The ability of fungi to colonize and persist within the human host is accompanied by an adaptation of fungal metabolism that allows them to withstand stress conditions, contend with the immune response, acquire nutrients, or simply secure a competitive edge during infection. Metabolites are the end products of cellular functions, and their levels reflect the fungal response to genetic or environmental changes. Despite the importance of metabolism for fungal fitness and pathogenicity, a comprehensive understanding of its impact on host-fungal interactions is still missing. Metabolomics, defined here as the simultaneous identification and quantification of the complete set of metabolites in a biological specimen, not only represents the chemical phenotype of an organism but also allows identification and interpretation of associations between genotype and phenotype. Investigations of the unique metabolic fingerprints of pathogenic microorganisms and the infection-associated changes to the host’s metabolism can provide a more complete impression of the infection process. Yet the utilization of metabolomics approaches to study host-fungal interactions are still few and far between. In this Pearl, we present an overview of the metabolome analyses in human pathogenic fungi to date, give examples of new discoveries made by such approaches, and discuss future research directions.Mass spectrometry (MS) and nucleic magnetic resonance (NMR) are the analytical tools of choice in most metabolomic studies, with a plethora of substrate- and study-specific variations available –3. An ovAspergillus fumigatus SM repository [http://www.ymdb.ca) is a database compiling information about metabolites found in or produced by Saccharomyces cerevisiae, which could be applied toward understanding metabolic responses of pathogenic yeasts . Another database collected curated literature information for 2,240 metabolic reactions in Aspergillus niger. The data were used to generate a mathematical metabolic model that was further validated with experimentally obtained transcriptional and metabolomic data [The required combination of analytical methods, complex data interpretation, and limited number of relevant studies in pathogenic fungi are the main reasons why researchers may hesitate to undertake metabolomic studies. However, the field is rapidly expanding due to the implementation of new methods, streamlined data analyses, and lower experimental costs. For instance, several fungal-specific metabolomic databases were recently launched, such as A2MDB, a comprehensive pository . A2MDB cmic data . Such daCryptococcus neoformans var. grubii versus C. neoformans var. neoformans and Cryptococcus gattii revealed a genomic translocation that disrupts TGR1, a gene encoding a newly described protein involved in metabolism. Metabolomic and subsequent phenotypic analyses showed that the deletion of TGR1 leads to an accumulation of intracellular trehalose that is critical for protection against a variety of host-associated stresses [Candida albicans growth under hypoxia and combined transcriptomics and metabolomics data to show that oxygen limitation stimulates lipid biosynthesis, resulting in structural rearrangements of the cell membrane [Candida auris [A few studies have linked metabolomics with other OMICs data, expanding our understanding of biological functions in a way that was not possible in single-OMICs studies. For example, comparative genomic analysis of stresses . Burgainmembrane . Anotherda auris . These aAspergillus detection [Aspergillus-specific linear tetrapeptide named aspergitide [A. pallidofulvus, Fusarium oxysporum, and Geothrichum candidum could clearly differentiate between the species. Further, Ahmed and colleagues developed sampling methods for volatile organic compounds (volatome) in A. fumigatus [Candida spp. and found that the C. albicans volatome was marked by increased 3-methyl-2-butanone and styrene, a feature absent in the other species [One key biological application of metabolomics is the identification of disease signatures and biomarkers. The routine measurement of single molecules or a pattern of several molecules as a part of fungus-specific metabolite imprints is rather inexpensive and could be used in diagnosis and therapeutic monitoring. Several metabolites have already shown promising characteristics for improved early etection . As an eetection . A studyergitide . Anotherergitide . A totalumigatus , which w species . These sC. neoformans increases the secretion of pantothenic acid, previously found to stimulate fungal growth [An important and thus far understudied aspect of fungal metabolomics is the ex vivo and in vivo metabolic sampling of the pathogen and the host during infection. The limited number of such studies already has revealed novel virulence characteristics. For example, metabolic profiles from meningitis rat model of cryptococcosis showed increased amounts of lactate, citrate, and polyols (mannitol and glycerol) and a decrease of glucose in the central spinal fluid . Others l growth . The impC. albicans metabolism [C. albicans [Mucorales species following exposure to sublethal concentrations of posaconazole revealed significant alterations in ergosterol biosynthesis compared to A. fumigatus, e.g., accumulation of the toxic sterol 14-methylergosta-8,24-diene-3,6-diol [C. neoformans iron regulatory protein Cir1 [Another major aim of metabolic studies in pathogenic fungi is to gain deeper insight in the effect of antifungal agents. Metabolomics-based approaches have been used, for example, to examine the effects of fluconazole on tabolism , 22. Thetabolism . Beside albicans –25 and oalbicans , 27 have3,6-diol . ErgosteC. albicans revealed that the transition from yeast to hyphae, a crucial virulence factor, is accompanied by impaired central carbon and nitrogen metabolism [N-Acetyl-D-glucosamine (GlcNAc), also affect C. albicans metabolism. GlcNAc-grown cells had low intracellular levels of amino acids compared to cells grown in glucose. This led to an amino acid starvation response, a known trigger of hyphal morphogenesis [C. albicans with mitochondrial inhibitors led to the suppression of hyphae formation, which correlated with changes in the redox state, decreased TCA cycle activity, and increased catabolism of fatty acids compared to nontreated cells [C. albicans Snf5, a subunit of the SWI/SNF chromatin remodeling complex, in controlling metabolic flexibility and fungal fitness specifically under hypoxia [The expression of virulence traits in human pathogenic fungi is frequently accompanied by distinct changes in metabolism. For example, metabolomic analysis of tabolism . Unlike tabolism , 30. Hypogenesis . Anotherogenesis . Furthered cells . A combi hypoxia .HVA1, in C. neoformans. The mutant strain had increased levels of phosphoenolpyruvate and decreased levels of 2-ketoglutarate relative to the wild type, suggestive of a block in the TCA cycle and lowered ATP production. Further investigations showed that HVA1 coordinates cell fitness (and thus virulence), likely via regulation of cellular NADPH levels [Central carbon metabolism was also found to play a critical role in other pathogenic fungi. Metabolomics was used to characterize the function of a newly identified gene, H levels . Thus, mC. albicans biofilm formation showed that mature biofilms are characterized by low TCA cycle and mitochondrial activity, whereas the intracellular levels of several amino acids and glycerol (cellular response to osmotic stress) are elevated [Several fungal pathogens can form robust biofilms on biotic surfaces and medical devices, which is a major health issue due to their reduced antifungal susceptibility. Metabolomic analyses of different stages of elevated . Moreoveelevated . Thus, mStaphylococcus aureus and C. albicans mixed biofilms showed that the symbiotic coexistence of the two species is signified by the high abundance of sedoheptulose-7-phosphate, an intermediate of the pentose phosphate pathway [Proteus mirabilis on C. albicans growth resulted in slower metabolism and energy consumption by the fungus within the mixed biofilm [Host-associated biofilms occur mostly as multispecies entities, which show different virulence characteristics compared to single-species biofilms. Metabolomic analyses of  pathway . By cont biofilm . Thus, mC. albicans–colonized mice had minimal changes in the cecum metabolites compared to the untreated animals [C. albicans growth and morphogenesis, including carbohydrates, sugar alcohols, and primary bile acids, were increased after treatment with this antibiotic, whereas the levels of growth-inhibiting carboxylic acids and secondary bile acids were decreased [Besides in biofilms, interspecies interactions take place en masse in the human body . Both metabolic modifications of the microbiome following antibiotic treatment and infection-associated changes to the host gut metabolome involving fungi have been investigated , 39. Spe animals . HoweverIn conclusion, metabolomics and the respective bioinformatics tools and databases are rapidly evolving and have the potential to reveal novel aspects of metabolic adaptations in fungal pathogens. In contrast to transcriptomics, which measures changes in gene expression that might lead to metabolic rearrangements, metabolomics reveals the most downstream effects of cellular activity. Therefore, this technique brings more concrete insights into metabolic regulation and adaptation to changing environments. However, since metabolomics provides only a snapshot of the organism’s physiological state at the moment of sampling, the examination of metabolic fluxes or a time course of sample collection should be considered to fully understand metabolic dynamics and reprograming.Neurospora crassa as a model organism for continuous in vivo monitoring of fungal metabolism [A. nidulans [A. fumigatus incubated in culture media, blood, or coculture with bacteria [S. cerevisiae to investigate potentially heterogeneous adaptations within a population to certain environmental conditions [In contrast to other well-established and widely used OMICs approaches, metabolomics is still a new tool. The studies performed in the field of fungal research to this date illustrate the broad applicability of the technique . Howevertabolism . In anotnidulans . Barkal bacteria . Additionditions . Moreove


Bacteroides is a highly abundant taxon in the human gut, and Bacteroides thetaiotaomicron (B. theta) is a ubiquitous human symbiont that colonizes the host early in development and persists throughout its life span. The phenotypic plasticity of keystone organisms such as B. theta is important to understand in order to predict phenotype(s) and metabolic interactions under changing nutrient conditions such as those that occur in complex gut communities. Our study shows B. theta prioritizes energy conservation and suppresses secretion of “overflow metabolites” such as organic acids and amino acids when concentrations of acetate are high. Secreted metabolites, especially amino acids, can be a source of nutrients or signals for the host or other microbes in the community. Our study suggests that when metabolically stressed by acetate, B. theta stops sharing with its ecological partners. Bacteroides thetaiotaomicron (B. theta) is a human gut symbiont thought to play an important role in maintaining host health. Untargeted nuclear magnetic resonance metabolomics revealed B. theta secretes specific organic acids and amino acids in defined minimal medium. Physiological concentrations of acetate and formate found in the human intestinal tract were shown to cause dose-dependent changes in secretion of metabolites known to play roles in host nutrition and pathogenesis. While secretion fluxes varied, biomass yield was unchanged, suggesting feedback inhibition does not affect metabolic bioenergetics but instead redirects carbon and energy to CO2 and H2. Flux balance analysis modeling showed increased flux through CO2-producing reactions under glucose-limiting growth conditions. The metabolic dynamics observed for B. theta, a keystone symbiont organism, underscores the need for metabolic modeling to complement genomic predictions of microbial metabolism to infer mechanisms of microbe-microbe and microbe-host interactions.Microbial metabolism and trophic interactions between microbes give rise to complex multispecies communities in microbe-host systems. IMPORTANCEBacteroides is a highly abundant taxon in the human gut, and Bacteroides thetaiotaomicron (B. theta) is a ubiquitous human symbiont that colonizes the host early in development and persists throughout its life span. The phenotypic plasticity of keystone organisms such as B. theta is important to understand in order to predict phenotype(s) and metabolic interactions under changing nutrient conditions such as those that occur in complex gut communities. Our study shows B. theta prioritizes energy conservation and suppresses secretion of “overflow metabolites” such as organic acids and amino acids when concentrations of acetate are high. Secreted metabolites, especially amino acids, can be a source of nutrients or signals for the host or other microbes in the community. Our study suggests that when metabolically stressed by acetate, B. theta stops sharing with its ecological partners. Microbes, whether in the environment or associated with host organisms, form complex multispecies communities that cooperate and compete to metabolize nutrients. The host gut ecosystem is a constantly changing landscape where symbiont organisms manage to establish long-term colonization despite the fact that the host regularly ingests and eliminates nutrients and transient microbes. This results in a constantly fluctuating environment where diverse microbes are secreting metabolic fermentation products and other secondary metabolic chemicals that may inhibit or stimulate neighboring organisms as they compete for nutrients. While there is recognition that microbes play important roles in host nutrition, health, and disease , it is dConsidering that bacterial virulence factors are often triggered by nutritional limitation  –4 or phy–Symbionts have evolved to cooperate with hosts to establish long-term colonization strategies that do not result in disease and even protect the host from pathogens. It has been shown that establishing host-symbiont trophic relationships protects hosts from virulent interlopers by physically and nutritionally limiting the ability of pathogens to establish infections , 12, stiBacteroides species are Gram-negative bacteria that are especially adept at metabolizing complex carbohydrates (Bacteroides thetaiotaomicron (B. theta) is a nonpathogenic human gut symbiont that colonizes infants within a day of normal birth  disease  phagocytes  bacterial colonization and virulence.It was previously shown that e medium –31. Bactagocytes , 33. Sucagocytes . Propionty acids . Acetatety acids  “switch”bacteria . SecretiB. theta secretes metabolites as a result of starch, extracellular matrix, or glucose metabolism (B. theta catabolizes glucose via the Embden-Meyerhof-Parnas (EMP) pathway (glycolysis) to pyruvate, which is a major intracellular metabolite used as the substrate in gluconeogenesis, the tricarboxylic acid cycle (TCA cycle), and for biosynthesis of acetyl coenzyme A (acetyl-CoA), enzyme cofactors, and amino acids. Acetate, the major secreted product, can be synthesized with ATP using two metabolic pathways: (i) by hydrolysis of the CoA thioether bond by acetyl-CoA synthase (Acs) in the acetyl-CoA pathway, or (ii) by phosphotransacetylase (Pta) and acetate kinase (Ack) enzymes in the Ack/Pta pathway , NADH , and reduced quinone (succinate dehydrogenase), and though enzyme steps are reversible, succinate synthesis by reverse TCA can only occur when there is a surplus of ATP generated as a result of forward TCA pathway flux.tabolism . B. thethway see  by-produsumption . In the 10.1128/mSystems.00252-20.3TABLE S1t test P values for metabolomics data in P values are given for each metabolite measured in spent medium as acetate is supplemented from 0 to 10 mM. Download Table S1, DOCX file, 0.1 MB.Student’s Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The next most abundant secreted products are formate and propionate. Formate is synthesized by pyruvate formate-lyase, which uses pyruvate and coenzyme A as the substrates to produce formate and acetyl-CoA. Formate is therefore an energy neutral overflow metabolite that nevertheless increases the enzymatic routes to acetyl-CoA. Propionate is synthesized from succinyl-CoA to propionyl-CoA by methylmalonyl-CoA mutase, methylmalonyl-CoA epimerase, and propionyl-CoA carboxylase enzymes, with subsequent thioesterase activity by the same Acs or Ack/Pta pathways used to synthesize acetate. Ultimately, propionate synthesis yields 2 ATP, but the pathway requires multiple enzyme steps and cofactors, suggesting this overflow pathway could be kinetically limited . A small1H NMR) data and the statistical methods to deconvolute complicated spectra has evolved considerably, making untargeted NMR metabolomics of B. theta cultures feasible  and uninoculated medium . Ellipses correspond to the 95% confidence interval for a normal distribution. (b) Back-scaled loadings plot generated from a validated OPLS-discriminant analysis (DA) model  comparing the spent culture medium and uninoculated medium 1D 1H NMR datasets. Positive peaks indicate an increase in spent culture medium and negative peaks indicate a decrease in spent culture medium. Positive peaks labeled and added in as inlets are numbered as follows: 15, formate; 13 and 14, histidine at 7.12 and 7.96 ppm; 12, hematin; 11, glucose; 2 and 10, alanine at 1.48 and 3.83 ppm; 8, cysteine at 3.05, 3.09, and 3.99 ppm; 9, cysteine at 3.20, 3.42, and 4.11 ppm; 7, asparagine at 2.97 ppm; 6, succinate; 5, glutathione at 2.50 ppm; 4, acetate; 3, lactate at 1.33 ppm. Peaks colored red are scaled 4× and colored green are scaled 2× to their actual intensities as depicted in the full-width spectrum in blue. Download FIG S1, PDF file, 1.8 MB.NMR metabolomics. (a) PCA scores plot generated from 1D Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the These endpoint metabolic products were then used to build a secretion flux map . NotablyThe secretion of amino acids is significantly lower than the major organic acid fermentation products (excluding lactate) but also suggests these metabolites are overflows for purine metabolism (histidine), the TCA cycle , and the serine cycle (cysteine/cystine and glutathione) . NotablyB. theta metabolism. We tested these hypotheses by growing B. theta with increasing acetate, formate, or a combination of both in a culture medium at physiological concentrations  with the addition of acetate despite the medium being buffered at pH 7.2 with 1 M potassium phosphate  with a pKa of 3.6 in water. An increase in the CO2 partial pressure produced by cells in sealed anaerobic culture tubes is known to decrease the pH of culture medium  was unmasked (xinit) from the amount of the metabolite observed (xobs) after acetate supplementation.The concentration for each of the metabolites in the culture medium changed independently as a function of the amount of supplemental acetate . The effunmasked  by subtr10.1128/mSystems.00252-20.4TABLE S2t test P values for metabolomics data in P values are given for the change in concentration of each metabolite measured in spent medium as acetate is supplemented from 0 to 10 mM. Download Table S2, DOCX file, 0.1 MB.Student’s Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00252-20.5TABLE S3t test P values for metabolomics data in r2) values are given for the change in concentration of each metabolite measured in spent medium as acetate is supplemented from 0 to 10 mM. Download Table S3, DOCX file, 0.1 MB.Student’s Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the xsec should also be equal to zero if the metabolite is neither a substrate nor product of acetate metabolism (null hypothesis). Instead, the concentration for each secreted metabolite changed as a result of the additional acetate in the culture medium. For instance, acetate only increased by 4.21 (± 0.55) mM in the culture medium after the 10 mM acetate treatment  between the observed and the null model was calculated assuming the secretions of acetate and the other metabolites are correlated  by adjusting gene expression to decrease metabolic flux, but at acetate levels up to 10 mM, no further changes in gene expression or other metabolic rerouting occur.In addition to a direct competitive feedback inhibition  , supplem10.1128/mSystems.00252-20.2FIG S2x. x axis, arbitrary time units; y axis, Δxsec, arbitrary change in concentration of metabolite x detected in the culture medium. Colors represent concentrations of supplemented acetate. (b) Secretion fluxes are constant with balanced secretion and absorption when x axis, arbitrary time units; y axis, Δxsec, arbitrary change in concentration of metabolite x detected in the culture medium. Colors represent concentrations of supplemented acetate. Symbols and lines overlap and may not be visible. (c) Feedback inhibition with no regulation when x axis, arbitrary time units; y axis, Δxsec, arbitrary change in concentration of metabolite x detected in the culture medium. Colors represent concentration of supplemented acetate. (d) Synergistic negative feedback inhibition with compensatory gene regulation when x axis, arbitrary time units; y axis, Δxsec, arbitrary change in concentration of metabolite x detected in the culture medium. Colors represent concentrations of supplemented acetate. Symbols and lines overlap and may not be visible. (e) Positive upregulation in response to increasing acetate when x axis, arbitrary time units; y axis, Δxsec, arbitrary change in concentration of metabolite x detected in the culture medium. Colors represent concentrations of supplemented acetate. Symbols and lines overlap and may not be visible. Download FIG S2, EPS file, 1.0 MB.Theoretical modeling of the effect of acetate supplementation on the observed secreted metabolites. Models assume a fraction of supplemented acetate is converted to secreted metabolite Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 and H2 or the redox state of ferredoxin. Thus, high formate concentrations may be synergistic, additive, or have independent effects on secretion fluxes compared to those for acetate alone. To differentiate between these possibilities, cultures were supplemented with 10 mM both acetate and formate. NMR metabolomics was then used to characterize the secreted metabolome. Growth experiments demonstrated that formate had a synergistic effect on population doubling time . Gray outlined circles represent undetected intracellular metabolic nodes. Black outlined circles indicate secreted metabolites. Shading is proportional to concentration in culture medium from 0% (red circles) to yellow (50%) to green (100%) carbon flux. Download Movie S1, GIF file, 0.1 MB.Animated secretion fluxes of organic acids and amino acids in defined minimal medium. Numbers represent percent mole carbon fluxes  mapped onto a metabolic network. Gray outlined circles represent undetected intracellular metabolic nodes. Black outlined circles indicate secreted metabolites. Shading is proportional to change in carbon flux compared to no acetate control from 0% (red circles) to yellow (50%) to green (100%). Download Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00252-20.9MOVIE S3Movie S3, GIF file, 0.1 MB.Animated effect of formate supplementation on acetate feedback inhibition. The effect of formate (10 mM) and acetate feedback inhibition (10 mM) mapped onto a metabolic network. Gray outlined circles represent undetected intracellular metabolic nodes. Black outlined circles indicate secreted metabolites. Shading is proportional to concentration in culture medium from 0% (red circles) to yellow (50%) to green (100%) carbon flux. Download Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 and/or H2 secretion fluxes. To determine if in silico B. theta metabolic models can reproduce the observed growth phenotypes, we conducted a series of in silico experiments to obtain flux values for all reactions in the metabolic model and exchange fluxes for all substrate compounds and product metabolites.Empirical modeling suggested that the bioenergetics of glucose metabolism does not change when acetate and/or formate accumulates in the culture medium. Therefore, the observed changes in secreted metabolites must be due to increased CO2O ↔ asparagine + glutamate + AMP + PPi) to using aspartate ammonium ligase (aspartate + NH3 + ATP → asparagine + AMP + PPi) when either acetate and/or formate is included in the culture medium (see B. theta under the culture conditions we modeled.A limited effect on the key reaction fluxes (defined in millimoles per gram cell dry weight per hour) was observed under high-glucose (0.02% [wt/vol]) conditions. In the high-glucose flux balance analysis (FBA) models, there were no changes in predicted biomass, which remained constant at 0.39 in all four experiments , or in edium see . However10.1128/mSystems.00252-20.10DATA SET S1Data Set S1, XLSX file, 0.1 MB.Model parameters and output flux data for high-glucose and low-glucose FBA models are included in an Excel spreadsheet. Download Copyright © 2020 Catlett et al.2020Catlett et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The presence of acetate under low-glucose conditions (0.002% [wt/vol]) results in an abundance of metabolic changes. When acetate was added, whether in the presence or absence of formate, biomass decreased to 0.14 . ExchangThe interchangeability of asparagine synthetase and aspartate ammonia ligase pathways for asparagine synthesis was also observed under low-glucose conditions, similar to what was seen under high glucose conditions . In addi2 and cysteine when acetate is supplemented into the culture medium, which is consistent with metabolomics data and the observed pH decrease. None of the simulations resulted in changes in the secretion of organic acids or amino acids to the culture medium. Modeling instead showed decreased production of a small amount of molecular oxygen, lack of nitrite secretion, and increased secretion of xanthine under low-glucose conditions with addition of acetate.The low-glucose model also predicts increased uptake of acetate and decreased uptake of COB. theta is a ubiquitous and abundant member of the human gut microbiome. Accordingly, B. theta is an attractive organism for investigating the interaction between genes, environment, and system-level behaviors. B. theta is a strict anaerobe grown in sealed culture tubes. Thus, by the law of conservation of mass, all mass inputs (culture medium ingredients) and outputs (biomass and secreted metabolites) are accounted for in the cell culture. Secreted metabolites, especially organic acids and amino acids, are important mediators in microbial food webs and may play simultaneous roles as nutrients, stimulators, and inhibitors. In this manner, secreted metabolites may affect overall system behavior.B. theta has two bidirectional acetate enzyme systems, Ack/Pta and acetyl-CoA pathways, and the metabolomics and modeling data are consistent with B. theta using both of these pathways to secrete acetate as an “overflow” of acetyl-CoA biosynthesis. Overflow metabolism has been studied extensively in E. coli  downstream of succinate biosynthesis. B. theta biomass yield was unaffected by supplemented acetate, as expected by the lack of a glyoxylate shunt. The biomass yield did not increase despite a decrease in the secretion of amino acids, indicating intracellular amino acid biosynthesis was sufficient for maximum growth.Metabolomics and cell growth data suggest fermentation products, acetate and formate, cause large metabolic changes even when biomass yield is unaffected.  E. coli , where i E. coli . In E. cry phase , where try phase . While t1 metabolism . Thus, the addition of formate increases the synthesis of amino acids, lactate, and pyruvate (through serine ammonia-lyase), which may compensate for the inhibition of acetate, succinate, and propionate.The addition of formate to the culture medium caused an inhibition in the secretion of acetate and a subset of amino acids, while metabolites derived from oxaloacetate and aspartate  were not affected. Formate and acetate are both synthesized from pyruvate but have different effects on downstream “overflow” metabolites. One possible explanation for this difference is the fact that formate is a substrate for CB. theta also seems unable to relax transporter specificity or to produce new transporters through changes in gene expression. In effect, B. theta did not relieve acetate or formate inhibition by secreting other biosynthetic intermediates.Unexpectedly, not all metabolites were secreted and no new metabolites were detected in culture medium as a result of acetate and/or formate inhibition. Several central metabolites are simply too large to be nonspecifically secreted (phosphosugars and CoA-oxoacids), but many nonsecreted TCA and amino acid biosynthetic intermediates are chemically similar to secreted metabolites. This suggests that metabolite secretion is highly discriminated by transporters. 2. Increased CO2 synthesis would manifest as a decrease in pH but would not necessarily affect CO2-yielding decarboxylation reactions. At the partial pressures tested here, decarboxylation reactions are virtually irreversible unidirectional reactions. Malate dehydrogenase reversibly catalyzes the decarboxylation of oxaloacetate to produce pyruvate and CO2. Malate dehydrogenase also reversibly oxidizes malate to produce pyruvate, CO2, and NADPH. Our results can be explained if the forward malate dehydrogenase reaction is favored, with increased pyruvate being secreted as acetate and formate. The low-glucose FBA models support this hypothesis and show that addition of acetate results in nearly a 50% decrease in CO2 uptake from the culture medium  were also likely unaffected, even though the rate of growth was significantly lower and there were large changes in secretion profiles. The FBA model was able to accurately predict that acetate and formate supplementation does not affect biomass  under hiB. theta were unable to model secretion of organic acids and amino acids  secrete accumulated metabolite pools as part of “overflow metabolism.” These discrepancies likely reflect technological limitations of in silico modeling, such as an inability to predict allosteric or competitive inhibition, gene expression changes that might result in specific or nonspecific activity of transmembrane transporters, or perhaps the activity of poorly characterized enzymes or nonspecific aminotransferases and decarboxylases (or other enzymes) that may affect exchange fluxes in unknown ways. By using untargeted NMR metabolomics, we were able to detect and quantify metabolites in culture medium with minimal sample processing in a relatively “agnostic” approach. NMR data sets can be used to produce secretion flux maps that describe metabolic behaviors without requiring genomic, biochemical, or transcriptomic information or, in the reverse direction, may be used to infer the existence of unknown biochemical pathways. We suggest that untargeted NMR metabolomics may be a useful tool to inexpensively curate genome-scale metabolic models and could be essential for developing accurate dynamic FBA models.The FBA models for no acids , and thono acids . One posB. theta culture supernatants supports the postulation that B. theta is primed to participate in such cross-feeding interactions in the gut . In this regard, a cell can dispose of its excess amino acids while also benefitting near neighbors . It is also possible that these secreted amino acids are used in mutually beneficial metabolite exchange (syntrophy) –55, for – the gut . Humans  the gut . The essB. theta. In gut epithelial cells, glutamate, aspartate, and glutamine are substrates for ATP synthesis, glutamine, glycine, and aspartate are used for nucleic acid synthesis, and threonine, cysteine, and proline are used for mucin synthesis. Thus, a symbiotic relationship may exist between B. theta and gut epithelium, where B. theta may provide essential amino acids critical for gut epithelial metabolism.Besides nutrition, amino acids also have a wide range of roles in gut epithelial metabolism and gut immune/neurological function. In fact, several amino acids are secreted at high concentrations by B. theta was observed to secrete 121 ± 16 μM glutathione into the culture medium. Since lactic acid bacteria produce H2O2 in the gut to compete with anaerobes such as B. theta for glycan nutrients  or oxidative stress . Thus, iutrients , 60, thenterface –63. The B. theta. Cysteine and cystine, like glutathione, can abiotically react with ROS or xenobiotic compounds to protect cells from oxidative damage. Histidine was also secreted at high levels (101 ± 12 μM), which was nearly equivalent to that of lactate (120 ± 25 μM). Histidine is an essential amino acid and is a precursor to the immunological effector histamine. Secretion of amino acids and glutathione by B. theta could potentially play an important role in host nutrition, oxidative stress, neurological function, and immunology.Cysteine (242 ± 22 μM) and cystine (209 ± 47 μM) were also secreted by B. theta’s metabolic efficiency, which leads to changes in secreted metabolites that, in turn, are sensed as chemical messages by the microbial community and host  was grown in minimal defined medium as described but with minor modifications , sodium formate (10 mM), or a combination of both 10 mM sodium acetate and 10 mM sodium formate. Growth was measured using optical density at 600 nm using a Spectronic D spectrophotometer (Thermo Fisher Scientific) fitted with a Balch tube (18 mm) sample chamber. Biomass and optical density were found correlate linearly with 0.54 ± 0.056 g dry weight OD−1 liter−1 in defined medium.ications –66. CultB. theta cultures were grown to late exponential stage in 10 ml defined medium with 0.05% glucose and one of the following concentrations of acetate: 0.5 mM, 1 mM, 5 mM, 10 mM, or a 10 mM formate and 10 mM acetate control. Cells were separated from medium with 0.2-μm filters by vacuum. Samples of filtered medium were flash frozen in liquid nitrogen and then lyophilized overnight.Five replicates of 1H NMR data collection and analysis were completed as described previously (4067–2O with 50 μM 3-(tetramethylsilane)propionic acid-2,2,3,3-d4 (TMSP). NMR spectra were recorded at 298 K on a Bruker Avance III-HD 700 MHz spectrometer equipped with a 5-mm inverse quadruple-resonance  cryoprobe with cooled 1H and 13C channels and a z-axis gradient. A SampleJet automated sample changer with Bruker ICON-NMR software was used to automate the NMR data collection. 1D 1H spectra were collected using excitation sculpting to remove the solvent signal and avoid any need for baseline corrections  iously 4042, 67–691H NMR spectra were processed and analyzed using our MVAPACK metabolomics toolkit (http://bionmr.unl.edu/mvapack.php)  . The 1D rrection . Residuarrection  or alignrrection . The datrrection .R2X and R2Y) were computed during the OPLS model training. The OPLS models were also internally cross-validated using 7-fold Monte Carlo cross-validation to compute Q2 values  significance testing . Fractio2 values , 77.1H NMR peaks identified by the back-scaled loading plots as a major contributor to group separation in the OPLS scores plot were assigned to a metabolite using the Chenomx NMR suite 7.0 . 1H NMR peaks with significant overlap and multiple metabolite assignments were excluded from further analysis.The validated OPLS models enabled the generation of back-scaled loading plots to identify the spectral features (NMR peaks) that primarily contributed to the observed group separation. The relative peak intensities in these “pseudospectra” highlight the magnitude of the metabolite’s contribution to the group separation in the OPLS scores plot. Similarly, the relative sign of the peak indicates if the metabolite’s concentration increases or decreases due to the effects of the growth medium. All nonoverlapping Secretion flux maps were generated using the following equation:xsec) is determined by subtracting the amount of each metabolite in the 0 mM control treatment (xinit) from the amount of the metabolite observed (xobs) following the addition of acetate.Feedback inhibition was estimated using the following equation:The fold variance in metabolite secretion was estimated byxobs in the observed concentration of each metabolite, Asup indicated the concentration of acetate supplemented, and xvar is the magnitude of each “missing” metabolite.Where In silico experimentation is conducted using the Department of Energy’s Systems Biology Knowledgebase (KBase) (https://narrative.kbase.us/narrative/ws.53087.obj.1). Applications used are part of the fba_tools module version 1.7.6  creating a draft metabolic model from the B. theta genome, (ii) defining the medium composition, (iii) gap filling the draft model to add in missing reactions, and (iv) running a flux balance analysis (FBA). FBA provides a measurement of growth resulting from flux through the biomass reaction (grams of dry weight of biomass) (https://kbase.us/metabolic-modeling-faq/). (KBase) . A publion 1.7.6 . Model cB. theta model we employed may have missing reactions (gaps) due to incorrect or incomplete functional genome annotations. We used the “gapfill metabolic model” application on the draft model to identify a minimal set of biochemical reactions that, when added to the draft model, allow it to achieve biomass on the specified media (https://kbase.us/metabolic-modeling-faq/)  . Gap filhttps://kbase.us/metabolic-modeling-faq/). This resulted in a rate of biomass production as a measure of growth. For each FBA, we used the gap-filled model on the medium of interest and used each medium as input to the FBA algorithm to maximize biomass (bio1). From the output of the FBA application, the objective value was used as a measurement of growth and to capture the reaction and exchange fluxes, which were used to find flux values of interest. All data and results are presented in our public KBase narrative (https://narrative.kbase.us/narrative/ws.53087.obj.1).The “run flux balance analysis” (FBA) application was used to run the simulation. The FBA algorithm is a constraint-based approach that estimates growth-optimal fluxes through all the reactions specified by the metabolic network constructed in the previous step (The FBA models were created with either high or low levels of glucose, which were combined with either the absence or presence of acetate and/or formate. This resulted in eight experiments: high glucose, high glucose with acetate, high glucose with formate, high glucose with acetate and formate, low glucose, low glucose with acetate, low glucose with formate, and low glucose with acetate and formate. The amount of each compound used in the model is specified in the medium file, which defines maximum uptake as measured in millimoles per gram cell dry weight per hour. Low glucose was defined as 0.1 maximum uptake, high glucose was defined as 2.78 maximum uptake, and the presence of formate or acetate was set to 10 maximum uptake. The absence of formate or acetate was set to 0 maximum uptake.


Numerous differences in the metabolic pathways were recorded in all cases. Seven metabolic pathways were detected in the control larvae. When the larvae were exposed to concentrations equal to LC50, LC50/2, and LC50/4 of non-doped CNDs, 12, 12, and 3 metabolic pathways were detected, respectively. In the case of N-doped CNDs, 4, 7, and 4 pathways were detected, while in the case of N,S-codoped CNDs, 8, 5, and 5 pathways were detected when exposed to concentrations of LC50, LC50/2, and LC50/4, respectively. In all cases, certain metabolic pathways were altered while others were either down-regulated or up-regulated. Some of these changes include the activation of alanine, aspartate, and glutamate metabolism, aminoacyl-tRNA biosynthesis, butanoate metabolism, D-glutamine, and D-glutamate metabolism, glutathione metabolism, selenoamino acid metabolism, valine, leucine, and isoleucine degradation pathways. Moreover, the deactivation of starch and sucrose metabolism, the glycine, serine, and threonine metabolism, among others, were recorded. Our findings underline the importance to further study the impact of CNDs on marine organisms. As zebrafish has been shown to share many similarities with humans in bioprocesses and genome, it can be assumed that CNDs may also pose a threat to human health.Recently, concern has been raised over the transport, transformation, and fate of carbon nanodots (CNDs) after their release into the environment. Their toxicity towards organisms and humans has recently been addressed as an important issue. In this study, a metabolomic approach was employed to obtain an insight into the effect of CNDs (either pristine or doped with nitrogen and nitrogen/sulfur) on zebrafish. Embryos were exposed to concentrations corresponding to lethal concentration (LC) LC The research interest involving nanomaterials continues to grow unabated as more and more researchers have been involved in this research field, aiming to exploit the expanding range of properties of nanomaterials . The advHowever, what would seem a highly promising and propitious material can backfire if used hastily without first examining its potential side effects ,15. ObtaUp to now, such studies are scanty and sparse. The first study regarding the toxicity of graphene quantum dots was published in 2015, in which, decreased heart rate, pericardial edema, bent tail, and alterations in the activity of embryonic zebrafish were reported . AnotherThis study aims to obtain a better insight into the metabolic alterations that occur in zebrafish upon their exposure to CNDs and may be responsible for the phenotypic alterations. To this end, a metabolomic approach was adopted, since it is the most relevant field that correlates the pools of biological molecules with the function/dynamics of an organism . Zebrafi2O) and 3-(trimethylsilyl)-1-propionic-2, 2, 3, 3-d4 acid sodium salt (TSP) were obtained from Deutero .Citric acid, urea, thiourea, benzoylated dialysis tubing membrane (MWCO: 2000 KDa), and all solvents were obtained from Sigma-Aldrich . Deuterated water  using a benzoylated dialysis tubing membrane (MWCO: 2000 KDa). The CNDs used in this study were synthesized according to our previous studies ,21. In bThe synthesized CNDs were characterized by obtaining their FTIR spectra and HRTEM images, and measuring their zeta potential. Details can be found in the −1 NaCl, 0.33 mmol L−1 CaCl2, 0.33 mmol L−1 MgSO4 and 0.17 mmol L−1 KCl). After 24 h post fertilization (hpf) eggs were dechorionated . Embryos were dechorionated using two forceps with sharp tips [50, LC50/2 and LC50/4) of each CNDs species . After 96 h, embryos were collected and washed several times with PBS, and then euthanized by snap freezing with liquid nitrogen. This also assists to quench the metabolism of the larvae. Then, the samples were freeze-dried to remove water (and further assist in the extraction of metabolites), and used for further experiments [All experiments conformed to the European Directive 2010/63. In this study, adult zebrafish of the wild type (AB) were maintained at 28 °C with a photoperiod of 14 h light/10 h dark and fed twice a day. Sexually mature zebrafish were used for spawning. Eggs were collected and placed in E3 embryo medium  and the new solution was transferred to a 5-mm NMR glass tube. The second residue was resuspended in 100 μL of acetonitrile for further analysis using LC-HRMS .The metabolomes of zebrafish larvae were extracted according to a previously described procedure . In briewww.hmdb.ca) and the Madison Metabolomics Consortium Database (www.mmcd.nmrfam.wisc.edu) were used for the analysis of the NMR spectra. The presence of the tentative metabolites that resulted from the above analysis was further confirmed by scanning the MS spectra for the accurate masses  of the metabolites and comparing fragmentation patterns with data from online databases. Metabolic pathways were obtained using the pathway analysis tool of the MetaboAnalyst suite  using the Danio Rerio metabolic pathway library. The processing of the obtained spectra was carried out according to our previous studies ,27,28. IAll information regarding the characterization of the CNDs is presented in 50 values of the three nanomaterials are 550 (non-doped), 400 (N-doped), and 150  μg mL−1. Our experiments were carried out at three concentrations, i.e., at LC50, LC50/2, and LC50/4, to acquire data on metabolic alterations under conditions consistent with the concentration-dependence of the three CNDs species. Representative 1H-NMR spectra of the obtained metabolomes can be seen in Based on previous results, the three kinds of CNDs exhibit different mortality to zebrafish larvae . The LC550/4), eight new metabolites emerged in the sample, i.e., citrulline, gamma-aminobutyric acid, glutathione, fucose, histidine, tyrosine, melibiose, and selenomethionine. Likewise, in the second tested concentration (LC50/2), six new metabolites appeared, i.e., alpha-ketoisovaleric acid, inosine, arabitol, cystine, pipecolic acid, and trehalose. On the contrary, the metabolome of larvae treated with the highest concentration (LC50) did not contain any new metabolites. The metabolic pathways that are associated with the above metabolites can be seen in 50/2, the pathways of amino sugar and nucleotide sugar metabolism, fructose and mannose metabolism, and biotin metabolism were also down-regulated, while at the LC50 concentration, the above pathways along with the galactose and nitrogen metabolism pathways were down-regulated. The number of common pathways among samples is visually described using a Venn diagram  while nine others  were not detected in any of the treated samples . Interespathways , three o50, while at the two other concentrations, three (at LC50/2) and four (at LC50/4) new metabolites were detected, which were absent in the control sample. The starch and sucrose metabolism, galactose metabolism, and the amino sugar and nucleotide sugar metabolism were the metabolic pathways not affected in all cases  were detected in the metabolomes of larvae exposed to the highest concentration, also existing in the control sample . The S-all cases . The rib50 values. Since the non-doped CNDs are more biocompatible, organisms exposed to them maintain the metabolic pathways that can be used to counterbalance their effect. This was plausible in our study because the most differences in the number of active metabolic pathways were observed in the case of non-doped CNDs compared with the two other nanomaterials. With respect to the N-doped and N,S-codoped CNDs, the latter has a higher (negative) surface charge compared with the former, but the number of the active metabolic pathways of zebrafish was comparable for both. Hence, the composition of the nanomaterials contributes to such differences [In all cases examined, numerous metabolic alterations were observed between the different materials and among the different concentrations of the same material. This is consistent with our previous study, where the observed phenotypic alterations of zebrafish  were dependent on the concentration and the type of the material used . The differences . The differences ,30,31. I50 values), the pathway of glutathione metabolism was not found to be up-regulated in any of the other samples. This can be attributed to the inherent antioxidant potency of these materials. In a previous study, we found that the N-doped CNDs exhibit the highest antioxidant activity , followed by the N,S-codoped CNDs, in contrast to the non-doped CNDs which exhibit negligible antioxidant activity [Glutathione exhibits significant antioxidant activity in organisms. Due to its significant role in the deactivation of reactive oxygen species, it is usually synthesized in ubiquitous amounts to cope with oxidative stress . In our activity . TherefoLipids are the building blocks of cell membranes and neural tissues, while they can serve as energy sources . GlyceroRiboflavin is the precursor of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) ,37. ChanBiotin acts as a coenzyme of carboxylases that take part in the normal metabolism of proteins, lipids, and carbohydrates. It is necessary for the normal progression of gluconeogenesis, fatty acid synthesis, and amino acid catabolism pathways, and is vital for the expression of some genes . More imIn a previous study, it was suggested that dysfunction of the nitrogen metabolism pathway can be attributed to problems in the normal function of the gills . As can 50 concentration of N,S-codoped CNDs. This indicates the creation of disturbances to the normal course of glycolysis/gluconeogenesis, which can lead to reduced energy levels in the larvae, making it difficult for them to survive [50, the activation of alanine, aspartate, and glutamate metabolism was recorded without detecting glutamate. The latter can serve as an energy source; since glutamate was not detected (despite the activation of the respective pathway), it implies that it served as an energy source [Glucose is one of the most important carbohydrates for living organisms, as it is the primary source of energy for cells. As shown in  survive ,41. GlycDuring the early development and given that the organogenesis takes place, amino acids are essential. When zebrafish were exposed to CNDs, essential amino acid-related pathways were up-regulated. The emergence of these metabolic pathways, therefore, hints towards muscle damage of the larvae after exposure to CNDs, so that essential amino acids can result from the breakdown of the muscle proteins . Muscle Multitudinous studies have demonstrated, so far, the biocompatibility of CNDs and their low cytotoxicity using eukaryotic cell models. However, only a few reports have examined the effect of CNDs on more advanced organisms, and even fewer have explored their effect at a metabolic level. According to all the results presented herein, the CNDs can significantly alter the metabolism of zebrafish. Multiple alterations can take place, which result in a distorted phenotype and can lead to acute toxicity. Furthermore, these alterations hint towards the development of other health issues, at a later developmental stage of the larvae. As can be seen from the results, most metabolic alterations were observed in the case of non-doped CNDs, followed by N,S-codoped CNDs and the N-doped CNDs. Although the non-doped CNDs cause the lowest mortality  they cause the most metabolic alterations and can potentially cause more dysfunctions in the long-term. Based on the above, the CNDs pose a threat to zebrafish and even more can threaten other marine organisms, and humans (zebrafish are considered as a representative vertebrate model that well-resembles human). Moreover, it is made apparent that the metabolomic approach employed herein can be used as a benchmark for the assessment of the metabolic alterations that other nanomaterials induce to organisms, as well as to study the effect of other factors, such as pure compounds  or even This study examined the effect of CNDs on the metabolism of zebrafish. The three CNDs, i.e., non-doped CNDs, nitrogen-doped, and nitrogen/sulfur codoped analogs were studied at three concentrations to obtain insights into the metabolic alterations. It is concluded that the exposure of zebrafish to CNDs brings about multiple metabolomic alterations that are responsible for the phenotypic alterations. They were found to be dependent on the different heteroatom dopants of the CNDs and their concentrations. In most cases, new metabolic pathways were up-regulated compared with the control larvae. The up-regulation of some pathways occurred to assist the survivability of the organisms and counterbalance the negative impact of the CNDs. The exposure of zebrafish to CNDs caused many unwanted side effects that can affect the viability, the reproducibility and the overall health of the organism. Most of these phenotypic alterations can be accounted for by the results of this study, highlighting the importance of metabolic alterations due to xenobiotics. As zebrafish is highly homologous to other mammals and has been shown to share many similarities in bioprocesses and genome with humans, it can be concluded that CNDs may also pose a threat to human health. Therefore, more research is needed before large-scale production of CNDs and mainly, before developing human-targeted health-related applications.


Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior. Multispecies microbial communities determine the fate of materials in the environment and can be harnessed to produce beneficial products from renewable resources. In a recent example, fermentations by microbial communities have produced medium-chain fatty acids (MCFAs). Tools to predict, assess, and improve the performance of these communities, however, are limited. To provide such tools, we constructed two metabolic models of MCFA-producing microbial communities based on available genomic, transcriptomic, and metabolomic data. The first model is a unicellular model (iFermCell215), while the second model (iFermGuilds789) separates fermentation activities into functional guilds. Ethanol and lactate are fermentation products known to serve as substrates for MCFA production, while acetate is another common cometabolite during MCFA production. Simulations with iFermCell215 predict that low molar ratios of acetate to ethanol favor MCFA production, whereas the products of lactate and acetate coutilization are less dependent on the acetate-to-lactate ratio. In simulations of an MCFA-producing community fed a complex organic mixture derived from lignocellulose, iFermGuilds789 predicted that lactate was an extracellular cometabolite that served as a substrate for butyrate (C4) production. Extracellular hexanoic (C6) and octanoic (C8) acids were predicted by iFermGuilds789 to be from community members that directly metabolize sugars. Modeling results provide several hypotheses that can improve our understanding of microbial roles in an MCFA-producing microbiome and inform strategies to increase MCFA production. Further, these models represent novel tools for exploring the role of mixed microbial communities in carbon recycling in the environment, as well as in beneficial reuse of organic residues.IMPORTANCE Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior. While anaerobic digestion technologies are well established, other strategies to expand the range of potential products of mixed-culture fermentations are only beginning to emerge. One example of such a mixed-culture fermentation is the carboxylate platform, which produces medium-chain fatty acids (MCFAs) as a potentially valuable set of bio-based products  and a guild-based microbial community model (iFermGuilds789) that offer expanded substrates and metabolic pathways compared to existing models. We used iFermCell215 and iFermGuilds789 to analyze pathways that lead to MCFA production. Simulations were performed with single organic substrates, including sugars, glycerol, lactate, and acetate, with ethanol and acetate or lactate and acetate as cosubstrates, and with complex organic mixtures. The iFermGuilds789 model was also used to evaluate predictions of observed bioreactor behavior and of the flow of carbon and electrons through different guilds within the mixed microbial community. In total, results of individual model simulations generated new hypotheses on how to increase MCFA production by mixed microbial communities. The metabolic networks in iFermCell215 and those in iFermGuilds789 are shown in 15 and a We first investigated predictions derived from an iFermCell215 model under conditions where single substrates were consumed within this unit. The iFermCell215 model depicts a simplified representation of fermentation in a mixed microbial community where interspecies transfer of metabolites is assumed to have no energetic penalty and is similar to previously published models of mixed-culture fermentations –13. For –2, C2, C4, C6, and C8 as predicted products  and acetate and lactate as cosubstrates (B). Values greater than 10Copyright © 2020 Scarborough et al.2020Scarborough et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In total, iFermCell215 predicts that the C2:EtOH ratio either could be used to improve production of C8 as a desired product or could be an indication that the environmental conditions in a bioreactor would lead to a mixture of fermentation products. At low C2:EtOH ratios (<0.56), iFermCell215 predicts that C8 should be the abundant product. At medium C2:EtOH ratios (0.56 to 1.20), iFermCell215 predicts that a mixture of C4, C6, and C8 would be produced, while at high ratios (>1.20) of these substrates, iFermCell215 predicts C4 would be the predominant product. Therefore, in a microbial community fermenting a mixture of substrates, iFermCell215 predicts that different ratios of ethanol and C2 will impact the composition and abundance of products. Additionally, iFermCell215 predicts that, if feeding a reactor with ethanol and C2, attention should be paid to creating a feeding regime that optimizes production of the target product.Megasphaera elsdenii has been shown to produce MCFAs from lactate , a coenzyme A transferase (CoAT), and acetate kinase (AckR) were key enzymes used in the network . When ethanol is the sole substrate , iFermCe2 . When C2 balance . Instead balance  and B. T balance .2, formate, and reduced ferredoxin  Disabling homoacetogenesis; (B) disabling homoacetogenesis and acetate kinase; (C) disabling homoacetogenesis, acetate kinase, and energy-conserving hydrogenases (Ech and HydABC); (D) disabling homoacetogenesis, acetate kinase, energy-conserving hydrogenases, and CoAT; (E) disabling homoacetogenesis, acetate kinase, and energy-conserving hydrogenases (Ech and HydABC); (F) disabling homoacetogenesis, acetate kinase, energy-conserving hydrogenases, CoAT, and an NADH-dependent lactate dehydrogenase (LDH) which requires the model to use an electron-confurcating lactate dehydrogenase reaction. Download Copyright © 2020 Scarborough et al.2020Scarborough et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the −1, corresponding to the bioreactor retention time of 6 days, and we again constrained the model only by requiring a minimum of 50 kJ of free energy release per mol of ATP produced. Flux sampling results from this scenario predicted a range of potential products  and of many pathway intermediates  between community members, ultimately constraining how individual organisms maintain redox balance, which can potentially impact ATP and product yields. For instance, the single unit model (iFermCell215) cannot predict the production of lactate via an NADH-dependent dehydrogenase and reconsumption of lactate via an electron-confurcating dehydrogenase, activities that may happen in a mixed microbial community that includes lactate producers and lactate consumers. The iFermGuilds789 model contains fermentation pathways in six guilds , with eaCandidatus Weimeria bifida” (SEOs), lactate-elongating “Candidatus Pseudoramibacter fermentans” (LEOs) (Lactobacillus species (SFOs), and three hydrogenic sugar-fermenting Coriobacteriaceae species (HSFs)  , five sus (HSFs) . We conss (HSFs) , and we mulation  predictemulation , with evCandidatus Pseudoramibacter fermentans” formance . This suTaken together, initial predictions from iFermCell215  and iFer−1, which is 74% of the expected growth rate (0.0069 h−1). This growth rate corresponds to a retention time of 8 days , and these differences may reflect difficulty in estimating the amount of metabolically active cells in the reactor or absence of unknown metabolic pathways that may increase the predicted growth rate.Our previous analyses predicted that microbes within the MCFA-producing microbial community perform different functions, compete for resources, and participate in interspecies transfer of metabolites that allows for conversion of lignocellulosic biorefinery residues to SCFAs and MCFAs . We used2 from carbohydrates. SEOs are further predicted to have a flux for C6 production of 0.46 mmol gDCW−1 h−1 and C8 production of 0.033 mmol gDCW−1 h−1  of low-abundance microbes to the overall activity of the mixed-culture fermentation. Further, the objective function to maximize biomass growth may not always represent the true objective of a fermenting microbial community. While there is no direct evidence that polymer storage is occurring in these organisms, other undescribed traits that enable resiliency may be present. Additionally, while most studies investigating the minimum required free energy release have found values close to 50 kJ per mol ATP , ATP pro2, and H2, which are potential intermediates in this mixed-culture fermentation  within the microbial community. The iFermGuilds789 network describes the same reactions and metabolites as in the iFermCell215 model, but these are separated into six functional guilds with each guild representing a subset of compartmentalized reactions . iFermGu10.1128/mSystems.00755-20.1TEXT S1Text S1, DOCX file, 0.1 MB.Additional descriptions of representative organisms in each guild, formation of the biomass equation, transport constraints, and the predicted impacts of reaction knockouts on lactate consumption. Download Copyright © 2020 Scarborough et al.2020Scarborough et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the When constructing each metabolic network, reactions were added to capture the exchange of substrates and products with the extracellular space . In addiFirmicutes, simplified biomass equations for both models were created based on the biomass composition of the well-studied Gram-positive bacterium Bacillus subtilis . All simulations were performed in Python 3.7 using the COBRApy package (v 0.18.1) (The production capabilities of both metabolic networks were simulated using standard constraint-based methods . Given a 0.18.1) .Gf0) of metabolites , which report the number of electrons potentially transferred to an electron acceptor if the compound is completely oxidized  contained in substrates and products from a mixed-culture fermentation of lignocellulosic biorefinery residue. Substrates represent the eeq consumed by the microbial community, and products represent the millielectron equivalents (meeq) of the measured products. We assumed that the complex carbohydrates remaining in conversion residue were comprised of 50% xylans and 50% glucans, by mass based on published compositions of this material . DownloaCopyright © 2020 Scarborough et al.2020Scarborough et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol The interconnected metabolic activities in these microbial communities underpin the biogeochemical cycles that feed into the Earth's ecosystem  and the Modelling microbial community dynamics is a significant challenge due to the complexity of these systems. Typical communities, for example those found in the human gut or AD reactors, are composed of hundreds to thousands of distinct microbial species ,6. The mMicrobial growth and metabolite dynamics are historically captured by empirical models such as the Monod growth function ,12. ThesTo capture thermodynamic inhibition effects, a simple thermodynamic model has been proposed that adjusts a Monod-type growth function with a thermodynamic factor based on the free energy of the growth-supporting metabolic conversion ,17,19,20vmax), substrate affinity coefficient (sK) and biomass yield from the substrate (s/xY) have been experimentally estimated for individual species that represent common functional groups seen in microbial communities. For some of these kinetic parameters, in particular biomass yield and substrate uptake rate, calibrated methods have been derived that can predict parameters from existing data and first principles approximations [Kinetic parameters of microbial growth have been collected over decades of research using monocultures grown under defined conditions. In particular, maximal growth rate (imations –26. The Desulfovibrio vulgaris (Dv), a sulfate reducer, Methanococcus maripaludis (Mm), a hydrogenotrophic methanogen and Methanosarcina barkeri (Mb), a methanogen capable of acetoclastic methanogenesis. Using daily metabolite measurement from mono-, co- and tri-cultures over a 21-day experiment, we show that the resulting model provides a superior fit to data, compared to a non-thermodynamic model, and that some of the thermodynamic parameters of the model can be calibrated using time-series data. These results show that thermodynamic models are appropriate and are needed to accurately capture metabolite dynamics in microbial communities, but that their full calibration requires a greater breadth of experimental data.Here, we aim to address this gap and develop a generic, readily extendable thermodynamic community model and use it to estimate the minimal free energy parameters from experimental time-series data. The model implements the multiple and distinct growth-supporting metabolic conversions possible in each organism and accounts for their possible thermodynamic limitations. It also accounts for metabolite phase exchanges and system pH. We calibrate the model using experimental data from synthetic communities composed of microbial species that represent key functional groups in AD systems; 2.2.1.https://github.com/OSS-Lab/micodymora.The model presented here aims to capture the population and metabolic dynamics within a microbial monoculture or a multi-species community. The model accounts for a set of growth-supporting metabolic pathways that involve either specific metabolites or cellular biomass . The che2.2.Dv, Mm and Mb populations, as illustrated in figure 1a and listed in Dv, Mm and Mb populations are considered to use lactate, carbon dioxide and acetate, respectively, as carbon source. In each of these reactions, biomass is represented as a generic molecule , having an associated Gibbs free energy of formation of −67 kJ mol−1 [G) calculated at a pH of 7.0 and a temperature of 310.15 K with reagents other than protons in their standard state.For the presented model, several growth-supporting (i.e. catabolic) and biomass forming (i.e. anabolic) metabolic pathways are considered to be encoded by kJ mol−1 . Table 12.3.Dv, Mm and Mb populations as implementing catabolic pathways available for each species. The overall dynamics of each population are governed by a differential equation that accounts for all of its catabolic pathways rates, as well as its anabolic biomass production. It is assumed that for a given population, the anabolic reaction has the same formula for each of its catabolic pathways . This factor is computed dynamically from the energetics of catabolic and anabolic reactions, as well as the overall Gibbs energy change during biomass formationi and j range over a given species (e.g. Dv) and catabolic pathway (e.g. lactate fermentation), respectively. The S −1), of the associated catabolic pathway j in a given species i, while ΔGjmet, is the overall Gibbs energy differential of the pathway . X −1) for species i. j index because we decided to use the same anabolic reaction stoichiometry for all pathways of a population. The Gibbs free energy of the catabolic and anabolic reactions are calculated dynamically during model simulation from the chemical species concentrations. The metabolic energy is that which is harvested by the population through its catabolism and which is not chemically stored as biomass. It encompasses a wide diversity of processes including heat and entropy emission and cellular maintenance. Its value is estimated based on experimentally measured dissipated energy for different microbial species [λcat factor relates to the growth yield associated with a metabolic pathway j of a population i according to the following relationship:iγD, is the stoichiometric coefficient for the electron donor in the anabolic reaction associated with the pathway.To model population dynamics, we consider the pathways . To acco species  −1) of a catabolic pathway j for a given species i is given byS (molX · h)−1 and specific to the pathway j and the population i, kth limiting substrate of the pathway j, j for species i, and R (in kJ (mol · K)−1) and T (in K) denote the gas constant and system temperature respectively. Note that in the main text, we refer to the first and second terms of equation (2.3) as kinetic (DF) and thermodynamic (TF) factors, similar to previous presentations in the literature [The specific rate ), however, iγX, is always one because the anabolic formula is normalized to the mole of biomass. Additionally, we account for the loss of biomass through death using a linear decay coefficient dk (1 h−1), resulting in the following differential equation for biomass:dk (=8.33 × 10−4 1 h−1) is the same for all species. This value is based on the experimental estimates made on Desulfovibrio vulgaris monocultures [dk for Mb and Mm (between 6.66 × 10−4 1 h−1 and 9.99 × 10−4 1 h−1) had no significant effect on the results of this manuscript .The dynamics of the biomass associated with a population cultures . Simulat2.4.A, is accounted for as two separate chemical species A(aq) and A(g), respectively. The concentrations of each species are accounted for by moles per litre of their respective phase volume. The transfer dynamics occurring between the two forms is captured through a set of differential equations given byLak is the mass transfer coefficient of the chemical (in 1 h−1) [H310.15 is the Henry constant of the chemical at 310.15 K (and expressed in mol (m3 · Pa)−1), Vaq is the volume of the liquid phase and gV is the volume of the gas phase (both in litres). Henry constants were obtained from the literature [Hsol) as follows: Some chemicals exist in both the gas and liquid phases. Any of such chemical species, n 1 h−1) , H310.15terature , and adj2.5.At the beginning of each timestep in the integration of the differential equation system (composed of equations (2.4)–(2.5), the pH of the solution is determined. This is done by solving the charge balance of the system using the Brent method , while c2.6.2(g) and CH4(g) concentrations). With this optimization procedure, the parameters are treated in a hierarchical fashion according to two properties; the extent (the number of variables affected upon changing a given parameter) and scale (the level of change in variables induced by changing a given parameter) of their effect. The parameter that produces the strongest effect among the least number of variables is selected first and the experimental time course data of the variables are weighted so that those variables that are most affected by the parameter have more weight in the calculation of the match between model prediction and experimental data during the optimization procedure. The selected parameter is then optimized against the weighted experimental data on variables using the truncated Newton method . This method minimizes the weighted sum of squared distance between the model predictions and the experimental data on the variables. Once a parameter is optimized in this way, its value is fixed and removed from the list of the parameters to be optimized. The optimization procedure then restarts with the remaining parameters until they have all been optimized and then repeated again for a different set of starting values. The whole process of optimization is repeated until it yields no significant improvement anymore in terms of distance between the model predictions and the experimental data on variables.The kinetic parameters used in the model are listed in electronic supplementary material, table S1, and are based on experimental estimates given in the literature. Henry constant and mass transfer coefficients  are compiled from the literature or measured in this study (see below). The thermodynamic parameters are either calculated dynamically (as explained above) or adapted from the literature . The onlrocedure . This aprocedure . In brie2.7.−1 was approximated from the experimental OD (at 600 nm) measurements at the start of the experiment  and using a previously calibrated relationship between OD and biomass using sulfate-reducing bacteria (predominantly Desulfovibrio vulgaris) [−1. We converted the resulting DW value to 1 C mol−1 by dividing it by the molecular weight of the generic molecule used to represent biomass  ; ln(DW) 0.5N0.2, ); 24.6 ghttps://github.com/OSS-Lab/micodymora). The calculations performed at each simulation timestep are the following: (i) determine the pH and the speciation of each chemical species (see ‘Modelling of medium pH’), (ii) compute the differential of each chemical species based on their current concentrations for gas/liquid transfer (see ‘Modelling of gas/liquid transfer’) and biochemical reactions (see ‘Modelling of metabolites and populations dynamics') separately, (iii) add the two differential terms together. The integration of the system is done using the ‘odeint’ function of the ‘scipy’ package.Simulations were done using the ‘micodymora’ package for Python v. 3.4  were prepared with 5 ml medium and 0.1 ml of 100 mM Na2S·9H2O in each tube, sealed in an anaerobic chamber station  and autoclaved. The headspace gas pressure and composition of the tubes were measured using a micro gas-chromatograph (GC)  and recorded. A gas mixture of H2, CO2 and CH4 was prepared by first flushing two 118 ml serum bottles with 80% H2/20% CO2 gas mixture for 3 min at 0.5 l min−1 flow rate and balancing the final pressure to 1 atm (101 325 Pa). Then, 10 ml of 90% CH4/10% CO2 gas mixture at 1 atm was injected into each serum bottle using a gas-tight glass syringe . Two millilitres of the resulting gas mixture is injected into each of the prepared Hungate tubes using a gas-tight glass syringe. The tubes were incubated under 37°C for more than 24 h, in order to let the added gas to be equilibrated between the headspace and the aqueous phase. The tubes were then flushed with 100% N2 for 2 min at a flow rate of 0.2 l min−1 and their headspace pressure brought to 1 atm using sterile needle and filter. The tubes were then returned to the 37°C incubator, and brought out in replicates of three for temporal measurement of headspace gas composition at pre-determined intervals of 0, 1, 2, 4, 8 and 24 h. The resulting temporal gas equilibration data are then used to estimate the Lak value for H2, CO2 and CH4. Specifically, the Lak values were obtained by minimizing the sum of squared error between average observed measurements and the integration of the dynamics of gas transfer considered (see equations (2.6) and (2.7)).The escribed , contain2.9.Dv, Mb and Mm and anaerobic medium preparations were done as previously described [Dv, Mb and Mm were cultivated in 5 ml anaerobic media for 4, 21 and 7 days respectively, to reach their late log phase. These monocultures were all grown at 37°C in the same anaerobic medium base  for continuous monitoring of the methane production over two weeks. The active methanogenic communities in all combinations were confirmed in this way and the cultures were considered and used as the stock cultures for the following step. Five millilitres of the stock cultures from each combination were extracted inside the anaerobic chamber, mixed separately with 5 ml fresh anaerobic medium OSM1.0 with 30 mM Na-lactate and 7.5 mM Na2SO4 in Hungate tubes, and incubated at 37°C for 7 days. These cultures formed the inocula for the following time-series experiment.The three strains of escribed . In brieribed in ), but wiDv monoculture, we designed a time-series experiment that involved starting a large number of replicate tubes and terminating a set of this large batch at different time points for gas and metabolite measurements. In total, 273 anaerobic Hungate tubes were prepared to collect data for 21 time points. Each tube contained 5 ml OSM1.0 medium with 30 mM Na-lactate and 7.5 mM Na2SO4. According to the full reaction of sulfate reduction by Dv in Dv to convert 15 mM lactate fully, while the conversion of the other 15 mM lactate would rely on Dv's other less thermodynamically favourable pathways. The tubes were numbered individually and separated into 21 batches. Each batch contains 13 tubes, of which one tube was used as a blank control and three replicate tubes were used each for the four cultures: Dv–Mb, Dv–Mm, Dv–Mb–Mm and Dv, respectively. The tubes were inoculated with the respective cultures using the stock cultures described above, and following the tube and batch numbers. The initial optical density (OD) at 600 nm and headspace pressure were recorded for each tube using a spectrophotometer  and a needle pressure gauge . All tubes were incubated at 37°C. Over the following 21 days, 13 tubes of one batch were terminated on each single day to measure their OD at 600 nm, pH , gas pressure , gas composition using Micro-GC  and the lactate, acetate, pyruvate and sulfate concentrations using ion chromatography  as described previously [To measure temporal dynamics of co- and tri-cultures, as well as eviously .3.Desulfovibrio vulgaris, Dv), and two different methanogens representing hydrogenotrophic  and hydrogeno/acetotrophic  methanogenesis pathways .To develop and calibrate a thermodynamic model of microbial growth and metabolite dynamics in a community context, we focus here on defined anaerobic synthetic communities. In particular, we use a recently developed experimental model system for studying syntrophic interactions among sulfate reducers and methanogens ,40, whic3.1.To capture community and metabolite dynamics, we developed a comprehensive and expandable thermodynamic model that also accounts for metabolite phase exchanges and medium pH . As is common for many microbes found in microbial communities, the species composing the studied synthetic communities can catalyse multiple, distinct metabolic pathways, sometimes starting from the same substrate. To account for these different metabolic activities of each species, we considered that each population can use any number of pathways at once, as previously described . Each paDF), expressing enzyme kinetics and a thermodynamic factor (TF), expressing the limitations arising from thermodynamic constraints . The ΔGmin represents the concept that cells must invest some of the energy associated with each catabolic into a metabolic driving force to run that reaction, as well as into maintaining cell viability. It is assumed that such an energy investment is pathway specific and its value can be estimated from population dynamics data, as attempted here. The resulting model is parameterized for kinetic rates using available estimates for Dv, Mm and Mb . After this parameterization, the only unknown parameters in the system are the ΔGmin, which we have estimated here from the data, and some of the metabolite phase exchange constants, which we have determined experimentally. This model is developed in a generic manner allowing its expansion to include additional species and metabolic conversions. This makes it adaptable to other monocultures and natural or synthetic communities .The specific rate of the catabolic reaction is determined by the product of a kinetic factor . When we do so and use previously determined kinetic parameters , we can apply the resulting model without thermodynamic inhibition to the experimental data. We find that such a model is not able to explain the observed experimental results , and as discussed in the next section, this feature allows it to better capture the experimental data.The eviously . The sameviously ,22 (figu results . In part3.3.Gmin parameters associated with the TF term in equation (2.3). As described above, this parameter captures the associated energy investment from each catabolic reaction into ‘running’ that reaction as well as the amount of energy harvested by the cell , and lies between −30 and −15 kJ mol−1 (a). There seems to be an alternative optimum at 0 kJ mol−1 as well, and while using this ΔGmin value for simulations can result in a good fit of experimental data , we do not consider this optimum due to its biological infeasibility (it would imply that none of the catabolic energy is stored into conserved moieties or membrane gradient). The ΔGmin of the hydrogenic and acetoclastic methanogenesis pathways cannot be given an exact estimate but rather boundaries; less negative than −20 kJ mol−1 for hydrogenic methanogenesis and less negative than −40 kJ mol−1 for acetoclastic methanogenesis (a). The ΔGmin parameters for Dv's sulfate respiration pathways could not be calibrated with the present experimental data, presumably because sulfate respiration occurs relatively quickly compared to the time-resolution of the available experimental data.After a set of parameters was determined by the calibration procedure for each combination of observed variables, a parametric sweep was performed to determine whether the obtained values correspond to an optimum that minimizes the distance between simulation and observation. We assess this by plotting an error function  for each calibrated parameter value ), while in the DvMm and DvMmMb cultures, the optimum is clearly around −17 kJ mol−1 (a). Consequently, simulating the Dv culture with the ΔGmin calibrated on the DvMm and DvMmMb cultures (−17 kJ mol−1) and simulating the DvMmMb co-culture with the ΔGmin calibrated on the Dv and DvMb cultures (−30 kJ mol−1) results in poor fit of experimental data . This finding suggests that the ΔGmin, i.e. the amount of energy harvested by the cell from a pathway as ATP or membrane gradient is not a constant. Rather, our observations suggest that it depends on the context in which the pathway is operating, and that some interactions with other organisms may elicit changes in the way cells tune their metabolism which may result in significant modification of their Gibbs free energy thresholds.Interestingly, we find that the lactate fermentation pathway has well-defined and distinct ΔkJ mol−1 a. ConseqGmin parameter has been attempted before in only few studies [Dv (lactate fermentation and sulfate respiration with lactate or hydrogen) has been calibrated against experimental data only from monocultures grown in the presence of sulfate, and using a model similar to that presented here [−1, relatively higher, i.e. more energy investment required as driving force and into maintenance, than found here. Possibly due to its use of higher sulfate concentration and shorter sampling intervals, that study was able to estimate the ΔGmin for both lactate and H2 respiration on sulfate, as −44.66 kJ mol−1. It should be noted that the model used by that study is different from the model used here, in that it uses gas partial pressure when calculating reaction free energies and has taken a static approach to model biomass yield. A theoretical study aimed at estimating energetic parameters for several metabolic pathways, including methanogenesis, using existing data [et al. [−1, as observed by Schink [Desulfovibrio vulgaris with various syntrophs [As far as we are aware, the estimation of the Δ studies ,32. The ing data , but it ing data  and Gonz [et al.  considery Schink . The valyntrophs .b). There are two possible explanations for this observation. Firstly, there may be metabolic pathways being catalysed by the populations in the different experimental batches that are not represented by the model. If such pathways involve a specific metabolite, then calibrations performed on that metabolite versus some other metabolite might differ. Such an explanation, while theoretically possible, does not fit with the fact that the presented model accounts for all key pathways known to be catalysed by Dv, Mm and Mb. The second possible explanation is that the minimum energy threshold of a given metabolic pathway depends on the experimental conditions. Indeed, the concept of a minimum energy threshold for growth aims to conceptualize energy invested as the metabolic driving force as well as cellular maintenance. Both these energetic investments are expected to be a function of culture and cellular conditions, including specific cellular details such as Mg2+ concentration [It is interesting to note that when a parameter sweep shows the existence of an optimal value or range, these depend on the experimental variable and culture used for calibration b. There ntration ). The mi4.Here, we have developed and presented a generic thermodynamic model for capturing population and metabolite dynamics in a microbial community. The model implements specific features that have been proposed and advocated over the last two decades ,28,33,41Our calibration results also shed light on the limitations of the thermodynamic approach employed. In particular, despite improvements over a non-thermodynamic model, the presented model was not able to fully capture experimental metabolite and population dynamics and its combination with experimental data did not allow the minimal free energies for all modelled pathways to be determined precisely. These limitations might be inherent in the structure of the model or in the experimental design used here, or a combination of the two. The latter can be addressed particularly by collecting more and higher resolution temporal data from similar experimental systems. The former will probably require increasing the complexity of the presented model. In particular, considering minimum energy thresholds as a constant feature of the system may be too simplistic and might instead require the inclusion of elements of metabolic pathway dynamics within the bacterial growth models.Thermodynamic constraints that we have endeavoured to predict here are one of the few phenomena that can be safely assumed to apply for all growth-supporting metabolic pathways. A sound basis for the description of this fundamental constraint applying to metabolic dynamics is thus necessary before attempting to assess and calibrate the extent of higher-order phenomena such as genetic regulation or resource allocation. While further dedicated experiments and more complex models are necessary to improve the accuracy of dynamics predictions, the presented work provides a step towards this aim. The presented model expands previous efforts of minimal energy estimates from monocultures  and comb