Microbial activity mediates the fluxes of greenhouse gases. However, in the global models of the marine and terrestrial biospheres used for climate change projections, typically only photosynthetic microbial activity is resolved mechanistically. To move forward, we argue that global biogeochemical models need a theoretically grounded framework with which to constrain parameterizations of diverse microbial metabolisms. Here, we explain how the key redox chemistry underlying metabolisms provides a path towards this goal. Using this first-principles approach, the presence or absence of metabolic functional types emerges dynamically from ecological interactions, expanding model applicability to unobserved environments.“Nothing is less real than realism. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.” –Georgia O’Keefe Marine microbial activities fuel biogeochemical cycles that impact the climate, but global models do not account for the myriad physiological processes that microbes perform. Here the authors argue for a model framework that reinterprets the ocean as physics coupled to biologically-driven redox chemistry. Since these transformations respond to, and feedback on, changes in climate  reactions that supply energy for metabolisms can provide an additional organizing principle for explicit descriptions of microbial populations in ecosystem models. This redox basis can be exploited to quantitatively resolve chemical transformations in terms of assimilatory and respiratory fluxes. While not yet incorporated into earth system models, this view has been advocated for such applications20, and has been embraced and employed in the field of environmental biotechnology, such as in the interpretation and modeling of wastewater bioreactors21. Just as models of ocean and atmospheric circulation are constrained by conservation of energy and potential vorticity, complementing mass balance with powerful redox and energetic constraints enables self-consistent descriptions of diverse microbial metabolisms.As we expand models to include the full metabolic potential of microorganisms, how can we organize and reduce the complexity of the descriptions of metabolic diversity? Non-photosynthetic organisms oxidize chemical species for energy, and thus their respiration is biogeochemically significant9. In analogy to the use of redox chemistry, trait-based functional type models of phytoplankton have used cell size as an organizing principle for understanding phytoplankton biogeography, biodiversity, and impact on biogeochemistry23. These types of theoretical constraints allow for the inclusion of more functional types without introducing as many degrees of freedom as would be necessary if each were empirically described. The guiding perspective is that organizing complex biological behavior by its underlying chemical and physical constraints gives more universally applicable descriptions of large-scale biogeochemistry.This approach aims to advance ecological modeling beyond species-specific descriptions to those that matter for biogeochemical function, in line with trait-based modeling approachesWhen incorporating a redox-balanced approach into ecosystem models, microbial function emerges from underlying chemistry as a consequence of interactions between populations modeled as metabolic functional types and their environment. Resulting theoretically grounded ecosystem models independently simulate microbial growth, respiration, and abundances in ways that we can compare with observations such as sequencing datasets. Thus, sequencing datasets are used as critical tests for the models, as external constraints rather than as input to the models, allowing for an iterative relationship between theory, observations, and models.In contrast with empirically informed models, this approach involves constructing a model of microbial activity theoretically, and then comparing the results with the observations in order to gain an understanding of the system. The goal is to understand why biology functions as it does, in addition to anticipating global impacts. From a first-principles biogeochemical perspective with respect to physical and chemical forcing, genes are an intermediate step between forcing and function, with the detailed complexity of biological reality following the underlying chemical and physical constraints . This does not equate to thinking that biology (or genetic information) does not matter or can be replaced entirely in models by physics and chemistry. Rather, we want to fundamentally understand biological activity as an integrated part of an ecosystem, and physics and chemistry become tools for doing so.Here, we outline the basis for using redox chemistry as an organizing principle and its translation into quantitative descriptions of microbial activity that are simple enough for global earth system models. We then discuss the benefits of this approach in the context of their implications for improved understanding and projections of global change impacts. Finally, we discuss limitations and possible future developments.28. The information from sequencing in particular has allowed for a huge expansion of insight into the detailed in situ activity of uncultivated species. When investigating global-scale impacts, how do we decide which of these details may be bypassed for simplicity? Or, if this simplification is impossible, must we incrementally construct a microbial ecosystem model that incorporates all known micro-scale detail?From one perspective, microbial communities are characterized by interactions at the micro-scale: gene expression, enzymatic capabilities, metabolites, species-specific interdependencies, etc., as well as the physical and chemical environment surrounding small cells30. For example, it is well known that microbial communities in sediments and anoxic zones organize according to the redox tower – the ranking of half-reactions by electrochemical potential32. Furthermore, respiration by living organisms increases the entropy of the environment by dissipating concentrated sources of chemical energy in accordance with the Second Law of Thermodynamics34.Another way forward arises from a macro-scale perspective, which examines how ecosystem function relates to the chemical potential utilized by organisms for energy35 demonstrated that external conditions cause the seemingly random fluctuations observed in microbial growth, rather than stochastic variation in gene expression. This provides direct evidence of deterministic behavior, and so the authors conclude that microbial systems can in principle be determined by macroscopic laws.This perspective suggests that chemical potential can be used to predict the activity of microbial communities and their biogeochemical impact. However, given the notorious complexity of microbial cells and systems, which is many steps away from governing chemical or physical equations, how can we be sure that this activity is indeed predictable? Frentz et al.37, as captured in the phrase “everything is everywhere, the environment selects”38. Furthermore, recent evidence also shows that gene acquisitions and deletions happen quickly enough to allow for horizontal gene transfer to dominate bacterial adaptation43, implying that evolution can occur within few generations and thus on timescales similar to ecological interactions. Perhaps consequentially, similar geochemical environments have been demonstrated to have high-microbial functional redundancy despite different taxonomic compositions44. This may be interpreted with the hypothesis that physics and chemistry selects for metabolic traits, and that these traits can be housed in different organisms with taxonomic composition shaped by micro-scale or biotic interactions45.How is this determinism manifested? If microbial communities can respond relatively quickly to changes to their local environment, they may predictably optimize the exploitation of locally available resources. In the ocean, dispersal in microbes is thought to be a highly efficient process such that microbial communities can in effect draw from an extensive seed bank52, and is conceptually similar to other redox-balanced approaches to understanding microbial activity in sediments, soils, subsurfaces, and aquatic systems58. Illustrating the power of these approaches, anticipating metabolism from chemical potential resulted in a prediction that anaerobic ammonia oxidation (anammox) should exist decades before it was observed60. Quantitatively understanding microbially mediated rates of conversion of substrates has practical implications for wastewater treatment, and thus the field of biotechnology has established methodologies for an approach in textbook form21. Flux balance analysis (FBA) models can be considered as much more highly detailed analogs of this approach that resolve the mass and electron balances among a multitude of chemical reactions within a cell62.The prediction of microbial activity from environmental chemical potential has a long history in microbiologyWe can resolve microbial activity in global ecosystem models using the underlying redox chemistry of diverse metabolisms as a constraint. One specific way forward is to model distinct metabolisms as populations of metabolic functional types. This systematically quantifies relative rates of substrate consumption, biomass synthesis, and excretions of transformed products associated with each metabolism. Coupled with estimates of substrate uptake, this replaces implicit parameterizations of processes such as organic matter consumption, oxygen depletion, and denitrification with electron-balanced respiratory fluxes of dynamic microbial populations. Box 48. The ratio of anabolism and catabolism can then be represented by the fraction f of electrons fueling cell synthesis vs. respiration for energy, following ref. 21. This provides a yield y (moles biomass synthesized per mole substrate utilized) of each required substrate that reflects two inputs: electron fraction f and the coefficients of the half-reactions , or the oxidation of ammonia or nitrite using oxygen (chemoautotrophic nitrification) as exemplified in Table 2 produced by heterotrophic metabolisms as well as the CO2 fixed by chemoautotrophic metabolisms  redox reaction Fig. . Such agBox 1 Incorporating metabolic functional types into ecosystem modelsμ is described asVi is the specific uptake rate of substrate i, and yield yi is the biomass yield with respect to that substrate. Yields for the different substrates and elements are interlinked in the metabolic budget derived from the underlying redox chemistry. Yields reflect Gibbs free energies of reaction among other factors. In the simplest model, non-limiting substrates are consumed in proportion to the limiting resource according to the metabolic budget, although in reality they may accumulate in the form of storage molecules.A metabolic functional type can be represented as a population with a growth rate that is limited or co-limited by multiple required substrates. If Liebig’s Law of the Minimum is employed, the limiting growth rate Each metabolic functional type population can be incorporated into a multi-dimensional environmental model  with physical transport asB, loss rate L, velocity u, and diffusion coefficient κ. The loss rate function varies with biomass and represents a combination of processes, including predation, viral lysis, maintenance, and senescence. These processes remain largely unconstrained, although efforts have been made to relate losses to ecological dynamics108.for biomass concentration Viyi, versus that exiting the cell in modified form as a waste product, Vi(1 − yi)  photoprotective cellular machinery.Descriptions of microbial growth that reflect underlying chemical potential can enable predictions of many other metabolic transitions, such as nitrogen fixation, nitrification, and the transition to sulfur oxidation and reductionnts Fig. , as wellReplacing implicit descriptions of organic matter remineralization: The fate of organic matter dictates the amount of carbon sequestered in the marine and terrestrial biospheres. Microbial consumption mediates the carbon stored in soils, the carbon stored in the ocean as dissolved organic matter (DOM), and the sinking flux of organic carbon that constitutes the marine biological carbon pump4, without which atmospheric CO2 would be 100–200 ppm higher than current levels. We want to understand how these carbon reservoirs respond to changes in climate, such as increased temperatures and changes in precipitation patterns. However, in biogeochemical models, simple rate constants often dictate the remineralization of elements from organic back into inorganic constituents.Replacing simplistic parameterizations with dynamic metabolic functional types means that electron-balanced descriptions of growth and respiration instead drive the fate of organic matter in earth system models Fig. . In addi2 excreted and the absolute amount of biomass sustained on a given substrate, independent of uptake kinetics . Untangling the relationship between abundances and biogeochemical function is also necessary for interpretation of genetic evidence that provide insight into this complex ecosystem structure.71.Redox-balanced metabolic functional type modeling links rates of biomass synthesis associated with a particular metabolism to its rate of respiration as well as the standing stock of limiting nutrients. As functional type modeling is coupled with estimates of population loss rates due to grazing, viral lysis, or other mortality, simulations also resolve the standing stocks of functional biomass. This quantifies the relationship between biomass concentrations and volumetric rates of chemical transformations, emphasizing how relatively low biomass may be associated with relatively high bulk rates18  and nitrite (NO2−) concentrations is consistent with observed differences75, and it reflects that the oxidation of one mole of NH4+ generates three times more electrons than the oxidation of one mole of NO2−, with differences in cell size further contributing to differences in abundances  themselves are not necessarily the most concise or useful currency given functional redundancies, unattributed function, and variation in gene dosage from horizontal gene transfer as well as growth rate40. Recent gene-centric models aim to resolve the abundances of key genes as proxies for a predetermined set of metabolic pathways17. However, the parameters used to describe metabolic pathways in these models are estimated similarly to the redox-balanced yields and efficiencies described here.17 resolves biomass and nutrient concentrations prognostically, and then uses a three-part formula—representing constitutive, regulated, and steady state transcription—to diagnostically calculate transcription rates from modeled biomass and growth rates17. Thus, the two types of modeling are complimentary, with redox chemistry providing estimates of metabolic activity from fundamental principles, and the careful calibrations between activity and sequencing providing a comparative metric.The innovation of gene-centric models is the sophisticated conversion of estimates of biogeochemical activity and biomass to genes. For example, the model of Coles et al.Nitrospina-like 16S rRNA genes with two conversion factors: the cell elemental quota (fmol N cell−1) and the number of cellular gene copies. Conversion error arises since cell mass and size vary with growth rate82. Maintaining transparency of the conversion from predicted microbial activity to genes and transcripts allows interdisciplinary audiences to understand and critique the models.The examples here externalize the conversion between modeled activity and sequencing information as a transparent process. In Fig. Using chemical potential as a theoretically grounding organizing principle for the resolution of diverse metabolisms can greatly improve microbial descriptions in global biogeochemical models. However, the approach does have its limitations, which generally increase in significance with increased temporal or spatial resolution.83. On one hand, this is beneficial for resolution of microbial processes in fine-grained ocean circulation models where flow can vary on the order of days. However, incorporating another constraint, such as proteome allocation69, is necessary to inform these choices. For example, considering enzymatic allocation in combination with energetics allowed for the prediction of both the division of nitrification into a two-step process in mixed environments and the combined, complete pathway in one organism (comammox) in biofilms, which preceded observations of the latter86.Modeling metabolic diversity with functional type populations requires choosing how metabolisms are distributed among the populations. This has consequences when interpreting time-varying states: model solutions become dependent on the partitioning of metabolism among the functional types as the timescales of physical change approach the timescales of microbial growth . Such versatility characterizes key players in large-scale biogeochemistry, such as nitrite-oxidizing bacteria and photoheterotrophs92. Some seemingly inactive populations may slowly metabolize over long timescales, requiring longer model integration times and careful attention to their loss rates for resolution, while some populations are periodically active as revealed by high-resolution observations in time37.Also, the metabolic functional type approach resolves only active functional biomass, while evidence suggests that less than 10% to more than 75% of the microbial community may be inactive66 have been empirically supported93. For organic matter, future work is needed to develop suitable descriptions of consumption rates, whether empirical or theoretical. For example, descriptions require attention to the hydrolysis of organic compounds by extracellular enzymes and the ecology of sinking marine particles—the diffusion of monomer away from the particle, within-particle transport, and dynamic ecological interactions on particle surfaces, among other processes95.The proposed modeling approach relies on estimates of the limiting uptake rates of required substrates. In lieu of suitable theoretical descriptions, the use of empirically derived uptake kinetic parameters still employs the benefits of the redox-informed yields (Supplementary Note 97. This could be used to improve the phenomenological description of organic matter in models as labile vs. non-labile, for example, with a more mechanistic underpinning.In Fig. 98. Incorporating this excretion into an energetic framework would enhance studies of phytoplankton ecology, such as studies of photoautotrophic-heterotrophic interactions in the ocean surface or photoautotrophic-chemoautotrophic interactions at the base of the euphotic zone where some phytoplankton excrete nitrite due to incomplete reduction of nitrate99.Descriptions of phytoplankton are currently much more sophisticated than of bacteria and archaea in models, reflecting a longer history of comprehensive sets of observations. However, further work could develop simple descriptions of photoautotrophic metabolisms from underlying energetics by connecting the supply of photons to available energy for biosynthesis within the cell. Many biogeochemical models account for an inefficiency of phytoplankton metabolism with a parameter that dictates their excretions of dissolved organic matter100? This may improve resolution of time-varying metabolic versatility. However, if both steps of nitrification were a part of such a consortium, would the characteristic accumulation of nitrite be predicted (Supplementary Fig. As a more radical extension, can we progress past population modeling and model microbial consortia as one aggregate community biomassWe have described a useful approach for understanding and anticipating microbial control of biogeochemical cycling that is suitable for global applications. The approach aims to represent microbial growth and respiration explicitly and consistently from knowledge of chemical gradients in the environment, towards a goal of building an independently constructed theoretical ecosystem model that can then be compared to observations. Describing microbial communities with underlying energetic constraints connects metabolisms dynamically with global geochemical distributions, such as those of carbon dioxide, oxygen, and biologically available nitrogen. This deepens our understanding of microbial ecosystems and enables the incorporation of the feedbacks of microbial activity to changes in global biogeochemistry and the climate system.Supplementary InformationPeer Review File


Escherichia coli being the best-studied. Several chemicals derived from acetate have already been successfully produced in E. coli on a laboratory scale, including acetone, itaconic acid, mevalonate, and tyrosine. As acetate is a carbon source with a low energy content compared to glucose or glycerol, energy- and redox-balancing plays an important role in acetate-based growth and production. In addition to the energetic challenges, acetate has an inhibitory effect on microorganisms, reducing growth rates, and limiting product concentrations. Moreover, extensive metabolic engineering is necessary to obtain a broad range of acetate-based products. In this review, we illustrate some of the necessary energetic considerations to establish robust production processes by presenting calculations of maximum theoretical product and carbon yields. Moreover, different strategies to deal with energetic and metabolic challenges are presented. Finally, we summarize ways to alleviate acetate toxicity and give an overview of process engineering measures that enable sustainable acetate-based production of value-added chemicals.Ecological concerns have recently led to the increasing trend to upgrade carbon contained in waste streams into valuable chemicals. One of these components is acetate. Its microbial upgrading is possible in various species, with  Escherichia coli, acetate formation can pose challenges. It has long been established that E. coli produces acetate together with other fermentation products like lactate and ethanol under anaerobic conditions [When producing value-added chemicals or recombinant proteins in nditions . Howevernditions . Additionditions ,4,5.E. coli strains. On the one hand, E. coli W exhibits only low amounts of accumulated acetate [E. coli strains JB101, JM105, and B on 20 g/L glucose and a dissolved oxygen level maintained above 20% were reported to be 0.88 g/L, 1.20 g/L, and 1.75 g/L, respectively [The amount of accumulated acetate may vary between different  acetate ,7. On thTo avoid the negative effects of acetate accumulation, extensive research on the cause of overflow metabolism has been done. For example, studies have identified the influence of acetyl-CoA synthetase, the cellular redox ratio, and the proteome efficiency of fermentative pathways ,10,11,12E. coli BL21(DE3) exhibits a reduced acetate accumulation in glucose batches, recombinant protein production has shifted towards this host strain [The implementation of different strategies helped to circumvent the challenges posed by acetate. Since t strain . Moreovet strain ,19,20,21E. coli processes, acetate is a part of many low-cost feedstocks such as lignocellulosic hydrolysates [Although often avoided as a substrate for olysates ,23,24. Folysates ,26.2 fixation via microbial electrosynthesis [In other cases, acetate is not only an additional part of feedstocks, but it is actively produced for further application in industrial processes. Potential acetate sources range from syngas fermentation to COynthesis ,30,31,32As a result, the implementation of acetate as a novel substrate in industrial biotechnology has been attempted in recent years and is showing promising results on the small scale. Recent achievements suggest that insights into the acetate metabolism of different microbial species combined with metabolic engineering of industrial host organisms seem to be the way towards sustainable production of value-added chemicals ,35,36,37E. coli. Therefore, this review will give an overview of acetate metabolism of different species and will then focus on acetate utilization in E. coli. We will present products that have already been successfully produced, highlight energetic aspects of growth on acetate, and discuss challenges that need to be overcome by metabolic and process engineering.Even though a wide variety of microorganisms are capable of growing on and metabolizing acetate, the focus for further process and strain development is E. coli, Vibrio natriegens, Pseudomonas aeruginosa, and the purple non-sulfur bacterium Rhodobacter capsulatus, members of the phylum of firmicutes , cyanobacteria  and sulfate-reducing bacteria  have exhibited growth on acetate [Chlamydomonas reinhardtii and Chlorella sorokiniana) can use acetate as a carbon source [The ability to use acetate as a carbon source is present in numerous microorganisms ranging from eukaryotes to bacteria. In the latter, growth on acetate is possible across different phyla. Next to proteobacteria, which include  acetate ,41,42,43n source ,45. Whet+ and NADPH/NADP+), and the formation of precursors for other metabolites  are distinctly different compared to sugar fermentation. As these factors have a major influence on how well cells grow and produce metabolites, we will focus our overview of the acetate metabolism in different organisms in these areas.When acetate constitutes the sole carbon source, the central energy metabolism (generation of ATP), the redox balance of the cells  and uses nitrate or fumarate as electron acceptors [Under anaerobic conditions, cceptors ,79. Onlycceptors ,81.C. kluyveri and the group of sulfate-reducing bacteria.Other anaerobic microorganisms exhibit a vastly different metabolism. Lacking oxygen as an electron acceptor, they use a wide range of alternative electron acceptors, including hydrogen, sulfur, sulfate and nitrite ,83,84,85C. kluyveri has gained biotechnological importance due to its ability to produce medium-chain organic acids from acetate and ethanol [ ethanol ,87. The 2 in the process [In this pathway, ethanol acts as the electron donor, whereas acetate assumes the role of an electron acceptor . Acetyl- process . The sec process .Once the conversion of crotonyl-CoA to butyryl-CoA is complete, butyryl-CoA can either leave the reverse β-oxidation cycle or be elongated further to caproate in another round .C. kluyveri to produce medium-chain organic acids in this way is frequently utilized in microbial consortia, where the respective substrates are produced by other microorganisms [The ability of rganisms ,86,87.2 [There are two main groups of sulfate-reducing bacteria. One group can metabolize organic compounds incompletely and produces acetate while the other one can completely convert organic compounds to CO2 . The latDepending on the organism, different metabolic adaptations  enable tDesulfobacter postgatei, acetate is completely metabolized to CO2 via the citric acid cycle , yeasts and fungi (Y. lipolytica and Cryptococcus curvatus), and even microalgae (Auraniochytrium limacinum SR21) [As can be seen, the range of microorganisms for the production of lipids is diverse, including bacteria (um SR21) ,99,100.Dinoroseobacter sp. JL1447, the cyanobacterium Synechocystis PCC 6803, and the bacteria Pseudomonas putida KT2440 and Aeromonas hydrophilia [Dinoroseobacter sp. JL1447, acetate as carbon source was even found to sustain a higher PHA production rate than others like glucose, glutamate, pyruvate, or citrate [Polyhydroxyalkanoates (PHAs) and especially polyhydroxybutyrate are another important class of value-added chemicals. Their production from acetate has been reported from the phototrophic bacterium rophilia ,102,103. citrate .Other chemicals such as histidine, caproate, malic acid, molecular hydrogen, and methane/biogas have a narrower range of hosts for production from acetate cf.  36,105,,110,111.Most organisms listed in However, coupling the consumption of acetate to the production of value-added chemicals in their natural producers can be challenging, especially if genetic or metabolic information on the organism is incomplete, the cultivation conditions are challenging or tools and procedures for genetic manipulation are lacking. Thus, the range of efficient, industrially applicable producers as well as products in these mostly non-model organisms is somewhat limited.E. coli, as a well-studied model organism, has become the focal point for the production of acetate. While some chemicals, like lipids and gases, are not commonly produced in E. coli, small organic acids, alcohols, and amino acids derived from metabolic intermediates can readily be generated. Other possible products include polyhydroxybutyrate, phloroglucinol, and β-caryophyllene . However, calculating the MTYs can be challenging and there seem to be two general ways by which they are obtained.In some instances, MTYs are calculated by considering only the carbon stoichiometry of reactions necessary to convert the carbon source to the product, regardless of the amounts of ATP, NADH, or NADPH required for the generation of the target molecule  ,114,125.2 [In other instances, it is taken into account that for the continuous supply of ATP, NADH, and NADPH, additional molecules of the carbon source need to be taken up and metabolized, which might also result in the release of CO2 ,119. In In a few cases  the production pathways are inherently energy-balanced. In these instances, follow-up reactions of the acetate molecules that are required for the carbon balance compensate for the energy consumption of acetate uptake and product generation. As an example, the energy expense for the formation of itaconic acid is covered by acetyl-CoA entering the TCA and glyoxylate cycle, where NADH, FADH, and subsequently ATP is regenerated . TherefoThis diversity in the calculations complicates comparability as shown in If acetone is produced from acetate, two molecules of acetate need to be taken up. The resulting two acetyl-CoA are subsequently condensed to acetoacetyl-CoA and react further to acetoacetate. In the last step of the acetone pathway, acetoacetate is converted to acetone by decarboxylation. Considering only these reaction steps, the carbon-balanced formation of acetone from acetate may be described by Equation (1). Molecules with no impact on the carbon or energy balance were omitted.As a result, the maximum theoretical yield of acetone from acetate is 0.50 mol/mol or 0.49 g/g and the theoretical carbon yield amounts to 75%.However, it should also be taken into account that the acetate uptake consumes ATP. Here, we assumed that acetate was taken up via the energetically more expensive acetyl-CoA synthetase, which requires two ATP per acetate. Therefore, Equation (1) needs to be amended towards Equation (2).2. As a result, we can formulate Equation (3).The consumed ATP may be replenished if another molecule of acetate is taken up and enters the TCA cycle. With the assumptions on ATP equivalents made above, the two ATP for the uptake are balanced out by 9 ATP gained from TCA cycle associated reactions, leaving a surplus of 7 ATP. This is enough to balance the ATP-requirement for the acetone synthesis, however, the reactions of the TCA cycle also produce two additional molecules of CO2, the final energy balanced reaction is given in Equation (4).To finalize the equation, we want to eliminate the ATP. Since we have established above that one molecule of acetate equals 7 ATP and two COThe new energy-balanced yield constitutes 0.39 mol/mol or 0.38 g/g and the theoretical carbon yield is 58%.2, while the other one follows the reactions of the glyoxylate shunt. In the glyoxylate shunt, the third molecule of acetyl-CoA needs to condense with glyoxylate to form malate. In the end, the reactions replenish the two molecules of oxaloacetate that were required at the start of the TCA cycle. Therefore, the carbon-balanced synthesis of itaconic acid can be given in Equation (5).Next, we examine the production of itaconic acid. First of all, it requires the uptake of three molecules of acetate. Two of the three resulting acetyl-CoA enter the TCA cycle and together with two molecules of oxaloacetate, they form two molecules of citrate and subsequently isocitrate. From here, one isocitrate can react to itaconic acid and COIf we examine the energy requirement, we can see that 6 ATP are needed for the uptake of 3 molecules of acetate. However, the subsequent reactions of the glyoxylate shunt generate two NADH and two FADH, which amounts to a total of 6 ATP. As a result, Equation (5) is inherently energy-balanced with the yield from acetate constituting 0.33 mol/mol or 0.73 g/g and the theoretical carbon yield 83%.The different calculation strategies can have a considerable impact. Firstly, it should be noted that no biological system can surpass an energy balanced yield, meaning if acetate is used as the sole carbon source, the MTY of acetone can never be above 0.39 mol/mol. Therefore, the highest reported yield for acetone (0.29 mol/mol) is already relatively close to the biological maximum . This shAnother issue might be the comparison of two products from acetate, especially, if the maximum theoretical carbon yield (MTCY) is involved as we outlined above for the two metabolites acetone and itaconic acid. If a non-energy-balanced calculation is applied, it might seem that acetone and itaconic acid have similar MTCYs, 75% and 83%, respectively. However, this is only because the pathway for itaconic acid is inherently energy-balanced while the one for acetone is not ,113. If Therefore, to avoid these pitfalls, it would be advisable to work with energy-balanced MTYs.+/ATP-ratio [Whether the MTY is energy-balanced or not, might not be the only challenge, though. If an energy-balanced approach is chosen, there is still the issue of how to weigh ATP, NADH, NADPH, and FADH against each other. In aerobic cultivations with acetate as the sole carbon source ATP is mainly gained via oxidative phosphorylation. The yield of ATP per oxygen is given by the phosphate/oxygen (p/o) ratio, which in turn depends on the electron transport efficiency and the HTP-ratio ,131.E. coli, different kinds of dehydrogenases with varying H+/e−-ratios have been identified. They are responsible for proton translocation across the membrane. The ratio of H+ to ATP is also a topic for debate, with reports ranging from 2 to 4 resulting in p/o-ratios between 1,5 and 3 [In ,5 and 3 ,132,133.,5 and 3 ,134.Based on the assumptions made above, acetate has an MTY of 7 ATP. Two ATP are needed for acetate uptake (irreversible uptake via acetyl-CoA synthase was assumed) while 9 ATP are generated during the oxidation of acetyl-CoA in the TCA cycle. Compared to other carbon sources like glycerol and glucose, the ATP yield of acetate is rather low.E. coli like glucose or glycerol are more favorable in terms of ATP yield under aerobic conditions. For glucose uptake and conversion to fructose-1,6-bisphosphate requires 2 ATP. Then, the conversion of two glyceraldehyde-3-phosphate to two 1,3-bisphosphoglycerate generates 2 NADH, and 2 ATP are recovered from two 1,3-bisphosphoglycerate to glycerate-3-phosphate. The reactions of 2 phosphoenolpyruvate to pyruvate finally yield two additional ATP. The remaining 18 ATP originate from 2 acetyl-CoA entering the TCA cycle according to the equivalence assumptions for the conversion of FADH, NADH, and NADPH to ATP. In total, 24 ATP are gained from glucose this way.As can be inferred from For the uptake of glycerol via glycerol-3-phosphate, one ATP is needed. Two ATP equivalents are generated in the next step, yielding dihydroxyacetone phosphate. The subsequent reactions towards pyruvate and the TCA cycle are the same as with glucose. However, this time only one 3C-unit is processed. This leads to an overall ATP gain of 14 ATP per glycerol.In terms of energy content, the main challenge concerning acetate is the carbon’s high oxidation state. By using glucose or glycerol some energy equivalents are generated independently of the reactions of the TCA cycle. As portrayed in Moreover, since most acetyl-CoA has to enter the TCA cycle for sufficient energy supply, the production of sugars can only be fueled by activation of the glyoxylate shunt . Even thAnother important aspect of the growth of any carbon source is the availability of cofactors. While the oxidation of NADH and NADPH can be used to build up a proton gradient across the membrane to generate ATP, there are also several other vital reactions in the cell, which require reduction equivalents ,137,138.However, despite all the challenges that acetate entails energetically, one of its advantages might be that its entry point into the metabolism (acetyl-CoA) is very close to the potential products’ branching points from the central carbon metabolism. With additional metabolic engineering, this “metabolic proximity” could allow for tight regulation of production.E. coli for better productivity, yields, and product titers on acetate can be conducted on two main levels.Engineering E. coli host strains, which is the main prerequisite for acetate-based production [The first level is metabolic engineering, which comprises several major aspects. Most importantly, it enables the introduction of non-native pathways into the oduction ,120,125.oduction ,123. Anooduction .The second level of engineering is process engineering. As process conditions like pH may substantially affect growth and metabolism on acetate, tight regulation is often necessary . In otheE. coli concerning the production of value-added chemicals from acetate is the introduction of the metabolic pathways leading to the desired product. Naturally, the first aspect of metabolic engineering of E. coli host strain, but the expression levels of the involved enzymes were optimized to favor tyrosine production [E. coli, resulting in the construction of expression cassettes containing extra copies of the E. coli genes and the heterologous ones [E. coli host strain [The pathways branching from the central carbon metabolism can be constructed in different ways. In the case of tyrosine, the pathway is natural to the oduction ,144. Forous ones ,121,123.ous ones ,125,145.t strain ,127,128.In some cases, additional gene deletions are necessary to disable unfavorable pathways, in other instances pathway genes are deleted and reintroduced into the cells under a promoter which allows expression control ,127,128.The types of deletions and modifications depend on the branching point from the central carbon metabolism. However, the starting point for all products is acetate uptake. Therefore, the strategy of enhancing acetate uptake to improve productivity has been employed with products from different branching points, such as acetone, isopropanol, tyrosine, succinate, and glycolate ,127,128.E. coli acetate can either be taken up via the reversible ackA/pta pathway or the irreversible acs-mediated way, these are the two points of action [ackA/pta pathway to improve acetate uptake and production of acetone, isopropanol, succinate, or glycolate [acs to enhance acetate uptake with products like succinate, phloroglucinol, tyrosine, or mevalonate [As for the question of how to increase acetate uptake, there are two approaches to tackle this challenge. As in f action ,63. Somelycolate ,117,125.valonate ,127,128.ackA/pta pathway usually include the lower energy requirement compared to uptake via acetyl-CoA synthetase (Acs) and its prevalence on high acetate concentrations [It is extremely difficult to tell if either option is generally better or worse than the other one. Arguments for choosing the trations ,117,125.trations .ackA/pta pathway instead of acs was detrimental to cell growth and did not improve production [acs-overexpression but rather worked better via enhancing the ackA/pta pathway [ackA/pta pathway which did increase acetate uptake and thereby the product titer and yield [ackA/pta pathway, plays an important regulatory role in the cells. Acetyl-phosphate affects protein acetylation and in extension glucose metabolism and acetyl-CoA pools as well as the phosphorylation of numerous response regulators like CheY, PhoB, OmpR, and CpxR [In the case of itaconic acid, it has been reported that overexpression of the oduction . Similar pathway . These fnd yield . The reaand CpxR ,147. Theand CpxR ,150,151.and CpxR .maeB  and pckA  increases isobutanol production [Apart from acetate uptake, with products branching from pyruvate, it seems that the most efficient way to increase the yield is enhancing pyruvate availability. It has been shown that the overexpression of oduction . The addoduction .For phosphoenolpyruvate-derived chemicals, the situation is less clear, since the only reported product is tyrosine. In this case, the expression of the glyoxylate cycle enzyme AceA was altered. With this strategy, it was possible to find a balance between carbon conservation via the glyoxylate shunt and energy generation via the TCA cycle thereby increasing the tyrosine yield .gltA) improved productivity [Some products branching from acetyl-CoA seem to benefit from enhancing the glyoxylate shunt. Flux through the glyoxylate shunt can either be increased by direct upregulation  or by blocking the TCA cycle via deletion of isocitrate dehydrogenase ,119. In uctivity . In a fuuctivity . As an euctivity . Other auctivity ,114,118.sdhAB is also disabled to prevent flux away from succinate and strains may also be lacking maeB and pckA to reduce losses of oxaloacetate [To successfully produce chemicals derived from the TCA cycle, the reactions leading away from them or their precursors need to be blocked in most cases. For glycolate, any pathway decreasing the glyoxylate pool, except the glycolate production, must be stopped . To obtaoacetate ,127.Next to that, it might also be necessary to actively replenish TCA cycle intermediate pools. For instance, the production of itaconic acid can be improved by enhancing the flux of the glyoxylate shunt. That way, isocitrate as the precursor for itaconic acid is replenished .In conclusion, pathway engineering strategies are highly product dependent and must also be adapted to the branching point from the central carbon metabolism.A repeatedly encountered challenge when using acetate as a carbon source is its potential toxicity to the cells. In concentrations above 5 g/L it can inhibit cell growth substantially, thereby complicating possible production processes . As a reThe question of how to deal with acetate is not only encountered when it is used as the sole carbon source but also when cells are grown on glucose . In someThere are several approaches to explain acetate or acid toxicity in general. One of the theories addressing this issue is the uncoupling theory. The reasoning behind this theory is that acetic acid in its protonated form can traverse the cell membrane and dissociate inside the cell. To maintain the proton gradient across the membrane, the resulting proton must be expelled from the cell again, which requires energy (ATP). Subsequently, this energy is not available for growth .A closely related explanation for acid toxicity is anion accumulation. It assumes that when acetic acid diffuses into the cells and dissociates, the intracellular pH decreases. This decrease leads to an accumulation of anions with potentially detrimental osmotic consequences for the cells .E. coli actually has a number of natural acid tolerance systems, which have been thoroughly reviewed elsewhere [To counter excessive acid stress, lsewhere . The moslsewhere ,157.E. coli strains, which is possibly the cause of strain-dependent acid resistance [However, different studies dealing with the production of n-heptanoic and octanoic acid showed that the responsible enzymes are expressed differently in various sistance ,159. ConE. coli strains have already been characterized several times. On defined medium and with an acetate concentration of 85 mM (approx. 5 g/L), E. coli C (ATCC8739) was found to have the highest maximum specific growth rate (µmax) with 0.41 ± 0.01 h−1, followed by E. coli W (0.37 ± 0.01 h−1). At the same conditions, E. coli BL21 and MG1655 exhibited a µmax of 0.30 ± 0.01 h−1 and 0.29 ± 0.01 h−1 respectively [E. coli W reached a µmax of 0.46 h−1, while E. coli BL21(DE3) and MG1655 grew at 0.36 h−1 and 0.23 h−1 respectively [Acetate tolerance of different ectively . When grectively .The expression and regulation of genes conferring acetate resistance was examined in a study about the cAMP receptor protein (CRP). There, it was discovered that one particular mutation (D138Y) led to an improved growth rate under acetate stress, albeit with a significantly longer lag-phase . UnfortuE. coli have identified the inhibition of methionine biosynthesis as a cause for reduced growth. When MetE, the first enzyme of the methionine synthesis pathway, is blocked, the toxic intermediate homocysteine accumulates [E. coli [Other studies concerning acid stress in umulates . Accordi[E. coli .Another possibly influencing factor is the intracellular concentration of acetyl-phosphate . Acetyl-E. coli under anaerobic conditions on glucose, it can be assumed that similar experiments can also lead to more acetate tolerant E. coli under aerobic conditions [To find further mechanisms for acetate tolerance and to create tolerant strains, adaptive laboratory evolution is a means that can be employed. As it was already successfully used to improve acetate tolerance and growth of nditions ,166.E. coli W strain [A topic related to acetate tolerance is product tolerance. For acidic products like 3-hydroxypropionic acid, the issue of acid tolerance arises too. While this has been addressed in the production of glycerol, the utilization of an acid-tolerant strain for 3-hydroxypropionic acid has yet to be implemented for acetate as the substrate ,167. ConW strain .E. coli [Phloroglucinol is another product that has a toxic effect on E. coli . To alleE. coli . While tE. coli ,169.relA, marC, proQ, yfgO, and rraA) that could enhance isopropanol tolerance [For other products, different solutions to improve tolerance are possible. In the case of isopropanol, adaptive laboratory evolution led to insights on genes involved in alcohol tolerance. The study identified mutations in five genes  [This issue seems to arise predominantly for the generation of alcohols. Concerns over the NADPH supply have led to the introduction of an additional NADPH regeneration system in the acetate-based production of isobutanol (E. coli) . The proE. coli) .pntAB or nadK was introduced into the cells and pntAB was deemed especially suitable to improve the yield as it replenished NADPH which was consumed by isopropanol production most effectively [Contrarily, cofactor engineering was successfully applied to increase the product yield of acetate-based isopropanol. Additional ectively .Another example is the pathway for mevalonate synthesis which also demands NADPH. In a recent study, the importance of NADPH regeneration for the production of mevalonate was revealed . HoweverTo facilitate cofactor engineering, helpful tools for metabolic engineering have been developed recently. In silico cofactor, balance estimation provides the possibility to evaluate different routes to a target product and allows a more efficient strain design .E. coli strain capable of being a platform for testing different enzymatic NADH regeneration systems might also be useful for future metabolic engineering. The strain in question is dependent on the introduction of an NADH regeneration system to grow on acetate. Subsequent determination of the growth rates of cells with different NADH regeneration systems enables the identification of a suitable cofactor regeneration strategy under the applied conditions [The emergence of an nditions .In the case of acetate-based production, process engineering has to deal with a few key factors to ensure stable and reproducible results.The first issue of special importance seems to be the tight regulation of the pH. Recent results show that the difference between pH 6 and 7 can have a major impact on the expression of acetate metabolism genes . AlthougAnother challenge is the reproducibility of biological replicates. Our own acetate-based preliminary shake flask and bioreactor experiments have shown that growth and productivity can differ significantly between replicates . This isAdditionally, process engineering can offer solutions for the issue of potentially toxic products. For instance, gas stripping has already been used to remove isopropanol from glucose-based fed-batch cultivations .In situ extraction could be a means to obtain more hydrophobic chemicals as has been demonstrated with the sesquiterpene alcohol α-bisabolol . Such anThe final challenge for process engineering is the coupling of acetate-based production to other processes. Syngas fermentation as a possible source for acetate was successfully tested with isopropanol . Other pSeveral topics still need to be addressed when it comes to acetate-based production.The first issue concerns media additives. As of yet, yeast or beef extract in different concentrations is utilized for isopropanol, acetone, itaconic acid, or β-caryophyllene to name a few examples ,113,114.E. coli.Investigations concerning metabolic engineering approaches constitute another field of potential future work. Obtaining knowledge on factors influencing the productivity of chemicals derived from different metabolic intermediates is certainly paramount ,153. ForE. coli, its reaction to oxygen, or the transition from glucose to acetate as growth substrate have already been established [Additionally, the use of metabolomics combined with precise metabolic models on acetate might identify future targets for metabolic engineering. Models for the acetate overflow metabolism in ablished ,176,177.ablished .To improve acid tolerance in general, further research and application of existing techniques could be a promising way. This includes studies on the expression of genes conferring acid tolerance, investigations concerning global regulators, or adaptive laboratory evolution experiments, which would also be applicable to enhance product tolerance ,159,170.Finally, developments that facilitate the industrial implementation of acetate-based production will be necessary. The establishment of continuous processes would be essential to make acetate industrially attractive. The combination of continuous acetate-based production with in situ product recovery would allow for high productivity while also maintaining high product titers. Such measures will be necessary to compete with petrochemical production, which already often employs continuous processes . HoweverE. coli is still required. In combination with efficient process design, this could lead the way towards “green” production of industrially relevant platform chemicals.Microbial conversion of acetate into value-added products can pose an ecologically sustainable alternative to fossil fuels. To be economically competitive, further research on the metabolism and metabolic engineering of potential host organisms like


Mycoplasma pneumoniae and Mycoplasma fermentans) were examined using a novel approach involving nuclear magnetic resonance spectroscopy and principle component analysis. Characterisation of metabolic changes was facilitated through the generation of high-density metabolite data and diffusion-ordered spectroscopy that provided the size and structural information of the molecules under examination. This enabled the discrimination between biofilms and planktonic states for the metabolomic profiles of both organisms. This work identified clear biofilm/planktonic differences in metabolite composition for both clinical mycoplasmas and the outcomes serve to establish a baseline understanding of the changes in metabolism observed in these pathogens in their different growth states. This may offer insight into how these organisms are capable of exploiting and persisting in different niches and so facilitate their survival in the clinical setting.Mycoplasmas are fastidious microorganisms, typically characterised by their restricted metabolism and minimalist genome. Although there is reported evidence that some mycoplasmas can develop biofilms little is known about any differences in metabolism in these organisms between the growth states. A systematic metabolomics approach may help clarify differences associated between planktonic and biofilm associated mycoplasmas. In the current study, the metabolomics of two different mycoplasmas of clinical importance ( Mycoplasmas are typically divided into fermentative and non-fermentative groups according to their ability to produce acid from glucose and degrade arginine respectively3. When grown under anaerobic conditions, lactate is the end product of the glucose oxidation, whereas under aerobic conditions, partial oxidation occurs and acetate and carbon dioxide (CO2) are formed4. Glucose and arginine are the substrates that are utilised as energy sources for mycoplasmas; thus, the majority of strains may be characterised according to whether they produce acid from glucose metabolism, ammonia from arginine hydrolysis or carry out both or neither of these reactions5. Amino acids, fatty acids, metabolic precursors and even cholesterol are essential growth factors and play a vital role in mycoplasma survival6. Due to the inability of mycoplasmas to synthesise macromolecules, they are dependent upon the nutrients found in host cells during infection7. Mycoplasma survival can be enhanced by biofilm formation9 and as a result, biofilms tend to be resistant to environmental conditions and host immune responses, relative to their planktonic counterparts10.Mycoplasmas are facultatively anaerobic mollicutes that require an enriched growth media supplemented with nucleic acid precursors, fatty acids as well as amino acids to sustain their culture in laboratory conditions. These organisms are therefore considered to have a minimal genome12 suggested that bacteria  attached to surfaces have differing metabolic activities compared to their planktonic counterparts due to physiochemical conditions and physiological characteristics expressed as a result of their growth. This phenomenon has been shown in biofilms produced by Vibrio parahaemolyticus, where a variety of cellular process including energy metabolism and biofilm development are influenced by certain polysaccharides13. In order to better understand mycoplasma biofilms and their influence in disease a comprehensive characterisation of their differing metabolic states is necessary within this complex cellular community. Metabolomics can provide such a systematic approach helping to clarify the complex mechanistics of biofilm states when compared with their free-living planktonic counterparts15.The view proposed by17.In the current study, NMR spectroscopy in conjunction with principle component analysis (PCA) was used to examine the mycoplasma metabolome (both planktonic and biofilm) and characterise metabolic changes in mycoplasma species. However, as the cellular metabolome normally incorporates thousands of metabolites it was not possible to exhaustively analyse every individual component. In order to manage the analysis of these metabolites, chemomx software was utilised to identify the major features within NMR spectrum, such as the absence or presence of peaks, the change in the shape of peak intensity, or the change in chemical shifts that distinguish between the two classes1H NMR) is commonly used for global analysis of a metabolome23. These experiments were able to identify the network of resonances associated with specific metabolites through J-coupling in a model system25. The current study also used diffusion ordered spectroscopy (DOSY) NMR methodology to study the metabolome of planktonic and biofilm states for human mycoplasma preparations, providing size and structural information about the molecules therein26. In DOSY- NMR, the diffusion motions of the components in samples are encoded and decoded using the application of a set of pulsed field gradients (PFG)31. A particularly novel application of DOSY NMR is the capacity for spectral binning and principal component analysis (PCA) of the diffusion coefficients for different regions. This is the first time this technique has been employed in mycoplasmal metabolome characterisation and significant metabolic changes between biofilm and planktonic mycoplasmas have been determined.One-dimensional proton NMR , respiratory tract (2 isolates), urine, urethra and a cell line isolate. All strains were stored as freeze-dried cultures and were grown and sub-cultured in fresh Eaton’s broth medium.A selection of clinical strains from the Kingston University culture collection were utilised in this study including eight clinical strains of M. fermentans and M. pneumoniae were inoculated into Eaton’s broth medium (at a cell density of 106 cfu/ml) and incubated at 37 °C for 3–7 days to grow planktonic cells, whilst M. pneumoniae strains were incubated at 37 °C for approximately 30 days.Isolates of both M. fermentans and M. pneumoniae (at a cell density of 106 cfu/ml) were inoculated into 10 ml tissue culture flasks  containing Eaton’s broth medium, and then incubated as previously described. Following incubation, biofilms were acquired from the flask using a cell scraper  and aliquoted into sterile bijous for processing.Aliquots (2 ml) of 1H NMR experiments. 5 mm Bruker Single Use NMR tubes (serum and DOSY). All spectra were acquired on Topspin 3.1 . All NMR experiments were carried out at 25 °C. The Pulsecal routine was run prior to all experiments and the pulse angles adjusted accordingly for total correlated spectroscopy (TOCSY) and diffusion ordered spectroscopy (DOSY) experiments.A Bruker Avance III 600 MHz NMR spectrometer with 5 mm TXI Probe and temperature control unit was used for all 1D 1H spectra for serum samples were acquired using 65,536 complex data points over a sweep width of 20.57 ppm using a pre-saturation of the water signal at 4.7 ppm and one spoil gradient, Relaxation delay (d1) was set to 10 s with the RGA set to 256 for quantitation (See Supplementary Information 2O.Diffusion spectra were obtained over 64 K data points (SW 10.3112 ppm) using longitudinal eddy current delay bipolar pulsed field gradient with 2 spoil gradients and pre-saturation sequence (LEDBPGPPR2s). This was used to obtain the diffusion series with δ = 4.6 ms and 31.Diffusion data was processed using a sine bell shaped window function phase over all data points prior to Fourier transformation  using Topspin 3.0 . Diffusion data was processed using DOSY Toolbox, created by Mathias Nilsson, Manchester University. Individual peaks were fitted exponentially after a 2nd order polynomial baseline correction was employedca 0.1 × 10–10 m2 s−131. The Residual Sum of Squares for each of the diffusion curves is less than 5 × 10–3 in all cases.Errors in diffusion coefficient were calculated based on the Standard Deviation for each diffusion curve and are in line with the estimated error as reported for a similar mixture of 29, internal reference standards such as trimethylsilyl propionate (TSP) can often complex with proteins in the analysis and it is highly questionable that reliable direct quantitation of individual metabolites is possible in such a mixture. The relative amounts of individual metabolites were of greater interest in a qualitative sense.All 1D datasets were processed using Chenomx . As has recently been reportedMetabolites used in the PCA were restricted to those for which a concentration was detected by Chenomx in all samples. All data points were normalised (area) using Unscrambler 10, using a constant weighting and cross validation.30.NMR spectra of metabolite mixtures are by their very nature complex and it was hypothesised that further information about metabolite mobility could be obtained using Diffusion Ordered Spectroscopy (DOSY). In addition, 1D NMR data provides many overlapping signals and while the Chenomx software is good at resolving some of the well-known metabolites, there was also a desire to investigate the existence of certain unknown metabolites. Through the use of a range of known metabolites as internal mass/diffusion standards it is possible to plot a LogMr vs LogD graph to calculate the approximate mass of an unknown and this has been used to good effect in complex mixtures of this typeTrying to automatically calculate diffusion coefficients from a pseudo 2D plot is not easily achieved and neither Amix nor Dosytoolbox are capable of dealing with the raw data to provide this output. DOSY spectra for both mycoplasmas were obtained for biofilm and planktonic samples in serum.2 > 0.95)All DOSY spectra were calibrated with respect to both chemical shift and also diffusion, using internal standards of acetate, glucose, alanine and tyrosine. The range of molecular mass (Mr) afforded by this range of metabolites would enable the identification of an unknown Mr with good linearity  with 0.02 ppm increments and all signal intensities for each bin for each of the 64 gradient incremented spectra.The diffusion coefficients for each bin were calculated using an Excel curve-fitting algorithm.The above three steps were carried out for each spectrum.Using the 8 samples for each of the strains of mycoplasma we obtained a spreadsheet 8 column wide by 515 rows long with the matrix showing the diffusion coefficients for each bin for each sample. As water suppression was used the area of suppression was discounted from the spreadsheet.For each strain of mycoplasma, the spreadsheet from 5 was analysed using Multibase 2015 addon to Excel and any differences analysed using PCA.Therefore, the following process was adopted;30.Using Chenomx Profiler  1D processed datasets were analysed and the components assigned with the aid of TOCSY where appropriate. The pH was verified for each of the samples prior to processing so no internal pH standards were used and the line width and shifts were calibrated to formate as per the standard procedure for Chenomx data processing. Where assignment was ambiguous DOSY NMR was used to approximate the relative molecular mass (Mr) for individual components as previously shownThis research raised no ethical concerns for consideration as no human or animal subjects included in the experimental work.M fermentans were considered in isolation. Following the same processing parameters described previously in the methodology section, PCA analysis was carried out . The media components used in this study (Eaton’s media) in the absence of any Mycoplasma species, were also identified by 1D 1H NMR in order to provide baseline recognition of known metabolites, Chenomx software was used to analyse and identify the different metabolite components . Further work would be required to automate the correlation of the signals with a specific component. The introduction of operator assignment error when the bin size is as small as it is could easily lead to incorrect assignment of a signal to a specific metabolite.M. fermentans initially showed little grouping in the scores plot and this was confirmed by the statistical output, which showed a negative value for Q2 and poor statistical explanation of the data. The residual variation was too high for this model and therefore further interrogation would have yielded unreliable pattern data  system37. Glucose, and other fermentable sugars, are a crucial energy source, especially for the fermentative mollicutes. Glucose may also act as a precursor for the synthesis of other saccharides that stimulate the growth rate of mollicutes40.Mycoplasmas are divided into fermentative and non-fermentative organismsM. fermentans and M. pneumoniae43. Physiological characterisation of biofilms is therefore vital to develop effective anti-mycoplasma treatments25.Host cell attachment by mycoplasmas, as a biofilm or part thereof, and cytotoxic metabolite production may contribute towards significant host cell damage, commonly observed in infection with these pathogens. Biofilm formation also frequently results in increased resistance to antimicrobial agents and persistence of many organisms including 27 the work presented here is the first DOSY-NMR metabolomics-based study in human mycoplasmas.Although global metabolite analysis has been proven as a powerful tool for understanding bacterial physiology17. The purpose of the evaluation of biofilm and planktonic mycoplasmas using 1D 1H NMR was to establish the amplification of one or more metabolites/media components (markers) dependent on the mycoplasma being grown either as a biofilm or in planktonic state. The use of 1D NMR techniques for the analysis of serum and also cytosolic contents are well established31 and analysis of Acinetobacter baumannii strains using 1D NMR, 2D TOCSY and PCA analysis demonstrate the usefulness of these types of investigations in potentially elucidating details of the different metabolic pathways. The aim of the current research was to use these techniques to identify differences in metabolite composition for M. pneumoniae and M. fermentans in both biofilm and planktonic forming conditions. In addition, we aimed to evaluate the potential of Diffusion Ordered Spectroscopy (DOSY) to assist in discriminating between planktonic and biofilm mycoplasmas and what significance these points of discrimination may have, either to assist in the classification of a mycoplasma as either biofilm or planktonic derived, or to provide more information about the constituents of the biofluid and potentially the differences in metabolic pathways. Additionally, this approach could also help to understand the potential changes in mycoplasmal metabolism under different growth conditions. The current study examines the overall metabolism of the organisms in the stated growth condition (planktonic or biofilm) and not the rate of metabolism. The measurement of bacterial metabolism for communities in a biofilm cannot reliably be measured and currently the metabolomic differences between the growth states as a whole is reported rather than growth rate measurements48.NMR-based metabolomics is a relatively new technology in biological applications and correspondingly has seen limited exploration in the investigation of microbial biofilms54. Oxidative phosphorylation deficient mycoplasmas can metabolise pyruvate via two alternate pathways to generate ATP: oxidation by pyruvate dehydrogenase to acetyl-CoA, followed by the action of phosphate transacetylase and acetate kinase to yield acetate and ATP. Alternatively, pyruvate is reduced to lactate by lactate dehyrdogenase with concomitant oxidation of NADH to NAD to facilitate continued ATP generation via the EMP53. Energy metabolism in mycoplasmas is therefore principally dependent on fermentation via the EMP to lactate under anaerobic conditions or acetate and CO2 under aerobic conditions34.In reference to carbohydrate metabolism. many mycoplamas lack hexokinase, therefore glucose is transported into cells and 'captured' by the phosphoenolpyruvate: phospho-transferase (PEP:PTS) system for degradation to pyruvate in the Embden–Meyerhof–Parnas (EMP) glycolytic pathwayM. fermentans, acetate was found to be greater in the biofilm suggesting that there is a bias toward the pyruvate dehydrogenase route for pyruvate metabolism. For both mycoplasma strains, the data indicates biofilms potentially produce more energy than planktonic cells. Glucose-6-phosphate was more abundant in planktonic cultures of both M. fermentans and M. pneumoniae, compared to biofilm serum . The results showed that the proline level (in the form of 4-Hydroxy-L-Proline) in M. pneumoniae was greater in biofilm cell serum compared to its planktonic counterpart  pathway, similar to that found in M. hominis58. The ADH pathway catalyses the conversion of arginine to ornithine, ATP, CO2 and ammonia. The current work showed that arginine levels in Mycoplasma fermentans were greater in planktonic cells than biofilms.In M. fermentans compared to its planktonic growth state . Depending on the examination of the phylogenetic distribution of the enzymes participating in isoleucine/valine metabolic pathway, it was found that all the genes coding for the enzymes of isoleucine/valine biosynthesis were missing in M. pneumoniae65. Thus, the presence of these metabolites in the profile might be related to the high nutritional value and complexity of the surrounding growth medium. This study also identified the presence of creatine in the biofilm of both M. fermentans and M. pneumoniae were higher than their planktonic counterparts (Figs. 66. Although creatine biosynthesis in mycoplasmas is still elusive and not fully described, these findings could be connected to arginine metabolism, as described above.The metabolite profiles in the current study showed some interesting observations, such as the presence of valine and alloisoleucine in ts Figs.  and 6. TM. fermentans and M. pneumoniae (Figs. 25. In Mycoplasma pneumoniae, Uridine is involved in the Leloir pathway that comprises a step in galactose phosphorylation67. Furthermore, EPS production requires both free galactose and the Leloir pathway in order to form a biofilm67.In relation to nucleotide Metabolism, levels of Uridine were higher in the biofilm than planktonic state after for both ae Figs.  and 6 UrM. fermentans and M. pneumoniae were regulated by multiple enzymatic reactions in order to fulfil key metabolic activities. Therefore, protein abundance in the metabolic network could provide a qualitative picture of M. fermentans and M. pneumoniae metabolic pathway activity in both biofilm and planktonic cells under laboratory growth conditions. There are examples of how metabolomic differences have been observed in in planktonic and biofilm cells of Mycoplasma genitalium46, Staphylococcus aureus45, Gardnerella vaginalis47 and Rhizobium alamii48. Whilst these studies all discuss the difference in metabolomic profiles in the species discussed between planktonic and biofilm growth states, none look at the rate of metabolism. As there is a range of growth states throughout a biofilm from the apical to the basolateral surface measuring the rate of growth is widely viewed as not possible. As a result, studies often analyse the production of materials, compounds metabolic markers from the biofilm as a whole and not the rate of metabolism as this is not currently possible. Hence the data presented here is the first use of DOSY-NMR to examine the metabolism of these mycoplasmas in their planktonic and biofilm growth states, as a whole and not on a rate wise basis. The clarification of metabolic pathway activity in both biofilm and planktonic cells enables us to gain a better understanding of vital and non-essential metabolites involved in the formation and establishment of human mycoplasma biofilms.This work is the first study to quantify a set of key metabolites, including glycolysis compounds, amino acids, and nucleotides, in the growth of human mycoplasmas. The results obtained suggested that metabolic pathways in human mycoplasmas, such as Supplementary Information 1.


Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.Leprosy, caused by  Mycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae- GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth. Mycobacterium tuberculosis and M. leprae, responsible for tuberculosis (TB) and leprosy respectively. Leprosy is one of the oldest plagues of mankind yet remains prevalent in developing countries. In 1981, the World Health Organization (WHO) recommended multidrug treatment (MDT) of disease with dapsone, rifampicin and clofazimine. Implementation of MDT achieved a 98% cure rate leading to reduction of prevalence by a remarkable 45% within three years. Yet, although MDT reduced global prevalence of leprosy, annual new case detection has remained fairly constant over the last decade ..MycobactM. tuberculosis. Both M. tuberculosis and M. leprae are intracellular pathogens that replicate primarily in macrophages but whereas the TB bacillus appears to primarily infect macrophages , the leprosy bacillus replicates in many cell types but particularly Schwann cells and endothelial cells . Th. ThM. tural ions , whereasplements . This la–14 days , 12]. T. TM. tubM. tuberculosis in 1998 . T. TM. tubd lysine . The biod lysine . Gene dethe body .M. tuberculosis and Mycobacterium bovis where they have been used to identify novel metabolic pathways and examine their growth and likely substrate utilization within their host cells , . I. IM. tubesponses .All animal studies were performed under the scientific protocol number A-105 reviewed and approved by the National Hansen’s Disease Programs (NHDP) Institutional Animal Care and Use Committee, and were conducted in strict accordance with all state and federal laws in adherence with PHS policy and as outlined in The Guide to Care and Use of Laboratory Animals, Eighth Edition.7M. leprae, strain Thai-53, in each hind footpad. At 4–5 months post inoculation mice were euthanized and footpad tissues removed. These tissues were either fixed in 70% ethanol for at least 48 h or minced and homogenized immediately for purification of viable M. leprae . S. S33]. Seviously , 16], . D. DM. lepd growth , 52]. D. DM. lepM. tuberculosis GSMN-TB at two different growth rates was validated with experimental data from M. tuberculosis grown in the chemostat at the two growth rates, highlighting the importance of the model to predict the metabolic state of M. tuberculosis, in experimentally challenging situation such as during infection [M. leprae in cultures. Our analysis did predict diaminopimelate, lysine, methionine, formate, tetrahydrofolate, pantothenate, cobalamin and protoporphyrinogen as nutrient supplements for growth and biomass production of GSMN-ML that could be further investigated.Genome scale metabolic reconstructions and flux balance analysis aims to find a state that maximizes growth of an organism in the given media. To validate these metabolic models, it is important to test experimentally if the bacteria grows optimally in the given media. Identification of discrepancies between model predictions and experimental data are informative in identifying errors in the model and gene annotation. The predictive capacities of our nfection . But, thM. leprae intracellularly [M. leprae’s metabolism for additional nutrients that are identified.GSMN-ML is applicable for interrogation and interpretation of RNA-seq data using DPA and for exploration, for testing scenarios, for scanning conditions, and for eliminating impossibilities in experimental design. A limitation of DPA analysis is that the predictions rely on the carbon substrate used for the analysis. In this study we chose glucose and glycerol based on our previous studies and evidences of these substrates available to llularly . As moreM. leprae relies primarily on the nutrient provision from the host, and that the pathogen probably does not maximize growth rate during infection, the use of an objective function based exclusively of maximization of growth rate is unlikely to correctly simulate the M. leprae's in vivo metabolic state. In the future, it will be useful to construct a host-GSMN-ML dual model and validate with experimental datasets to explore the M. leprae’s metabolism in an infected cell.A limitation of GSMN-ML model is that it will test the metabolic abilities of the bacteria alone. Considering that metabolism of M. leprae compared to M. tuberculosis. This is likely to be associated with a lower degree of metabolic robustness in the leprosy pathogen, compared to the TB pathogen; a vulnerability that might provide novel opportunities for new drugs. We believe that GSMN-ML will be a useful model that will be made freely available to researchers aiming to help researchers perform in silico DPA analysis of RNA-seq data for metabolic predictions and pre-experimental gene essentiality analysis and growth tests aiding experimental design.With new research being available in the future, GSMN-ML will be developed by continuous curation of the metabolic model, improved gene annotation and integration of biochemical and high throughput datasets. The predictive capacities of the GSMN-ML confirmed the expected higher degree of gene essentiality in M. leprae has never been grown in the laboratory, severely hampering research on this important pathogen. In this study, we describe the construction of an in silico genome scale metabolic model of M. leprae GSMN-ML that is able to simulate growth. The model was used to devise a base medium for efforts to grow the pathogen in the laboratory. We also used the model to interrogate gene expression data from M. leprae grown in mouse foot pads and identified the active metabolic pathways during intracellular growth of the pathogen. We also found evidence for major differences in the intracellular metabolism of M. leprae compared to the related pathogen, M. tuberculosis. The availability of the GSMN-ML model provides a new tool for the further exploration of the biology of this important pathogen that could lead to development of new approaches for control of leprosy.Despite intensive efforts, S1 FileM. tuberculosis and the problem file (p-file) including the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis. The network was constructed using SurreyFBA software.The network file includes metabolite lists, pathways and reactions for (XLSX)Click here for additional data file.S2 FileThis composition was used to test and incorporate pseudogenes and orphan reactions that facilitated the growth of GSMN-ML. The p-file includes the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis.(XLSX)Click here for additional data file.S3 FileM. leprae and the problem file (p-file) including the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis. The network was constructed using SurreyFBA software.The network file includes metabolite lists, pathways and reactions for (XLSX)Click here for additional data file.S4 FileThe p-files include the list of nutrients and their uptake rates upper and lower limits.(XLSX)Click here for additional data file.S5 FileM. leprae freshly harvested vs. 48h incubation in axenic medium.The file includes expression data and fold changes in expression of (XLSX)Click here for additional data file.S6 FileMetabolites are listed for GSMN-ML and RNA-seq analysis using glucose and glycerol as sole carbon sources. The p-files include the list of nutrients and their uptake rates- upper and lower limits.(XLSX)Click here for additional data file.S7 FileFile shows flux variability analysis and gene essentiality analysis for the model. Two media p-files was set for this test. One is to use glycerol as only carbon source and the other is to use glycerol as only carbon source and the other necessary molecules. Table 'FVA' shows results for all reactions with maximized biomass. Table 'media' shows media settings, those substrates whose lower-bound < 0 are allowed to be uptaken. Table 'metabolite_name' shows metabolite IDs and their names. Table 'KO_gene' shows gene essentiality analysis results on the media set in 'Media'. GRateKO: growth rate when the gene knocked out. Ratio: calculated as GRateKO/GRateWT(wildType). Genes are considered as essential If Ratio < 0.01 (marked red).(XLSX)Click here for additional data file.S8 FileFile shows flux variability analysis and gene essentiality analysis for the model. Two media p-files was set for this test. One is to use glycerol as only carbon source and the other is to use glycerol as only carbon source and the other necessary molecules. Table ‘FVA’ shows results for all reactions with maximized biomass. Table 'media' shows media settings, those substrates whose lower-bound < 0 are allowed to be uptaken. Table 'metabolite_name’ shows metabolite IDs and their names. Table 'KO_gene' shows gene essentiality analysis results on the media set in 'Media'. GRateKO: growth rate when the gene knocked out. Ratio: calculated as GRateKO/GRateWT(wildType). Genes are considered as essential If Ratio < 0.01 (marked red).(XLSX)Click here for additional data file.S1 FigM. leprae using palmitic acid as the only carbon source [Radiorespirometry (RR) was used to measure oxidation rate of n source . For the(TIF)Click here for additional data file.


Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of  In the era of global climate change and increasing food demand caused by population increases, it may become challenging to grow enough agricultural crops to produce sufficient amounts of foods for human consumption. Drought is among the major environmental problems in agriculture worldwide, as water deficits limit plant growth and reduce crop production ,2. TherePlants have evolved phenotypic plasticity under changing environments and have altered their metabolism to balance their growth and specific stress responses. Plants utilize various strategies to cope with drought stress of different degrees and durations. The complex regulatory circuits under drought stress lead to physiological or morphological changes across a range of temporal and spatial scales. One well-known regulatory circuit is the abscisic acid (ABA) biosynthesis and signaling pathway. When water deficit occurs, plant cells undergo osmotic changes and induce regulatory genes. Subsequently, ABA levels are transiently increased ,4. ABA tTo understand the whole metabolic system response, simulation using biochemical metabolic networks, known as a genome-scale metabolic model (GEM), is performed . Based oIn general, a GEM contains all possible reactions that organisms of interest can process. This means that some reactions may not occur in specific cell types or under specific conditions. Consequently, integration of omics data representing gene expression, protein expression, or metabolite accumulation levels enables recapitulation of the metabolic pathway in a specific cell type under specific conditions . ParticuArabidopsis thaliana in response to water deficit. We used publicly available transcriptome data to tailor the Arabidopsis GEM and reconstructed context-specific metabolic network models. The context-specific GEMs were used to estimate the flux distribution under drought stress and well-watered control conditions. The biomass production rate, which is considered as an important agricultural trait, was predicted and compared to the actual leaf biomass production rate. By comparative analysis of the flux distribution under drought and control conditions, we identified reactions and metabolites associated with drought adaptation. Finally, we discussed the limitations of this method and provided considerations for further applications.In this study, we established a pipeline for generating context-specific GEMs to understand the metabolic changes in plants under specific conditions. We performed an in silico investigation of metabolic adaptation of A. thaliana [A. thaliana rosette leaves under progressive drought stress [AraGEM, a genome-scale metabolic network model of thaliana , was uset stress  were rett stress ,18.The data set consisted of time-series data from days 1–13 under drought stress and control (well-watered) conditions. The average expression value of four replicates was used as the gene expression value. We selected the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm  as a taiIn AraGEM, the reactions are grouped into metabolic subsystems according to metabolic pathways in which they are involved. Concerning the subsystem “flavonoid biosynthesis”, more reactions remained in drought GEMs than in control GEMs at the later stage of treatment c. This iTo examine whether the reconstructed context-specific GEMs represented actual metabolic states, we defined the occurrence percentage of metabolites. Among the metabolites in the global GEM, 66 metabolites were detected by metabolite profiling in Bechtold et al. . They weWe reconstructed the context-specific GEMs so that the time-dependent change in the biomass production rate, the objective function to be maximized b, was coWe further investigated which reactions are important for metabolic adaptation in terms of biomass production. First, we identified reactions that accelerated the biomass production rate in control GEMs on days 12 and 13 b. To thiEscherichia coli gdhA were reported to increase their biomass production by enhancing drought tolerance [Transgenic tobacco and maize lines overexpressing olerance ,22. ImprArabidopsis plants under progressive drought stress showed the greatest physiological changes at day 13 [We investigated the difference in the flux distribution between drought and control GEMs on each day by flux comparative analysis. We considered that, if the flux size of a reaction is larger under drought conditions than in controls, the reaction was more active under drought conditions. Next, we calculated the fold-change in the flux size (drought/control) a and cout day 13 . For inst day 13 .2, and amino acid transporters and reactions associated with the TCA cycle.Further, cluster analysis of these reactions was performed c and metNext, we employed the flux-sum, which is defined as half of the summation of all consumption flux (efflux) and generation flux (influx) related to a specific metabolite under a pseudo-steady state ,24. As tThis analysis clarified the metabolic changes from the perspective of metabolites, while cluster analysis of the reactions c revealeAs distinct differences were observed at day 13, the metabolic change on day 13 was summarized in a pathway map . SeveralArabidopsis, wheat (Triticum aestivum L.), and shoreline purslane mangrove (Sesuvium portulacastrum) [Arabidopsis enhanced the susceptibility to drought stress [Some of the abovementioned reactions were reported to be involved in the drought stress response, validating our analytical pipeline for identifying significant reactions related to the drought stress response . Reactiocastrum) ,26,27. Scastrum) ,29. Hydrt stress .We aimed to establish a systematic pipeline to gain insight into metabolic adaptation to environmental changes. The pipeline integrating genome-scale model and omics data allowed us to capture the reactions and metabolites that may enhance drought stress tolerance without prior knowledge. As an example, we clarified that the reaction R00243_c (glutamate dehydrogenase), which was lacking in drought GEMs, is an important reaction affecting the biomass production rate. Previous physiological and molecular genetic studies indicated the significance of glutamate dehydrogenase in drought tolerance ,22. HoweIn this study, we tailored context-specific GEMs by using the established tailoring algorithms. The tailoring algorithms were categorized into three families: GIMME-like, Integrative Metabolic Analysis Tool (iMAT)-like, and The Model Building Algorithm (MBA)-like families . Among tTo perform in silico analysis using this pipeline, two points should be carefully considered. The first point is setting the threshold of the expression value of transcriptome data. The threshold setting has a major effect on the content of the tailoring model . In thisThe pipeline proposed in this study can be applied in studies aimed at understanding metabolic adaptation to stresses other than drought. Plant GEMs have been reported for several species, including rice, maize, and soybean . The pipA. thaliana, was downloaded as an sbml file from [AraGEM, a genome-scale metabolic model of ile from . The dowile from ,34, wherMicroarray data were retrieved from Gene Expression Omnibus under accession number GSE65046  using paThe rate of fresh weight increase in rosette leaves was used to represent the actual biomass production rate. From the experimental data reported by Bechtold et al. , the freZ) was the biomass production rate  was regularized by subtraction with a strictly concave function equation [−6. The FBA was performed using COBRA toolbox version 3.0 in MATLAB (version 9.5-R2018b) [FBA was performed on context-specific GEMs to simulate the maximum possible flux of the feasible flux distributions . The objequation . The min-R2018b) .n reactions calculated from a setting threshold value and gene expression values mapped to each reaction, m metabolites and n reactions, a and b were lower and upper bounds, respectively. The maximal value of the objective value was set to 90%. The lower bounds, corresponding to the objective function (Z), were set to the maximal value, whereas other bounds were set as the original bounds.Reconstruction of context-specific GEMs was performed using Gene Inactivity Moderated by Metabolism and Expression (GIMME) . BrieflyThe expression threshold was set to be the same on all days and under all conditions. The expression thresholds ranged from 0.7 to 0.9  were tested . Then, wThe occurrence percentage of metabolites in context-specific GEMs was calculated for each GEM by comparison with the metabolites for which contents were reported in Reference . If a meEach single reaction in the day-13 control GEM was deleted individually using the singleRxnDeletion function from COBRA toolbox version 3.0 in MATLAB (version 9.5-R2018b) . Next, ti participating in reactions (j) and j).Flux-sum is defined as one-half of the summation of all incoming fluxes (influx) and outgoing fluxes (efflux) concerning individual metabolite (tion (5) :(5)vxi=1m is the number of context-specific GEMs and n is the number of reactions. The missing values in the matrix were filled with one-tenth of the minimum value of flux sizes. The fold-change in flux size under drought to control conditions was calculated in Equation (6) and transformed into logarithm to base 2.All reactions present in each context-specific GEM were listed to create a matrix of simulated fluxes, m is the number of context-specific GEMs and n is the number of metabolites. The missing values in the matrix were filled with one-tenth of the minimum value of flux-sum values. The fold-change in flux-sum under drought to control conditions was calculated in Equation (7) and transformed into logarithm of base 2.All metabolites present in each context-specific GEM were listed to create a matrix of flux-sum values, Cluster analysis was performed using agglomerative clustering using a correlation metric with average linkage. Reactions showing a correlation greater than 0.5 were grouped into clusters and are shown in different colors. Heatmap representation was performed using python library SciPy and library Seaborn ,39.In this study, context-specific GEMs were reconstructed using transcriptome data under drought stress. In silico single reaction deletion experiments revealed the reactions important for increasing the biomass production rate. Comparative analysis between drought and control conditions clarified the candidate reactions associated with the drought stress response, which can be utilized, after further biological validation, to improve the drought tolerance of crops. This strategy can be applied to other stresses to gain a better understanding of metabolic pathways affecting the stress response involved in growth and yield, which will help improve future agricultural strategies.