C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers.Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of  Clostridium thermocellum and Thermoanaerobacterium saccharolyticum are thermophilic anaerobic bacteria with complementary metabolic capabilities that utilize distinct glycolytic pathways for the conversion of cellulosic sugars to biofuels. We integrated quantitative metabolomics with 2H and 13C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the central metabolic networks of these two microbes. We found that the glycolytic pathway in C. thermocellum operates remarkably close to thermodynamic equilibrium, with an overall drop in Gibbs free energy 5-fold lower than that of T. saccharolyticum or anaerobically grown Escherichia coli. The limited thermodynamic driving force of glycolysis in C. thermocellum could be attributed in large part to the small free energy of the phosphofructokinase reaction producing fructose bisphosphate. The ethanol fermentation pathway was also substantially more reversible in C. thermocellum than in T. saccharolyticum. These observations help explain the comparatively low ethanol titers of C. thermocellum and suggest engineering interventions that can be used to increase its ethanol productivity and glycolytic rate. In addition to thermodynamic analysis, we used our isotope tracer data to reconstruct the T. saccharolyticum central metabolic network, revealing exclusive use of the Embden-Meyerhof-Parnas (EMP) pathway for glycolysis, a bifurcated tricarboxylic acid (TCA) cycle, and a sedoheptulose bisphosphate bypass active within the pentose phosphate pathway.IMPORTANCE Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers. Thermoanaerobacterium saccharolyticum and Clostridium thermocellum are thermophilic, anaerobic bacteria with complementary metabolic capabilities being developed for industrial-scale production of biofuels, such as ethanol, from lignocellulosic biomass   , 5. Meta mobilis –9. In co, and H2 , 10. T. olyticum . Severalmocellum –19.T. saccharolyticum and C. thermocellum possess distinct glycolytic pathways. While genome annotation suggests that T. saccharolyticum uses the canonical Embden-Meyerhof-Parnas (EMP) pathway, the glycolytic pathway in C. thermocellum has been shown to possess several unique characteristics (20–i) rather than ATP hydrolysis (C. thermocellum lacks a pyruvate (Pyr) kinase  and instead converts phosphoenolpyruvate (PEP) to Pyr via one of two routes: (i) pyruvate phosphate dikinase  and (ii) the malate shunt, which converts PEP to Pyr via three steps, phosphoenolpyruvate carboxykinase , malate dehydrogenase , and malic enzyme   per glucose but also can be expected to significantly impact the thermodynamic driving force of individual reactions and of the overall pathway.In addition to the differences outlined above, eristics , 20–23  , 26  . These uin vivo Gibbs free energies (ΔG) of metabolic reactions have been applied to investigate the thermodynamics of glycolytic pathways in a few model organisms and biofuel producers  (+J) to backward (−J) flux ratios (+/J−J) from isotope tracer experiments (R is the gas constant and T is the temperature in kelvin)   and the  kelvin) , 29. The kelvin) –34.2H and 13C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the glycolytic pathways and central metabolic networks of T. saccharolyticum and C. thermocellum. We show that glycolysis in C. thermocellum is highly reversible at every step of the pathway and operates close to thermodynamic equilibrium. In comparison, the glycolytic pathway in T. saccharolyticum is highly thermodynamically favorable, comparable to that of anaerobically grown Escherichia coli. We also found that the ethanol fermentation pathway of C. thermocellum was substantially more reversible than that of T. saccharolyticum. Besides revealing differences in pathway reversibility, our analyses also represent the first experimental isotope-based reconstruction of the T. saccharolyticum central metabolic network. We found that this bacterium metabolizes glucose exclusively via the EMP pathway, while the Entner-Doudoroff (ED) pathway and the oxidative pentose phosphate pathway (oxPPP) are inactive. Our analyses also revealed a bifurcated tricarboxylic acid (TCA) cycle and a sedoheptulose bisphosphate bypass within the pentose phosphate pathway (PPP). Finally, we present evidence supporting the activity of several currently unannotated amino acid biosynthetic pathways in T. saccharolyticum.Here, we integrated quantitative metabolomics with 2H and 13C metabolic flux analysis to measure in vivo reversibility and thermodynamics of metabolic reactions requires prior knowledge of the underlying metabolic network topology. Recent studies have used isotope tracers, metabolic flux analysis (MFA), proteomics, or biochemical assays to elucidate the central metabolic network of C. thermocellum , we present a quantitative MFA that corroborates the metabolic network reconstruction presented in this section.The application of mocellum , 35–38. ysiology , 39, 40,T. saccharolyticum has genes encoding all of the enzymes in the EMP pathway, but its genome annotation does not support a complete ED pathway. T. saccharolyticum does not have sequences associated with the ED enzymes phosphogluconolactonase  or phosphogluconate dehydratase , although it does have genes annotated as glucose-6-phosphate dehydrogenase  and ED pathway aldolase  (The Embden-Meyerhof-Parnas (EMP) and the Entner-Doudoroff (ED) pathways are the two most common glycolytic pathways found in microbial species, although several other variants have been described , 41–43. 41–ac_0268) , 23.T. saccharolyticum was grown on [1-13C1]- or [6-13C1]glucose, the glycolytic intermediates 3-phosphoglycerate (3PG) and phosphoenolpyruvate (PEP) both were ∼50% M + 0  and ∼50% M + 1 , which was consistent with their production via EMP glycolysis , consistent with its production solely via the EMP pathway (13C2]Pyr), which would have been indicative of ED pathway activity H] to form ribulose-5-phosphate (Ru5P), losing what was originally the first carbon of glucose as CO2 (T. saccharolyticum has annotated genes for g6pdh (Tsac_1610) and 6pgd (Tsac_0154) genes, but there are no annotated pgl sequences  to 6PG via the G6pdh and Pgl reactions, which are shared with the ED pathway. 6PG then is decarboxylated by 6-phosphogluconate dehydrogenase glucose . Nondete]glucose , 47.T. saccharolyticum, R5P and Ru5P were predominantly M + 2 and M + 4 labeled in glucose and M + 1 and M + 2 labeled in [1-13C1]glucose , a transaldolase reaction , and a second transketolase reaction . Additionally, ribose phosphate epimerase  and ribose phosphate isomerase  interconvert Xu5P, Ru5P, and R5P. In ]glucose  and C, w13C2]- and [1-13C1]glucose that could not be explained by canonical nonoxidative PPP reactions  bypass comprising an SBP aldolase  and an SBP phosphatase   are annotated as both Fbas and Sbas  of M + 0 S7P in both ) , 26. Whiand Sbas , 23, andand Sbas .13C1]- and glucose. Similarly, during [6-13C1]glucose labeling, the large fraction of M + 2 S7P (∼32.5%) was also indicative of SBP bypass activity (This SBP bypass can explain the production of unlabeled S7P from unlabeled DHAP and E4P during growth on glucose labeling indicated that formaldehyde transketolase (Ftkt) was unlikely responsible for the formation of Ru5P and R5P. Dihydroxyacetone phosphate (DHAP) was either unlabeled or M + 2 labeled and can be dephosphorylated to form dihydroxyacetone (DHA). Ftkt condenses the first two carbons of DHA with glyceraldehyde-3-phosphate  to form xylulose-5-phosphate (Xu5P), releasing the third carbon of DHA as formaldehyde. Because DHA is symmetrical, carbons 1 and 3 are equivalent, so a 13C carbon from M + 2 DHA has a 50% chance of being incorporated into Xu5P. Thus, Xu5P produced by Ftkt inherits 0 or 2 13C-labeled carbons from GAP and 0, 1, or 2 13C carbons from DHA. Xu5P is epimerized to form ribulose-5-phosphate (Ru5P), which is isomerized to form ribose-5-phosphate (R5P). Therefore, if Ftkt was involved in the production of pentose phosphates, we would expect Xu5P, Ru5P, and R5P to be approximately 17% M + 0, 17% M + 1, 33% M + 2, 17% M + 3, and 17% M + 4. However, labeling of Ru5P and R5P in cells fed glucose did not match these patterns but were primarily M + 2 and M + 4 labeled, with only small fractions of M + 1 or M + 3. These labeling patterns are better explained by canonical nonoxidative PPP reactions supplemented with an SBP bypass (T. saccharolyticum. (B) glucose labeling also indicated that the reverse ribulose monophosphate (rRuMP) pathway was not responsible for the formation of Ru5P and R5P. The rRuMP pathway isomerizes fructose-6-phosphate (F6P) to hexulose-6-phosphate (Hu6P). Hu6P is split into (Ru5P) and formaldehyde, with carbons 2 to 6 retained in Ru5P. Thus, the carbon lost as formaldehyde is 13C labeled during glucose labeling, leading to the production of M + 1 Ru5P. Ru5P then is isomerized to form R5P. Ru5P and R5P were primarily M + 2 and M + 4 during glucose labeling, with only a very small M + 1 fraction, indicating that pentose phosphate generation via the rRuMP pathway was unlikely. This agrees with the lack of an annotated gene encoding hexulose-6-phosphate synthase (Hps) in T. saccharolyticum. (C) [6-13C1]glucose labeling indicated that phosphoketolase (Pkt) activity was unlikely to be responsible for the formation of Ru5P and R5P. GAP produced by the EMP pathway was M + 0 or M + 1. GAP is further processed to form acetyl-phosphate (AcP), which was M + 0 or M + 1 labeled. GAP and AcP can be condensed by Pkt to form Xu5P, which thus inherits no or one 13C-labeled carbon from GAP and 0 or 1 13C-labeled carbon from AcP. Thus, Xu5P, Ru5P, and R5P produced via Pkt were expected to be ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2. However, we observed approximately equal proportions of M + 0 and M + 1 Ru5P and R5P, with only a small fraction of M + 2, indicating that Pkt was unlikely to be responsible for pentose phosphate generation in T. saccharolyticum. This agrees with the lack of an annotated gene encoding a phosphoketolase in T. saccharolyticum. (D) Labeling of TCA cycle intermediates in T. saccharolyticum fed [1-13C1]glucose. M + 2 AKG production was consistent with production from oxaloacetate and acetyl-CoA via the oxidative TCA cycle. A lack of M + 2 succinate indicates that succinate was not produced oxidatively from AKG and that the TCA cycle was bifurcated. The presence of only M + 0 and M + 1 succinate, malate, and aspartate indicates these metabolites were produced by a reductive TCA. (E) Labeling of TCA cycle intermediates in T. saccharolyticum fed glucose. M + 4 AKG production was consistent with production from oxaloacetate and acetyl-CoA via the oxidative TCA cycle. A lack of M + 3 succinate indicates that succinate was not produced oxidatively from AKG and that the TCA cycle was bifurcated. The presence of only M + 0 and M + 2 succinate, malate, and aspartate indicates these metabolites were produced by a reductive TCA. The increased proportion of M + 0 α-ketoglutarate, pyruvate, succinate, and malate were due to variable extracellular pools of these metabolites that do not participate in metabolism. In addition to its production via Fumh, fumarate may be produced by deamination of aspartate, which takes place in a number of biosynthetic pathways, such as purine biosynthesis. This route of fumarate production would result in the same labeling patterns as its production from oxaloacetate via Mdh and Fumh. CO2 labeling was inferred from the MIDs of citrulline and ornithine, as described in Materials and Methods. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Pyruvate formate lyase (Pfl) was omitted from panels D and E for clarity but serves the same role as Pfor. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Carbons with an approximately equal chance of being 13C or 12C labeled are represented by a half red and half black circle. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Abbreviations: Dap, dihydroxyacetone phosphate phosphorylase; DHA, dihydroxyacetone; FAL, formaldehyde; Ftkt, formaldehyde transketolase; Hps, hexulose-6-phosphate synthase; Hu6P, hexulose-6-phosphate; Phi, phosphohexuloisomerase; Pkt, phosphoketolase. Download FIG S1, PDF file, 0.6 MB.Alternative routes for pentose phosphate generation and bifurcation of the TCA cycle in P bypass . This agCopyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the T. saccharolyticum genome, including citrate synthase and malate dehydrogenase. Our 13C tracer data indicated that the TCA cycle in T. saccharolyticum functions as a branched pathway to produce essential biosynthetic precursors: α-ketoglutarate (AKG) was produced oxidatively despite the lack of an annotated citrate synthase, while oxaloacetate (OAA), malate , and succinate all were produced reductively. During growth in [6-13C1]glucose, α-ketoglutarate (AKG) labeling patterns were consistent with its production from oxaloacetate and acetyl-coenzyme A (AcCoA) via citrate and isocitrate  Labeling of aspartate, asparagine, and threonine was consistent with all three amino acids originating from oxaloacetate (OAA). OAA can be produced via carboxylation (CO2 addition) of PEP by Pepck or via carboxylation of Pyr by ME to produce malate, followed by its conversion to OAA by Mdh. Because PEP and Pyr were both ∼50% M + 0 and ∼50% M + 1 and CO2 was unlabeled, production of ∼50% M + 0 and ∼50% M + 1 OAA was expected. Aspartate, produced via amination of OAA, had the same labeling pattern as Mal, PEP, and Pyr (∼50% M + 0 and ∼50% M + 1). Aspartate is further converted to threonine (Thr) or asparagine (Asn) without carbon transitions, resulting in Asn and Thr labeling that matched that in PEP and Pyr (∼50% M + 0 and ∼50% M + 1). (B) Labeling of amino acids derived from α-ketoglutarate (AKG) was consistent with canonical synthesis pathways. OAA is formed by the addition of CO2 to PEP by Pepck or to Pyr by the combined action of ME and Mdh. Because PEP and Pyr are both ∼50% M + 0 and ∼50% M + 1 and CO2 is unlabeled, M + 0 and M + 1 OAA are produced. Pyr is also decarboxylated by Pfor, losing an unlabeled CO2 to form acetyl-CoA (AcCoA) that is ∼50% M + 0 and ∼50% M + 1. Citrate synthase combines OAA with AcCoA to form citrate, which is ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2, and has a 50% chance of inheriting one 13C carbon each from AcCoA and OAA. Citrate then is decarboxylated to form AKG, losing an unlabeled carbon in the process, leaving AKG with the same labeling pattern as citrate . AKG is aminated to form glutamate, which serves as the precursor to glutamine, proline, ornithine, and citrulline, all of which are approximately ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2 labeled, consistent with the formation of all of these amino acids from AKG. (C) Labeling of T. saccharolyticum with glucose indicated that valine and leucine were produced via canonical synthesis pathways from pyruvate and acetyl-CoA. Briefly, two pyruvate molecules are condensed to form the intermediate ketoisovalerate (KIV), losing the carboxyl group of one of the pyruvate molecules as CO2. Each pyruvate has a ∼50% chance of being labeled at the carbonyl and methyl groups, meaning KIV should be primarily ∼25% M + 0, ∼50% M + 2, and ∼25% M + 4. KIV is aminated to form valine, and we detect valine labeling that approximates what we expect from KIV. KIV is also used for leucine biosynthesis, where it receives an additional two carbons from AcCoA, itself formed from the carbonyl and methyl carbons of pyruvate and, thus, ∼50% M + 0 and ∼50% M + 2. During leucine synthesis, the carboxyl carbon of KIV  is lost as CO2. Thus, leucine independently inherits 3 sets of 2 carbons, and each set may be both labeled or unlabeled and should be labeled ∼12.5% M + 0, ∼37.5% M + 2, ∼37.5% M + 4, and ∼12.5% M + 6. As expected, leucine labeling is consistent with production from KIV, although it is enriched for the M + 0-labeled form due to incomplete label penetration. The presence of only M + 0 and M + 2 labeling in acetyl group carbons confirms the EMP pathway as the sole glycolytic route, as ED pathway activity would lead to the formation of M + 1 acetyl groups via decarboxylation of pyruvate due to the alternate orientation of carbons in pyruvate formed from the first three carbons of glucose via the ED pathway. (D) [1-13C1]glucose labeling in T. saccharolyticum confirmed the synthesis of aromatic amino acids via the shikimate pathway. Erythrose-4-phosphate (E4P) is produced by transketolase from the bottom 4 carbons of F6P. The reverse activity of the Pfk and Fba reactions in the EMP pathway can result in F6P that is labeled at the 6th carbon. Thus, E4P can be M + 0 or M + 1, although the M + 0 form is much more prevalent. The shikimate pathway condenses E4P with PEP  to form a seven-carbon intermediate, 3-deoxy-d-arabinoheptulosonate-7-phosphate (DAHP), which is processed to become shikimate-3-phosphate (Sh3P). Sh3P can contain 0, 1, or 2 labeled carbons. Sh3P is further processed and combined with another PEP molecule (which is M + 0 or M + 1) and dephosphorylated to form chorismate, which is M + 0, M + 1, M + 2, or M + 3. Chorismate mutase converts chorismate to prephenate, which is decarboxylated, dehydrated, and aminated to form phenylalanine (Phe) or decarboxylated, oxidized, and aminated to form tyrosine (Tyr). The CO2 lost by decarboxylation is unlabeled , meaning the labeling of Phe and Tyr is the same as that of chorismate. Therefore, Phe and Tyr should inherit up to one 13C carbon from E4P and up to one 13C carbon from each of the two PEPs, resulting in M + 0, M + 1, M + 2, or M + 3 Phe and Tyr. The experimental labeling patterns in Phe and Tyr matched these predictions. Pyruvate formate lyase (Pfl) was omitted from panels A and B for clarity but serves the same role as Pfor. The increased proportion of M + 0 α-ketoglutarate and malate in panels A and B was due to extracellular pools of these metabolites that do not become labeled. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Carbons with an approximately equal chance of being 13C or 12C labeled are represented by a half red and half black circle. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Abbreviations: Asn, asparagine; Citr, citrulline; Gln, glutamine; Leu, leucine; Orn, ornithine; Phe, phenylalanine; Sh3P, shikimate-3-phosphate; Tyr, tyrosine; Val, valine. Download FIG S2, PDF file, 0.6 MB.Routes for amino acid synthesis. When the EMP pathway is used for glucose catabolism, TCA cycle intermediates have equivalent labeling during glucose confirmed production of serine from both 3PG and glycine. Serine was 56.0% M + 0, while 3PG was 52.3% M + 0 and glycine was 99.8% M + 0, indicating serine production from both 3PG and glycine. Labeling of methionine (5.5% M + 2) while threonine was only M + 0 and M + 1 confirms that the C1 unit in 5,10-methylenetetrahydrofolate (MeTHF) was labeled due to Shmt activity. (B) Labeling of serine when T. saccharolyticum was fed glucose confirmed production of serine from both 3PG and glycine. Serine was 52.3% M + 0, while 3PG was 50.7% M + 0 and glycine was 53.1% M + 0. Additionally, serine was 42.3% M + 2 while 3PG was 50.8% M + 2 and glycine was 1.0% M + 2. Together, these data indicate serine production from both 3PG and glycine. Labeling of methionine (1.5% M + 3) while threonine was only M + 0, M + 1, and M + 2 confirms that the C1 unit in 5,10-methylenetetrahydrofolate (MeTHF) was labeled due to Shmt activity. (C) Labeling of purine nucleotides served as additional confirmation that C1 units in one carbon metabolism were 3C labeled during growth on [6-13C1]glucose. Purines are synthesized de novo via a series of reactions that incorporate ribose-5-phosphate (R5P), glycine (Gly), CO2, and two C1 units from 10-formyltetrahydrofolate (FTHF). During [6-13C1]glucose labeling in T. saccharolyticum, R5P is at most M + 2 labeled and CO2 and Gly are M + 0. The increased proportion of the M + 2 and M + 3 forms of ATP and GTP relative to R5P was due to the addition of labeled C1 units from FTHF during de novo purine biosynthesis. We used the MIDs of threonine and methionine to infer labeling of C1 units in MeTHF (represented in the figure as THF carbon). MeTHF is oxidized by one carbon metabolic reactions to form 5,10-methenyl-THF, which is dehydrated to form FTHF. We combined the inferred MeTHF labeling pattern with the labeling in R5P, Gly, and CO2 to produce an expected labeling pattern of a purine molecule synthesized from these precursors (represented on the figure as predicted purine), resulting in a labeling pattern that matched that of measured purines ATP and GTP. The match between the predicted purine labeling pattern and the measured pattern indicated that the labeling in MeTHF was able to substitute for that of FTHF, indicating the two metabolites are close to isotopic equilibrium. (D) Labeling with an equimolar mixture of [U-13C6] and [U-12C6]glucose confirmed the activity of the citramalate pathway. Production of isoleucine from threonine during labeling with an equimolar mixture of [U-13C6] and [U-12C6]glucose would result in 25% M + 3 isoleucine, but we observe only 1.5% M + 3 isoleucine, suggesting at most 6% contribution of the pathway starting from threonine. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Download FIG S3, PDF file, 0.7 MB.One-carbon metabolism, purine biosynthesis, and the citramalate pathway. (A) Labeling of serine when Copyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 13C1]glucose and [1-13C1]glucose, consistent with its production either via threonine aldolase  of M + 2 methionine when only M + 1 aspartate was present indicated that at least some glycine was produced via reverse Shmt to generate 13C-labeled C1 units, from the third carbon in serine, in MeTHF, which can be converted to 5-methyltetrahydrofolate (MTHF) and used to produce M + 2 methionine from M + 1 aspartate (13C labeling of C1 units (9.2% M + 1 during [6-13C1]glucose labeling) in 10-formyltetrahydrofolate (FTHF) was also apparent from labeling patterns in purine nucleotides  or spartate . 13C lableotides .T. saccharolyticum has annotated genes encoding most of the enzymes required for isoleucine biosynthesis from threonine, but it does not have an annotated threonine deaminase, which is the first step in this pathway. Similarly, most of the enzymes required for isoleucine synthesis via citramalate are annotated, but citramalate synthase is missing  and unlabeled glucose  revealed substantial differences in the reversibility of upper glycolytic reactions between C. thermocellum, T. saccharolyticum, and E. coli  (C. thermocellum (∼23%) versus T. saccharolyticum (∼3%) and E. coli (∼5%) indicates that phosphofructokinase is substantially more reversible in this bacterium (P < 0.005). This observation is consistent with PPi-Pfk in C. thermocellum being less energetically favorable than the ATP-Pfk used by T. saccharolyticum and E. coli (13C1]- and [6-13C1]glucose labeling.Labeling with an equimolar mix of - and [1-13C1]glucose labeling . The fractions of M + 1 DHAP in C. thermocellum, T. saccharolyticum, and E. coli were 49.8%, 46.4%, and 43.5%, respectively, indicating that Tpi is highly reversible in all three organisms. The data indicated that Tpi was significantly more reversible in C. thermocellum than E. coli (P < 0.005), although the difference between C. thermocellum and T. saccharolyticum did not reach statistical significance (P = 0.07).Reversibility of triose phosphate isomerase  was revealed by glucose labeling, [1-2H1]GAP  is produced via Fba. Conversion of [1-2H1]GAP to 1,3-bisphosphoglycerate (BPG), catalyzed by glyceraldehyde-3-phosphate dehydrogenase (GAPDH), removes the deuterium from [1-2H1]GAP and places it on NAD+, generating 2H-labeled NADH. Reverse GAPDH flux then can produce unlabeled GAP if unlabeled NADH is used. The M + 0 GAP produced can propagate to DHAP (via reverse Tpi) and FBP (via reverse Fba), as well as to other upstream glycolytic intermediates. We observed that during growth on [4-2H1]glucose, C. thermocellum displayed a larger loss of 2H labeling across all glycolytic intermediates than T. saccharolyticum and E. coli, which indicated increased reversibility of glycolytic reactions in this bacterium. For example, although we could not reliably measure GAP, higher fractions of M + 0 DHAP and M + 0 FBP in C. thermocellum, i.e., 92% and 72%, respectively, versus 82% and 62% in T. saccharolyticum or 89% and 43% M + 0 in E. coli, suggested greater reversibility of its GAPDH/Tpi and GAPDH/Fba pairs of reactions .As shown in 2H1]glucose, the intermediates GAP, BPG, 3PG, and 2-phosphoglycerate (2PG) become deuterated at the second carbon (2H1]GAP to M + 0 DHAP via reverse Tpi or by the forward phosphopyruvate hydratase reaction (Eno) converting [2-2H1]2PG to M + 0 PEP. Once produced, propagation of M + 0 DHAP or M + 0 PEP to upstream metabolites provides a measure of the reversibility of Tpi, Eno, and other glycolytic reactions. As with [4-2H1]glucose, the greater loss of 2H labeling across glycolytic intermediates in C. thermocellum during [5-2H1]glucose labeling indicated increased reversibility of glycolysis in this bacterium compared to that of T. saccharolyticum or E. coli. For example, taking Tpi reversibility into account (C. thermocellum (98.4%) and T. saccharolyticum (96.6%) suggested increased reversibility of the Pgm/Eno reaction pair in these thermophilic organisms compared to that in E. coli .During growth on glucose and unlabeled glucose, the Pyr initially produced via glycolysis is either M + 0 or M + 3, and its decarboxylation produces acetyl-CoA containing an M + 0 or M + 2 acetyl group. Complete or partial  reversibility of Pfor or Pfl resulted in the interchange of the carboxylic group in Pyr with unlabeled CO2 or formate to produce M + 2 Pyr (E. coli (33.2% M + 2 Pyr) and C. thermocellum (26.5% M + 2 Pyr) than in T. saccharolyticum .When cells are grown in an equimolar mixture of [U- + 2 Pyr  Fig. 5)13C6]glucP < 0.01) in C. thermocellum (10.2% M + 2 PEP) than in E. coli (2.0% M + 2 PEP). Data suggested that PEP-to-Pyr conversion was also more reversible in C. thermocellum than T. saccharolyticum (3.9% M + 2 PEP), but this difference was not significant at P < 0.05. Considering the reported absence of pyruvate carboxylase or oxaloacetate decarboxylase activity in C. thermocellum H→acetaldehyde + NAD(P)+] and then to ethanol [acetaldehyde + NAD(P)H→ethanol + NAD(P)+] by a bifunctional aldehyde/alcohol dehydrogenase, AdhE  (C. thermocellum and T. saccharolyticum in the presence of [1-13C1]ethanol and unlabeled cellobiose (for C. thermocellum) or unlabeled glucose (for T. saccharolyticum). The incorporation of 13C from [1-13C1]ethanol into acetyl-CoA was significantly larger (P < 0.005) in C. thermocellum (67.9% M + 1) than T. saccharolyticum (46.0% M + 1), indicating increased reversibility of the conversion of acetyl-CoA to ethanol in C. thermocellum . We also observed significant propagation of 13C from [1-13C1]ethanol into upper glycolytic intermediates, such as G6P (5.5% M + 1) and FBP (8.8% M + 1), in C. thermocellum (P < 0.05 for both metabolites). However, these metabolites remained essentially unlabeled in T. saccharolyticum (<0.2% M + 1). Overall, this analysis indicated a substantially more reversible ethanol fermentation pathway in C. thermocellum than T. saccharolyticum.Since se, AdhE . T. sacce (AdhA) . To invemocellum . Additioin vivo flux ratios (+/J–J) and compare the thermodynamics of the distinct glycolytic pathways of C. thermocellum and T. saccharolyticum, we integrated data from five 13C and 2H tracer experiments  into a single flux map for each organism  as well as models lacking reactions that we identified as necessary to explain labeling patterns of specific metabolites . The results of this analysis agreed with our previous conclusions and predicted an inactive ED pathway , an inactive oxidative PPP , and a bifurcated TCA cycle with oxidative production of AKG and reductive production of succinate. Although Pepck typically is considered a gluconeogenic reaction , our MFA results supported the activity of a PEP carboxylating enzyme producing OAA from PEP. We also found that the SBP bypass was necessary to explain labeling patterns in PPP intermediates and aromatic amino acids. This analysis also supported qualitative conclusions regarding amino acid biosynthetic routes, such as isoleucine synthesis via the citramalate pathway instead of from threonine. Additionally, the production of serine from 3PG and its consumption by Shmt to form labeled THF intermediates was necessary to achieve a statistically acceptable model fit.For C. thermocellum, our MFA results were also consistent with the previously reported central metabolic network (C. thermocellum.For  network . SpecifiΔG = −RT ln(+/J−J), we estimated ΔG of glycolytic reactions  from MFA-derived flux ratios (+/JJ−) in C. thermocellum and T. saccharolyticum  and was comparable to the glycolytic pathway in anaerobically grown E. coli  (Using the relation olyticum . For a sx ratios . Estimat kJ/mol)  , so we calculated the free energy of the Pfk reaction using our measured intracellular metabolite concentrations. We used our calculated ΔG range for the Pfk reaction and assigned the remainder of the ΔG of the Pgi-Pfk combined reaction to Pgi. Error bars shown represent the 95% confidence intervals of the free energy estimates. Error bars smaller than the icon used to represent the average are not visible. Download FIG S4, PDF file, 0.4 MB.Thermodynamic profile of glycolysis in  E. coli  and Z. m mobilis . T. saccCopyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. thermocellum could be attributed in large part to the small driving force of the Pfk reaction, which had a ΔG of only −1.45 kJ/mol compared to −22.57 kJ/mol in T. saccharolyticum and −18.84 kJ/mol in E. coli. The small ΔG of Pfk in C. thermocellum was likely due to the use of PPi instead of ATP as the high-energy phosphate donor. Indeed, the standard free energy for the ATP-dependent Pfk (ΔG = −15.0 kJ/mol) is ∼10.6 kJ/mol more favorable than the PPi-dependent Pfk (ΔG = −4.4 kJ/mol) due to the greater free energy of ATP hydrolysis than PPi hydrolysis (The limited thermodynamic driving force of the glycolytic pathway in drolysis .T. saccharolyticum (−0.70 kJ/mol) and C. thermocellum (−0.47 kJ/mol) but was more thermodynamically favorable in anaerobic E. coli (−3.64 kJ/mol) (T. saccharolyticum (−1.93 kJ/mol) than in C. thermocellum (−0.74 kJ/mol). The ΔG of lower glycolytic reactions (GAPDH to Eno) were close to equilibrium in both C. thermocellum and T. saccharolyticum but were generally more favorable in T. saccharolyticum. For example, the ΔG of the Pgm/Eno pair of reactions was −0.49 kJ/mol in C. thermocellum compared to −0.77 kJ/mol in T. saccharolyticum.Consistent with our qualitative observations regarding its reversibility, the Fba reaction operated close to thermodynamic equilibrium in both  kJ/mol) . Also inC. thermocellum and T. saccharolyticum, most of the reactions in the PPP, including Sba, SBPase, Rpi, and Rpe, as well as the transketolase half reactions TktA (Xu5P→GAP + C2) and TktB (S7P→R5P + C2), all had ΔG no more favorable than −1.3 kJ/mol, putting them close to thermodynamic equilibrium  and also higher than those in E. coli . Higher levels of G6P and F6P in C. thermocellum potentially reflect the need to accumulate larger amounts of upper glycolytic intermediates to drive the PPi-Pfk reaction forward. Indeed, the FBP/F6P ratio was considerably lower in C. thermocellum (∼1) than E. coli (∼15) or T. saccharolyticum (∼3.7). Thus, higher intracellular FBP concentrations maintained by the more favorable upper EMP reactions in E. coli and T. saccharolyticum may help drive subsequent reactions in glycolysis.Intracellular concentrations of glycolytic intermediates across the bacteria in this study reflected the thermodynamic profile of their glycolytic pathways see . For exaC. thermocellum  than E. coli (66.0 and 42.4) and T. saccharolyticum (59.9 and 61.4). The higher ATP/ADP ratios in E. coli and T. saccharolyticum likely contribute to the favorability of the ATP-Pfk reaction and other reactions in upper glycolysis in these organisms. In contrast, the low ATP/ADP and GTP/GDP ratios in C. thermocellum may help make ATP/GTP-generating reactions in lower glycolysis, such as GAPDH and Ppdk, more favorable but may also slow biosynthetic reactions that rely on ATP/GTP hydrolysis, such as amino acid, protein, fatty acid, nucleotide, and mRNA synthesis.Interestingly, ATP/ADP and GTP/GDP ratios were considerably lower in ΔG = −RT ln(+/J−J), enzyme efficiency  is directly proportional to the drop in Gibbs free energy (ΔG) of a biochemical reaction (−J) increases exponentially as ΔG approaches equilibrium, decreasing the net reaction rate (+J– −J)  , 65. Thi(J+– J−) , the smaΔG→0) and effectively stops further substrate utilization and product formation. Therefore, it is likely that the limited thermodynamic driving force of the C. thermocellum glycolytic pathway represents a key contributor to its low ethanol titer (25 g/liter) (T. saccharolyticum (70 g/liter) (S. cerevisiae (75 g/liter) (Z. mobilis (>85 g/liter)   comparedg/liter) , S. cereg/liter) , and Z. g/liter) , 68, 69.olyticum  g/liter olyticum , 16, 70.C. thermocellum is to replace PPi-Pfk with an ATP-dependent Pfk. In addition, replacing the malate shunt and Ppdk routes for conversion of PEP to Pyr with Pyk also may increase flux and ethanol titers by providing a single more thermodynamically favorable route. Indeed, C. thermocellum strains expressing an exogenous Pyk achieve higher ethanol titers  and Ppdk (−6.92 kJ/mol) reactions than the values we report in this study . This discrepancy may be due to in vivo constraints not accounted for in the MDF analysis, such as limits on enzyme abundance and the need to maintain adequate metabolite pools to drive biosynthetic pathways .The previously reported MDF-based analysis of ycolysis . Our in pathways , 33, 34.C. thermocellum mirrors that of C. cellulolyticum, another cellulose-degrading organism that also utilizes PPi-Pfk, Ppdk, and the malate shunt (i-Pfk (which uses PPi in place of ATP) and Ppdk (which produces ATP from AMP and PPi), by cellulolytic microbes represents a potential mechanism for increased energy yield. However, recent research suggests that the PPi needed for glycolysis is in excess of what can be generated as a byproduct of biosynthetic pathways and is instead generated by ATP-consuming glycogen cycling reactions , which is transcribed and expressed in C. thermocellum (+ oxidoreductase (Rnf)  , 77, witse (Rnf) , 18. Thu−1 hour−1) or the cell yield (grams of cells × gram substrate−1), the product of which is the specific growth rate (per hour). For microbes growing on cellulosic biomass, however, the specific substrate consumption rate is highly constrained, leading to strong selective pressure for cellulolytic bacteria to maximize the cell yield by increasing glycolytic ATP yield. Conversely, the use of highly thermodynamically favorable pathways with lower ATP yield, such as the ED pathway in Z. mobilis, likely represents an adaptation to glucose-rich environments  were purchased from Cambridge Isotope Laboratories . All other chemicals were purchased from Sigma-Aldrich .Tracers (glucose and then extracting intracellular metabolites into solvent containing known concentrations of unlabeled standards for metabolites of interest. The solvent containing 13C-labeled intracellular metabolites and unlabeled standards was analyzed by LC-MS, and the ratio between the 13C and 12C peak intensities was used to determine intracellular metabolite concentrations -N′-ethylcarbodiimide hydrochloride (EDC) solution and 10 μl 6 M aniline solution was added. Samples were vortexed for 2 h, and then 5 μl triethylamine was added to stop the reaction. Samples were centrifuged to remove debris and then subjected to LC-MS analysis as described above.To enhance chromatographic separation and detection of some metabolites, we performed an aniline derivatization protocol for some samples used for metabolite quantitation . After tN-acetyl-glucosamine-6-phosphate.Positional labeling in pyruvate was determined by comparing the labeling patterns in ornithine (Orn) with those in acetylornithine (AcOrn). These compounds differ by a single acetyl group, received from acetyl-CoA, which is itself produced by decarboxylation of pyruvate. Thus, the acetyl group always contains only the 2nd and 3rd carbons of pyruvate, and the gain of labeled carbons by Orn during acetylation to AcOrn can be used as a readout of the position of labeled carbons in pyruvate. Acetyl group labeling was also measured by comparing the labeling patterns of glucosamine-6-phosphate to those in 13C labeling, unlabeled CO2 may come from two sources: (i) glucose dissimilation and (ii) carryover from inoculation. For glucose dissimulation, in cultures fed an equimolar mixture of [U13C6]glucose and unlabeled glucose, ∼50% of CO2 released during glucose fermentation to ethanol or acetate is expected to be unlabeled; the decarboxylation of pyruvate to form acetyl-CoA likely releases most of the cell-produced CO2. In cultures fed [1-13C1]- or [6-13C1]glucose, almost all CO2 released by the culture would be unlabeled. The 13C carbon under these conditions becomes the methyl carbon of pyruvate , which differs from Orn by incorporation of a single CO2 molecule. Thus, the difference between the labeling patterns of the two metabolites is due to the labeling of CO2. We used CO2 labeling derived this way solely to assist in our qualitative analysis of metabolism; CO2 was not assigned an MID during MFA.To determine the labeling of intracellular CO2, into a single hypothetical MID for purines. This simulated MID showed agreement with measured labeling of purines ATP and GTP (THF intermediate labeling was measured by comparing the labeling patterns of threonine (Thr) to the labeling pattern of methionine (Met). Met is formed from threonine by the addition of a single carbon from 5-methyltetrahydrofolate (MTHF), derived from the same pool as FTHF. Thus, the difference between Thr and Met labeling allowed us to determine FTHF labeling. Labeling of “mock purine” was produced by combining the labeling patterns of all precursors to purine formation, ribose-5-phosphate (R5P), glycine (gly), 2 THF intermediates, and CO and GTP .T. saccharolyticum metabolic model was constructed from the KEGG genome-scale metabolic model, manually curated  equals forward flux (+J) minus backward flux (−J), and exchange flux (Jexch) equals min. Using the optimal solution, we calculated 95% confidence intervals for all estimated fluxes by performing a parameter continuation, which varies each flux to determine how sensitive the optimal SSR is to that flux. Upper and lower bounds are assigned to each flux by finding how far they can be varied before the SSR is perturbed past a critical point, corresponding to a chi-square distribution with a single degree of freedom.MFA was performed using the INCA software suite . INCA isisotopes . We rest10.1128/mSystems.00736-19.10Table S613C and 2H labeling data in C. thermocellum and T. saccharolyticum used for MFA analysis. Download Table S6, XLSX file, 0.05 MB.Copyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2, which was modeled by allowing intracellular CO2 to exchange with a pool of unlabeled CO2. We used a previously published biomass equation  and the derived free energies across them. We found a <1.0% cumulative difference in free energy of glycolytic reactions between the two models, with the single most affected reaction having only a 5.5% difference in free energy. Thus, the omission of malic enzyme had a negligible effect on ΔG measurements, and this model was used for all flux and free energy calculations presented in the text and figures.Measured uptake and excretion fluxes for biomass, lactate, ethanol, acetate, and glucose in t titers , 39. Our2 test was used to determine whether the estimated fluxes adequately describe the measured data. The optimized SSR of a correct model and data set is a variable with a χ2 distribution with degrees of freedom equal to the number of fitted measurements (n) minus the number of estimated independent parameters (p). Fitted measurements are fitted external fluxes, namely, uptake, excretion, growth rates, and all non-zero MIDs. Estimated parameters are all free fluxes, including net, exchange, and dilution fluxes. We required that our models pass the χ2 test with a critical threshold of 0.05 (95% confidence), meaning the optimized SSR fell between C. thermocellum, the acceptable SSR range was 422.1 to 543.7, and the SSR after convergence was 499.2. For T. saccharolyticum, the acceptable SSR range was 434.3 to 557.5, and the SSR after convergence was 507.3.A χΔG = −RT ln(+/J−J). For two reactions (Pfk and Pyk) in the glycolytic pathway of T. saccharolyticum, the 95% confidence interval lower bound for the reverse flux was 0, implying the reaction free energy is unbounded in the negative direction . For these reactions, we supplemented our flux-based ΔG values with those calculated from metabolite concentrations using the relation ΔG = ΔG°′ + RT lnQ, where Q is the ratio of the concentrations of products to reactants, R is the gas constant, and T is temperature in kelvin. Reaction standard free energies (ΔG°′) were retrieved from the online tool eQuilibrator, using a pH of 7 and ionic strength of 0.1 as settings (E. coli (Reaction free energies were calculated from MFA-derived flux measurements using the relation settings , 88, 89.(E. coli  combine (E. coli .


Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions , and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites. Multi-tissue models based on these reconstructions, or from scratch9, are used to investigate the interplay between different tissues. And while there are profiles for uptake, and release of substances from individual cell lines , this information is often lacking for human tissues, and has to be inferred from other species. Thus, it is difficult to properly constrain a multi-tissue model, as literature sources vary, and bounds might be too stringent. Recently a study by Hyötyläinen et al.11 suggested to relax these hard constraints by using a quadratic programming approach in which hard bounds are replaced by penalties for data violation. This approach enables the direct use of data in a model, while contradicting bounds are relaxed in an automatic fashion, with many smaller violations preferred to large individual violations. However, this approach still does not address another issue with multi-tissue models of higher organisms. What is the metabolic objective of the organism? For microbes, biomass accumulation has been extensively used as objective yielding good results12, and this has also shown promising results for plants13, and even infant growth14. This objective is also commonly used for cancerous tissues which grow rapidly15. However, for a mature organism that no longer grows, this objective is questionable. Other suggested options include enzymatic optimization, or energetic optimization, the former trying to minimize the overall fluxes, the latter trying to minimize the use of ATP16. But while efficiency is a reasonable target, it is likely not to be the major driving force in an organism. In human, one observable factor is that blood metabolite levels are tightly regulated17, and metabolism is likely to play an important role in keeping these levels steady. Abnormal levels of metabolites commonly indicate diseases, e.g., Diabetes Mellitus and Inborn Errors of Metabolism (IEMs). Temporary perturbations of metabolite levels occur primarily due to nutrient uptake or food scarcity. This would imply that a good objective drives metabolite levels towards the healthy range after perturbations by nutrient uptake and simultaneously tries to keep metabolite levels stable while fasting.Genome scale reconstructions of the human metabolism have become ever more sophisticated in the recent years , each one was evaluated independently to determine how many known tissue-specific functions each model was able to perform, as defined in Gille et al.18. Two examples of tested functions are: (1) the ability to produce ATP from glucose or a fatty acid, in presence or absence of oxygen; (2) the ability to degrade an amino acid to ammonium. In total, the liver model could perform 139 out of the 142 tested functions, the muscle model 88 out of the 98 functions, and the adipose tissue model 91 out of the 98 tested functions. To conclude, each model was able to perform a minimum of 90% of the known tissue specific functions , protein and amino acids stores, if applicable, and with the blood compartment. The latter contains a storage for the different blood metabolites, thus allowing for the description of the dynamics of these metabolites. Each of these stores were initialized with the average amounts generally found in healthy individuals, as described in the Methods section. To mimic food absorption from the gut to the blood, a previously established differential equation20 based approach was employed to simulate the final coupled model. In contrast to the general FBA approach, where the metabolic flux can be calculated for a given specific time point and condition, the dFBA approach enables to account for the dynamics of metabolites by integrating the individual FBA solution over time. In both settings, constraints on reaction fluxes, and the optimization of an objective function are usually employed to narrow down the solution space and to obtain biological relevant flux solutions, respectively. The model in this paper represents the dynamics of metabolism of three interconnected tissue models, and to properly simulate it, an objective function, integrating multiple sub-objectives, has been developed as follows: (1) blood homeostasis should be maintained; (2) energy ingested in excess may be stored in the tissue energy stores, and may be used in periods of food scarcity; and (3) metabolic transitions are smooth over time, and between conditions. For a more detailed description of the model simulation workflow, and the objective function, please refer to the Methods section.In this work, a dynamic Flux Balance Analysis (dFBA)A second evaluation on the coupled multi-tissue model was performed. The second set of tests consisted of simulating different physiological conditions and a set of IEMs, and validating the model predictions using published data. In the next sections, the model predictions will be presented and discussed thoroughly.After setting up the model, we validated it through the simulation of different metabolic conditions occurring throughout the day. I.e. in the morning when we wake up we are in a fasting state, and after having breakfast the metabolism needs to adapt to a fed condition. Thus, these states were simulated and the ability of metabolism adaption between conditions was verified.starch and sucrose metabolism flux was at the maximum in the liver, but once the glycogen stores became depleted it became inactive. This pathway contains all the reactions involved in either the production or hydrolysis of glycogen. The effect was further confirmed by the flux through the glycolysis/gluconeogenesis pathway, and through the tissue storage degradation pathway. The latter one, in the liver, contains the reaction which leads to the degradation of the glycogen stores. These two pathways followed a similar pattern as the starch and sucrose metabolism. They became fully inactive after the glycogen stores were depleted. To compensate for this effect, and to fulfill the energy demands of the multi-tissue model, there was an increase in the flux through the tissue storage degradation, and the triacylglycerol synthesis pathways in the adipose tissue. The triacylglycerol synthesis pathway contains reactions producing and degrading TAG. In the liver, the flux through the glycolysis/gluconeogenesis pathway was decreased, while the flux through the fatty acid oxidation increased. To conclude, the model predicted that after 2 days of prolonged fasting, the liver glycogen stores were completely depleted, which led to an increase of TAG hydrolysis rate in the adipose tissue.We started by simulating a fasting condition for 3 days, which corresponds to 4320 min, to determine if the model was able to capture known effects occurring during the fasting state, such as liver glycogen depletion. As can be seen in Fig. 21. These two meals, representing a healthy and a high fat breakfast, were compared to each other and to the fasting condition  on the energy-associated metabolic pathways, the fluxes were normalized to the maximum flux per pathway, and per tissue Fig. .glycolysis/gluconeogenesis), or inactivation  of metabolic pathways, which as the post-absorptive state progressed, slowly returned to the fasting flux values. The effect of the meals on the majority of the energy-associated pathways was still highly pronounced 6 h after the meal ingestion.When compared to the fasting condition, the fed state led to the activation  were used and the surplus was stored in the internal energy stores. The activation of these metabolic pathways, following a meal, are in good agreement with known human metabolism22. In addition, the ingestion of a high fat meal led to an increase of ROS detoxification in the liver, and to a higher degree in the muscle. It has already been shown that meals rich in carbohydrates and lipids lead to the increased production of ROS27, which has been associated with the development of insulin resistance28. These results confirm the potential detrimental effect that high fat diets might have on muscle and liver metabolism, and show the value of the availability of such a modeling approach.The results correctly suggest that the liver was the main metabolizer of amino acidsAs shown in Supplementary Fig. To conclude, these results confirm that during fasting the internal energy stores were used for energy production. In the fed state, during the analyzed time-course, the internal energy stores were not depleted. Instead meal metabolites were used for energy production. The surplus of energy obtained from the meal was stored in the internal energy stores as glycogen or TAG. As expected, the high fat meal caused an important increase of TAG storage, which was minor after the consumption of a low fat meal, showing the potential detrimental effect of high fat meals consumption in the development of obesity.22. Thus, to determine if the model was able to capture key metabolic changes occurring during exercise, exercise was simulated for various intensities and durations. Exercise was mimicked by increasing the ATP consumption in the muscle model. I.e. an intense exercise load of 90% of the maximum oxygen consumption rate  or exercise (M → E), and exercise followed by a low fat meal (E → M). Specific tissue fluxes and tissue stores amounts were compared among these three conditions , the whole-model fatty acid oxidation was increased and the glucose one was decreased when compared to the M → E condition.The model predicted a different effect, if exercise was performed before or after a meal. The muscle glucose uptake was larger in the E → M condition, the degradation rate of TAG in adipose tissue and glycogen in muscle was larger than in the M → E condition.During exercise, the liver glycogen usage was similar in both conditions. However, in the On the other hand, the ingestion of a low fat meal led to a slight storage of energy in the different tissues  and predicted their biomarkers. To validate the results, the set of predicted biomarkers for 17 IEMs was compared to biomarkers previously32 manually extracted from OMIM.IEMs are inherited metabolic diseases characterized by an impairment in the metabolic activity of an enzyme or a transporter. If people affected by one IEM have unrestricted diets, toxic metabolites might build up in the body fluids  and tissues. Therefore, it is important to identify potential biomarkers for all IEMs to get a quicker diagnosis and to avoid health complications at long termFigure 4, which is toxic for the human body. In the healthy fasting condition, we observed a basal urea production  biomarker. Thus, we investigated the dynamics of tyrosine at the different storage and excretion levels to determine if it decreased in this condition  maintenance of blood homeostasis. During fasting conditions, blood levels should remain stable, and in the fed conditions they should return to the basal levels within a few hours after the meal. This sub-objective was supplemented by two others: (2) all energy ingested in excess will be stored, i.e., as triacylglycerol in the adipose tissue, and will be used, to a higher extent, when the body goes through periods of food scarcity, i.e., long periods of fasting. Most of the reactions’ flux in constraint-based metabolic models are not constrained, and during the optimization of the model, multiple flux solutions might be equally valid. Thus, a third sub-objective was added, (3) which aim was to prevent sharp changes over time, in the metabolic fluxes. The choice of this multi-objective function allowed the model to mimic quite well the metabolism and the metabolic flow between the three tissues and the blood. Each of the sub-objectives are optimized simultaneously, and can be weighted equally, or not. As an example, in this model, the same weight was selected for each of the blood metabolites. The effect of this assumption led to each metabolite being penalized similarly, as will be further described during the discussion of the IEMs results. In the future, the penalization coefficients could be further tuned to further improve the prediction capacity of the model.The proposed model was simulated using a dynamic Flux Balance Analysis approach11 can be applied on the reactions’ bounds. During model optimization, the flux solution will try to remain inside the expected flux range, and it will only deviate from the expected range, if really necessary. Because of the large nature of the model, and the complexity of the objective function, and to avoid infeasible solutions, we decided to apply flexible constraints on the reaction bounds.Commonly, reactions flux are constrained with hard bounds. However, due to model incompleteness, experimental errors, and noise, imposing hard constraints on reaction fluxes can lead to infeasible solutions. In order to prevent it, “flexible” constraintsOne advantage of constraint-based metabolic models is that they necessitate much less parameters, when compared to those models based on ordinary differential equations (ODEs). As described, constraint-based metabolic models rely on the optimization of one objective function, and the application of specific constraints in order to obtain condition specific flux solutions. Due to the small number of parameters, these models can be easily scaled to describe the complete metabolism of one, or more tissues. As demonstrated, the model was able to describe the complete metabolism of three tissues, showing the enormous potential of this approach. The proposed model was validated by simulating different physiological , and unhealthy conditions (different IEMs).34, therefore the predicted time of the complete glycogen depletion is in the same range as the reported one. When glycogen stores deplete, the body relies mainly on fatty acids issued from the degradation of adipose tissue TAG22, to avoid the depletion of blood glucose. The model was able to capture this metabolic change by predicting an increase in the triacylglycerol synthesis and fatty acid oxidation fluxes. Concomitantly, the glycolysis/gluconeogenesis fluxes decreased, confirming the usage of fatty acids as main energy source.During the first hours of fasting, the model showed little dynamics, and the metabolic pathways usage was stable. At around 2 days of fasting, the model showed a dynamical switch, when the liver glycogen stores were completely depleted, forcing the system to adapt and choose other energy sources. It is known that liver glycogen depletion occurs after 48 to 60 h of fasting22. However, the aromatic amino acids, phenylalanine, tyrosine, and tryptophan were predicted to be mainly metabolized in the muscle. This was unexpected, as in reality the degradation of these amino acids occurs primary in the liver22. This occurred because prior to the model reconstruction, we collected the complete canonical pathway of the aromatic amino acids degradation, and used these reactions as part of the set of core reactions for the contextualization of the tissue models. Thus, this will be addressed in future iterations of this model.It has been reported, that the liver is the primary metabolizer of amino acids, with the exception of branched chain ones 24, and the internal energy stores are no longer used. If energy is ingested in excess, it is stored either as glycogen or TAG22. The model predicted that the energy storage pattern after a low, or a high fat meal was different. The results show that a high fat meal elicited a larger energy storage than the low fat meal. This is expected as the high fat meal contained a larger amount of TAG and of glucose.The predicted metabolic pathways activities during the fasting, and the fed condition are in agreement with the literature. It is known that in the fed state, fatty acid oxidation rates decrease25, which was predicted to be increased in the three tissues. In addition, the increase of the glycolysis rate was proportional to the amount of glucose contained in the meal.Furthermore, it has been shown that high levels of blood glucose lead to the increase of the glycolysis rate27. Previously, ROS has been associated with the development of insulin resistance28, which is the first detectable defect in type 2 diabetic patients and it can occur 10 years before the apparent disease35.The activity of the ROS detoxification pathway was affected by the fed condition, mostly in the liver and in the muscle, and this effect was more pronounced after the high fat meal. Increased levels of blood glucose, and fatty acids lead to the production of increased amounts of ROS2 uptake. All the other exercise intensities were calculated linearly. The approach used to calculate the different exercise intensities relies on two main assumptions. The first assumption is that an average individual was used in this work. In the future, the model could be further adapted to represent the metabolism of athletes, by i.e., adapting the maximal O2 uptake. The second assumption, which was used due to the lack of data in this field, assumes that the linear increase of ATP consumption in the muscle, leads to the linear increase of O2 uptake in humans.During a normal day, individuals go from a state of fasting to feeding, and also do some kind of physical activity, which can be done either in the fasting state or after eating. Therefore, to further validate the model, we predicted and partially validated the effect of the exercise intensity and the meal composition on metabolism. This was achieved by modifying the ATP consumption in the muscle, to reflect the exercise intensities of interest. First, we determined by how much the ATP consumption in the muscle would need to be increased to reach 90% of the maximal O37, at high intensity glycogen becomes the main one38. The developed model was able to correctly reflect this switch in energy source depending on the intensity level. It correctly predicted that the muscle glycogen usage was proportional to the exercise intensity38. The fatty acids uptake in the muscle reached the maximum flux at 60% OIt has been previously established that fat, and carbohydrates are the main energy sources during exercise, and depending on the intensity of the exercise, one source is favored over the other. While at lower to moderate exercise intensities, fatty acids are the main source40, which is close to the maximal predicted fatty acid oxidation rate , rather then after a meal (M → E)23. We demonstrated that performing exercise during fasting promotes fatty acid oxidation, through the increase the TAG degradation rates. If a meal was ingested after the exercise, the TAG level in the adipose tissue remained lower when compared to the level where a meal was ingested prior to the exercise. This effect was similar for both meals. Carbohydrates ingestion after exercise allows the glycogen stores to replenish41. In the model, this effect was more pronounced when a high fat meal was ingested before or after exercising. It is important to note again that, not only this meal contained more TAG, but also more glucose.Performing exercise in the fasting state has been suggested to be more favorable regarding the lipid metabolismM → E level.Overall, these results suggest that more TAG is burned in the adipose tissue, if exercise is performed in the fasting state. If a meal is ingested after exercising, it allows the slight replenishment of glycogen, and simultaneously, it keeps the adipose tissue TAG levels below the These results can be relevant for the treatment of human obesity. To maximize fat oxidation and potentially fat loss, exercise should be performed in the fasting state, and at low to medium intensity levels, as for high intensities, fatty acid oxidation was predicted to be almost suppressed.After analyzing the metabolic effect of different physiological conditions, the model was further validated, by predicting biomarkers for a set of IEMs and by comparing them to literature data. The model mainly predicted urine biomarkers, which is explained by the blood homeostasis assumption used to constrain the model. Metabolites could be exported through the urine, if the exporting rate was kept within the healthy range, while their storage in the blood was penalized. Therefore, it was preferable to export biomarkers through the urine, instead of accumulating them in the blood.The model predicted blood biomarkers, that were also predicted as urine biomarkers. This happens when the urine secretion rate is at the maximum allowed flux, or outside the healthy range. When the urine secretion fluxes are saturated, the model minimizes the objective function value by splitting the penalization among different reactions. Thus, the metabolites that accumulate in the blood are also exported through the urine. To summarize, the model only predicted a blood biomarker, if the respective urine metabolite was also predicted as a potential biomarker.4, which is one by-product produced during the degradation of amino acids. Due to the impairment on urea synthesis, NH4 could not be removed through the urea synthesis pathway. Thus, the model tries to remove NH4 from the system by eliminating it through the urine. When this elimination is saturated, NH4 might accumulate in the blood. As the final step of the amino acids degradation is impaired, amino acids are eliminated through the urine, and to some extent they are stored as protein in each tissue, even if protein storage is penalized. One way to avoid this effect would be to have a specific penalization coefficient for each metabolite, which would reflect the preference for the elimination, or the accumulation of the metabolite. However, this kind of data would be very hard to obtain. E.g., the biomarkers data, collected in Supplementary Table Arginase deficiency led to the prediction of almost all urine amino acids as potential biomarkers. During this condition, urea synthesis was completely impaired. The urea synthesis pathway is involved in the detoxification of NHThe model predicted 5 false positive biomarkers at the urine level, of which 3 corresponded to the increase of phenylalanine in 3 IEMs . In these conditions, the tyrosine degradation pathway is impaired and this impairment leads to the known accumulation of tyrosine. Phenylalanine, which is the tyrosine precursor, will also accumulate as tyrosine cannot be degraded, and will be secreted through the urine to avoid any blood concentration changes. Similar to the Arginase deficiency, this occurs because the model penalizes big changes at the blood level, and favors the sum of small changes. Thus, the objective function value is lower if the model accumulates small amounts of the two amino acids, instead of having a high accumulation of tyrosine.DLD gene associated with MSUD, also regulates one reaction belonging to the downstream degradation pathway of lysine. This impairment in the lysine degradation pathway leads to the possible accumulation of oxoadipic acid, which is also the degradation product of tryptophan. Thus, an increase in the levels of lysine and tryptophan was not surprising, because of the impairment in the degradation pathways of these two amino acids. These results might suggest that either these amino acids have not yet been reported as potential biomarkers, or that the degradation pathway of these two amino acids is less affected than the one from the branched chain amino acids, potentially because an unknown enzyme might be able to compensate for this loss.Two other false positive biomarkers were predicted for Maple Syrup Urine disease (MSUD), and corresponded to lysine and tryptophan. After an in depth investigation, we observed that the We have demonstrated that this approach shows good agreement with literature data and allows to prioritize potential biomarkers when performing metabolomics analysis.In conclusion, we developed a dynamic multi-tissue model which not only captures the physiological effects, but is also able to identify potential biomarkers. This framework can be applied in the field of personalized sports nutrition, such as in the improvement of sports performance. This could be done by predicting which diet would elicit the faster muscular glycogen and protein storage repletion. In addition, this approach can be extended to the analysis and biomarker prediction of other metabolic diseases, by performing gene knock-outs for each gene present in the model. In addition, personalized models of any metabolic disease could be contextualized with individual expression data and metabolomics data, which would serve as a basis for the development of personalized treatments and adapted meals for each type of studied disease.7, high quality models of individual tissues are still rare. However, algorithms for automatic reconstruction of draft context-specific models are available and can be used to create a starting point for tissue specific models, with the FASTCORE family of algorithms having shown the ability to generate good quality draft models42. Here, an outline about how these algorithms were adapted to generate draft models for three tissues  is presented. Further details are given about how the quality of the generated models was assessed. Finally, the approach used to connect these models and the concepts applied to interrogate the system are described.While genome-scale reconstructions of human metabolism are readily available45 discretization approach, and integrated in the FASTCORMICS workflow46. In short, the microarrays were read into R version 3.2.0, with the affy package (1.48.0). They were subsequently normalized with the fRMA package (1.22.0), and then discretized with Barcode using the hgu133plus2frmavecs vector (1.5.0).Tissue specific transcriptomics data from the GEO database was extracted for the three tissues of interest. Tissue datasets were selected by filtering for the following keywords: liver, skeletal muscle, and subcutaneous adipose tissue. Only transcriptomics datasets in the healthy condition, generated by the platform U133 Plus 2.0, were selected  were reconstructed from Recon2.04Reactions belonging to canonical pathways which should be present in the tissue models i.e., degradation of amino acids.Reactions not expressed based on the transcriptomic level, but expressed based on proteomics data (data from the Human Protein Atlas database).Reactions involved in the recycling of co-factors.These supplementary core reactions were comprised of:The full list of supplementary reactions is given in Supplementary Data 18  was used, if human data was not available. All included fluxes and their references are listed in Supplementary Data Tissue coupling is important to allow a systematic investigation of the reconstructed model, which was achieved by the following approach:50. For each of these metabolites, Recon2.043, and Recon35 were searched for possible transport reactions. If the reaction was present, then it was added to the model, if not the following transport reaction was added: met[c] ↔ met[e]. These transporters were added because during the reconstruction process many transporters were removed, and some IEMs biomarkers (either in urine or in blood) were metabolites which were only present in the cytosol of the models.In the first step, after each tissue model was reconstructed, transport reactions between the cytosol and the extracellular space were added for metabolites which were present in the model cytosol and have already been detected in either blood or urine, according to the data from the human metabolome database (HMDB)50, together with the respective ranges.In the second step, a blood compartment was added to allow metabolite exchange between the different tissue models. Furthermore, a blood pool, and a virtual gut, mimicking food absorption Fig. , from whThe blood concentrations were then converted to amounts, assuming a total blood volume of 5 L (Supplementary Data The metabolite concentrations in urine were given in μmol per mmol of creatinine51 (9.4 ± 2.68 mmol per 24 h)The healthy creatinine amount secreted over a period of 24 h in urine was gatheredThe 24 h creatinine value was converted from mmol per 24 h to mmol per minThe urine metabolite concentrations were multiplied by the creatinine amount to obtain metabolite secretion fluxes . A general overview of the model structure can be found in Fig. Finally, glycogen stores were added to the liver, and the muscle models, while triacylglycerol (TAG), and protein stores were added to the adipose tissue, liver, and the muscle models. Tissue weights, and tissue stores initial conditions are listed in Supplementary Table That metabolism aims to maintain a blood concentration homeostasis.That energy might be stored if in excess, to be used during scarcity conditions.That metabolic changes are smooth and not sharp.A common issue for metabolic modeling in mature higher organisms is the selection of an appropriate objective function that properly reflects the aim of metabolism. The modeling approach proposed here was based on three main assumptions:11.This objective must then be subjected to limitations and requirements, such as flux bounds or known turnover and maintenance costs. Extensive literature search was performed to collect tissue specific metabolic fluxes to constrain the multi-tissue model , which are defined as:i from reactions in RK, respectively. i ∈ RK. For all supplementary transport fluxes (RCS), and protein degradation reactions (RPD), similar constraints with Constraints on the fluxes of reactions with known flux ranges  The maximum allowed flux for the liver glycogen storage reaction was very high, as this reaction can carry a higher or a lower flux depending on the physiological state of the body. Therefore, a flexible constraint, based on the fasting rates, was set to a very high coefficient value to further penalize the deviation from these bounds. The same procedure was employed for the CS reactions. (2) The flexible bounds for the oxygen uptake in the muscle are bounds in the resting state. However, during the simulation of exercise, the model needs to uptake more oxygen. Thus, the uptake of oxygen was slightly penalized. (3) When going from one metabolic state to another, the model needs to adapt its metabolism. Since it might be impossible for the model to have a smooth transition from the previous to the current time step, a coefficient of 0.001 was given to still penalize the deviation from the previous time step, while still allowing some flexibility during the metabolic adaptation. (4) All the other coefficients for all the other reactions included in the quadratic objective function were set to a value of 1.In addition to the aims detailed above, there are several flexible constraints  are restricted by:vm as defined in Eq.  are incorporated in two different ways. For all blood metabolites which are available in the food (NU), the uptake rates  is retaCplex.Param.timelimit.Cur = 60 ⋅ 10Cplex.Param.barrier.algorithm.Cur = 1Cplex.Param.barrier.convergetol.Cur = qp.Param.barrier.convergetol.Cur ⋅ 10Cplex.Param.emphasis.numerical.Cur = 1A set of Cplex parameters were modified to address the numerical difficulties occurring during the optimization steps.Cplex.Param.preprocessing.presolve.Cur = 0In addition, if the solver was still unable to identify an optimal solution, the presolver was turned off, which in some cases helped to optimize the model.Furthermore, because of the numerical difficulties, occasionally the solution became infeasible during the second optimization step (flux minimization), while it was optimal during the first optimization step. Therefore, for each time step the solution from the first optimization step was used to update all the stores, i.e., the blood concentrations and the internal tissue stores.20, and optimized for the objective function described above, using the COBRA Toolbox for MATLAB and the ILOG Cplex solver. Each simulation was performed for a period of time, i.e., 360 min, using a time step of 1 min.The multi-tissue model was simulated using a dynamic Flux Balance Analysis-based approachInitialize the model (only for time = 1 min)Calculate the food amount that is absorbed from the gut to the blood using Eq. Update the blood amountsvm using Eq.  using the flux solution from step 6Restart from step 2 until the end of the simulation, i.e., time = 360 minThe pipeline for the simulation of each time step is described by the following steps:21. In this dataset, 20 metabolite profiles  were available. These metabolites were measured at baseline, and then every 60 min for 300 min, after ingestion of a low or a high fat meal. The average metabolite profiles of young individuals were used to fit the model.Diet information, and blood metabolite profiles were obtained from Milan et al.55. If they could not be found in this database, the SelfNutrition database56 was used instead.The foods included in the low and in the high fat meal were converted to nutrients using the Frida databaseIf starch values were present in the database, it was assumed that half of the starch was starch1 and the other half was starch2, to match the starch metabolites in Recon2.04. If the starch value was not available from the database, then the following calculation was performed:This value was assumed to be the value of starch missing. Similarly, it was assumed that half of this value corresponded to starch1 and the other half to starch2.Starch1, starch2, disaccharides, fructose, and galactose were converted to glucose with the conversion factor of 11, 3, 2, 1, and 1, respectively. The conversion step was performed as the carbohydrate hydrolysis in the gut was not explicitly modeled.19 employed Eq.  is the absorption rate of a metabolite m from the pool, Dm is the amount of metabolite m in the pool, Am is the bioavailability of m, and m in the blood. For each time step, before optimising the model, the amount determined by Um(t) was added to the current blood metabolite level. All the blood metabolite amounts were then converted back to concentrations by multiplying them by a blood volume of 5 L. Note: in the fasting condition, the gut is empty and Um(t) is equal to 0.Hovorka’s equation:Sm], was used to limit the maximum uptake rate of the food metabolites from the blood stores to the tissues:m]basal is the basal blood metabolite concentration. Due to numerical errors, [Sm] might become negative , dependm considered in the food pool, the parameters 21. The optimization was performed using the lsqnonlin function from the Optimization Toolbox in MATLAB.For each metabolite The upper bounds of the fitting parameters were set in the following way: the 2 consumption in the model. This was achieved by simulating exercise by increasing the ATP demand in the muscle until the expected O2 consumption was observed. Thus, by multiplying the muscle basal ATP demand by a factor of 42, an exercise eliciting the consumption of 90% O2 consumption. I.e. to determine by how much the ATP consumption in the muscle needed to be increased to reach 80% OTo simulate exercise at different intensity levels, it is necessary to represent the exercise in the model. Since exercise mainly consists of energy expenditure, exercise was simulated by varying the ATP demand in the muscle. First, we investigated by how much the ATP demand in the muscle would need to be increased to reach 90% of the maximal O29.Simulation 1: 90 min of 40% O29.Simulation 2: 60 min of 60% OSimulation including steady exercise at different intensity levels during fasting:30 were simulated.Simulation 3: 5 min of 50% OSimulation including steady and incremental exercise at different intensity levels during fasting:23.Simulation 4: 60 min of 55% O23.Simulation 5: the meal was applied. After 60 min resting, 60 min of 55% OThe following simulations were performed for a low fat meal:Simulation 6: the meal was applied, and no exercise was simulated.An additional simulation was performed to compare the effect of exercising, or resting in the fed state:The fatty acid oxidation was calculated in multiple steps. First, the individual fatty acid oxidation reactions were collected. Depending on the reaction, more than one molecule of acetyl-coa could be produced. The acetyl-coa coefficient was extracted, and each flux was multiplied by the respective coefficient. The fluxes were summed up, and the AUC calculated.Multiple energy precursors can be used to provide energy during exercise to the muscle: (1) free fatty acids absorbed from the blood; (2) other fat sources, representing the sum of blood TAG absorption and the degradation of TAG stores in muscle; (3) blood glucose absorbed; and (4) glycogen degraded in muscle.The AUC of the flux of the different energy precursors for the muscle was calculated.TAG and glycogen were broken down into free fatty acids, and glucose molecules, respectively. TAG contains 3 fatty acids, thus the AUC value of "other fat sources" was multiplied by 3. Glycogen degradation produces 8 molecules of glucose and 3 molecules of glucose-1-phosphate, thus the AUC value of muscle glycogen degradation was multiplied by 11.57, respectively. Therefore, to obtain the ATP contribution of each energy source, the AUC values of glucose, and glycogen-derived glucose were multiplied by 31, and those from free fatty acids, and TAG-derived free fatty acids were multiplied by 106.The degradation of one molecule of glucose, and an average free fatty acid leads to the production of around 31, and 106 ATP moleculesThe calculation of the availability of each of these sources for ATP production was performed in several steps:59 to simulate IEMs in the multi-tissue model. These studies were selected, as literature biomarkers were included in the papers. The IEMs were mapped to a list of IEMs impaired genes/enzymes60, and the respective genes extracted. IEMs were simulated only if the impaired genes directly affected a metabolic reaction of the multi-tissue model .A list of IEMs was collected from several published studiesIn total, 65 IEMs were simulated under different conditions . The simulation of IEMs was performed by setting the targeted reaction(s) fluxes to 0 . In addition, 100 "individual" models for each condition were simulated . For each of these models, the flux bounds of all the flexible constraints were randomly decreased, or increased to mimic the metabolism differences between individuals. The random change ranged from 0 to 10%.65 IEMs ⋅ 3 conditions100 “Individual” healthy models ⋅ 3 conditionsAverage healthy model ⋅ 3 conditionsIn total, 498 models were simulated:Metabolic biomarkers are metabolites whose levels are outside the healthy range, either above or below. Thus, one method which might help in their identification is by predicting which metabolite levels might be outliers. This was achieved by performing several steps: (1) preprocessing, (2) “outliers” identification, and (3) filtering.−7, and occurred because of numerical errors during the optimization process.For each metabolite, the minimum amount among all simulations was calculated. For some metabolites, the blood amount became negative during the simulation, as well as the urine fluxes. These negative values were in the order of 10For those blood metabolites where a negative amount was found during the simulation, the absolute value of the negative change was added to all the simulations in order to obtain only positive blood amounts.For the urine metabolites, any negative flux was replaced by zero.Data was pre-processed as following:n = 498). The number of clusters represents the following: a cluster number of 1 indicates that the metabolite was inside the healthy range for all the conditions; a cluster number of 2 indicates that at least one metabolite level was outside the healthy range ; finally, a cluster number of 3 indicates i.e., that in some conditions the metabolite level was decreased, while in others it was increased.After the preprocessing, the AUC of the blood level/urine excretion flux over time was calculated for each metabolite in each condition, and hierarchical clustering analysis was performed to identify potential biomarkers. This was achieved by using the MATLAB cluster function from the Statistics and Machine Learning Toolbox. Default parameters were selected, with the exception of the linkage measure which was set to single. The silhouette function, from the same toolbox, was used to identify the best number of clusters (ranging from 1 to 3), for each metabolite among all conditions (n = 303).In the first step, metabolites with AUC larger or smaller than the healthy range were kept, and the others removed from the list. The healthy range is defined as the minimum and maximum AUC values obtained from the simulations of the healthy models  that a biomarker was identified in. The score value ranged from 0 to 3. A score of 0 represents non biomarkers ; a score of 1 represents biomarkers identified in only one condition ; a score of 2 represents biomarkers identified in two conditions; and a score of 3 represents biomarkers which were identified in all 3 conditions. Furthermore, a sign was added to the score value to represent an increase (plus sign), or a decrease in the biomarker (minus sign) level, when compared to the healthy range.After extracting the potential list of biomarkers, two steps of filtering were performed.61. The figures were either created with R version 3.4.0 or MATLAB 2014b 32bit.The micro array data processing was performed with R version 3.2.0. All the simulations were performed with MATLAB 2014b 32bit using ILOG Cplex 32 bit (cplex1261). The model manipulation, and simulation was performed using the COBRA Toolbox (v2)Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7