Escherichia coli, enabling a biosynthetic production route for many isoprenoids. In this paper, we develop and solve a mathematical model for the concentration of metabolites in the mevalonate pathway over time, accounting for the loss of acetyl-CoA to other metabolic pathways. Additionally, we successfully test our theoretical predictions experimentally by introducing part of the pathway into Cupriavidus necator. In our model, we exploit the natural separation of time scales as well as of metabolite concentrations to make significant asymptotic progress in understanding the system. We confirm that our asymptotic results agree well with numerical simulations, the former enabling us to predict the most important reactions to increase isopentenyl diphosphate production whilst minimizing the levels of HMG-CoA, which inhibits cell growth. Thus, our mathematical model allows us to recommend the upregulation of certain combinations of enzymes to improve production through the mevalonate pathway.The mevalonate pathway is normally found in eukaryotes, and allows for the production of isoprenoids, a useful class of organic compounds. This pathway has been successfully introduced to  In plants, isoprenoids are the cause of many aromas and, in animals, isoprenoids form steroids and sterols. The wide range of isoprenoid products is one reason why the successful introduction of a viable isoprenoid pathway to D-erythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate (MEP/DOXP) pathway, and also converts pyruvate to IDP. The mevalonate pathway was the first to be discovered, and occurs naturally in eukaryotes. The non-mevalonate pathway mainly occurs in bacteria (with some exceptions), and some plants.There are two main pathways from pyruvate to isopentenyl diphosphate (IDP) and dimethylallyl diphosphate (DMADP), and these two products can react to make isoprenoid compounds. IDP and DMADP are essentially interchangeable due to the enzyme isopentenyl diphosphate isomerase that allows conversion between the two. The first pathway is known as the mevalonate pathway, and starts from acetyl coenzyme A (acetyl-CoA), mainly derived from pyruvate, which is converted to IDP via the key pathway intermediate mevalonate. The second pathway is known as the non-mevalonate or, alternatively, the 2-C-methyl-E. coli, a bacterium that naturally expresses only the non-mevalonate pathway, is to bypass the natural negative feedback mechanisms in place that would ordinarily prevent the overproduction of isoprenoids. We are interested in mathematically modelling this mevalonate pathway, with the goal of understanding how to further modify the pathway by, for example, upregulating genes that control certain enzymes, in order to produce more IDP. The mevalonate pathway we will model comprises the following:The reason for introducing the mevalonate pathway to In general, the aim of our model is to determine which reactions are the most important for IDP production without resorting to expensive and time-consuming experiments. We are also interested in determining which reactions have the most significant control over the levels of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA), linked to the inhibition of cell growth due to its inhibition of fatty acid biosynthesis . SpecifiWe make several key modelling assumptions to facilitate analysis of our system. Firstly, we assume that the formation rate of enzyme complex is much quicker than the rate of substrate consumption, and thus the reaction rates are governed by Michaelis–Menten-type laws, the specific form of which we obtain from the literature. We also consider a system that is well mixed and thus spatially independent. Additionally, we consider the case where pyruvate is instantaneously introduced to a system containing all of the relevant enzymes, but none of the intermediate metabolites, allowing for a cleaner mathematical analysis. We first investigate the case where pyruvate is continuously replenished and held at a constant concentration, then the case where pyruvate is never replenished. We show that the second case shares many similarities with the first until the pyruvate is depleted to a certain critical level, which we determine. Understanding these extreme cases allows us to determine the key reactions in this pathway, and to suggest targets for upregulation.As is the case with many biological systems, there are many parameters in the system. Thus, a comprehensive understanding of the system using a purely experimental approach would be very time consuming. This reasoning also applies to investigating our mathematical model using a fully numerical approach, although the time taken to investigate the system would be shorter than the purely experimental approach. To get around this issue, we supplement and guide our numerical simulations by determining asymptotic approximations . We show that mevalonate can be produced by our bacterial chassis, and confirm that our experimental results are successfully predicted by our model.Finally, to test our theoretical predictions, we introduce part of the mevalonate pathway  into The outline of this paper is as follows. We introduce a mathematical model to describe the nonlinear reaction kinetics in 2The dimensional system we consider is derived from Michaelis–Menten-type laws found in the literature, where each variable is defined in A represents the total loss of acetyl-CoA to other metabolic pathways, for example, the citric acid cycle or fatty acid biosynthesis, and we assume that this occurs with first-order kinetics. While this parameter is difficult to measure experimentally, we will bypass this issue by considering a distinguished limit in the dimensionless system when we perform an asymptotic analysis. The parameters iE, where Most of the terms in the governing equations (2) are standard Michaelis–Menten reaction velocities, for which we obtain the relevant kinetic parameters from the references indicated in S0 represents the initial or typical level of pyruvate present in the system. As any given metabolite may already be present in the real-world system, our approach to the initial conditions is a modelling choice. That is, we choose to reduce the number of uncertain parameters in the system in order to facilitate a more simplified analysis of the system.We use initial conditions that correspond to the scenario where pyruvate is instantaneously introduced to a system containing all of the relevant enzymes, but none of the intermediate metabolites. That is, we use S0, the initial concentration of pyruvate. Additionally, we scale time with S0/(k1E1), the characteristic time of the first reaction, which occurs between pyruvate and acetyl-CoA. We summarise these scalings in cϵj, where c is an O(1) parameter (between 0.1 and 10), and j is an integer. The resultant dimensionless parameters in our system are given in O(1) parameters are multiplied together, we will show that our asymptotic and numerical results show excellent agreement, and thus the approach is reliable for this system.To nondimensionalize the system variables, we scale each dimensional metabolite concentration with Therefore, we nondimensionalize using the dimensionless variables defined in In forming the dimensionless variables, we have used the physically plausible value of S1(t) ≡ 1. This corresponds to the scenario where pyruvate is continuously replenished and held at a constant value. The second case is where We solve this system for two cases. The first case is where 33.1k with a subscript) given in We solve the system presented in §E3 increases both the maximum levels of HMG-CoA and of IDP production, whereas increasing E4 decreases the maximum levels of HMG-CoA but has a negligible affect on IDP production. Comparing the continuously and never replenished pyruvate cases, we see that the initial dynamics appear to be similar between cases for the same parameter values (until t ≈ 2 for HMG-CoA and t ≈ 12 for IDP), but the dynamics diverge after a longer time. In the continuous replenishment case, the concentration of HMG-CoA and the production rate of IDP tends to a constant value whereas, in the no replenishment case, the concentration of HMG-CoA increases to a maximum level before decreasing, and the concentration of IDP tends to a constant value. To understand these phenomena in more detail, and to determine how we can increase IDP production whilst minimizing the maximum levels of (cell growth inhibiting) HMG-CoA in terms of the parameter values, we now perform an asymptotic analysis.Although the dynamics for each case are different, we can observe general trends. Most notably, we see that increasing 3.23.2.1S2), it is difficult to obtain accurate estimates of Here, we consider the system (3b–h), and impose The leading-order version of (4) is given byconsider .Fig. 4Thv5, v6, and v7 is significantly reduced as t → ∞, and we may determine that the leading-order long-time behaviour in this regime is given byAlthough the leading-order solution for In dimensional terms, the long-time behaviour of the isopentenyl diphosphate (IDP) concentration isE1 and E3, whereas the maximum concentration of HMG-CoA depends on the enzyme concentrations E1, E3, and E4. Recalling that our goal is to maximize IDP whilst minimizing HMG-CoA, and noting that the dependence on E1 and E3 is the same for both metabolites of interest, the only way our goal can be achieved by varying enzyme concentration is to significantly increase E4, so that it compensates for any increase in E1 or E3. That is, our model suggests that we should overexpress HMG-CoA reductase, the enzyme that catalyses the reaction from HMG-CoA to mevalonate. Importantly, we are able to deduce that this is the most significant leading-order effect, and this explicit result is possible due to our asymptotic analysis. The significance of this reaction is in agreement with E3, the enzyme that catalyses the reaction from acetoacetyl-CoA and acetyl-CoA to HMG-CoA would increase the levels of IDP produced by a single cell, but would also produce more HMG-CoA, which would reduce the number of cells in the system . The same is true of increasing E1, the enzyme that catalyses the reaction from pyruvate to acetyl-CoA, and decreasing A, any enzyme that catalyses reactions from acetyl-CoA to sinks of acetyl-CoA. Finally, we note that these results all agree with the numerical results in From the explicit results (9), we see that the long-time production of IDP depends on the enzyme concentrations 3.2.21/2, and using the solution  decay of pyruvate occurs over the timescale As suggested by ω is defined in (9c).For completion, we show the dynamics of this depletion, where the levels of pyruvate become exponentially small, in Hence, our main conclusions from the continuous replenishment case are still valid for this case. For this no-replenishment-of-pyruvate case, we are also able to further determine that a lower value of 4in vivo experiments in Cupriavidus necator H16, a gram-negative bacterium previously known as Ralstonia eutropha. C. necator is a facultative chemolithoautotrophic microorganism of relevant biotechnological interest since it can be exploited as a bacterial chassis for the production of chemicals. As proof of concept, we sought to introduce the upper part of the mevalonate pathway, leading to the production of isoprenoid precursors, into C. necator. To this end, C. necator H16 was transformed with a plasmid (pBBR1JW3) carrying the mvaE and mvaS genes from E. faecalis of the L-arabinose inducible promoter PBAD. These two genes code for the enzymes Acetyl-CoA acetyltransferase/HMG-CoA reductase (MvaE) and Hydroxymethylglutaryl-CoA synthase (MvaS), respectively. MvaE is a bifunctional enzyme that catalyses two reactions, from acetyl-CoA to acetoacetyl-CoA and from HMG-CoA to mevalonate, while MvaS converts acetoacetyl-CoA and acetyl-CoA to HMG-CoA. In our model, increasing the expression of MvaE corresponds to increasing E2 and E4, whereas increasing the expression of MvaS corresponds to increasing E3 (a schematic of the pathway is shown in S1 to S5) within the modified C. necator.To validate the predictions made in this model, we carried out 4.1C. necator H16/pBBR1-USERcassette1 and C. necator H16/pBBR1JW3 (pBBR1::araC/PBAD-mvaES) were used to inoculate 5ml of LB solution with 300µg/ml kanamycin (in 50ml tubes) and grown overnight at 30 °C with shaking (200 rpm). The following morning, the optical density (OD600) of the cultures was measured and normalised to OD600 of around 0.6 - 0.7. At this point, 1ml samples were collected from each culture, centrifuged at 14000 rpm for one minute and the cell pellets were stored at -20 °C to be used as pre-induction protein samples for SDS-PAGE. In addition, 1ml of 1% (w/v); 2% (w/v); or 20% (w/v) L-arabinose solutions were added to the corresponding cultures, thus obtaining L-arabinose final concentrations of 0.01%; 0.02%; and 0.2%, respectively. Following 18 hours of incubation at 30 °C with shaking (200 rpm), the OD600 of the cultures was measured. The bacterial cultures were then centrifuged (8000 rpm for five minutes), concentrated to OD4Cl with 300µg/ml kanamycin and transferred to 250ml flasks. After 24 hours of incubation at 30 °C with shaking (200 rpm), 500µl samples were taken from the cultures and centrifuged (14000 rpm for one minute). Supernatants were then analysed by HPLC to quantify the mevalonate titres produced by C. necator H16/pBBR1JW3 in response to the different L-arabinose concentrations taken into account.Single colonies of 4.2S4] remain the same and, in dimensional terms, the long-time results for [S5] areP], so our previous predictions for maximizing IDP while minimizing HMG-CoA will also be our current predictions for maximizing mevalonate while minimizing HMG-CoA in this experiment.We can use our model to predict the behaviour of the shorter pathway from pyruvate to mevalonate, by taking the limit L-arabinose leads to significant increments in the amount of mevalonate produced after 24 hours of growth in the presence of fructose as a carbon source  constant. As discussed above, our model predicts that this will decrease the maximum levels of HMG-CoA in the system, and thus produce more IDP. Hence, our model is verified by these experimental results.HPLC data show that increasing the concentration of the inducer n source a. From tncreased b. Howeve5We developed and solved a mathematical model for the kinetics of the mevalonate pathway, derived using generalised Michaelis–Menten kinetics and including the effect of a sink from acetyl-CoA into other metabolic pathways. We considered two extreme cases, namely where the pyruvate was continuously and never replenished. We used asymptotic analysis to gain physical insight into the system behaviour, allowing us to evaluate the effect of upregulating different reactions without resorting to an expensive parameter sweep. The system we considered here has eight dependent variables with 20 kinetic parameters. Our asymptotic analysis enabled us to give analytic expressions for each dependent variable in the continuous replenishment case, and to reduce the entire system to numerically solving a coupled nonlinear system of two dependent variables with one parameter in the never replenished case. We then validated our model by performing experiments that agreed with our predictions.E3, the enzyme that catalyses the reaction from acetoacetyl-CoA and acetyl-CoA to HMG-CoA will have a positive effect on both IDP production and maximum levels of HMG-CoA. Thus, over-expressing this enzyme by itself will not have significant effects. However, we additionally note that over-expressing E4, the enzyme that catalyses the reaction from HMG-CoA to mevalonate, will decrease the levels of HMG-CoA without having a significant effect on IDP production. Therefore, over-expressing both E3 and E4 should have a much larger effect on IDP production than just over-expressing E3. The importance of the reaction from HMG-CoA to mevalonate has been previously noted by experiments  in our main analysis, we also show in Our results also suggest that the reactions between acetyl-CoA and acetoacetyl-CoA can have a significant effect on both IDP production and maximum levels of HMG-CoA. However, this effect cannot be achieved by over- or under-expressing In the case where pyruvate was never replenished, we found that the depletion of pyruvate occurred slowly enough that, for intermediate time, our system reproduced the steady-state behaviour we would eventually expect from the continuously replenished pyruvate case. However, whilst the system was initially robust to this slow depletion, we additionally calculated the point at which the low levels of pyruvate affected the entire system, then calculated the dynamics of this depletion. This required the use of the method of matched asymptotic expansions with logarithmic matching terms involving the small variable to track metabolite concentrations up to the first correction order over two timescales, thus allowing us to obtain the leading-order depletion at the third and final timescale.C. necator. This was achieved by transforming C. necator with a plasmid harbouring the E. faecalis mvaE and mvaS genes under the control of a promoter induced by the presence of L-arabinose (PBAD). The proteins MvaE and MvaS, respectively encoded by these two genes, are responsible for the conversion of acetyl-CoA to mevalonate. A protein expression analysis showed that production of MvaE increased as the levels of L-arabinose present in the cultivation medium were increased. On the other hand, expression of MvaS appeared to be independent of L-arabinose concentration. In any case, the effect of increasing MvaE expression was to produce more mevalonate, an experimental result that agreed with the theoretical predictions from our model. We note that our model could be verified further by directly measuring the levels of HMG-CoA. However, as HMG-CoA is not secreted by the cell and is likely to be unstable, direct measurement is significantly more technical; as carried out in In our experiments, we were able to introduce a pathway from pyruvate to mevalonate into the bacterium In the derivation of this model, we chose initial conditions that modelled the instantaneous addition of pyruvate to a well-mixed solution of enzymes. These conditions were chosen for mathematical convenience and are likely to differ significantly from the conditions within a cell producing isopentenyl-diphosphate. However, as the systems we have considered both tend to stable steady states, this difference is unlikely to be a practical issue for the long time results, although it would affect the initial transients.Finally, we note that this work highlights how a simple mathematical model can be used to predict the biologically relevant behaviour of a system. Moreover, this work shows how asymptotic analysis can play an important role in reducing the computational complexity of a derived system and be used to overcome uncertainty issues with parameter values. We hope that the predictions from our model can help to build a more efficient path to biological discovery.


Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine Phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on different leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identification in cancer.Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve  CBM integrates different types of mathematical and biophysical constraints, enabling prediction of metabolic phenotypes. In particular, CBM allows us to conduct gene essentiality analysis (GEA), which aims at determining (i) essential genes, defined as those genes which individual deletion is incompatible with organism/cell growth; and (ii) synthetic lethal genes, which refer to two (or more) non-essential genes which simultaneous deletion becomes incompatible with cellular proliferation.Constraint-based modeling (CBM) represents one of the most popular approaches within computational systems biology. It focuses on building, contextualizing and analysing genome-scale metabolic networks2. Growth is here modelled as the flux of a physiological reaction, typically named biomass equation, which involves the metabolic requirements, in terms of building blocks and energy, to produce biomass. GEA is accomplished by computationally finding those genes whose individual (or simultaneous) deletion prevents the biomass reaction from being active. Essential  genes are identified by obtaining the subset of reactions fully dependent on them and then confirming that the maximum biomass production becomes zero when these reactions are deleted from the metabolic model. Note here that enzymes catalyzing metabolic reactions are usually a gene product or, in the case of heterodimers or multimers, the junction of several gene products. The set of genes needed for a reaction to take place can be inferred based on the boolean rules typically included in genome-scale metabolic networks3.CBM and GEA have been successfully applied in the field of microbiology4 and extensive availability of transcriptomic and proteomic data. The first successful application of GEA in cancer was presented in Folger et al.5. They revealed that haem oxygenase is synthetically lethal with the tumor suppressor fumarate hydratase. This result was later experimentally validated, showing its relevance to treat leiomyomatosis and renal cell cancer6.Over the last few years, different studies have emerged to extend this same approach to the field of cancer research, exploiting the release of high-quality human genome-scale metabolic networks7, iMAT8, FastCore9 and others10. With these approaches, the construction of different cancer-specific metabolic networks is now possible.In order to further continue and extend this seminal work, much effort has been done in developing algorithms for contextualizing a reference human metabolic network for each particular scenario mainly based on gene expression data. In essence, these methods aim to select the consistent subset of reactions active for a given cancer sample. A non-exhaustive list of this type of methods includes: GIMMEet al.5, which involves 42 essential metabolites in human tissues, including nucleotides, deoxynucleotides, amino acids and lipids. Despite its novelty at the time, this biomass definition is limited and requires further development. The process of cell growth and proliferation requires metabolic reactions that do not directly provide metabolites used for biomass, but rather molecules that will support the activation of signalling and regulatory programs. One remarkable example is 2-hydroxyglutarate, a metabolite arising from mutated IDH in a number of tumors11, which has been shown to modify the epigenetic profile in cancer. Other important oncometabolites are acetate12, succinate or fumarate13. These observations emphasize the need to consider additional cancer-specific metabolic phenotypes, which can be systematically impaired via gene essentiality analysis. Two recent studies have focused on this growing need, providing an extended biomass equation14 or metabolic requirements associated with highly migratory cancer cells15.However, less work has been directed to contextualize and enrich the biomass equation in cancer. The biomass equation allows us to simulate the biosynthesis of essential metabolites to produce new cells, a feature of particular relevance in cancer given the highly proliferative phenotype of malignant cells. The first biomass equation was released by Folger 17. Polyamines are oncometabolites that exert their action, at least in part, by regulating different cancer signalling pathways19 and are over-produced by cancer cells22. Inhibition of polyamine biosynthesis has been largely investigated as a potential anti-cancer strategy, leading to a number of promising targets and inhibitors24. However, polyamines are typically neglected in the biomass equation of existing in-silico cancer metabolic models and, therefore, a gene essentiality analysis for polyamine biosynthesis in cancer is still lacking. In this study, we focused on the identification of genetic perturbations that hamper polyamine production and propose Adenine Phosphoribosyltransferase (APRT) as a candidate target to inhibit the production of these biomolecules and, in turn, curb cancer cell growth.In this study we focused on the requirement of polyamines  for tumor cell proliferationet al.5, which was built based on the expression data of the NCI-60 human cancer cell line panel and the standard RPMI-1640 growth medium conditions used in these cell cultures. The biomass equation presented in Folger et al.5 was amended by including polyamines at similar levels as found in other reconstructions25 (see Methods section). Under this new scenario, we repeated the gene essentiality analysis as conducted in Folger et al.5. Table et al.5, as their role is intrinsically linked to the biosynthesis of polyamines. Given that polyamine biosynthesis plays a prominent role in cancer cells over healthy tissues22, we hypothesized that the set of essential and synthetic lethal genes shown in Table We used the generic cancer model presented in Folger 17. Indeed, inhibition of ODC1 with difluoromethylornithine (DFMO) is suggested to reduce the risk of colorectal cancer26 and targeting AMD1 with SAM486A reduces prostate cancer growth with negligible systemic toxic effects22. MTAP deletion is frequently observed in cancer27, suggesting that additional pathways compensate the lack of this gene. In particular, in the light of the recently elucidated synthetic lethality of MTAP with PRMT528, MTAP-deleted cells seem to rewire their metabolism to elicit an epigenetic switch that explains their selective advantage. Our computational analysis is not capable of predicting the side-benefits of depleting MTAP due to its specific nature on metabolic modeling.AMD1, MTAP, ODC1 and SRM are directly involved in the polyamines biosynthesis and their role in cancer has been previously studiedet al.5 does not take into account tissue-specific gene expression. Since MAT1A is predominantly produced in the liver29, our results would suggest that MAT2A and MAT2B are essential genes in other cancer tissues. MAT enzymes catabolize methionine and produce S-adenosylmethionine (SAM), the major methyl donor in the cell and the precursor of decarboxylated SAM for polyamine synthesis. The results are therefore coherent with the essential nature of enzymes that are required for the production of polyamine precursors. In fact, the inhibition of MAT2A has been proved to decrease cell growth in colon cancer30.MAT1A & MAT2A and MAT1A & MAT2B were revealed as synthetic lethal genes in our analysis. Interestingly, the metabolic model presented in Folger et al.5 capable of producing ornithine, key precursor of polyamines. In turn, the synthetic lethality scored in our model is consistent with the negative consequences of ornithine ablation on polyamine production. Indeed, experiments in yeast showed that their inhibition significantly decreases the level of polyamines31.ARG1 and OAT are the only enzymes included in the metabolic model in Folger 32. This led to the development of different PNP inhibitors, such as forodesine, which has been tested in a number of clinical trials33. The cytotoxicity derived from PNP inhibition in these tumors is mediated by the accumulation of dGTP, which leads to the apoptosis of lymphocytes34. Interestingly, the association of PNP inhibition with the biosynthesis of polyamines has not been reported and requires further consideration. On the other hand, the role of APRT in cancer is poorly understood and constitutes an attractive target to be explored. It catalyzes the reaction that synthesizes AMP from adenine. Importantly, lack of APRT activity results in the accumulation of a toxic by-product of adenine, 2,8-dihydroxyadenine (DHA)35.Strikingly, two genes with no apparent relationship to polyamine production were highlighted as synthetic lethal in our model, purine-nucleoside phosphorylase (PNP) and adenine phosphoribosyltransferase (APRT). On the one hand, the inhibition of PNP was previously identified as a promising therapeutic strategy in T-cell acute lymphoblastic leukemia (T-ALL) and cutaneous T-cell lymphoma (CTCL)et al.5. Putrescine is the precursor for the formation of both spermidine and spermine; 5-methylthioadenosine (MTA) is a by-product in such biosynthetic pathway. There is only one enzyme consuming MTA, namely MTAP, which produces a molecule of adenine. It can be also observed that PNP and APRT consume adenine in order to produce adenosine/deoxyadenosine and AMP, respectively. In the cancer metabolic network presented in Folger et al.5, these reactions are the unique mechanisms consuming adenine.Figure 36, we focused on hematological malignancies, which additionally show a major dependence on polyamines than solid tumors37. In particular, B-ALL constitutes an interesting case where the synthetic lethality of PNP and APRT (if existing) could be relevant, since preliminary results about PNP inhibition show a significantly lower effect than in T-ALL38.Based on the above, the possible synthetic lethality of PNP and APRT arises as a consequence of the need to consume the generated adenine when spermidine and spermine are produced. We next proceeded to evaluate the synthetic lethality underlying APRT and PNP inhibition. In line with previous studies about PNP inhibitionin vitro gene silencing experiments in the B-ALL derived cell line CEMO-1. We measured cell proliferation over time in the following gene knockdown scenarios: i) control; ii) PNP knockdown; iii) APRT knockdown and iv) PNP and APRT knockdown. Results are summarized in Fig. We conducted an 39. Therefore, the degradation of adenine is incorrectly annotated to PNP, which explains the lack of synthetic lethality. In turn, in the absence of a compensatory PNP activity to metabolize adenine, our results would suggest that APRT is an essential gene in this setting. To confirm this, we amended the reactions annotated to PNP in the cancer metabolic model presented in Folger et al.5 and repeated the computational analysis carried out in the previous section, finding that APRT is now essential, in line with the reduction in cancer cell growth elicited by APRT silencing in Fig. We then re-evaluated the function of PNP and APRT in order to explain the lack of experimental validation. A detailed analysis of the reactions associated to PNP shows that it can accept multiple substrates: adenine, guanine, hypoxanthine and uracil ), PEER, MOLT-4 (T-ALL), and MY (B-ALL). We used three siRNAs and each of them decreased APRT expression in all cell lines tested as detected by Q-RT-PCR , which was amended based on existing literature. This inconsistency was experimentally validated with gene silencing analysis of PNP and APRT in the ALL derived CEMO-1 cell line. In contrast to the information in the metabolic model considered, there is strong evidence suggesting that PNP does not consume adenine in human cellsal Table , playing41 and results in renal damage42. This could open a therapeutic window for drug development. With the proof-of-concept here presented, we believe that further investigating the role of APRT in leukemias and other cancer types is promising and should be further considered in the future.The results observed for the silencing of APRT in AML and ALL are remarkable. To our knowledge, the importance of APRT in cancer has not been explored. Instead, the APRT deficit has been found in a rare autosomal-recessive disorder that is compatible with life42 that is not considered in the metabolic model presented Folger et al.5), which in turn would hamper cancer cell proliferation  . On the other hand, the expression of SRM was upregulated in sensitive cell lines. This may indicate that the production of spermidine and adenine would be higher than in the resistant counterparts and, consequently, the pool of excessive adenine upon APRT inhibition would be increased. To reinforce this hypothesis, we computed the ratio of APRT/SRM  is denoted as xc. For readers unfamiliar with metabolic modeling, for each reaction r , it is assigned a continuous flux variable vr that represents its underlying activity. The total number of molecules produced (consumed) by each reaction is precisely its flux (vr) multiplied by the number of molecules produced (consumed) per unit of flux (Scr). Scr  are the stoichiometric coefficients, which are grouped into the matrix S. As usual in the literature46, substrates have a negative stoichiometric coefficient, while products have a positive stoichiometric coefficient.i.e. there is no effective variation in the concentration of considered metabolites over time. After forcing the steady-state assumption, equation  is the mass balance equation (Equation ). This ec c = , …, C isc c = , …, C isentclass1pt{minimaRev), i.e. reactions that can perform in both directions. In these cases their flux can be positive and negative depending if it performs in the forward and backward direction, respectively. On the other hand, if the reaction is defined as irreversible (Irr), it can only operate in the forward direction, therefore its activity must be greater or equal to zero. This is reflected in equation  and lower (lr) bound for metabolic fluxes, as observed in equation  in the amounts found in other reconstructions  is not active, denoted as N(g). This constraint is added into CBM (through equation (vbio) is recalculated. If the maximum growth rate is below a certain arbitrary threshold, then such gene g is included in the list of essential genes. This arbitrary threshold represents the minimum biomass production to guarantee cellular proliferation. In the study presented in this manuscript, we considered the minimum biomass production rate to be 10-4 units, measured in grams per dry weight per hour (gDW/h). A sensitivity analysis for this threshold is included in Supplementary Note Firstly, based on boolean rules that provide the relationship between genes and their encoded enzymes (proteins), available in the cancer metabolic model presented in Folger equation ) and thene g g = , …, G isWe used IBM Ilog Cplex in a Matlab environment to solve the underlying linear optimization problems defined by equations –5 upon d2. All cell lines were tested for mycoplasma  and no positive results were obtained. Gene silencing were done using a Nucleofector II device . Cells were nucleofected with Silencer Select siRNA s9655 for PNP, s1505 (APRT A), UGAGCUGGAGAUUCAGAAA (APRT B) and AGCUGGAGAUUCAGAAAGA (APRT C) for APRT and Silencer Select Negative Control-1 siRNA  at a final concentration of 50nM. Briefly, 1 × 106 of cells were resuspended in 100 µL of supplemented culture medium with 50nM of APRT siRNAs or Silencer Select Negative Control-1 siRNA  and nucleofected with the Amaxa nucleofector apparatus using the following programs: G-009 (CEMO-1), C-005 , C-013 (PEER) and V-001 (KG-1). Nucleofection was performed twice with a 24h interval. 48h after the second nucleofection, PNP and APRT mRNA expression was analyzed by Q-RT-PCR (GUS was employed as the reference gene). Nucleofection efficiency was determined by flow cytometry using the BLOCK IT Fluorescent Oligo .Acute leukemia cell lines were maintained in culture in RPMI 1640 medium supplemented with fetal bovine serum at 37 °C in a humid atmosphere containing 5% COCell proliferation was analyzed by MTS-based CellTiter 96® Aqueous Assay  that was used following the manufacturer’s instructions. Data were calculated as the percentage of total absorbance of APRT nucleofected cells/absorbance of control cells.Total mRNA was extracted with Trizol© Reagent 5791  following the manufacturer’s instructions. RNA concentration was quantified using NanoDrop Specthophotometer . Reverse transcription was performed on 1 µg of total RNA, after heating at 70 °C for 5 min, with random hexamers as reaction primers. The reaction was carried out at 42 °C for 45 min in the presence SuperScript® II Reverse Transcriptase .GUS) transcript was performed as a reference gene using specific primers and TaqMan probe . The relative expression of each gene was quantified by the Log 2(−ΔΔCt) method using the gene GUS as an endogenous control.Real time PCR was performed in a 7300 Real Time System , using 20ng of cDNA in 12 µl reaction volume with 0.25 µmol/l of each primer , and SYBR® Green PCR Master Mix . Amplification of glucuronidase beta  and 3 samples of KG-1 (sensitive) against 5 samples of MOLT-4 (resistant). For the rest of cell lines considered in the main text, we could not find public microarrays data available. Microarrays data were obtained from Gene Expression OmnibusSupplementary Information


Targeted kinase inhibitors are an important class of agents in anticancer therapeutics, but their limited tolerability hampers their clinical performance. Identification of the molecular mechanisms underlying the development of adverse reactions will be helpful in establishing a rational method for the management of clinically adverse reactions. Here, we selected sunitinib as a model and demonstrated that the molecular mechanisms underlying the adverse reactions associated with kinase inhibitors can efficiently be identified using a systems toxicological approach.First, toxicological target candidates were short-listed by comparing the human kinase occupancy profiles of sunitinib and sorafenib, and the molecular mechanisms underlying adverse reactions were predicted by sequential simulations using publicly available mathematical models. Next, to evaluate the probability of these predictions, a clinical observation study was conducted in six patients treated with sunitinib. Finally, mouse experiments were performed for detailed confirmation of the hypothesized molecular mechanisms and to evaluate the efficacy of a proposed countermeasure against adverse reactions to sunitinib.In silico simulations indicated the possibility that sunitinib-mediated off-target inhibition of phosphorylase kinase leads to the generation of oxidative stress in various tissues. Clinical observations of patients and mouse experiments confirmed the validity of this prediction. The simulation further suggested that concomitant use of an antioxidant may prevent sunitinib-mediated adverse reactions, which was confirmed in mouse experiments.A systems toxicological approach successfully predicted the molecular mechanisms underlying clinically adverse reactions associated with sunitinib and was used to plan a rational method for the management of these adverse reactions. Despite the development of novel technologies that enable comprehensive measurements of off-target interaction profiles,8 standard methods for elucidating the molecular mechanisms underlying development of adverse reactions in a clinical setting based on off-target data have not yet been established. Development of a method to identify means to mitigate adverse reactions based on the identification of toxicological targets and the molecular mechanisms is urgently required. In this study, we selected sunitinib as a model compound to demonstrate an efficient method to elucidate the molecular mechanisms underlying clinically adverse reactions associated with kinase inhibitors and to design a possible countermeasure for adverse effects.Targeted kinase inhibitors are an important class of therapeutics that are widely used to treat various types of cancer,9 It is indicated for the treatment of metastatic renal cell carcinoma (RCC), imatinib-resistant gastrointestinal stromal tumors, and metastatic pancreatic neuroendocrine tumors.10 Sunitinib targets multiple tyrosine kinases, including vascular endothelial growth factor receptors, platelet-derived growth factor receptors and stem cell factor receptors.11 However, despite its potent antitumor effects, sunitinib also causes numerous adverse reactions. These reactions are markedly more prevalent and severe in patients treated with sunitinib than in those treated with sorafenib, another multi-kinase inhibitor used in the treatment of RCC, even though their primary target profiles are similar.12 Among the adverse reactions associated with sunitinib, thrombocytopenia has an exceptionally high incidence and is generally recognized as a dose-limiting toxicity.5 Hyperthyrotropinemia is also more prevalent in patients treated with sunitinib than in those treated with sorafenib.13 Other associated adverse reactions include hepatic and cardiac dysfunction.13 In general, although sunitinib is currently recognized as the initial drug of choice for the treatment of advanced RCC, its toxicity is a matter of clinical concern.14Sunitinib is a multi-kinase inhibitor with antitumor and anti-angiogenic activities.16 In addition, in silico simulation enables the examination of working hypotheses without time-consuming and costly experiments, thereby helping to develop plausible hypotheses that warrant experimental investigation. Although such approaches may ultimately contribute to the elucidation of the molecular mechanisms underlying physiological drug activities, few reports demonstrating concrete methodologies are available. In the present study, we used systems toxicology approach to efficiently elucidate the molecular mechanisms underlying the clinically adverse reactions associated with sunitinib and to identify a method to attenuate these adverse reactions based on an understanding of the molecular mechanisms.Understanding possible molecular mechanisms of such adverse reactions requires a systems toxicology approach where diverse data and knowledge are integrated to uncover dynamics of molecular interactions networks linked to physiological responses. It can be a power approach especially where the polypharmacological properties of a compound obscure the physiological mechanism of drug action.Kd values of a number of kinase inhibitors obtained with high-throughput measurements.8 The full list of Kd values of sunitinib and sorafenib to human kinases is available as the 8 The steady state mean unbound plasma concentrations of these agents under clinical conditions  were obtained from the information displayed on the Japanese package inserts. From a pharmacokinetic standpoint, the free drug concentration in cells is in equilibrium with the plasma-unbound concentration of the drug. Thus, the occupancy rates can be calculated using the Kd value for drug binding to the kinase and the mean plasma unbound concentration  of the drug at steady state as The occupancy rates of sunitinib and sorafenib at various human kinases were calculated from a previous report that included the 18 and public databases, such as PANTHER19 and BioModels.20 Specifically, the glycogen metabolic pathway was retrieved from previous reports,18 and the glycolysis pathway, tricarboxylic acid cycle and pentose-phosphate pathway were retrieved from the PANTHER database . The glutathione metabolic pathway was retrieved from previous reports22 and from the BioModels database (ID: BIOMD0000000268). These pathways were then integrated to create a system-wide map using CellDesigner4.3.23Metabolic pathway maps were constructed utilizing previous reports24 the pentose–phosphate pathway,25 and the glutathione metabolism pathway26 are available. For glycogen metabolism and the glycolysis pathway, the inhibitory effects of sunitinib were added to the model by assuming competitive inhibition of phosphorylase kinase (PHK) with an inhibition constant of 5.5 μM, a reported value for the dissociation constant.8 This simulation model was published as a MATLAB script in the original paper.24 MATLAB 2012b was used to simulate the effects of varied sunitinib concentrations on PHK content, glycogen phosphorylase and glycogen synthase activity, and glycogen and G6P content, as measured at the end of the simulation. The kinetic behavior of the pentose–phosphate pathway was described by the kinetic laws published in a previous report25 and the SABIO-RK database.27 These kinetic data were implemented into a model formed using systems biology markup language (File S2). Simulations based on this model were performed using MATLAB 2012b. The glutathione metabolism model was taken from BioModels ID: BIOMD0000000268 in systems biology markup language format, and simulations were performed on COPASI.28 To simulate the effect of antioxidants, a node representing vitamin E was added to the glutathione model, which assumed direct quenching of H2O2 by vitamin E.Although there are no publicly available simulation models that include the entire relationship between PHKG1/2 and GSH, models for glycogen metabolism and glycolysis,M L-glutamine , and 1% penicillin-streptomycin . HEK 293 cells were cultured in DMEM supplemented with 10% FBS and 1% PCSM. Primary hepatocytes were isolated as described previously,29 seeded onto collagen-coated plates, and cultured in William E medium  supplemented with 10% FBS, 2 mML-glutamine, and 1% PCSM.293FT cells were cultured in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum , 2 mGenes encoding human and mouse PHKG1 and PHKG2 were engineered to include a His-tag at the N-terminus and then subcloned into pcDNA3.3 vectors (Life Technologies), which were then transfected into 293FT cells using Lipofectamine 2000 (Life Technologies). At 48 h post-transfection, cells were lysed in lysis buffer containing phosphate-buffered saline (pH 8.0), 1% Nonidet P-40, and a protease inhibitor cocktail . Recombinant proteins were purified using Profinity IMAC Ni-Charged Resin .M ATP in kinase buffer  and 10 μM β-mercaptoethanol (Sigma-Aldrich) for 30 min at 37 °C. The concentration of the remaining ATP was determined using a Kinase-Glo Plus luminescent kinase assay . To determine the concentration for 50% inhibition (IC50), drugs were added to the reaction mixture at the indicated concentrations. ATP concentrations were fitted to the standard model, and IC50 values were determined according to Powell’s nonlinear least squares method with a uniform weighting factor. Analyses were performed using Scientist software .To measure the kinase activity of PHKG1/2, 100 ng of the recombinant protein was incubated with 10 μg of κ-casein , 2 μPatients with advanced RCC scheduled for sunitinib treatment were recruited for the observational study. Written informed consent was obtained from each patient and blood samples were collected at the indicated times.Six-week-old male C57BL6/J mice were purchased from Japan SLC  and fed an MF diet  containing sunitinib  or sorafenib  for 14 days. The doses were adjusted to sustain plasma drug concentrations at levels comparable to those achieved in patients. To evaluate the effects of the concomitant use of an antioxidant, mice were fed a diet containing both α-tocopherol nicotinate  and sunitinib for 14 days. To examine the contribution of oxidative stress on sunitinib hepatotoxicity, buthionine sulfoximine was administered intraperitoneally (6 mmol/kg at −1 and 11 h) and sunitinib was administered orally . After 36 h, the mice were killed, and their organs and cells of interest were analyzed.g for 5 min at 4 °C. Supernatants were analyzed according to the manufacturers’ protocols. Tissue glutathione levels were measured in excised tissue samples homogenized with 6% metaphosphoric acid (Wako) and centrifuged at 20,000g for 5 min at 4 °C. Supernatants were divided into two aliquots, one of which was used to assay total glutathione, while 2-vinylpyridine  was added to the other sample to mask GSH. Total glutathione and GSSG were assayed at an absorbance of 405 nm in the presence of 25% triethanolamine , 6 mM 5,5’-dithiobis-(2-nitrobenzoic acid) , 0.3 mM NADPH (Wako), and 5 units/ml of glutathione reductase (Sigma-Aldrich). Troponin T measurements were conducted by SRL  using an electrochemiluminescence immunoassay. The N-terminal fragment of the prohormone B-type natriuretic peptide (NT-proBNP) was measured using an NT-proBNP ELISA kit  according to the manufacturer’s protocol. Platelet counts were performed by Mitsubishi Chemical Medience Corporation  using an electrical resistivity method. Thyroid-stimulating hormone levels were measured using a rodent thyroid-stimulating hormone assay kit  according to the manufacturer’s protocol. Total triiodothyronine (T3), total thyroxin (T4), free T3 (FT3) and free T4 (FT4) concentrations were measured using an ARCHITECT i1000SR analyzer  with the corresponding kits.Serum ALT levels were measured using an L-type Wako GPT J2 (Wako) kit and a Dimension Xpand analyzer  according to the manufacturers’ protocols. To measure tissue glycogen and G6P levels and the NADPH/NADP+ ratio, excised tissue samples were homogenized with the extraction buffers provided in the glycogen assay kit , the glucose-6-phosphate assay kit (BioVision), or the NADP+/NADPH quantification kit (BioVision), and then centrifuged at 20,000m/z 399.48 to 283.1 for sunitinib, m/z 371.27 to 283.1 for N-desethylsunitinib, m/z 465.35 to 252.1 for sorafenib, and m/z 268.23 to 116.03 for metoprolol, which served as an internal standard. Plasma samples were deproteinized with 4 volumes of acetonitrile  and the supernatants were used for analysis.Plasma drug concentrations were measured using liquid chromatography coupled with tandem mass spectrometry  in a device equipped with an ACQUITY UPLC BEH shield  and a 2.1×100 mm column (Waters). All the data were acquired and processed using MassLynx software (version 4.1) with QuanLynx (Waters). Positive-ion electrospray tandem mass spectroscopy, operated under the multiple reaction monitoring mode, was used to detect mass transitions (parent to daughter ion), with PacI and transfected into HEK 293 cells to yield adenoviruses encoding shRNAs. Both virus production and amplification were performed according to the manufacturer’s protocols. A CsCl density gradient ultracentrifugation was used to purify crude adenoviral preparations. The viral titer was determined using an Adeno-X Rapid Titer Kit (Clontech Laboratories). Mice were intravenously injected with 1×109 infectious units of adenovirus. Seventy-two hours after adenovirus administration, the mice were killed, and the relevant tissues and cells were analyzed.A small hairpin RNA (shRNA) sequence that inhibits murine PHKG2, specifically, 5′-GCCTTAAGCAGTCACCGTTTA-3′, was designed using BLOCK-iT RNAi Designer . The shRNA duplex contains a loop sequence (5´-TTCAAGAGA-3′) that was initially subcloned into an RNAi Ready pSIREN vector  and then into pAdeno-X (Clontech Laboratories), which was digested with Total RNA was extracted from mouse liver using RNA isoplus reagent . Reverse transcription was performed using ReverTra Ace  and mRNA expression levels were measured using real-time quantitative PCR with SYBR GreenER qPCR SuperMix Universal (Life Technologies), an Eco Real-Time PCR System  and the provided software. The following primers were used: 5′-GCACCAGAGATCCTTAAAT-3′ and 5′-TAGCATCAGGATTTGGCGC-3′ for mouse PHKG2, and 5′-CCGGAAGGAAAACTGACAGC-3′ and 5′-GTGGTGGTGAAGCTGTAGCC-3′ for β-actin.t-test, analysis of variance followed by the Tukey–Kramer post hoc test, or analysis of covariance, where applicable.All the data are expressed as the mean±s.d. of at least five independent experiments. Statistical analysis was performed using Student’s Experiments using human samples were conducted according to a study protocol approved by the Institutional Review Board of the Graduate School of Medicine, The University of Tokyo. All animal procedures were approved by the Institutional Animal Care and Use Committee of the Graduate School of Medicine, The University of Tokyo.31 and reported dissociation constant (Kd) values8  is a typical radical scavenger involved in eliminating the reactive oxygen species produced by numerous physiological processes.39 However, it remains unclear whether GSH redox balance is affected by phosphorylase kinase inhibition or the influence of phosphorylase kinase inhibition is diminished before affecting GSH redox balance owing to the buffering or feedback effects of metabolic pathways. Therefore, in silico simulations were used to examine whether sunitinib-mediated inhibition of PHKG1/2 affects GSH levels. Although models of glycogen metabolism and glycolysis,24 the pentose–phosphate pathway,25 and glutathione metabolism26 are available, to the best of our knowledge, there is currently no single available model that includes the entire relationship between PHKG1/2 and GSH. Therefore, we performed sequential simulations using all these models , six patients receiving sunitinib as a therapy for advanced or relapsing RCC were enrolled in the study  because intravenously administered adenoviruses preferentially infect and accumulate in the liver.40 Indeed, hepatic PHKG2 mRNA , was added to the original model (in silico prediction by examining the in vivo effects of a vitamin E preparation consisting of α-tocopherol nicotinate (α-TN) in sunitinib-treated mice. Mice were administered sunitinib with or without α-TN, and the metabolite content of the tissues was measured. In all four tissues examined, co-administration of α-TN prevented sunitinib-induced decreases in the GSH/GSSG ratio, which was maintained at levels similar to those observed in the tissues of control mice  and lung weight, which reflects the metastasis of Lewis lung carcinoma cells . These e tissues . As expe2 Although these agents are less toxic than classical anticancer chemotherapeutics, the spectra of adverse reactions associated with kinase inhibitors are often different from those of anticancer chemotherapies, causing an emerging problem in clinics.5 Clinical management methods for frequent adverse reactions associated with anticancer chemotherapies, such as diarrhea, nausea and myelosuppression, have been established on the basis of empirical evaluations.44–46 However, clinical management methods have not been well established for adverse reactions that are rarely observed by clinicians.47 Fast-paced launches of new kinase inhibitors might also make it more difficult to establish clinical management methods for adverse reactions in a timely manner. To overcome this problem, development of a system-oriented framework is critical so that countermeasures for adverse reactions can be designed on the basis of an understanding of the molecular mechanisms and system dynamics underlying adverse reactions. Various in silico tools have been developed to predict the toxicological properties of new chemical entities, including quantitative structure–activity relationship-based approaches and rule-based expert systems. However, most of these tools presupposed their utility during the drug designing steps, aiming to predict properties of a compound related to general toxicity such as genotoxicity, carcinogenicity and irritancy based on its chemical structure.48–50 These tools are useful for reducing the risk of discontinuation due to serious safety issues at the later stages of drug development; however, they are not intended to provide information on the detailed molecular mechanisms underlying clinically adverse reactions. Accordingly, we examined the usefulness of a systems toxicological approach in the present study and demonstrated a method to predict the reactions that may be triggered by a compound at the molecular level based on the experimental measurement of its kinase interaction profile.Various targeted kinase inhibitors are available for clinical use, and these drugs have contributed to the drastic improvement in treatment outcomes for anti-cancer therapies.in silico simulations to examine whether a change in an input actually affects the output. The challenges in implementing this approach lie in the preparation and use of mathematical models. Obtaining quantitatively accurate predictions requires simulations using an integrated model that encompasses the entire processes of interest. However, it is not always practical to develop a large-scale integrated model and therefore, the use of multiple models that represent a subset of the entire interaction network may be necessary. A simple merger of independently derived models often results in inconsistency in the kinetic parameter values and therefore, these values have to be re-tuned after model integration. The constraint here is that fine-tuning the kinetic parameters in an integrated model may not work owing to different levels of abstraction among models. Even if the level of abstraction and methods of model description have been made consistent, it is a computationally intensive and time-consuming task that often requires additional experimental data for accurate estimates of kinetic parameter values.The present analysis was guided by the off-target interaction profile associated with sunitinib. Off-target candidates that are affected at clinically relevant concentrations can be identified using the calculations shown in 51 This degree of accuracy may be attributable to the modularity and robustness of the biological network. If the entire network is so tightly connected, perturbation of any part of the network affects the behaviors of all other parts. This results in an extremely unstable system, leading to an evolutionary dead-end.52 Thus, in practice, we can define a subset of the interaction network and consider the rest to be a ‘black box’ from which effects can be implicitly represented within a specific parameter setting of the model  is an effective strategy to examine toxicological issues associated with kinase inhibitors. Results of this study have major implications for the use of sunitinib in clinical practice, as it is essential to overcome adverse reactions associated with sunitinib tolerability.13 Suppression of sunitinib-mediated thyroid dysfunction may improve patients’ quality of life by ameliorating subjective symptoms, such as fatigue. More importantly, since the sunitinib-mediated reduction in platelet counts is a dose-limiting toxicity, an improvement in thrombocytopenia will allow the continued use of higher doses, which is desirable in treating the primary disease. Indeed, the therapeutic efficacy of sunitinib in RCC correlates positively with the area under the plasma concentration curve for the drug.53 The experiments conducted in the present study strongly indicate that the multi-organ toxicity suffered by patients receiving sunitinib may be alleviated by the concomitant administration of α-TN.The results reported herein demonstrate that In conclusion, we demonstrated that a systems toxicological approach could be successfully utilized to elucidate molecular mechanisms underlying adverse reactions associated with kinase inhibitors. This approach may be applicable to other kinase inhibitors to maximize their clinical benefits.


In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganismsMetabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge,  Escherichia coli [Saccharomyces cerevisiae [. However, only a few of these biosynthesized chemicals are able to be industrially commercialized due to low production levels with unsatisfactory titers, yields, and productivities [Using microorganisms to produce various chemicals from renewable resources could be environmentally friendly and reduce strong dependence on petroleum. Recently, with the rapid development of metabolic engineering and synthetic biology, a wide range of bulk chemicals ,2,3,4, bhia coli ,20,21,22revisiae ,24,25,26tivities ,28. TherOne of the main reasons for the low production level of engineered microorganisms is the high complexity of cell metabolism. Microbi13C-Metabolic Flux Analysis (13C-MFA) has been in development since the 1990s [13C-MFA, carbon isotopes have been used to trace the cell metabolism. The carbon flux distributions in metabolic network of microorganisms can be determined using computational algorithms with the development of a metabolic model and the measurement of 13C-labeling patterns of the key metabolites [13C-MFA successfully helped to enhance the microbial production of chemicals [13C-MFA has been widely considered as one of the most important tools to diagnose complex microbial metabolism and develop novel metabolic engineering strategies [To demystify the complex metabolic rewiring of engineered microorganisms and more importantly, derive the appropriate strategies to engineer microorganisms for better biochemical production, a technology named he 1990s ,43,44,45abolites ,47,48,49abolites ,53,54,55hemicals ,57. Therrategies ,34,35,5313C-MFA and metabolic engineering from cases that attempted to improve microbe-based chemical production in the past decade. We will first briefly introduce the techniques of 13C-MFA, and then categorize studies that integrated 13C-MFA with metabolic engineering in different industrial microorganisms, including (1) Saccharomyces cerevisiae; (2) Escherichia coli; (3) Bacillus subtilis; (4) Corynebacterium glutamicum; and (5) other microorganisms. Finally, we envision the emerging areas where breakthroughs of 13C-MFA could potentially promote rational metabolic engineering for improved microbe-based chemical production in future.In this review, we aim to summarize the integrated tactics of 13C-MFA have been developed for more than two decades [13C-MFA, which includes three steps in general: cell cultivation, isotopic analysis of metabolites, and 13C-assisted pathway and flux analysis , has often been used to guarantee that high 13C abundance will be observed in various metabolites [13C-labeled substrate as the sole carbon source is often required for the 13C-labeling experiments. Two culture modes, i.e., batch mode and chemostat mode, are most often used for 13C-MFA to reach the required steady states for sampling, namely, metabolic and isotopic steady states, in which the concentration and isotopic labeling of intracellular metabolites are both constant. Next, cell biomass and culture medium will be used for further isotopic analysis.Cell culture on abolites ,59. On tabolites . A stric13C-labeling in metabolites is often achieved by using mass spectrometry, such as gas chromatography–mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). When using GC-MS for isotopic analysis, a derivatization process using TBDMS or BSTFA as the derivatization agents is always required to render molecules , for the metabolites of interest by using several well-established algorithms [i.e., MDV, will then be used for the pathway and flux analysis [The measurement of hemicals  and protgorithms ,63,64. Sanalysis .i.e., pathway analysis) and quantitatively . On one hand, the 13C-assisted pathway analysis often aims to answer whether or not a metabolic pathway is active in non-model microorganisms by tracking the 13C-labeling patterns  in key metabolites and the fate of biomolecule synthesis in these denoted biochemical pathways. On the other hand, the 13C-assisted flux analysis aims to quantify the carbon fluxes in multiple metabolic pathways by simulating the 13C-labeling patterns  in key metabolites and searching for the “real” metabolic fluxes that could lead to the best fit of the measured MDV. In short, while 13C-assisted pathway analysis is suitable for pathway discovery in non-model microorganisms, 13C-assisted flux analysis is more useful in identifying the metabolic rewiring in industrial microorganisms.Based on the MDV of various metabolites, the metabolic behaviors of microorganisms can be elucidated both qualitatively  to c13C-MFA has been widely used to provide insightful information on metabolism of various microorganisms, thus helping metabolic engineers to successfully improve biochemical production. In the following sections, we have summarized recent successes on integrating 13C-MFA and metabolic engineering  Escherichia coli; (3) Bacillus subtilis; (4) Corynebacterium glutamicum; and (5) other host microorganisms.The ultimate goal of metabolic engineering is to design and build engineered biological systems that can produce chemicals, materials, food, and drugs at high yield using the appropriate microorganisms . Howeverineering  based onSaccharomyces cerevisiae, an important microbial cell factory [S. cerevisiae has been accumulated for over three decades, the complex metabolism in S. cerevisiae still impairs the improvement of chemical productivity in various bioprocesses. For example, the insufficient supply of several key precursors could be a bottleneck in biochemical production and the introduction of a heterologous sugar utilization pathway could lead to serious cofactor imbalance issues. In addition, high maintenance energy required by host cells will compete for the limited carbon source that keeps cells alive. Besides, various inhibitors in industrial fermentation process could dramatically inhibit cell growth, and even lead to cell death. To elucidate the complex yeast metabolism, 13C-MFA has been widely applied on S. cerevisiae to provide biological insights behind all above obstructions. Following these insights, metabolic engineers have developed versatile strategies to overcome rate-limiting steps for further improvement of biochemical production. In this section, as shown in 13C-MFA and the corresponding metabolic engineering work for S. cerevisiae.The yeast  factory  and a mo factory ,74, has  factory ,75, bulk factory ,76,77,78 factory ,80, prot factory ,82,83 by13C-MFA analyzes the metabolic flux distribution in industrial microorganisms at steady state. By using 13C-MFA to elucidate the flux distributions of genetically perturbed cells, the bottleneck step in supplying key precursors for biochemical production can be identified. These elucidated major bottlenecks would provide direct instruction to design appropriate strategies for different biochemical production. For example, as a central metabolite, acetyl-CoA is a key precursor in the biosynthesis of sterols, amino acids, fatty acid-derived chemicals, polyketides, and isoprenoid-derived drugs [13C-MFA has been applied to investigate the acetyl-CoA biosynthesis in S. cerevisiae. By comparing S. cerevisiae strains growing under purely oxidative, respiro-fermentative and predominantly fermentative conditions [S. cerevisiae was found to be the activated pyruvate bypass pathway. In this pyruvate bypass pathway, pyruvate is converted to acetaldehyde and then to acetate for synthesis of cytosolic acetyl-CoA. Although the pyruvate bypass pathway was activated, it was not strong enough to supply sufficient cytosolic acetyl-CoA when S. cerevisiae is engineered to produce acetyl-CoA-derived chemicals, such as n-butanol.Improving the supply of key precursors is one of the most effective strategies to boost the biochemical production in metabolic engineering. ed drugs . To acconditions , the maiS. cerevisiae , in a genetically engineered yeast strain with the expression of genes xpkA and ack from Aspergillus nidulans [A. nidulans, the utilization of this metabolic pathway increased carbon flow towards acetate and acetyl-CoA through the action of a phosphotransacetylase [S. cerevisiae strain, which was also accounted for the improved acetate production. Encouraged by this discovery, co-expression of the same PHK pathway and a wax ester synthase successfully improved the titer of fatty acid ethyl esters by 1.7 fold [In addition to elucidating the bottleneck of cytosolic acetyl-CoA biosynthesis for wild-type yeast, nidulans . The PHKnidulans  and filacetylase . Flux di1.7 fold . Such pr13C-MFA was recently applied to four different engineered S. cerevisiae strains that were engineered to produce shikimic acids at different amounts (manuscript in preparation). By comparing flux distributions of the four strains, a higher flux through PP pathway was positively correlated with higher production of shikimic acid. This suggested that the low flux into the PP pathway, especially a limited supply of the precursor E4P from the PP pathway, could be the bottleneck for producing the shikimic acid. Indeed, it was found that when removing the original phenylalanine and tyrosine synthesis pathway, and overexpressing aro1, aro4 and tkl genes to improve the metabolic fluxes in PP pathway  also play a vital role in the synthesis of value-added compounds. For example, several chemicals, such as shikimic acid  and muconic acid (an important biopolymer precursor) could be produced from metabolites in the PP pathway . To inve pathway , shikimi pathway .+ and NADPH/NADP+, play a key role as the redox carriers for catabolic and anabolic reactions, as well as important intermediates in cell energetics. However, the utilization of some cofactor-dependent production systems may lead to over-production or depletion of certain cofactors, and hence, break the original balances of cofactor usage in the cell metabolism. For example, one of the commonly used strategies to engineer S. cerevisiae for xylose utilization is introducing a fungal xylose pathway from native xylose-utilizing yeasts, such as Pichia stipites. Specifically, xylose could be converted to fermentable xylulose through the consecutive redox reactions catalyzed by NADPH-dependent xylose reductase (XR) and NAD+-dependent xylitol dehydrogenases (XDH), with xylitol produced as the intermediate , no carbon would be lost when producing NADPH for the XR, which would provide more carbons for the ethanol production [+ regeneration directly by introducing the heterologous genes could increase the xylitol consumption by XDH and improve the ethanol production by obtaining more carbons [13C-MFA study, the lower activity of glycolysis that led to inefficient re-oxidation of NADH could be a new bottleneck step when using xylose isomerase in S. cerevisiae [The cofactor engineering strategy has proved to be functional in several studies. For example, by replacing the native oduction ,94,95,96 28%~41% ,99,100. oduction . Also, i carbons . Anotherrevisiae .S. cerevisiae, in order to produce non-natural products with multiple genes inserted, deleted, replaced, or overexpressed. As a result, multiple kinds of chemicals can be produced with the implementation of complex genetic modification. However, the productivity achieved is often unsatisfactory due to the severe metabolic burden from the heterologous protein overexpression, a biological function that could be energetically expensive during transcription and translation. Such an issue, however, has not yet been well studied in the field of metabolic engineering.Metabolic engineering is frequently equated with the heterologous production of a series of recombinant proteins. Nowadays, a large number of heterologous pathways have been introduced into a host cell, such as 13C-MFA studies  catalyzed by malonyl-CoA ACP transacylase (fabD), and the cyclic chain elongation process [Fatty acids and fatty-acid-derived chemicals are the precursors to produce transportation fuels and industrial chemicals including surfactants, solvents, and lubricants . Severalubstrate . E. coli process .13C-MFA has been performed by using an engineered fatty acid over-producing E. coli DH1 strain with the overexpression of tesA, and fadR genes as well as the knock-out of fadE gene [E. coli strain, the flux in the engineered strain was significantly diverted from acetate synthesis to fatty acid synthesis, which suggested that the increased supply of key precursors in the fatty acid synthesis played a crucial role to increase subsequent fatty acid synthesis. The flux of the pentose phosphate pathway also dramatically increased to compensate large amounts of reduction powers, mostly NADPH, for the fatty acid production in the engineered strain. Finally, since more carbon fluxes were required to supply cytosolic acetyl-CoA (the starting point of fatty acid biosynthesis), the flux of the anaplerotic pathway into the TCA cycle decreased 1.7-fold in the engineered strain. Overall, as indicated by 13C-MFA, the supply of fatty acid precursor, cytosolic acetyl-CoA, and the reduction power, NADPH, were recognized as the key bottlenecks in microbial engineering for fatty acid production.Recently, various fatty acid over-producing strains have been created to use different strategies to boost the fatty acid production. However, most studies focus on engineering terminal enzymes in fatty acid biosynthesis pathways and little is known about the central metabolism responses to fatty acid production. To uncover the key bottleneck steps in fatty acid production, adE gene . ComparefadE in E. coli. Combined with the over-expression of tesA and fabF, the yield of fatty acids was increased by nearly 3-fold [fabZ and a thioesterase from Ricinus communis in a fadD (a key gene in fatty acid degradation) deletion mutant could enhance the fatty acid titer by nearly 3-fold [. In summary, the bottleneck of precursor supply identified by 13C-MFA has now been well addressed in metabolic engineering for fatty acid production. The insufficient supply of NADPH, another bottleneck uncovered by 13C-MFA, could be the next direction to further improve the fatty acid production and requires the attention of metabolic engineers.Various engineering strategies have been suggested and explored  in ordery 3-fold . Similary 3-fold . In summE. coli strains via 13C-MFA. For example, 13C-MFA has been used to analyze the cell metabolism in wild type and fatty acid over-producing E. coli strains. As revealed by 13C-MFA, the engineered strain requires excessive NADPH compared to the wild-type strain, i.e., 255 units compared to 179 units NADPH when the flux of glucose uptake was normalized to 100 units. However, the sum of NADPH supplied from central metabolism could only reach 100 units [E. coli. In order to compensate for the NADPH deficiency, the transhydrogenase pathway was activated in the engineered E. coli with flux increasing by 70% compared to that in wild type strains  for fatty acid biosynthesis.The imbalance issue of NADPH has been founded in several engineered 00 units , which iE. coli strains, metabolic engineers have adopted various strategies to overcome this challenge to enhance biochemical production. One strategy is switching the specificities of glycolytic enzymes, e.g., GAPDH, from NAD+-dependence to NADP+-dependence. Such a switch increased the availability of NADPH by building a NADPH-producing glycolysis pathway, and further improved the NADPH-dependent lycopene production by ~100% [zwf that encodes glucose-6-phosphate dehydrogenase (G6PDH) [pfkA and pfkB that encode the phosphofructokinase (PFK) [pgi that encodes phosphoglucose isomerase [E. coli. Overall, following the metabolic rewiring identified by 13C-MFA, the availability of NADPH has been increased for biochemical production in E. coli.Realizing the significance of cofactor balance, particularly the NADPH supply, in engineered by ~100% . Also, s (G6PDH) ,115,116,se (PFK) , or delesomerase ,119  were achieved to increase the biochemical productivity  that have detrimental effects on cell metabolism and other physiological activities. By perfBacillus subtilis, most well known as the microorganism engineered for commercial production of riboflavin [13C-MFA for both wild type and engineered strains in the past two decades [B. subtilis have been elucidated to better understand the cell metabolism and to further improve the biochemical production. The flux distributions of a riboflavin-producing B. subtilis strain has been rigorously investigated under three different dilution rates in chemostats [13C-MFA, the activated PP pathway was observed, which not only supplied sufficient precursors but also produced sufficient NADPH  have been well studied for a relatively long time with a better understanding of the cell metabolism, various non-model microorganisms have been recently highlighted for biochemical production due to their unique advantages. For example, Pichia pastoris has been used as an industrial microorganism to produce recombinant protein for more than three decades [Aspergillus niger is another preferable industrial producer of various extracellular enzymes and antibiotics due to the advantages for protein secretion and post-translational modification [Penicillium chrysogenum has been used to produce the penicillin G as a secondary metabolite of P. chrysogenum, which has been used as a narrow spectrum penicillin antibiotic against gram-positive organisms for more than 60 years [Rhodopseudomonas palustris, a photosynthetic bacterium, could metabolize acetate and produce hydrogen at the non-growth stage when starved for nitrogen. Many studies also attempt to use some novel non-model microorganisms for producing versatile chemicals. For instance, Basfia succiniciproducens is found to be a novel producer with a high yield of succinate, a key platform chemical. By specifically revising the universally existing central metabolism model for various microorganisms, 13C-MFA has been performed for these non-model microorganisms. Here, as shown in 13C-MFA studies on these non-model microorganisms that identified various issues related to biochemical production and listed the corresponding metabolic engineering strategies to solve these issues.Although the above model microorganisms  uncovered the parallel in vivo activity of the oxidative and reductive branch of the TCA cycle in B. succiniciproducens. In addition, it revealed two undesired pathways catalyzed by pyruvate-formate lyase (PflD) and lactate dehydrogenase (LdhA), which consumed the precursor pyruvate for succinate production  measuring the isotopic labeling of the numerous low-abundant metabolites to provide more information needed for expanding the model; (2) providing a high quality genome-scale model along with the detailed atom maps of every reaction in the network; and (3) the huge computational burden of simulating isotopic labeling of all metabolites in genome-scale metabolic networks. However, with the continuous efforts committed to overcome these obstacles, several successful studies have been reported to extend the 13C-MFA to genome-scale. Thanks to the rapid development of high-resolution mass spectrometry, the accurate measurement of isotopic labeling of low-abundant metabolites became possible [13C labeling patterns, an efficient network decomposing method, the elementary metabolite units (EMU) method [The conventional possible ,158,159.possible  has beenpossible , has beepossible . To use ) method , was use13C-MFA study based on refined genome-scale mapping model (GSMM) of E. coli strain has been reported, which showed several advantages when using the genome-scale model instead of the central metabolic model [13C labeling patterns obtained a better prediction of 13C-labeling patterns, resolved the alternative pathways in secondary metabolism for some key metabolites, and refined the cofactor and energetic metabolism. Therefore, genome-scale 13C-MFA could provide a more thorough investigation of microbial metabolism for biochemical production. This valuable information could guide the development of rational metabolic engineering strategies in a similar way that has been used for improving chemical production.Recently, the first ic model . In shor13C-INST-MFA was recently developed [13C-MFA for various autotrophic systems including cyanobacteria and plants. In brief, other than collecting 13C-labeling patterns at the isotopic steady state, 13C-INST-MFA tracks the dynamics of 13C-labeling in intracellular metabolites and applies computational algorithms to calculate the steady-state metabolic fluxes that can best fit the 13C-labeling kinetics. 13C-INST-MFA has been performed to determine the autotrophic metabolism of Synechocystis sp. PCC6803 [Arabidopsis thaliana [13C-MFA because all of the metabolites will be universally labeled at the isotopic steady state when the autotrophic organisms are fed with 13CO2 and the information about pathway usage is completely lost. The merit of 13C-INST-MFA for metabolic engineering is the capability to rigorously determine the metabolisms of numerous autotrophic systems, which used to be mysterious. Since various autotrophic systems are promising cell factories [2, into valuable chemicals, we can envision that 13C-INST-MFA could guide metabolic engineers to rationally modify such systems for improving the production of chemical products.eveloped ,166,167  PCC6803  and Arabthaliana . Such auactories ,171,172 13C-DMFA has recently been developed as an approach to investigate microbial metabolism at the metabolic non-steady state [13C-DMFA investigated the E. coli metabolism in a fed-batch fermentation process for overproduction of 1,3-propanediol. By introducing several additional parameters to describe the fed-batch fermentation process, a time-resolved flux distribution map was obtained. Based on this time-resolved flux map, it is found that the intracellular flux associated with PDO pathway increased by 10% and the split ratio between glycolysis and pentose phosphate pathway decreased from 70/30 to 50/50. 13C-DMFA has provided a way for metabolic engineers to elucidate the dynamic metabolism during industrial fermentation, especially the fed-batch fermentation. The extension of 13C-DMFA to study microbial metabolism at the stationary growth phase is also expected since numerous high-value secondary metabolites are often produced during the stationary growth phase. With the insightful information about the metabolic rewiring at non-steady states, metabolic engineers could develop more appropriate strategies to improve the biochemical production, particularly microbe-based drug production.dy state . One of 13C-MFA is the implementation of parallel labeling experiments (PLE) by using multiple isotopic tracers to track the cell metabolism, which has been proved to improve the flux estimation and observability of 13C-MFA [13C-MFA [Another recent advance in  13C-MFA ,174,175. 13C-MFA ,174,175.[13C-MFA  are the [13C-MFA ,174 and 13C-MFA has been continuously developed and successfully applied to assist the rational design of metabolic strategies for both model and non-model microorganisms by rigorously quantifying the carbon flux distribution in central metabolism. As shown in this review, multiple issues in biochemical production, such as bottleneck pathways in biochemical synthesis, cofactor imbalance issue in host cells, and energetic requirements in cell maintenance, have been revealed by 13C-MFA, which guides the development of the appropriate metabolic engineering strategies for successful improvements of the target chemicals to different extents. To further advance 13C-MFA, several techniques have been recently developed to demystify the secondary metabolism, improve the accuracy and resolution of the flux distribution, and investigate the metabolism for autotrophic organisms as well as the microbial metabolism at the metabolic non-steady state. We believe that, by using 13C-MFA for various cell factories, metabolic strategies will be more rationally designed and successfully applied to improve the biochemical production.Metabolic engineering has been rapidly developed for various industrial microorganisms to produce bulk chemicals, fuels, and drugs from renewable feedstock, in order to free the modern society from the depleting fossil fuel feedstock. However, the complex microbial metabolism is one of the most challenging obstacles for metabolically engineered microorganisms to reach an industrially satisfactory yield, titer or productivity. In order to overcome this challenge,