules , optimiz of CFME .The limited availability and challenges in the synthesis of structurally homogenous glycosylated natural products has restricted our understanding of the glycosylation process as well as its applications in biotechnology. Unlike DNA and protein biosynthesis, glycosylation is not a template-driven process; instead, it is carried by a series of glycosylation reactions catalysed by specific glycosyltransferase (GT) enzymes, localized at different subcellular locations . The glyStreptomyces antibiotics and YjiC from many Bacillus species are most commonly used for cell-free glycosylation of small molecules, and these enzymes can accept a diverse set of NDP-sugars as glycosyl donors and have promiscuous substrate specificity [Bacillus licheniformis [Many enzyme cascades and biochemical pathways have been established in the cell-free format for the synthesis of natural products . As mentcificity ,99,100. niformis ,101. CelE. coli CFPS with self-assembled monolayers for matrix-assisted desorption/ionization (SAMDI) mass spectrometer and was used to investigate the enzyme’s substrate specificity using 3480 unique peptides and 13,903 unique reaction conditions, finally revealing the optimal glycosylation sequence [A novel mass spectrometry based high-throughput screening (MS-HTS) technique has been developed for the characterization of enzymes produced through cell-free protein synthesis (CFPS) lysate . The plasequence . Recentlsequence . This syThe selection of appropriate enzymes is an important step for the manipulation or construction of a metabolic pathway in native or heterologous host. The field of enzyme engineering has also made significant progress and enhanced the substrate scope, selectivity and activity and, quite interestingly, enzymes with non-natural activities have also been added to the tool box .E. coli strain and the synthesis of unnatural terpenoids was achieved [Enzyme engineering can help synthetic biology and metabolic engineering in two ways. By making the preset enzymes more efficient, enzyme engineering can help in the microbial biosynthesis of flavonoids and, through creating de novo enzymes, it can help in the synthesis of novel flavonoids. The development of artificial metalloenzymes (ArMs), specifically, P450 class enzymes, is a notable example. Recently, an ArM that contains an iridium porphyrin complex has been assembled in the terpene-producing achieved . The useEnzyme compartmentalization, to mimic nature’s systems of colocalization of enzymes in space, is an intensive area of research in protein engineering as it has multiple advantages ,107,108.In vitro prototyping and the rapid optimization of biosynthetic enzymes (iPROBE) is another interesting avenue that can help in the synthesis of novel flavonoid derivatives. There is no need for large-scale DNA assembly or the metabolic engineering of living cells, so many enzymes and enzyme combinations and cofactor requirements and optimal conditions can be tested in a short period of time . It can An exciting avenue for synthetic biology and metabolic engineering is the implementation of machine learning algorithms to help understand the metabolic and regulatory networks of host organisms to optimise existing pathways and develop new synthetic routes ,115.For modeling gene regulatory networks (GRN), the convolutional neural network (CNN) is a popular machine learning method due to the availability of relevant datasets, reliable prediction and excellent performance in learning unique features linked with biological sequences . RecentlThe reconstruction of a metabolic pathway in a heterologous host is always a difficult task, and is therefore aided by machine learning approaches ,122. RecFlavonoids are an important natural repository for drug discovery, but ready access is limited from the low yields in plants, and their use has been further constrained by stability, bioavailability, and pharmacokinetics in vivo. Synthetic biology and metabolic engineering offer solutions to both the low availability of flavonoids in plants, and their challenging in vivo pharmacokinetic properties. Using these methods, the synthesis of flavonoids and their methylated and/or glycosylated forms can be transferred into ex planta production, either using microorganisms or using cell-free biosynthesis. Additionally, the application of protein engineering techniques and machine-learning algorithms can also provide greater insights into ex planta flavonoid production and yield. A number of flavonoids were successfully produced using microbial hosts in recent years, demonstrating the utility of the approach.Key to the future of flavonoids in the clinical setting is the understanding and optimisation of their properties for improved efficacy, stability, bioavailability and pharmacokinetics. Recent advances in genome sequencing, coupled with bioinformatics, have provided ready access to data for the identification of endogenous plant methylation and glycosylation enzyme genes, which can then be integrated into ex planta flavonoid biosynthesis pathways. It is this integration of different flavonoid modification enzymes that it is envisaged will open the landscape for the optimisation of flavonoid biophysical and pharmacokinetic properties.The possible applications of synthetic biology are exciting and almost limitless, but to achieve this, greater insight into how microbial and cell-free systems function is required. Together with this, efforts to use the cheaper and more sustainable substrates such as agricultural leftovers and waste streams will significantly reduce the overall cost as well as potentially increase the productivity. The successful synthesis of flavonoids in a sufficient quantity for research and commercial space will prove groundbreaking for the development of flavonoids as effective drugs.


During photosynthesis, organisms respond to their energy demand and ensure the supply of energy and redox equivalents that sustain metabolism. Hence, the photosynthetic apparatus can, and in fact should, be treated as an integrated supply-demand system. Any imbalance in the energy produced and consumed can lead to adverse reactions, such as the production of reactive oxygen species (ROS). Reaction centres of both photosystems are known sites of ROS production. Here, we investigate in particular the central role of Photosystem I (PSI) in this tightly regulated system. Using a computational approach we have expanded a previously published mechanistic model of C3 photosynthesis by including ROS producing and scavenging reactions around PSI. These include two water to water reactions mediated by Plastid terminal oxidase (PTOX) and Mehler and the ascorbate-glutathione (ASC-GSH) cycle, as a main non-enzymatic antioxidant. We have used this model to predict flux distributions through alternative electron pathways under various environmental stress conditions by systematically varying light intensity and enzymatic activity of key reactions. In particular, we studied the link between ROS formation and activation of pathways around PSI as potential scavenging mechanisms. This work shines light on the role of alternative electron pathways in photosynthetic acclimation and investigates the effect of environmental perturbations on PSI activity in the context of metabolic productivity. Thereby a proton gradient is formed, which is used to drive the synthesis of ATP by the ATP synthase. The second part of the photosynthetic process is the Calvin-Benson-Bassham (CBB) cycle, regulated by the thioredoxinsystem   Asada,  and affe) Asada, .To fine-adjust the formation of ATP and NADPH in the PETC, alternative electron transport pathways evolved  cycles during photosynthesis  that clearly showed a high impact of the Sedoheptulose-bisphosphate enzyme (SBPase) on the ROS scavenging mechanism, CBB and the PETC. Finally, the role of the SBPase was further elucidated. With this scientific work, we formalised a connection between the CBB cycle, PETC, and ASC-GSH cycle. We showed the interconnection between these parts of photosynthesis and also shed light on the control each part has over others via mathematical modelling. We therefore expanded our understanding of the complex interplay between different acclimatory processes in photosynthesis and created a computational framework to stimulate future scientific efforts in this direction.2O2) by the superoxide dismutase (SOD). This implementation required changing the description of the PSI mechanism from the original model  is twice the simulated rate through PSII. Likewise, the rate of CEF is twice the rate mediated by FNR.−1 and inside the stroma in mM. To convert the concentrations of ATP, NADPH and H2O2 produced in the lumen to the unit of the stroma, where these metabolites are consumed/scavenged, we employ a conversion factor where 1 mmol (mol Chl)−1 corresponds to 3.2·10−5 M in the stroma  concentrations .We first investigate the steady-state behaviour of the model under various light intensities . In the trations . Neverth2O2 production rates are not affected, also the ratio of NADPH required for scavenging ROS is unaltered in the MDA reductase knock-down.A key enzyme in the ASC-GSH cycle is the MDA reductase, which reduces MDA back to ASC using NADPH as an electron donor . Interes2O2 levels. The reduced photosynthetic capacity of PGR5 mutant plants has been demonstrated experimentally  and low (PPFD 100 μmol m−2s−2) light conditions. A selection of flux and concentration control coefficients are depicted in The above investigations illustrate that electron flow around PSI apparently affects not only the PETC itself but also downstream metabolism, in particular carbon fixation. In order to understand which processes carry the strongest control in this complex supply-demand system, we performed MCA and systematically determined flux and concentration control coefficients for high (PPFD 1000 μmol m6f complex) predominantly increases the Mehler reaction. This behaviour changes dramatically under low light. Here, CBB enzymes exert almost no flux control, but electron transport and carbon fixation rates are mostly controlled by the activities of the photosystems. Increasing PSII leads to more reduced redox pools and lower ATP levels, whereas increasing PSI leads to more oxidised redox pools and higher ATP levels. Both photosystems have a positive control on CBB cycle intermediates RuBP and PGA, while only PSI positively affects the bisphosphates FBP and SBP. Altogether these analyses confirm the previous observation  under Germany's Excellence Strategy - EXC-2048/1 - project ID 390686111 (AM and OE), the Deutsche Forschungsgemeinschaft Research Grant MA 8103/1-1 (AM), the Deutsche Forschungsgemeinschaft (DFG), project ID 391465903/GRK 2466 (TN), and the EU's Horizon 2020 research and innovation programme under the Grant Agreement 862087 (MA).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Danio rerio) using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. However, there is no significant information available regarding the characterization of organic molecules, and metabolites with SWCNT exposure. Noninvasive biofluid methods have improved our understanding of SWCNT metabolism in zebrafish in recent years. Here, we used targeted metabolomics to quantify a set of metabolites within biological systems. SWCNT at various concentrations was given to zebrafish, and the metabolites were extracted using two immiscible solvent systems, methanol and chloroform. Metabolomics profiling was used in association with univariate and multivariate data analysis to determine metabolomic phenotyping. The metabolites, malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted. The metabolism of amino acids, energy and nucleotides are influenced by SWCNT which might indicate a disturbance in metabolic reaction networks. In conclusion, using high-throughput analytical methods, we provide a perspective of metabolic impacts and the underlying associated metabolic pathways.The present study aims to investigate the metabolic effects of single-walled carbon nanotubes (SWCNT) on zebrafish ( In the past few decades, the carbon nanotubes (CNT) have gained a great attention in biomedical applications that are observed as pipe-shaped atomic layers of carbon particles. It is made up of layer-by-layer orientation of graphite sheets . Single-The hydrophobic nature of CNT, as well as poor biocompatibility in solvent media, is its drawbacks. Furthermore, surface changes in materials, such as chemical alterations in CNT, may improve solubility in an aqueous environment . When wo50), the path of inhalations, and solvent materials played a lead role in toxicological responses (Tigriopus japonicas), frogs , rat, and mice to profile the toxicological responses and folic acid (FA) coated CNT are achieved higher anti-proliferative effects. The cytotoxicity is found to be associated with DOX and FA in MCF-7 cells . CNT sizesponses . Recentlresponses. In in vy levels .2−) in dose dependent manner  is used as a model organism. Zebrafish is a tiny tropical freshwater fish. Zebrafish are an important vertebrate model organism that is frequently used in toxicological research due to their ease of maintenance in laboratory conditions. , chloroform (CHCl3), sulfuric acid (H2SO4) and nitric acid (HNO3) were obtained from Carlo Reactifs . SWCNT , and sodium salt of 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid (TSP-d4) were purchased from Sigma-Aldrich, United States. According to datasheet, carbon content (≥95%), color, black; appearance, the powder was confirmed by Thermogravimetric (TGA) investigation. Fish food was obtained from Tera . Deuterium water (D2O: 99.8% purity) was obtained from Cambridge Isotope Laboratories, United States. Food was purchased from Tera .Methanol  were obtained from a local dealer. The age ranges from 5 to 6 months. Zebrafish were maintained for more than two weeks in glass tanks, which contained a fresh 60 L dechlorinated water. Fish were fed 1.0% body weight twice daily with commercial food. All animals were standardized at 26.0 ± 1°C and maintained on 14:10 h light/dark for a reproductive cycle. All examinations were carried out with various dosages of SWCNT  for 72 h. Each experimental exposure contains eight fish  which contains equally in a treated tank. These concentrations were determined based on the previous publications (Zebrafish (ications . The feeThe metabolites were extracted according to previous reports . Briefly2-edited CPMG pulse sequence (RD-90°-[τ-180°-τ]n) was applied to the visualization of small molecules/metabolites. With relation to solvent peaks (TSP at 0 ppm), 1H-NMR chemical shifts were described in ppm (δ). The chemical shifts from −2 to 14 ppm were covered and the multiplicity stated as s, singlet; d, doublet; t, triplet; q, quartet; and m, multiplet. Heavy water acts as field frequency locking. The acquisition time, 3.0 s; relaxation delay, 1.0 s; width, 9,615.6 Hz; and 128 scans were acquired from each spectrum. Total time per sample was taken for 10 min.For temperature equilibration, the samples were kept for 10 min inside the NMR probe. NMR spectra were recorded on a 600 MHz spectrometer using one NMR probe. A water-suppressed t1H- spectral data.All Fourier transform spectra were manually corrected for phase and baseline distortions. TSP calibration at 0 ppm was processed. Mnova software  was used to visualize, process the 1D NMR data. To avoid statistical error, the water domain (δ 4.7–4.9 ppm) was removed. δ 0–10 ppm was uniformly divided as buckets that have 0.001 ppm. Further, binned data were exported to MetaboAnalyst software (v 5.0) for multivariate statistical analysis. Initially, to eradicate outlier data, principal component analysis (PCA) was analyzed (data not shown). Using supervised techniques, particle least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were applied to NMR data. From score data, each point represents a https://www.metaboanalyst.ca/) was used to analyze all of the samples. The newly aligned and filtered metabolite lists were then processed. The metabolic expression, metabolic networking, and biological interpretation were determined after a logarithmic transformation. GraphPad Prism eight software was used to perform statistical analysis. Numerical values were delivered as the mean ± SD. Two-way analysis of variance (ANOVA) has been used to examine multiple subjects. The significant changes of metabolites were recognized by p < 0.05.The data normalization (to avoid concentration differences among metabolites) and validation for score plot analysis was accomplished using the Pareto scaling algorithm, which measured each variable using the square root of its standard deviations. MetaboAnalyst 5.0 program  profiled and quantified. Mnova software was used to organize the spectral data. Before and after normalization with SWCNT exposed metabolites, Pareto Scaling-based Kernel density plots and Box plots analysis are shown in The chemical structure of SWCNT is shown in There was a strong distinction between the four classes , suggesting that zebrafish has a unique metabolic profile. The possible metabolites  are highlighted in the volcano map. A univariate OPLS-DA score plot of SWCNT-applied samples shows that they are widely separated from each other and starving fish  (controlPLS-DA scores were used to select the candidate quantified metabolites with a variable importance of projection (VIP) score >1 . FifteenR2 (0.8: 80%) and Q2 (0.4: 40%) revealed satisfactory goodness of fit and goodness prediction, respectively (2 indicates that a four-component model is the best (marked with a red star). R2 and Q2 values were indicating near 1 that was considered as robust score plots. The evidence of R2 and Q2 values from score plots indicate that the metabolic disconcertion was found in fish by SWCNT exposures.As shown in ectively . Q2 indiAdenosine phosphates are involved in energy hydrolysis in cells (ATP to ADP to AMP), and these phosphates serve as a caption of energy yield. AMP, ADP, and ATP are increased in low to high concentrations. These changes also perplexed the ratio of total adenosine phosphate groups, which could disrupt the zebrafish's equilibrium between energy production and ATP consumption. The AMP-activated protein kinase (AMPK) pathway is activated by metabolic changes in adenosine phosphate . In cellAnaerobic conditions can develop in zebrafish as a result of adenosine phosphate degradation. The metabolic disturbance caused by SWCNT exposures, which involves increased ADP, ATP, and decreased glucose, disrupts ATP intake in cellular environments, which serves as cellular energy. When the zebrafish was given large doses of SWCNT, the metabolic chemical reaction took place primarily via the oxygen-dependent pathway. ATP, ADP's primary metabolite, is formed during purine metabolism. As a result, these findings indicate that SWCNT disrupts purine metabolism.As shown in Metabolites of NAD-utilizing reactions such as oxaloacetate are found to be significantly increased. The NAD involved oxidation in TCA cycle intermediates such as acetyl CoA, alpha-ketoglutarate, and succinyl-CoA can perplex the ATP energy production and metabolic pathway process. Pyruvate, oxaloacetate, NADPH, and NAD + have significantly increased which may lead to an intra and extracellular oxidative chemical reaction. Furthermore, upregulated NADPH/NAD + ratio has been perplexed and lead to ROS development which could affect the oxidative chemical reaction in lipid metabolisms, glycolysis, and galactose metabolism. As shown in According to the latest updates, carbon nanoparticles and carbon nanotubes (CNTs) uptake causes oxidative stress. The internalization of nanoparticles and their aggregation in cells have been explored . The accAs shown in pareto-scaling, normalization, and trans-formation, the metabolomic dataset was deferred. Kernel density estimation, which used to estimate the probability density functions. This analysis may yield the practical applications of probability distributions.This is the first study to use a zebrafish model to evaluate SWCNT's effects on metabolites and determine if those metabolites are upregulated or downregulated in their metabolism. The density plots that before normalization (left side), after normalization (right side) are delivered based on all the metabolites datasets. These box plots defined the distribution of each variable or metabolite concentrations . According to R2 and Q2 values are reported, which decreases as the score plot becomes robust . We first analysed the metabolites and associated metabolic pathways in zebrafish metabolisms with SWCNT exposures. The detection of top fifteen metabolites such as malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, AMP, ADP, ATP, valine, pyruvate, serine, and niacinamide has been significantly impacted by SWCNT exposures. Here, an asparagine, glycine, histidine, malate, and oxalacetate are increased from control zebrafish. The TCA metabolites, malate, fumarate and oxalacetate contribute to the production of energy  exhibited clear separation between the groups. The f energy .1H-edited NMR-detected fish metabolites.The amino acid of histidine is required for protein biogenesis. These metabolites were then selected as prognostic biomarkers through differential expression, survival, and aquatic vertebrate model analysis. Many studies have delivered new therapeutic metabolites for disease prevention, and toxicity with limited success in aquatic zebrafish model . We tookWhen analyse each metabolite, we found a pattern of changes in the metabolic intricate of zebrafish. The most remarkable alteration found in our analysis was reduction in the abundance of metabolites that contribute directly or indirectly to metabolic reprograming in zebrafish . InteresRemarkably, we found that SWCNT induced major metabolic pathways in zebrafish metabolism and are quantified by MESA investigations. The creatine and creatine phosphate are modified that may fail to serve as energy in cell. Here, purine metabolism might be disconnected from energy metabolisms such as Krebs cycle and glycolysis . The enrAccording to these data, we quantified a set of metabolic pathways, glycolysis/gluconeogenesis, primary bile acid biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, taurine and hypotaurine metabolism, ether lipid metabolism, citrate cycle might play important role in 10 mg/L of SWCNT exposure in zebrafish. The cysteine and methionine metabolism, ether lipid metabolism, pantothenate and CoA biosynthesis, taurine and hypotaurine metabolism, primary bile acid biosynthesis, glycine, serine and threonine metabolism are modified in 50 mg/L of SWCNT treatment in zebrafish. Finaly, we extended the quantification of metabolic pathway in 100 mg/L of SWCNT treatment. Purine metabolism, glycolysis/gluconeogenesis, taurine and hypotaurine metabolism, citrate cycle and primary bile acid biosynthesis are impacted.1H-NMR technique, SWCNT exposure caused significant phenotypic changes in metabolites. 1H-NMR offers significant benefits to the metabolomics field via the detection of metabolites integral to critical cellular metabolism and cell signaling. Malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted by SWCNT. We believe that these metabolome alterations and metabolic pathways in zebrafish, such as energy, amino acid, and nucleotide metabolisms, may contribute in the development of future treatments that target diseases, especially as SWCNT exposure becomes more important as a pharmacological target. However, more investigations are necessary to better understand the SWCNT exposure and underlying mechanisms.This study convincingly demonstrated the impacts of metabolites caused by SWCNT exposure in zebrafish for the first time at the omics scale. According to our findings based on


Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady‐state chemostat system. We demonstrate that Mtb efficiently co‐metabolises either cholesterol or glycerol, in combination with two‐carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt.The co‐catabolism of multiple host‐derived carbon substrates is required by  Mycobacterium tuberculosis growing slowly in a steady state chemostat system.Quantitative metabolic analysis using stable isotopes, lipid fingerprinting, and mathematical modelling are applied to investigate the metabolic network of  Mycobacterium tuberculosis (Mtb) is the causative agent of a global tuberculosis (TB) pandemic which has now reached staggering levels making Mtb once again a leading cause of death globally  cycle, pentose phosphate pathway (PPP) and Embden–Meyerhof–Parnas (EMP) pathway, as well as enzymes providing a metabolic link between glycolysis and the TCA cycle  at different growth rates in carbon limited conditions , which is an essential gene for the conversion of glycerol to glycerol 3‐phosphate   =  69 h). We adopted this dilution rate as we have previously demonstrated that the transcriptional response at this growth rate has many similarities to the transcriptional response characteristic of the adaptation of Mtb to the macrophage environment, and others have shown a similar profile from Mtb isolated from sputum . Samples were taken every volume change for a total of four volume changes to ensure that an isotopic stationary state was attained. Labelling of proteinogenic amino acids and intracellular metabolites was measured using gas chromatography–mass spectrometry (GC‐MS) or liquid chromatography–mass spectrometry (LC‐MS) as previously described , pyruvate (PYR) and the pyruvate‐derived amino acids , valine (VAL) and leucine (LEU)) had ≥ 50% unlabelled carbon indicating that the carbon backbone of these metabolites was predominantly derived from unlabelled cholesterol. This is expected as cholesterol enters central carbon metabolism as succinate and pyruvate. Canonical 13C isotopologue analysis of proteinogenic amino acids. As expected, the profiles of all metabolites derived from CHL‐ACE were different to that derived from GLY‐OLA grown Mtb  and pyruvate carboxylase (PCA) with high confidence so the summed flux is shown between pyruvate and malate/oxaloacetate , indicating that the other 13C label has been lost as CO2 via the TCA cycle consistent with the metabolic flux prediction that Mtb lacks PPDK activity , in either condition. There was flux towards pyruvate mediated by pyruvate kinase (PYK = EMP R6) and this was higher on GLY/OAA than with CHL/ACE Fig . We prevurce Fig  This expvity Fig .et al,We observed only a very low gluconeogenic flux Fig  during get al,The MCC in mycobacteria is required for the utilisation of the propionyl‐CoA derived from sterols, branched chain amino and fatty acids that are acquired from the host Fig . Here, w13C acetate into SL‐1 or an increased chain length due to the incorporation of carbon from unlabelled cholesterol, as predicted by our flux profiles, we also measured the lipid fingerprint of control samples derived from the same chemostat cultures but prior to attaining an isotopic steady state. In accordance with our metabolic flux predictions, we identified an increase in chain length in SL‐1 in these unlabelled control samples which confirmed our expectations that lipids derived from CHL‐ACE‐grown Mtb had significant differences in the composition of the methylmalonyl‐CoA‐derived SL‐1s, specifically an increase in chain length and abundance when compared to GLY‐OLA grown Mtb  significant amounts of unlabelled glutamate which Mtb can use as both a nitrogen and a carbon source  are unable to grow in the presence of cholesterol because they cannot metabolise propionate due to the role of this enzyme in the MCC  complements for the absence of the MCC pathway 13C MFA, conditions in which the glyoxylate shunt would not be expected to be active  was grown in a 2 litre bioreactor (Adaptive Biosystem Voyager) under aerobic conditions and at pH 6.6 as previously described . Culture samples were withdrawn from the chemostat to monitor cellular dry weight, viable counts, optical density and nutrient utilisation, and CO2 and O2 levels were measured in the exhaust gas  100% U‐[13C2] acetate and unlabelled cholesterol, or (ii) 30% U‐[13C3] glycerol and unlabelled Tween 80 were used as carbon substrates in Roisin’s minimal media. To confirm isotopic steady state, cultures were withdrawn at regular intervals and the amount of 13C label incorporation was measured using gas chromatography–mass spectrometry (GC‐MS) analysis for every volume change.Once metabolic steady state was reached, 13C2] cholesterol as the sole carbon source until mid‐log phase (OD600 = 0.6–0.8) before being harvested to analyse isotopologue incorporation into intracellular metabolites. To evaluate the incorporation of 2H into the amino acids of Mtb, deuterated cholesterol (0.0625 g/l) was used as the single carbon source in Roisin’s media. Deuterated cholesterol was prepared using a recombinant Pichia pastoris strain CBS7435 Δhis4Δku70 Δerg5::pPpGAP‐ZeocinTM‐[DHCR7] Δerg6::pGAP‐G418[DHCR24] (54). Cultures were adapted to growth in deuterated minimal in the Deuteration Laboratory (D‐Lab) within the Life Sciences of the Institut Laue‐Langevin  + ion. To obtain further information relating to the 13C incorporation at individual positions within individual amino acids, isotopologue abundances were also determined for fragments formed by cleavage of the C1–C2 bond in some samples. Within a subset of these, isotopologue abundances for the fragment formed by cleavage of the C2–C3 bond in serine were also determined allowing comprehensive characterisation of the isotopic incorporation into serine. Mass spectra of the derivatised amino acids were corrected for the natural abundance of all stable isotopes  fitted with a DB‐5ms capillary column  and deactivated quartz wool packed FocusLiner (SGE) coupled to a Pegasus III time‐of‐flight (TOF) mass spectrometer (Leco) equipped with a splitless injector. The injector temperature was initially held at 70°C for 2 min followed by heating to 350°C at a rate of 17°C/min. This temperature was held for 1.5 min. The He Carrier flow was held constant at 1.4 ml/min. The injection volume was 5 µl, and inlet temperature was 250 °C, interface temperature at 310 °C and source temperature at 245°C. The system was operated with a mass range of 40–800 For the detection of intracellular metabolites, cultures were quenched in a solution of 60% methanol and 0.9% NaCl at −20°C. The metabolites were extracted in a pre‐chilled (−20°C) solution of methanol:chloroform (1:1), and cells were lysed twice by bead beating in the FastPrep at 6.5 for 20 s with careful cooling in between. Soluble extracts were filtered twice and then stored at −80°C until analysis by LC‐MS.13C isotopomer profiling of organic acids and sugar phosphates were separated using a synergy hydro C18 reversed phase column on an Agilent 1200 chromatography system . Free amino acids were separated using a Luna SCX cation exchange column on a JASCO HPLC system. In both cases, the HPLC outlets were coupled to an API 4000  mass spectrometer equipped with a TurboIon spray source. The separation methods, optimal MS parameter settings as well as extraction and integration of ion chromatograms as described  matrix in chloroform/methanol (CHCl3/MeOH) 90:10 v/v. The mycobacterial sample and matrix were mixed directly on the target by pipetting and allowed to dry under a stream of air. MALDI‐TOF MS analysis was performed on a 4800 Proteomics Analyzer (Applied Biosystems) using the reflectron mode. Samples were analysed by operating at 20 kV in the negative ion mode using an extraction delay time set at 20 ns.et al,13C2] acetate for CHL‐ACE and 30% [U‐13C3] glycerol for GLY‐OLA. Flux values for net and exchange rates were derived from 86 independent flux parameters that are estimated using 108 labelling measurements for CHL‐ACE and 120 for GLY‐OLA, respectively (supplementary data file S1). Substrate consumption rates for cholesterol, acetate, glycerol and Tween 80 and overall CO2 net production rate (Table Metabolic modelling was conducted using INCA, version 1.7 Young, . An exteet al,Flux estimations were performed using INCA Young,  using noKB designed and conducted experiments, analysed data and wrote the manuscript. TM analysed data and wrote the manuscript. NDH conducted the experiments. JLW and MHB analysed data. GLM conducted experiments and analysed data. AB wrote the manuscript. MM, MH, GS, HP and VTF synthesised the deuterated hydrogen and designed experiments. SN analysed data. CWG analysed data. JM obtained funding and wrote manuscript. DJVB designed the experiment, obtained funding, analysed data and wrote manuscript.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Review Process FileClick here for additional data file.