Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of  An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments. Genomic selection (GS) is currently considered the most promising breeding method to speed up the development and release of new genotypes2. It uses machine learning to integrate phenotypic data of a given trait with molecular markers (e.g. single nucleotide polymorphisms (SNPs)) in a statistical model for a training population. The model for the trait is then used to predict genomic estimated breeding values (GEBV) of genotypes in a testing population which have been genotyped but not phenotyped4 ed4 Fig. . The pre8. For instance, high-quality large-scale metabolic network models of A. thaliana, maize, and rice have been used to generate insights into genotype-phenotype relationships by using the constraint-based modeling framework that includes simplifying, but biochemically relevant constraints11. Metabolic network models include all known enzymatic functions of primary metabolism that influence growth. They further incorporate a biomass reaction that characterizes the chemical composition of a gram dry weight of the modeled plant or tissue in a specific environment12. Plant metabolic models have been employed to: (i) predict reaction rates through major pathways14, (ii) study the effect of manipulating pathways (e.g. photorespiration15 or introducing photorespiratory bypasses16), (iii) estimate the impact of nutrient deficiency on growth11, and (iv) compare the different types of photosynthesis18.Although GS simultaneously estimates effects of markers by foregoing statistical testing, it does not integrate information of cellular networks available for model plants and some cropsHere, we focus on plant growth as an agronomically relevant trait largely determined by the rate at which the available nutrients are transformed into the building blocks of biomass. We show that metabolic reaction rates (i.e. fluxes) are polygenic traits which can be predicted by GS and employed in estimating growth for a given genotype. As a result, we propose an approach for network-based GS  that uses metabolic models and improves the prediction accuracy of classical GS for growth within and across environments Fig. . The for19 with predictions of steady-state fluxes from accession-specific metabolic models of 67 A. thaliana accessions with biomass reactions for optimal nitrogen (N) conditions    in the metabolic model for Col-0 have non-zero fluxes allocation as well energy demand between accessions. Indeed, we find that the largest flux ranges across the 67 accessions are observed for reactions involved in: photosynthesis, i.e. the Calvin–Benson cycle and light reactions, glycolysis, oxidative phosphorylation, pentose phosphate pathway, gluconeogenesis, glutamate synthesis and degradation, glycine synthesis, and pyruvate metabolism  for six enzymes: nitrate reductase, glucose-1-phosphate adenylyltransferase, glutamine synthase, malate dehydrogenase, glutamate synthase, and fumarate hydratase24. We expect that if the predicted fluxes are biochemically feasible, they must not exceed the accession-specific Vmax. Indeed, we find that the predicted fluxes in every accession are in line with this expectation for five out of the six enzymes, and there are only very small deviations in nitrate reductase for 30 accessions , while all other pairs show negligible correlations  show coefficients of variation greater than 10%  with 3-fold cross-validationp-value = 9.01×10−5, paired t-test). We also investigate the effect of using only the flux GEBVs from statistical models with accuracy above a given threshold. Following this strategy, we demonstrate that the accuracy of netGS can be further increased to 32.6% relative to the classical GS when using only flux models with prediction accuracy larger than that for fresh weight .However, estimations of GEBV for fluxes as traits cannot be directly used in netGS for growth, since the predictions resulting from statistical models may not respect the basic physico-chemical constraints . To address this problem, we determine the closest steady-state flux distribution to the predicted flux GEBVs which exceed the GEBVs of the flux through the biomass reaction Eq. . The res27. However, this remarkably does not hold for netGS: in the cross-validation case with the largest population difference, with a coancestry coefficient28 of 0.21, we find that the accuracy of the classical GS is nearly zero, but that of netGS reaches 0.31 . Altogether, netGS achieves better predictions within the same environment than the classical GS when using enzymatic SNPs and similar performance when using genome-wide SNPs, and the robustness analysis indicates further improvements  fluxes on the boundary of the metabolic network. The alterations in the exchange fluxes then propagate to and affect the rest of the fluxes in the network. To determine these alterations, we use a steady-state flux distribution of the reference genome, in this case Col-0, in a second environment to determine the ratio of exchange fluxes between the two environments. To arrive at the reference flux distribution for Col-0 in the second environment, we identify the closest flux distribution compatible with the change in biomass between the two environments (Eq. Successful deployment of GS in plant breeding depends on the availability of data for phenotypes in possible future environmentsents Eq. .−1 d−1) . Similar trends are observed with genome-wide SNPs , while the across-environment prediction accuracy is improved by 67.8% in comparison to the classical GS . To examine the effects of the population structure, we employ the first ten principal components (PCs) with the enzymatic as well as genome-wide SNP. We find that the inclusion of the first ten PCs improves the prediction based on the enzymatic SNPs for both GS and netGS within and across environments using rrBLUP, respectively; however, the effects with the genome-wide SNPs are negligible. In all examined cases with the different statistical approaches (i.e. rrBLUP and BayesC), netGS consistently outperforms the classical GS  and 19.6%  higher than those of classical GS. Therefore, the findings between pFBA and FBA are qualitatively similar.The flux distribution of the reference and the other genotypes in netGS are determined based on FBA in addition with constraints that render a robust and biologically meaningful flux distribution. Nevertheless, there are other approaches that can be used to estimate fluxes, including the parsimonious FBA (pFBA)p-value = 2.29×10−6, paired t-test).The formulation of netGS allows transferability of the statistical models for metabolic fluxes between two environments as long as there are estimates for the flux distributions of the reference genotype for the respective environments. While we showed that netGS improves prediction accuracy between environments which differ in the same factor, namely, availability of nitrogen, it remains questionable if netGS leads to increase in performance if models are transferred between two environments which show differences in two factors, namely nitrogen and carbon. To test the applicability of netGS in such a scenario, we applied our netGS approach estimating from optimal nitrogen condition to a low carbon condition. By using a constraint on the ratio of carbon dioxide import flux and statistical models for the fluxes from optimal nitrogen condition, we show that netGS lead to an improvement up to 50.4% comparing with the classical GS , which cast agronomically relevant traits, such as yield and growth, as a function of other morphological and physiological traits as well as environmental variables31. Two approaches already allow for integration of genome-wide SNPs in CGMs by simultaneously inferring the physiological model parameters and effects of genome-wide SNPs on the parameters in a Bayesian framework33. While these approaches have been shown to improve the accuracy of predictions over the classical GS and facilitate the modeling and inference of genotype-by-environment interactions, they use information about the ranges of the physiological parameters over all genotypes and require environmental variables as input. In addition, CGM approaches are statistical in nature and do not provide mechanistic insights about the reasons for the particular performance of specified genotypes. In contrast, netGS does not directly include environmental variables; the environment is reflected in the metabolomics data which are used in the development of the environment-specific models for the reference genotype. Furthermore, by using metabolic network models and the flux distributions, as an intermediate phenotype, netGS provides mechanistic understanding for the differences in performance between two genotypes.We demonstrate that netGS provides the means to integrate molecular markers in large-scale metabolic models, which on the data set from 67 A. thaliana. The approach is tested by using accession-specific models which integrate metabolomics data from A. thaliana rosettes in respective condition- and accession-specific biomass reactions for the relatively small population. Future simulation studies will examine how changes in the size of the used population may affect the prediction accuracies, which in the examined datasets show comparable uncertainties with those resulting from the classical GS.Our study provides a proof-of-principle that netGS provides a feasible approach to predict growth for the model plant 37 and, thereby, impose additional constraints and explore their effect on prediction accuracies, as done in classical prediction of fresh weight38. The current state of plant metabolic modeling does not yet allow incorporation of information about catalytic rates, as plant-specific information about this is still lacking. Future studies may aim to expand netGS to integrated models which consider multiple, interconnected metabolic networks of different tissues40.As a constraint-based modeling approach, netGS can be extended to integrate transcriptomics, proteomics and metabolomics dataA. thaliana population show that environment-specific metabolic models are needed only for the reference genotype to facilitate improved accuracy of prediction across environments. To further decrease the effort for generating genotype-specific metabolic models for a single environment, necessary in netGS, future studies may investigate the generation of biomass reactions directly from SNPs data following the classical GS.Our findings suggest that the improved prediction accuracies within and across environments may be due to the consideration of accession-specific metabolic networks and flux phenotypes that tacitly include interactions between molecular markers which are otherwise challenging to integrate in statistical models. Most importantly, our results from the studied Due to the constraint-based formulation, netGS appears facile to apply for traits directly related to metabolism in agronomically relevant crops. However, testing the performance of netGS on metabolic traits in crops, e.g. maize and rice, that have experienced recent history of intense selection will necessitate the assembly of high-quality metabolic models. Another difficulty is that the models must be able to reproduce growth and other metabolic traits of representative organs or the plant as a whole, before they can be employed for the estimation of fluxes, as intermediate traits. In addition, dedicated experiments will have to be designed to assemble accession-specific biomass reactions which are able to simulate metabolic traits of interest in specific contexts. Therefore, the usage of netGS in decreasing the phenotyping effort and expediting the development of superior crop genotypes remains to be validated in future studies that address the aforementioned challenges.A. thaliana accessions in a previous study41. For two nitrogen (N) conditions , all accessions were grown under 12-h photoperiod; for low carbon (C) condition, all accessions were grown under 8-h photoperiod42. In the low N condition, the soil was constituted of 50% (v/v) white peat (Gramoflor GmbH) and 30% (v/v) fine and 20% (v/v) coarse-grained vermiculite . Additionally, 260 mg K2HPO4, 396 mg GRANUKAL 85 , 1.6 mg Fetrilon-Combi micronutrient fertilizer (BASF AG), and 30 mL of tap water was added per 100-mL pot. In the optimal N condition, a supplement of 90 mg solid NH4NO3 was added to low N soil per 100-mL pot. The inorganic N per pot in low N and optimal N was ca. 1.25 and 31.5 mg, respectively43. For the low C condition, the soil substrate was GS90  and vermiculite (Gebrueder Patzer GmbH & Co.). At 21 days, plants were transferred to a controlled small growth chamber for two additional weeks. Harvests were performed at the end of the light period. The fresh weight (i.e. biomass) and metabolites including amino acids, sugars and TCA-related metabolites, as well as the total protein and starch were measured for every accession in all conditions42. These data were used to help obtain accession-specific biomass reactions in a bottom-up assembled model of Arabidopsis metabolism.We used data gathered from a panel of 97 diverse 9. The network consists of 407 metabolites and 549 reactions. It includes a biomass reaction denoting the percentage contribution of different metabolites and cellular components to a gram dry weight. Therefore, this synthetic reaction allows us to simulate biomass yield under specific conditions similar to the microbial studies44. The network provides three biomass reactions corresponding to optimal N and low N conditions as well as low C condition based on the measurement of soluble metabolites, starch, cell wall precursors, lipid precursors and nucleotides of A. thaliana accession Columbia-0 (Col-0) in the three conditions.We used the Arabidopsis core model covering the major characterized metabolic reactions from primary plant metabolismA. thaliana accessions for which there were genotypic data of same coverage available. Altogether, we had access to 214,051 SNPs, here referred to as genome-wide SNPs19. To determine the power of consideration of only the genes included in the model, we used only SNPs in the coding regions of the genes included in the model. After filtering the 5% minor allele frequency (MAF) SNPs, GS was conducted with 180,859 genome-wide SNPs and 1824 enzymatic SNPs. To examine the effects of population structure, we also considered the first ten principle components (PCs) of the genome-wide SNPs.We used 67 45, and model steady-state growth in the light. A steady-state reference flux distribution, 21, wherein the flux through the biomass reaction is maximized under the constraints of: (i) steady-state of the model, specified with a stoichiometric matrix 20. The resulting linear maximization program is as follows:In the following, we avoid consideration of effect of photoperiod which has effects on partitioning of plant resources16. This optimization program was solved with the help of the COBRA package46 in MATLAB.where 20. In the absence of information about genotype-specific ratios, we assume the ratios of carboxylation to oxygenation reactions and starch to sucrose syntheses to be bounded in the ranges between 0.94 and 3.81 and between 0.79 and 3.37, respectively, obtained from the measured ratios in Col-0 assuming a variance of (2.88 + 1)/2 and (2.58 + 1)/2.The flux distribution of another genotype Z, Two more constraints were added to the optimization program: (i) the genotype-specific biomass reaction and (ii) that the ratio of fluxes through the biomass reaction in Col-0 and genotype Z equals the ratio of measured biomass, s.t.−4. All quadratic programs were solved with the help of the cvx package in MATLAB47.We used the average of the maxima over all genotypes to define the model scaling parameter entclass1pt{minima9. In total 30 soluble metabolites including free amino acids, sugars, TCA-related metabolites and others, as well as starch, were measured in all accessions and converted into the unit of μmol per gram dry weight. The protein-bound amino acids were calculated from the fraction of 20 amino acids in the total protein concentration for every accession. Because there are no genotype-specific measurements available for cell wall precursors, lipid precursors, nucleotides and ATP, we assumed them to be the same for all accessions 48.Instead of constraining the biomass flux to the ratio of measured biomass, this program only constrained the biomass flux to be positive. Thus, the resulting flux distribution contains the entry for 1 may have poor performance in another environment E2 due to the usually large genotype-by-environment interaction observed for yield-related traits49. We extended our approach to allow for prediction of flux and biomass GEBV across environments. To this end, we propose an approach which relies on the reference flux distribution of Col-0 in two environments. Again, given the flux distribution, 1, we obtain the flux distribution, 2 under the assumption that it minimizes the distance while ensuring that (i) the Col-0 biomass reaction in E2 and (ii) the ratio of measured biomasses coincides with the ratio of biomass fluxes in two environments. This can be obtained by solving the following quadratic program:2 and 1 and E2, respectively.The developed flux models in environment E2, given the flux GEBVs Let 1. For prediction in an unseen environment, we additionally require only access to a reference genotype-specific biomass reaction from environment E2. In the program, we considered the exchange reactions of photon, CO2, H2O and NO3 to belong to the set 2 when predicting from optimal N to low C condition. Similarly, the predictability was determined by the mean correlation coefficient between measured and predicted biomass using 3-fold cross-validation with 50 repetitions.We note that the genotype-specific biomass reaction used in this program is from the measurement in environment EThe differences in the effect of the environment on a genotype may in part be due to the presence of genotype-environment interactions. Our approach accounts for such differences since the last program does not impose that all genotypes respond in the same fashion to the environmental change, particularly not with respect to their internal fluxes. The reason is that the statistical model for the fluxes, −4. For the reason that we observed very small variance between 50 random flux distributions, the means of the resulting flux distributions were used as a reference flux distribution in the netGS approach to determine the robustness of the predictions.To test the robustness of the reference genome flux distribution, we randomly sample 50 values Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Software 1


Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.In this work, we developed kinetic models of P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant  Pseudomonas putida recently emerged as one of the most promising production hosts for a wide range of chemicals, due to its fast growth with a low nutrient [P. putida compared to heavily used industrial workhorses like E. coli is its superior tolerance to toxic compounds such as benzene, toluene, ethylbenzene, xylene, n-hexane and cyclohexane [P. putida strains such as DOT-T1E, S12, and VLB120 are able to grow in high concentrations of n-butanol [E. coli [nutrient  and cellnutrient  demand, nutrient , abilitynutrient , 5, suitnutrient , and itsnutrient . One of lohexane , 9. For [E. coli .P. putida metabolism resulted in several genome-scale reconstructions. The first reconstructed Genome-Scale Model (GEM) of P. putida KT2440, iJN746, was published in 2008 and it comprised 911 metabolites, 950 reactions, and 746 genes [P. putida KT2440, iJN1411, was reconstructed in 2017 by Nogales et al. [P. putida including the enhanced production of poly-hydroxyalkanoates [Recent efforts toward understanding and improving the behavior and systemic properties of 46 genes . It was 46 genes , 13. The46 genes . The so s et al. , and it kanoates , reconcikanoates . Howeverkanoates . TherefoP. putida metabolism were developed to model the growth and changes in extracellular concentrations [P. putida MTCC 1194 [P. putida ATCC 49451 [Multiple small-scale kinetic models of trations –29. BandTCC 1194 . Wang anCC 49451 , tolueneCC 49451 –26, 28 aCC 49451 , growth CC 49451  and dibeCC 49451 , 27.P. putida KT2440 that contained mass balance equations for both extracellular and intracellular metabolites described by mechanistic rate expressions based on in vitro investigation of the participating enzymes [P. putida KT2440 while taking into account the dynamics of gene expression, protein stability, enzymatic activity and the concentrations of intracellular and extracellular metabolites [More recently, Sudarsan et al. in 2016 developed a kinetic model of the β-ketoadipate pathway in  enzymes . Chavarrabolites .All these kinetic models are of limited size and with ad hoc stoichiometry, i.e., their stoichiometry was built for a specific purpose and without justifications how their metabolites and reactions were chosen , 33. TheP. putida central carbon metabolism. We provide the models of three different sizes to allow modelers to make a trade-off between the accuracy of the models and the model complexity. The level of details of important metabolic interactions described in the model affects the model accuracy. The more detailed model, the better is its accuracy. However, as the model complexity increases, the portion of available data of intracellular metabolite concentration and metabolic flux is rapidly decreasing, i.e., uncertainty in the system is increasing [P. putida KT2440. The potential of developed kinetic models for the design of improved production strains of P. putida was demonstrated through two studies: (i) predicting metabolic responses of a wild-type P. putida strain to single-gene knockouts; and (ii) improving the responses of this organism to the stress conditions of increased ATP demand.In this work, we first performed a thermodynamic curation of the iJN1411 GEM, i.e., we estimated the standard Gibbs energy of formation of metabolites, adjusted these values for pH and ionic strength in the studied physiological condition, and used these values together with the concentrations of metabolites to calculate the transformed Gibbs free energy of reactions –40. We tcreasing . Next, wcreasing , 41–50, Methods that use thermodynamic data such as the thermodynamics-based flux analysis TFA –39 allowWe used Group Contribution Method (GCM) , 62 to aP. putida might be inaccurately determined in iJN1411 which would lead to large discrepancies in ATP yield on glucose [E. coli, a well-studied species of Gram-negative rod-shaped bacteria [4. The production of adenosine 5′-phosphosulfate catalyzed by SADT2 is coupled with GTP consumption, whereas this coupling is absent in SADT. Since the experimental evidence supports that GTP hydrolysis enhances the rate of adenosine 5′-phosphosulfate formation [E. coli), the iJO1366 could not predict experimentally measured values of ATP in E. coli [We integrated experimental measurements of glucose uptake and biomass yield on glucose  and meta glucose , 66. Her glucose  and late glucose . The gap glucose , 69, whe glucose , MetaCyc glucose , and Atl glucose ; (ii) gebacteria . The solormation , we incl E. coli .P. putida of different complexity. The reduced models were constructed in two steps.Using as a basis the curated iJN1411, we employed the redGEM  and lumpIn the first step, the redGEM algorithm produced core networks focused around six central carbon subsystems of iJN1411: glycolysis and gluconeogenesis, pentose phosphate pathway, pyruvate metabolism, TCA cycle and oxidative phosphorylation Fig. ”. In theP. putida by generating alternative lumped reactions towards BBBs. The lumpGEM appended to the D1, D2, and D3 core networks 550, 397, and 407 lumped reactions, respectively  of the iJN1411 biomass reaction (Methods). lumpGEM generates lumped reactions that account for the production of BBBs from the metabolites of the core metabolic networks, i.e., it allows for modeling the fate of all metabolites along the synthesis routes and quantifying the cost of all precursor metabolites and cofactors . MoreoveThe resulting D1 model contained 828 reactions and 286 metabolites distributed over cytosol, periplasm and the extracellular space  and 6-phosphogluconate dehydratase (EDD) [P. putida were not able to predict the essentiality of these genes [The Entner–Doudoroff (ED) pathway is essential for the growth of se (EDD) , 78, 79.se (EDD)  and founse (EDD) . The prese genes .We analyzed how the directionalities of reactions from the pentose phosphate pathway impact the essentiality of EDA and EDD in D2. We found that the directionalities of three reactions that have glyceraldehyde 3-phosphate (g3p) as reactant  determine if EDD and EDA are in silico essential. When directionality of TKT2 was imposed towards production of g3p, TALA and TKT1 became exclusively unidirectional towards consumption of g3p and production of g3p, respectfully  available for the Pseudomonas genus in the Brenda database [We used ORACLE , 41–50 tdatabase –84. The To evaluate the predictive capabilities of the constructed models, we computed the flux control coefficients of the intracellular fluxes in the metabolic network. The flux control coefficients quantify the relative steady-state change in fluxes in response to relative changes in parameters, and allow us to identify how control of the carbon and energy flows within the metabolic networks is redistributed , 85, 86.A closer inspection of the flux control coefficients of glucose uptake revealed that for four enzymes  the error bars were spread around zero values Fig. a. This mWe used iSCHRUNK to eliminate the inconsistencies with the experimental data observed for some of the predicted responses “”. The meWe generated a novel population of kinetic models with ORACLE with constrained ranges of these seven parameters as defined by iSCHRUNK, and with integrated experimental values for 21 km’s from the Brenda database, and we then computed the distributions of corresponding control coefficients for the glucose uptake and specific growth rate. Out of 50,000 models, 29,979 (~ 60%) models correctly predicted the changes in the glucose uptake rate to six single-gene knockouts  Fig. 3cc3c, whilWe discovered with iSCHRUNK that operating regimes of only a few enzymes determine metabolic responses to multiple single-gene knockouts. This emphasizes the significance of accurately determining the kinetic parameters of such important enzymes in order to obtain model responses consistent with the experimental observations. It will also be interesting to consider complex kinetic phenomena such as crowding when modeling kinetic properties of certain enzymes .Km’s from the BRENDA database [Km’s from BRENDA showed that ORACLE could capture the ranges for 17 out of 21 km’s  and P. taiwanensis (metabolomics data); and (ii) large uncertainty in available and partially available experimental data. In general, the more experimentally measured data are available for integration in the models by ORACLE, the better their predictive capability will be.For the four remaining parameters such as Michaelis constant of ude Fig. . The disS. cerevisiae to high ethanol concentration, osmotic stress, and high temperature is an increased demand for ATP [The robustness of microorganisms to environmental stresses encountered in industrial processes is a significant factor for choosing hosts for the production of biofuels and biochemicals. While stress-specific responses differ between various stresses such as product toxicity, heat, or osmotic stress, and different organisms can have different mechanisms for stress adaptation, counteracting stress requires energy . For exa for ATP . The act for ATP .P. putida metabolism by titrating 2,4-dinitrophenol (DNP), and they demonstrated that DNP concentrations below 300 mg/l did not impact the specific growth rate of P. putida [P. putida’s specific growth rate and increase of the glucose uptake  pathway operates in a gluconeogenic fashion [E. coli has functional both EMP and ED pathway. However, the glucose metabolism in this organism is carried out through the EMP pathway, while the ED pathway remains mostly inactive. Hollinshead et al. in 2016 showed that about 90% of flux in E. coli is channelled through EMP pathway while the flux through ED pathway was negligible [P. putida to generate NAPDH which is required to counteract environmental stresses [In comparison, 138 mg/l . It is aycolysis , 78, 93.gligible . The actstresses , 78.P. putida has the potential to produce enough ATP to cope with the stress. To this aim, we first used our stoichiometric model to assess the stoichiometric capacity of this organism to produce ATP, and then, we used the developed kinetic model to identify metabolic engineering strategies to steer the system towards attaining that capacity.We undertook to investigate does the biochemical network of P. putida metabolism by gradually increasing the minimally required flux through ATP hydrolysis in increments of 1 mmol/gDCW/h  and used it to simulate the impact of increased ATP demand on the W/h Fig. . We set  and usedIn agreement with the experiments, the model predicted that the minimal glucose uptake of 7.51 mmol/gDCW/h is required to attain the specific growth rate of 0.73 1/h when the lower bound of the flux through ATP hydrolysis is set to 0 mmol/gDCW/h  over the specific growth. Together with pyruvate carboxylase (PC), ME2 forms the pyruvate shunt that in oacetate , 97. Sin over theWe found a strong correlation between the control coefficients of the specific growth and the concentration control coefficients of the cytosolic ATP  we  we 4) weMoreover, if we denote the physiological lower and upper bound on intracellular metabolite concentrations as LB= 1 μM and UB=50 mM, respectively, then the upper bound on To prevent the case M in the compartment i, Analogously for the lower bound on the concentration of the metabolite i constraints on the compartment species of metabolite M in the form of i + 2 constraints providing more flexibility and relaxing the assumption on equal concentrations of metabolite M in all compartments:Therefore, instead of using her with  and11\doed as in , 9)..i constrThe volume fractions of cytosol, and 0.12 .E. coli. More precisely, we appended reactions from iJO1366 into iJN1411 to obtain a composite model. We then removed duplicate reactions from the composite model along with phosphofructokinase (PFK) that is experimentally shown to be absent from P. putida metabolism [In the gap-filling procedure , we carrtabolism . CompareWe used the redGEM  and lumpSmin [Smin + 2. Finally, lumpGEM collapses the identified subnetworks into lumped reactions, which together with the core networks constitute the core reduced model.We then used the lumpGEM  algorithSmin . For somThe D1 model consisted of: (i) the D1 core network formed by the reactions and metabolites from the six subsystems and the reactions that belonged to one-reaction-step pairwise connections between these six subsystems  We performed a battery of tests to validate the consistency of the systemic properties of the core reduced models D1, D2 and D3 with their GEM counterpart, iJN1411. Here we present and discuss results for D2, the results for D1 and D3 are provided in Additional file μ = 0.94 h−1 for both D2 and iJN1411, meaning that D2 was able to capture well the physiology of the growth on glucose.We first performed FBA and TFA for the glucose uptake of 10 mmol/gDCW/h, and we found the identical maximum specific growth rate of We then carried out the comparison of essential genes between D2 and GEM. In silico gene deletion represents one of the most common analysis of metabolic networks, and it is used to assess the predictive potential of the model  or to idE. coli where the discrepancy in concentration ranges was reported for only few metabolites [We next performed thermodynamic-based variability analysis (TVA) on all common reactions and metabolites of D2 and GEM and compared their thermodynamically allowable ranges. We obtained consistent flux ranges for the majority of the reactions, and 131 reactions were less flexible in D2 than in GEM  have less than 10 carbon atoms and do not contain phosphate or CoA; and (ii) do not have an existing transport reaction in the model. This was done to model a possibility that small amounts of these metabolites were produced during fermentation but in insufficient quantities for experimental detection. The expanded model contained 768 reactions and 339 metabolites across cytosol, periplasm, and extracellular space.d-gluconate and 2-dehydro-d-gluconate by putting a lower bound on their exchange reactions to 0.3 mmol/gDCW/h. For the remaining carbon-based by-products, we allowed only their basal secretion by constraining their transport rates to the extracellular space (10−6–10−3 mmol/gDCW/h) following the common observation in the literature that P. putida can break the carbon down almost without any by-product formation [Based on the data provided in del Castillo et al. , we inteormation . Furtherormation . In the l-glutamate; (iii) dihydrolipoamide S-succinyltransferase was generating NADH from NAD+ [d-ribulose 5-phosphate to d-xylulose 5-phosphate; (vi) adenylate kinase (ADK1) and nucleoside-diphosphate kinase (NDPK1) were consuming ATP; and (viii) GTP-dependent adenylate kinase (ADK3) was consuming GTP.We then imposed constraints based on several additional assumptions: (i) TCA cycle was complete , 78;  acl-glutamate; (iv) dihydrolipoamide S-succinyltransferase was operating towards production of NADH from NAD+ [The stoichiometric model was reconfigured in the following way: (i) we constrained the specific growth rate in the range 0.43 ± 0.2 1/h and the glucose uptake in the range 11.6 ± 1.2 mmol/gDCW/h. These values correspond to the concentration of 700 mg/l of DNP in the experimental study or 44 mmol/gDCW/h in the simulation study Fig. d; (ii) trom NAD+ .We performed TFA with so configured stoichiometric model, and we found that six reactions  could operate in both directions whilst still satisfying the integrated data. To fix the directionalities of these six reactions, we performed another TFA where we minimized the sum of the fluxes in the metabolic network under the constraint that at least 99% of the observed specific growth rate should be attained.P. putida can take a wide range of values. For instance, in optimally grown P. putida, oxygen uptake rate is about 15 mm/gDCW/h [Depending on physiological conditions, maximal rates of oxygen uptake and ATP synthase in m/gDCW/h , while im/gDCW/h . To inveWe varied the allowed maximal oxygen uptake between 30 and 70 mm/gDCW/h  that we found for the Pseudomonas genus in the Brenda database [Km’s) [To construct the kinetic models, we employed the ORACLE framework. In ORACLE, we remove the mass balances for the extracellular metabolites from the stoichiometry because we consider the concentrations of extracellular metabolites as parameters. The mass balances for water and the corresponding transport reactions were also removed. We then assigned a kinetic mechanism to each of the enzyme catalyzed reactions in the model, and we integrated experimental values for 21 Michaelis constants (database –84. We ne [Km’s) , 43, 45.The details about this framework are discussed elsewhere , 41–50.Additional file 1: Table S1. Steady state solution used in building kinetic model. Table S2.Comparison of experimentally observed and in silico obtained growth rates. Table S3. Abbreviations from Fig. Table S4. Differences in directionalities between FBA and TFA. Table S5. Gene essentiality in rGEM (D2) and GEM. Table S6. Correlation between control coefficients of specific growth and ATP concentration with respect to most important enzymes. Table S7. Content of glucose minimal media. Table S8. Values of Michaelis constants for the Pseudomonas genus acquired from the Brenda database. Table S9. Allowable thermodynamically feasible concentrations of ATP with and without including SADT2 in the metabolic network of P. putida and E. coli together with the available experimental values.Additional file 2: Figure S4. Distribution of the control coefficients of glucose uptake (GLCtex) with respect to most important enzymes in the stress conditions.Additional file 3: Figure S5. Distribution of the control coeicients of 2-dehydro-3-deoxy-phosphogluconate aldolase (EDA) with respect to most important enzymes in the stress conditions.Additional file 4: Figure S6. Distribution of the control coefficients of 6-phosphogluconate dehydratase (EDD) with respect to most important enzymes in the stress conditions.Additional file 5: Figure S7. Distribution of the control coefficients of cytosolic NADPH with respect to most important enzymes in the stress conditions.Additional file 6: Figure S8. Distribution of the control coefficients of pyruvate carboxylase (PC) with respect to most important enzymes in the stress conditions.Additional file 7: Figure S9. Distribution of the control coefficients of glucose dehydrogenase (GLCDpp) with respect to most important enzymes in the stress conditions.Additional file 8: Figure S10. Distribution of the control coefficients of cytosolic ATP with respect to most important enzymes in the stress conditions.Additional file 9: File S2. List of reactions and metabolites of D2 reduced model.Additional file 10: File S3. List of reactions and metabolites of D3 reduced model.Additional file 11: File S4. Consistency checks for the D1, D2, and D3 reduced models.Additional file 12: Figure S3. Thermodynamic-based variability analysis (TVA) on reactions from glycolysis, gluconeogenesis, pentose phosphate pathway and citric acid cycle of D2 (red) and GEM (black).Additional file 13: File S1. List of reactions and metabolites of D1 reduced model.


Mycobacterium tuberculosis requires extensive sensing and response to environment for its successful survival and pathogenesis, and signalling by cyclic adenosine 3′,5′-monophosphate (cAMP) is an important mechanism. cAMP regulates expression of target genes via interaction with downstream proteins, one of which is cAMP receptor protein (CRP), a global transcriptional regulator. Previous genomic works had identified regulon of CRP and investigated transcriptional changes in crp deletion mutant, however a link to downstream metabolomic events were lacking, which would help better understand roles of CRP. This work aims at investigating changes at metabolome level in M. tuberculosis crp deletion mutant combining untargeted LC-MS analysis and 13C isotope tracing analysis. The results were compared with previously published RNA sequencing data. We identified increasing abundances of metabolites related to nitrogen metabolism including ornithine, citrulline and glutamate derivatives, while 13C isotope labelling analysis further showed changes in turnover of these metabolites and amino acids, suggesting regulatory roles of CRP in nitrogen metabolism. Upregulation of diaminopimelic acid and its related genes also suggested role of CRP in regulation of peptidoglycan synthesis. This study provides insights on metabolomic aspects of cAMP-CRP regulatory pathway in M. tuberculosis and links to previously published transcriptomic data drawing a more complete map. Mycobacterium tuberculosis requires extensive sensing and response to environment for its successful survival and pathogenesis, and signalling by cyclic adenosine 3′,5′-monophosphate (cAMP) is an important mechanism. Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is one of the leading causes of death among infectious diseases, leading to 10 million infections and 1.5 million deaths across the world in 2018.1 Latent TB infection is estimated to be found in one quarter of the global population, acting as a potential reservoir for the disease, while emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) resistant to first-line and second-line anti-TB drugs remains a threat to global health. Mtb is transmitted between human via inhalation of bacterium-containing droplets, and enter the lungs where it infects pulmonary macrophages.2 Its intracellular lifestyle requires extensive sensing of environmental stresses and quick bacterial responses to ensure survival and pathogenicity.3 Presence of secondary messengers, such as cyclic nucleotides, guanosine pentaphosphate ((p)pGpp) and nitrogen oxide, allows successful communications between bacterial machineries to achieve this aim.3 Cyclic adenosine 3′,5′-monophosphate (cAMP) is a critical signalling molecule first studies in Escherichia coli catabolite repression,4,5 followed by discovery of its wider regulatory roles in bacterial pathogenicity.6 Synthesis of cAMP from adenosine triphosphate (ATP) is catalysed by adenylyl cyclases (ACs). The Mtb H37Rv genome encodes 16 ACs, 10 of which have enzymatic activities, enabling integration of multiple stimuli.7,8 The ability of Mtb to utilize multiple carbon sources reduces its need for catabolite repression,9 however the importance of cAMP in Mtb has been shown in both virulence and host interactions.10 The high concentration of intracellular cAMP found in Mtb which increases during macrophage infection also suggests its importance.11via interaction with downstream proteins. cAMP-receptor protein  is one of these proteins identified in Mtb, being activated by direct binding to cAMP and working as a global transcriptional regulator.3 It differs from its E. coli homolog, in that binding of cAMP to CRPMt is weaker and does not affect its DNA-binding properties.12 Point mutations found in the DNA- and cNMP-binding domains of CRP in Mycobacterium bovis BCG inactivate the protein and were linked to lower virulence of the strains.13 Deletion of crp in Mtb resulted in growth defects in murine macrophages,14 while overexpression of crp resulted in rapid growth of Mtb under stress conditions.15 The putative CRP regulon consisting of 114 genes was predicted using previously-identified E. coli CRP binding sites,16 and the regulon was extended to 207 potential genes by using promoter sequences from the GlxR regulon, an ortholog of CRP in C. glutamicum.17 Several genes with important roles in Mtb were found to be regulated by CRP, including rpfA, a resuscitation-promoting factor that is involved in resuscitation of bacteria from dormant states,14,18 and whiB1, an essential transcription factor responding to nitrogen oxide.12,19,20 CRP was also found to regulate serC pathway in serine metabolism, contributing to slower growth of the mutant which could be restored by supplementation of serine in liquid cultures but not in macrophages.21 Kahramanoglou et al., studied the role of CRP on the overall transcriptome by chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing, and identified 191 binding sites in both intragenic and intergenic regions of the Mtb genome. A widespread transcriptional alteration covering more than one fifth of the total genome could by identified in a crp mutant.22cAMP regulates expression of target genes Mt regulation but there has been no downstream metabolomics study showing overall changes in bacterial metabolome in the absence of crp.23 The aim of this study is to investigate the role of CRP at the metabolomics level. Specifically, untargeted metabolomics was performed to identify changes in the overall metabolome, while 13C isotope tracing was performed to measure rate of interconversion of key metabolites. Integration of metabolomics profile with transcriptomic regulation of CRPMt will help better understand its roles in cAMP signalling, survival and pathogenicity of Mtb.Previous studies worked on transcriptomic levels of CRPUnless otherwise stated, all chemicals and reagents were purchased from Sigma-Aldrich.M. tuberculosis H37Rv parental, knockout for Rv3676 (CRP) and complemented strains were provided by Dr Roger Buxton MRC-National Institute for Medical Research.22 Mycobacterial strains were cultured up to mid-exponential phase in 7H9 liquid medium supplemented with 0.5 g l−1 fraction V bovine serum albumin, 0.05% tyloxapol, 0.2% dextrose, 0.2% glycerol, and 10 mM NaCl. For metabolomics profiling studies, mycobacteria were cultured on 7H10 agar supplemented with 0.5 g l−1 fraction V bovine serum albumin, 0.2% dextrose, 0.2% glycerol and 10 mM NaCl. Throughout the study, mycobacteria were cultured in a shaking incubator set at 125 rpm and 37 °C.The 24–26 Briefly, mycobacteria were initially grown in 7H9 liquid medium containing the carbon sources of interest until the OD600 reached ∼0.8–1. Bacteria were then inoculated onto 0.22 μm nitrocellulose filters under vacuum filtration. Mycobacterial-laden filters were then placed on top of chemically equivalent agar media (described above) and allowed to grow at 37 °C for 5 doubling times to generate enough biomass for targeted metabolomics studies. Filters were then transferred into 7H10 plates supplemented with 0.5 g l−1 fraction V bovine serum albumin, 0.2% dextrose and 0.2% glycerol, 10 mM NaCl. Bacteria were metabolically quenched by plunging the filters into the extraction solution composed of acetonitrile/methanol/H2O (2 : 2 : 1) pre-cooled to 4 °C. Small molecules were extracted by mechanical lysis of the entire bacteria-containing solution with 0.1 mm acid-washed zirconia beads for 1 min using a FastPrep (MPBio®) set at 6.0 m s−1. Lysates were filtered twice through 0.22 μm Spin-X column filters (Costar®). Bacterial biomass of individual samples was determined by measuring the residual protein content of the metabolite extracts using the BCA assay kit (Thermo®).9,27For targeted and untargeted metabolomic profiling studies, mycobacteria were cultured as described.−1 was used. Elution of polar metabolites was carried out using solvent A consisting of deionized water (resistivity ∼18 MΩ cm) and 0.2% acetic acid and solvent B consisting of 0.2% acetic acid in acetonitrile. The following gradient was used: 0 min 85% B; 0–2 min 85% B; 3–5 min to 80% B; 6–7 min 75% B; 8–9 min 70% B; 10–11 min 50% B; 11.1–14 min 20% B; 14.1–25 min hold 20% B followed by a 5 min re-equilibration period at 85% B at a flow rate of 0.4 ml min−1. Accurate mass spectrometry was carried out using an Agilent Accurate Mass 6545 QTOF apparatus. Dynamic mass axis calibration was achieved by continuous infusion, post-chromatography, of a reference mass solution using an isocratic pump connected to an ESI ionization source operated in the positive and negative-ion mode. The nozzle voltage and fragmentor voltage were set at 2000 V and 100 V, respectively. The nebulizer pressure was set at 50 psig, and the nitrogen drying gas flow rate was set at 5 l min−1. The drying gas temperature was maintained at 300 °C. The MS acquisition rate was 1.5 spectra per sec, and m/z data ranging from 50–1200 were stored. This instrument enabled accurate mass spectral measurements with an error of less than 5 parts-per-million (ppm), mass resolution ranging from 10 000–45 000 over the m/z range of 121–955 atomic mass units, and a 100 000-fold dynamic range with picomolar sensitivity. The data were collected in the centroid 4 GHz (extended dynamic range) mode, for optimal data storage and improved the speed of isotologues extractions. Detected m/z were deemed to be identified metabolites based on unique accurate mass-retention time and MS/MS fragmentation identifiers for masses exhibiting the expected distribution of accompanying isotopomers. Typical variation in abundance for most of the metabolites remained between 5 and 10% under these experimental conditions.Aqueous normal phase liquid chromatography was performed using an Agilent 1290 Infinity II LC system equipped with a binary pump, temperature-controlled auto-sampler (set at 4 °C) and temperature-controlled column compartment (set at 25 °C) containing a Cogent Diamond Hydride Type C silica column . A flow rate of 0.4 ml min13C3] glycerol (99%) and [U-13C6] glucose (99%), the extent of 13C labelling for each metabolite was determined by dividing the summed peak height ion intensities of all 13C-labelled species by the ion intensity of both labelled and unlabelled species using the software Agilent Profinder version B.8.0.00 service pack 3.Under the experimental conditions described above using  glycerol and [U-13C6] glucose were used as carbon sources to be incorporated into central carbon metabolism through glycolysis and the TCA cycle, and later into amino acids and downstream metabolites. Samples were taken at a 1/4 and 1 doubling time after 13C supplementation in each group. An in-house compound database and library containing major metabolites in the TCA cycle and nitrogen metabolism was used to extract the isotopologue profiles of the metabolites of interest . However, the expression level of prpC was upregulated by more than 4-fold (log2 FC = 2.01), which does not explain the reduction of carbon turnover in the metabolite. The previous transcriptomic analysis also suggested changes in expression levels of enzymes related to glycolysis, pentose phosphate pathway and TCA cycle,22 however in our metabolomics study, the changes in either abundances or turnover of related metabolites were minimal. A possible explanation is that the ability of Mtb to respond to and co-catabolise multiple carbon sources led to changes in different aspects of carbon metabolism, and differences in growth conditions could enhance such differences.Among metabolites investigated, malate and 2-methylcitrate were found with significant changes in via glutamate synthase (GltB).29 In our labelling assay glutamine did not show changes in turnover. After ¼ doubling time incubation with 13C-labelled carbon sources, 27% of glutamate was labelled in at least one carbon atom, and the number increased to 46% after 1 doubling time. Similar proportions of incorporation were achieved in complemented bacteria, while M. tuberculosis Δcrp showed a lower rate of incorporation, reaching 36% after 24 hours. The isotopologue profile also indicated lower proportion of labelling in m + 4 fraction, suggesting lower turnover rate of the metabolite in M. tuberculosis Δcrp. 13C incorporation into gamma-aminobutyric acid (GABA), a metabolite closely linked to glutamate and involve in interconversion between glutamate and TCA cycle metabolites via the GABA shunt, was also decreased by 26% in M. tuberculosis Δcrp with a lower percentage of labelling in m + 3 and m + 4 forms. Opposite changes in incorporation was found in urea cycle metabolites citrulline and ornithine, with the mutant showing an approximately 40% increase in overall incorporation after 1 doubling time. Higher proportions of labelling in m + 2 and m + 3 fractions could be found in both metabolites, indicating higher rates of turnover. The result might be correlated with untargeted metabolomics, with increased urea cycle activity leading to higher abundances and turnover rates of ornithine and citrulline. Meanwhile, lower turnover of glutamate and its related metabolites suggested reduced activity of related pathways, while the glutamine pool of might be used for maintaining nitrogen availability though glutamate.Previous untargeted metabolomics suggested upregulation of nitrogen metabolism. We therefore investigated incorporation of carbon into related metabolites. Glutamine is the major nitrogen donor in Mtb and can be converted to glutamate 13C incorporation and isotopologue distribution, while most of them showed lower turnover in M. tuberculosis Δcrp compared with WT, the levels of incorporation were similar between complemented and WT bacteria, confirming the complementation of our phenotype. This could be explained as the result of abnormal function of nitrogen metabolism and slower growth of M. tuberculosis Δcrp.14 Serine was found to have similar overall levels of labelled metabolites among groups, however when comparing fractions of differently-labelled molecules, the higher proportion of fully-labelled serine (m + 3) could be found in M. tuberculosis Δcrp (50%) compared with WT or complemented bacteria (30%), suggesting a higher rate of turnover. Serine provides the backbone for synthesis of other amino acids including glycine, cysteine and tryptophan.21,30 It is also the direct indicator of glycolysis as it is related to the pyruvate pool in central carbon metabolism29 and the serine dehydratase gene sdaA (Rv0069) converting serine to pyruvate was upregulated (log2 FC = 2.57).22 A higher turnover of serine was therefore related to maintenance of carbon metabolism and amino acid synthesis levels in M. tuberculosis Δcrp. Serine was also predicted to be an important nitrogen and carbon source using in silico approaches, with multiple metabolic pathways converging in Mycobacterium species.32 SerC (Rv0884) is a phosphoserine aminotransferase responsible for interconversion from glutamate and 3-phospho-hydroxypyruvate to phosphoserine and 2-oxoglutarate, and was identified as an essential gene in Mtb.31 Phosphoserine is then used for production of serine through phosphoserine phosphatase SerB (Rv3042c). Previous studies investigated the regulatory effect of CRP on the serC pathway in Mtb and found that the mutant showed decreased expression of serC and its divergently transcribed gene Rv0885 with impaired bacterial growth which could be complemented by supplementation with serine, glycine or cysteine.21,22 Here our result suggested that although serC pathway is reduced, the abundance of intracellular serine remained stable, perhaps supplemented from other routes of serine metabolism, further reinforcing the importance of this amino acid in M. tuberculosis.29,32A group of amino acids including tyrosine, valine, alanine and aspartic acid were found to have changes in levels of M. tuberculosis Δcrp mutant in terms of metabolite abundance and turnover, and established correlations with previous transcriptomic results. Knockout of crp in Mtb affected nitrogen metabolism as shown by the higher abundances and differential 13C turnover rates of related metabolites, while central carbon metabolism was minimally affected. Moreover, changes in the abundance of diaminopimelic acid and upregulation of related genes suggested possible remodelling of peptidoglycan.32Overall, our study analysed the metabolome of the 35–37 Further, as one of the pathway is the synthesis of the precursor of peptidoglycan, one could investigate the antimicrobial susceptibility of M. tuberculosis Δcrp to antimicrobials such as d-cycloserine which targets alanine racemase and d-alanine ligase38 and vancomycin which binds the d-Ala-d-Ala terminal of the growing peptide chain.39 Based on the data presented here, we can anticipate an increase in susceptibility of M. tuberculosis Δcrp compared to the parental strain.In order to validate the changes observed in the present study, one could use assay to increase the intracellular cAMP levels which CRP will respond to and determine if the pathways mentioned here are affected by the changes in cAMP levels. Such approach can be achieved by the stimulation of Mtb by adenylate cyclases activator such as Forskolin which has been shown to stimulate the adenylated cyclase Rv1625c  or the cin vivo, further studies using different carbon sources such as fatty acids are needed, while system biology approaches to probe variation between the metabolic states of different strains or different growing conditions could also be utilised.Although some of these metabolome results could be mapped to changing expression levels of enzymes, this was not always the case, and enzyme abundance and post-translational modifications should be also taken into consideration. Therefore, to link the phenotypes observed YI, SRR and GLM conceived, designed experiments, performed experiments, analysed data, and wrote the paper.This work was supported by an EPSRC-EMBRACE pump-priming award (EP/M027007/1) and by the ISSF Wellcome Trust (105603/Z/14/Z). Sonia Rebollo Ramirez was funded by the Department of Life Sciences from the Faculty of Natural Sciences Imperial College London, UK.The authors declare that they have no conflicts of interest regarding the content of this article.RA-010-D0RA05153E-s001RA-010-D0RA05153E-s002


Cellular metabolites are generated by a complex network of biochemical reactions. This makes interpreting changes in metabolites exceptionally challenging.To develop a computational tool that integrates multiomics data at the level of reactions.Changes in metabolic reactions are modeled with input from transcriptomics/proteomics measurements of enzymes and metabolomic measurements of metabolites.We developed SUMMER, which identified more relevant signals, key metabolic reactions, and relevant underlying biological pathways in a real-world case study.http://summer.salk.edu and the code is available at https://bitbucket.org/salkigc/summer.SUMMER performs integrative analysis for data interpretation and exploration. SUMMER is freely accessible at The online version of this article (10.1007/s11306-020-01750-7) contains supplementary material, which is available to authorized users. With recent advances in untargeted metabolomics, thousands of steady-state metabolites can be profiled simultaneously in a standardized setting  to enable mechanistic interpretation of steady-state metabolomics data. SUMMER integrates metabolomics with enzyme activities estimated from transcriptomics or proteomics data by calculating changes in reaction rate potentials. The reaction rate potential is dependent on enzyme and substrate concentrations while inversely dependent on product concentrations. Thus, a reaction potential is designed to model the regulation of a reaction in a perturbed condition compared to a reference condition assuming that the ratio between substrates and products is balanced at the reference state. By looking for significant alterations in the ratio with correlated changes in enzyme activity, SUMMER is able to identify the hotspot reactions of a perturbed system. After the change in rate potentials is estimated, SUMMER visualizes the network graph of all significant metabolic changes and runs pathway enrichment analysis for data exploration and functional interpretation. Our simulation study shows that the integration of the enzyme activities significantly improved the accuracy of estimating the change in reaction rate potentials compared to a non-integrated model. To demonstrate its utility, particularly for datasets with weak metabolic changes, we apply SUMMER to a case study to reveal underlying biological signals. SUMMER has a user-friendly web interface for researchers to upload their data, perform quality checks and quantitative analyses, and interactively explore outputs. SUMMER was developed in the R shiny framework  , which provides a global summary of the data.2 fold-change (log2FC) > 0.5.To determine fold changes in metabolites, a univariate test is performed for metabolites by limma , fold-change of reaction rate potentials between a reference state and a test state can be estimated by combining changes in substrates, products, and enzyme between test and reference states:In addition to a network-based analysis, SUMMER also performs a standard over-representation analysis on the DE metabolites and DE reactions using WebGestaltR  of 0.01, 0.1, 0.2, and 0.3. A Receiver Operating Characteristic (ROC) curve was used to compare the Area Under the Curve (AUC). As expected, metabolites with greater log2FC led to higher AUC of rate potential estimates when the enzyme data was not available  cycle was profoundly altered with aging in SAMP8 mice, which was partially prevented by the treatment with CMS121  due to the nonlinear signal response of electrospray ionization . Supplemental Dataset 1. Area Under the Curve for simulated metabolites and enzymes at different log2 Fold-ChangeElectronic supplementary material 2 (XLSX 392 kb). Supplemental Dataset 2. Differential Expression analysis results and pathway enrichment results computed by SUMMER for metabolites and reactions of young vs old and old+121 vs old comparisons. Only metabolites mapped to KEGG identifiers were included in the resultsElectronic supplementary material 3 (HTML 1311 kb). Supplemental File 1. Interactive network graph constructed for old+121 vs old comparison by SUMMER using default cutoff log2FC > 0.5, FDR < 0.05, and ranking score < 0.05Below is the link to the electronic supplementary material.


Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies. Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing (HLB), or citrus greening, a devastating vector-borne disease causing millions of dollars of agricultural damages every year1. CLas species infect the phloem of some plants in the family Rutaceae  and Solanaceae 2. HLB causes poor vegetative growth, fruit drop, diminished fruit quality, and tree decline7.CLas infections have been documented across most citrus-producing areas in Asia, the Americas and Africa8, and are projected to spread further10. CLas is naturally spread in citrus through a psyllid host, Diaphorina citri Kuwayama10. The basic HLB management scheme is based on the use of HLB-free nursery stock, inoculum reduction by removal of HLB-affected trees and insecticide treatments for control of psyllid populations11. In addition, various combinations of citrus rootstocks and interstocks11, cocktails of antibiotics12 and small molecule bacterial inhibitors13, or brassinosteroids14, as well as thermotherapy and nanoemulsion technology15 have been deployed. However, none of these options has been proven to be very successful, economically viable, or environmentally sustainable, making HLB a major threat to the citrus industry worldwide.16 and to understand complex metabolic capabilities17. These models could provide useful information for best practices to treat or prevent HLB. However, few detailed models of HLB currently exist18. To identify novel alternatives for combatting HLB, detailed knowledge about the metabolic dependencies and capabilities of the pathogen (CLas) is required. While CLas was identified as the likely infectious agent responsible for HLB in 1994 using molecular methods19, CLas has never been consistently cultivated axenically in vitro, limiting our ability to functionally characterize this pathogen. On the other hand, Liberibacter crescens, the closest culturable relative to CLas, was isolated and cultured in vitro from the phloem sap of defoliating mountain papaya in Puerto Rico21. Advances in metagenomic sequencing have enabled the assembly of genomes from several CLas strains obtained from HLB-infected citrus22. Genome sequences are the primary input used to reconstruct genome-scale metabolic models. These models have been successfully validated as a systems biology framework and deployed for a variety of uses. For example, models have been utilized for elucidating fundamental metabolic processes26, optimizing culture media and growth conditions28, and have been essential for metabolic engineering efforts29. These metabolic models are genome-scale knowledge databases, which contain manually curated annotation related to gene-protein-reaction associations for all possible metabolic reactions inside a cell. Reconstructed models have been used to understand and channel the metabolism of different pathogenic and non-pathogenic microorganisms30, as well as to contextualize metabolic states based on omics data24. Here, we reconstructed genome-scale models for seven Liberibacter strains and evaluated their physiology and metabolic response. Furthermore, we used in vivo expression data to determine strain-specific interactions of CLas while hosted by the psyllid Diaphorina citri Kuwayama or Citrus spp.Mathematical models have been critical in developing treatment options for infectious diseasesCLas strains gxpsy, Ishi-1, psy62, and almost complete genomes obtained by shotgun sequencing of strains A4, FL17, and YCPsy. Strains A4, FL17, Ishi-1, psy62, and YCPsy were obtained from citrus, while sequences for gxpsy were obtained from the psyllid. Additionally, we reconstructed a comparative metabolic model using the complete genome sequence of the microorganism L. crescens BT-1 (BT-1), the closest culturable microorganism to CLas , previously annotated as hypothetical, were associated with a metabolic reaction or transport reaction in these models. A list of these reactions and their gene-protein-reaction associations is provided in Supplementary Table 32. The final model properties are shown in Table CLas models, defined as “Pan-capabilities”, were 447 and 601, respectively . Another ~30% of those reactions were associated with the cell envelope . The rest of the reactions were divided among carbohydrate, glycan, and nucleotide metabolism  , and developed an optimized defined medium , which improved the growth rate33. Using OD measurements we determined the growth rates of L. crescens BT-1 while growing in BM-7, reaching a growth rate of 0.011 ± 0.007 1/h, which results in a 63 h generation time. Defined media compositions, such as M13, M14, and M15 can also support growth of this strain, resulting in growth rates of 0.009 ± 0.0006 1/h, 0.0081 ± 0.0004 1/h, and 0.012 ± 0.003 1/h, respectively.Cruz-Munoz et al.in silico by the BT-1 model, obtaining growth rate predictions of 0.011, 0.009, 0.011, and 0.015 1/h for the culture media BG-7, M13, M14, and M15, respectively. Complete data and calculations about constraints are shown in Supplementary Table 33. To determine the impact of amino acids and intermediaries of the TCA cycle on growth rate, we interrogated the model for metabolites most affecting the growth rate of L. crescens. We found that citrate, aspartate, and serine have an interwoven growth effect. Simulations performed while varying serine, aspartate, and citrate uptake rates are shown in Fig. 34.These growth rate phenotypes observed experimentally were reproduced CLas models were evaluated using different culture media compositions to unravel the most important metabolites contributing to CLas growth. The culture media compositions tested for L. crescens BT-1 were used as constraints to simulate CLas growth as shown in Supplementary Table CLas strains were able to grow, suggesting that co-metabolism with the host could play an important role for these bacteria. Modeling results showed that arginine, glucose, glutamate, glutamine, proline, ornithine, citrate, and alpha-ketoglutarate could each support the growth of L. crescens BT-1 individually. However, we found that CLas strains were highly dependent on co-metabolism to stimulate growth, necessitating the combination of two or more carbon sources at the same time . The growth rate improvement due to the addition of single metabolites in the culture media containing glucose was evaluated for each metabolite . However, when the effect of each metabolite was analyzed independently, we found that media composition affected CLas growth rates differentially  and urea were predicted to improve growth rate only for the BM-7 media. Overall, modeling predictions showed that serine, malate, fumarate, and aspartate will improve the growth rate of CLas strains in culture medium M13, M14, and, M15. On the other hand, predicted metabolites limiting the growth rate are nicotinate, pantothenate, riboflavin, and aminobenzoate when BM-7 culture media was used.Part of the well established modeling protocols is the determination of the average metabolite connectivityins Fig. . For exaram Fig. . GlucoseCLas growth rates, they also suggest that phenotypic outcomes depend on the media composition to which each metabolic model is being subjected. The heatmap in Fig. CLas gxpsy and Ishi-1 the media composition can strongly affect the ability of a strain to synthesize metabolites, varying from 60 to over 220 metabolites. The M15 media composition resulted in maximal metabolite production for every model, except for CLas gxpsy. The culture medium BM-7 resulted in a similar number of produced metabolites for all seven models. We also evaluated the growth phenotype under eight different carbon sources assayed experimentally, predicting accurately the growth rate for oxoglutarate, glucose, and malate and an overall growth increase for fructose, fumarate, malate, maltose, and succinate  of the changes in the observed growth rate for the different carbon sources33.While these results hint at specific metabolites that could improve ate Fig. . The BT-CLas, a cocktail of predicted metabolites was added to in vitro citrus hairy root cultures infected with CLas. Culture media B5, which is used to propagate the hairy root cultures, was supplemented with three concentrations  of an amino acid cocktail containing glycine, serine, proline, aspartate, glutamine, and glutamate. Measurements of CLas in the hairy root cultures obtained over the course of seven days showed that the treatment of 1 mM significantly improved CLas growth rate compared to 0.1 and 5 mM treatments, as well as controls   Fig. .L. crescens BT-1 genome-scale metabolic model, we evaluated the response of CLas models using host-specific RNA-sequencing data. Expression data was collected from phloem-enriched samples (referred to as phloem in the rest of the paper) from three Citrus cultivars  and from the alimentary canals of Diaphorina citri (psyllid). Using RNA-sequencing reads we generated strain-specific counts that were used to constrain the boundaries of the reactions in each model. Supplementary Table CLas strains and the differentially expressed genes using a cut off of p-value < 0.05 (t-test).After validating the CLas models from each host, as well as the fold change between samples from the two hosts obtained from RNA-sequencing data. Data from the individual phloem and psyllid samples were combined into two separate datasets that were used to constrain the models, giving insight into specific metabolic traits operated by CLas under each host. Constrained models are provided in at https://github.com/cristalzucsd/Liberibacter.As expected, gene expression fold change between phloem and psyllid samples was normally distributed across genes. The maximum fold change observed was 28. Figure CLas models  resulted in a growth rate decrease of up to 68% and 74% for phloem and psyllid, respectively, in comparison to media-constrained conditions in which the predicted and experimental growth rate was around 0.013 ± 0.0009 1/h. Phloem-constraint flux distributions revealed increased amino acid metabolism, fatty acid metabolism, gluconeogenesis, nitrogen metabolism, one-carbon pool of folate, nicotinamide and nicotinate metabolism . The overall flux distributions across all constraints showed that subsystems, including glycolysis, transporters, TCA cycle, pyruvate metabolism, and nitrogen metabolism were highly activated, which was the opposite of riboflavin metabolism, steroid biosynthesis, and cysteine and methionine metabolism. Furthermore, the pentose phosphate pathway was predicted to be active only under host-constraints while glycine, serine, and threonine metabolism were reduced in flux.Figure CLas models constrained with phloem expression data clustered with models constrained with culture media M15 and CLas models constrained with psyllid expression data clustered with the flux distribution of BT-1  associated to each gene, while maintaining the host-dependent constraints for the rest of the network. Figure CLas models and the BT-1 model.Identification of potential CLas strain YCPsy was the most sensitive, with 94 essential genes in comparison with the strains gxpsy, Ishi-1, psy62, FL17, and A4, which had 93, 91, 90, 89, and 87 essential genes, respectively , inorganic diphosphatase (3.6.1.1), coproporphyrinogen oxidase (EC 1.3.3.3), and protoporphyrinogen oxidase (EC 1.3.3.4) were overexpressed  and identified as essential. Additionally, we compared all predicted essential genes (91) with genes overexpressed  in the citrus cultivars Valencia and Washington navel orange (C. sinensis L. Osb.) and Tango mandarin (C. reticulata Blanco) and the CLas enzymes phosphoglycerate mutase (EC 5.4.2.12), dihydroorotic acid (menaquinone-8) (EC 1.3.5.2), ribonucleoside-diphosphate reductase (UDP) (glutaredoxin) (EC 1.17.4.1), and glutaredoxin reductase (EC 1.20.4.1) were selected. Together, these results suggest eight distinct enzymes, whose inhibition could reduce CLas pathogenicity. The full dataset of metabolic reactions that are potential genetic targets in the CLas strains studied here are shown in Supplementary Table Previously, a study of random transposon mutagenesis of EC 2.7.4., inorganCandidatus Liberibacter asiaticus (CLas) during growth in the psyllid host Diaphorina citri Kuwayama and infection of the plant host Citrus spp. This work represents a systems biology modeling approach to understand the metabolic role of CLas, the putative vector-borne causal pathogen of HLB, in citrus infection. We generated high-quality, manually curated genome-scale metabolic models of the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy  and multiple substrates   are reliable sources of information for modeling uncultivable microorganisms. Host-specific (psyllid or plant expression data) constrained models predicted growth rates ~70% slower than media-constrained models. The predicted growth rate in the psyllid was higher than in the plant, as was previously observed experimentally42. These findings suggest different behaviors of CLas are dependent on its host . We also found that fatty acid metabolism was highly activated in CLas from citrus phloem samples, including enoyl-acyl reductase, which has been associated with antibiotic resistance50.Using genome-scale metabolic models, we focused on understanding the metabolic behavior of CLas and its hosts, and systems biology and omics tools can help to further unravel metabolic mechanisms associated with HLB initiation and progression, as well as to identify targets in CLas that can be used to develop HLB management strategies. Our results are consistent with and expand on prior findings. For example, other studies in L. crescens have shown that supplementation of amino acids to the culture media increases growth rates36. Gene essentiality simulations  creation of an automated draft reconstruction, (ii) draft refinement by manual curation, (iii) conversion from reconstruction to mathematical model, and (iv) model evaluation.52. This method results in draft models, which require refinement through manual curation. Draft models are generated based on protein-homology comparison between each protein sequence in the genome of the target microorganism  and the protein sequence of a manually curated reference model(s).Semi-automatic reconstruction methods reduce building time, while maintaining high-quality architecture and prediction accuracy53. Supplementary Fig. Liberibacter strains and bacteria with available reference models in the repository. Pseudomonas putida KT2440, iJN74654 was the closest related microorganism to Liberibacter, followed by Yersinia pestis CO92, iPC81555 and the model of Escherichia coli str. K-12 substr. MG1655, iML151556. Table CLas strains A4, FL17, gxpsy, psy62, YCPsy and L. crescens BT-1, which were used as input to The COBRA32 and RAVEN Toolboxes57 for MATLAB .The reference models used here were chosen from the BiGG Database58 to compare protein sequences between each strain of Liberibacter in the multi-strain model with sequences of E. coli, P. putida, and Y. pestis using UniProtKB/Swiss-Prot databases59. Transporter protein sequences were identified and compared using the TCBD database60. Metabolic reactions where no gene association could be found underwent another round of curation, where literature was reviewed to find evidence for the presence/absence of these proteins. Reactions with no supportive literature or matching sequences were included in the model for gap filling to ensure the completeness of relevant pathways37. The Supplementary Table Each metabolic reaction in the reconstructed models was manually curated for their correct gene-protein-reaction association (GPR) using protein BLAST32. Metabolic models were shared following the standard protocols for computational analysis61.The manual curation process was followed by model evaluation and validation. The reconstructions were analyzed for connectivity, mass and charge balance and converted into a functional mathematical model for simulation using The COBRA ToolboxCLas and BT-1 metabolic model reconstructions were constrained identically using the culture media BM-7, M13, M14, and M1533. All media compositions were simulated by setting a lower bound of −100  on the exchange reactions for Co2+, Fe2+, H+, H2O, Na+, NH4, PO4, SO4. Supplementary Table 62. Growth simulations were performed using the flux balance analysis procedure32. Constraints on biomass composition were imported from the reference model of P. putida KT2440, iJN74654. Stoichiometry of the biomass composition was estimated to be 1 g of dry weight of biomass.The seven 35. Methods listed under growth simulations of alternate substrates of Orth et al. protocol were used to calculate the degree of metabolite connectivity  and reactions (columns) in the model. Reaction essentiality by metabolite was calculated by scanning the matrix S across all reactions. For the reactions in which each metabolite was found to participate the boundaries were set to zero and compound growth rate was estimated . The essentiality fraction was determined by the ratio between compound growth rate and the growth rate determined without any modification to the boundaries .The model topology was evaluated following the constraint-based modeling standard protocoly D, Eq. 35, ident2•2H2O (1320 mg/L), MgCl2 (1068.2 mg/L), MgSO4•7H2O (2778 mg/L), KCl (2240 mg/L), NaH2PO4•H2O (1007 mg/L), L-alanine (447.24 mg/L), L-arginine-HCl (1777 mg/L), L-asparagine monohydrate (1075.45 mg/L), L-cysteine-2HCl (56.38 mg/L), L-glutamic acid (1502.2 mg/L), glycine (859.51 mg/L), L-histidine hydrochloride monohydrate (2366.11 mg/L), L-isoleucine (687.36 mg/L), L-leucine (592.89 mg/L), L-lysine-HCl (1464.85 mg/L), L-methionine (678.9 mg/L), L-phenylalanine (789.62 mg/L), L-proline (940.61 mg/L), l-threonine (459.8 mg/L), L-tryptophan (373.73 mg/L), L-tyrosine disodium salt (391.37 mg/L), L-valine (644.31 mg/L), betaine monohydrate (0.36 mg/L), dl-ornithine hydrochloride (293.39 mg/L), methionine sulfoxide (18.2 mg/L), D-biotin (0.1 mg/L), choline chloride (1000 mg/L), folic acid (0.2 mg/L), myo-inositol (0.2 mg/L), niacin (0.2 mg/L), D-calcium pantothenate (0.2 mg/L), para-aminobenzoic acid (0.2 mg/L), pyridoxine-HCl (0.2 mg/L), riboflavin (0.2 mg/L), thiamine-HCl (0.2 mg/L), L-aspartic acid (2500 mg/L), dl-serine (2500 mg/L), L-glutamine (358.04 mg/L), and citric acid (2500 mg/L)33. All ingredients were combined with the exception of tyrosine, which was first dissolved in 1 M HCl before being added. Once all ingredients were dissolved, the medium was adjusted to pH 5.92 with 5 M KOH and filter sterilized. Other derivatives of M15 (the different treatment media types) were prepared in the same way, but with differing concentrations of the components being examined . The concentrations of all other components were kept the same as the original M15 recipe. M15-basic media was made with minimal (0.1 mg/L) amounts of the treatment components  but was otherwise kept the same as the original M15 recipe.M15 media consists of CaClLiberibacter crescens  was used for all experiments21. Glycerol stocks of L. crescens strain BT-1 in BM-7 complex media were used to inoculate M15 media, which was then shaken at 150 r.p.m. and 28 °C for 3–5 days to grow sufficient quantities for the experiments. Bacteria were pelleted via centrifugation at 6000 rcf for 10 min, re-suspended in M15-basic medium, which does not contain the treatment components  and shaken at 150 r.p.m. and 28 °C for 1 h to remove any large traces of the treatment components. OD600 was measured, and bacteria were re-pelleted using the same conditions described above. Pelleted bacteria were re-suspended in sterile DI water and used to inoculate treatment media for growth to stationary phase: OD600 = 0.8. Treatment media tubes were grown in 5 mL volumes in 16 × 100 mm tubes at 150 r.p.m. and 28 °C. Growth was measured every 12–24 h for 300 h using a Spectronic-20  spectrophotometer and OD600.CLas-citrus hairy root cultures were generated using methods described previously63, with CLas-infected sour orange tissues (Citrusxaurantium L.) as ex-plant/inoculum source for CLas. Briefly, quantitative polymerase chain reaction (qPCR) validated CLas containing sour orange were identified and 5–10 cm shoots were excised for hairy root induction. The cut-end of the ex-plant was inoculated with fresh culture of Rhizobium rhizogenes  under gentle vacuum infiltration (~200 kPa). R. rhizogenes is a soil bacterium that naturally transforms plant cells to induce hairy roots at the point of contact by reprogramming plant hormone signaling64. In citrus, hairy root formation typically occurs in ~90 days, and because of the vascular connectivity between the shoot ex-plant, CLas naturally migrates into the newly formed hairy roots. Presence of CLas in the hairy root cultures was further confirmed by qPCR, using CLas-specific primers as described below. To determine the effect of amino acids on CLas titers, in vitro assays were set up using the validated CLas-citrus hairy roots63. Briefly, the CLas-citrus hairy roots were surface sterilized with 70% ethanol and 2.5% sodium hypochlorite for five minutes followed by six washes with sterile water. Approximately 100 mg of CLas-citrus hairy roots were added to a multi-well culture plates and supplemented with B5 + amino acid cocktail  concentrations . An oxytetracycline (500 p.p.m. = 500 mg/L) treatment was included as a CLas-inhibitor control for the in vitro assay. Four to five independent biological replicates were included for all treatments. The samples were vacuum infiltrated at 200 kPa for ~15 min to facilitate penetration of the media and nutrients into the hairy root cultures. The assay plates were placed on an orbital shaker at 40 r.p.m. at~25 °C and in dark. Fresh B5 medium was replaced at 3 and 5 days after incubation. Samples were collected at 0, 3, 5, and 7 days after treatment and flash-frozen in liquid nitrogen and stored at −80 °C until further use.The ex vivo CLas-citrus hairy root cultures were lyophilized and homogenized in a MiniG 1600 (Spex Sample Prep) homogenizer at 1500 x r.p.m. for 30 s, with a single steel bead . Total DNA extraction was carried out according to Almeyda et al.65. qPCR reactions were carried out in a CFX-384 Real-Time PCR Detection System  with 25 ng of DNA as template, using Sso Advanced Universal SYBR Green Supermix , and the following primers for citrus GAPC2 (CsiGAPC2-F 5′-TCTTGCCTGCTTTGAATGGA-3′and CsiGAPC2-R 5′-TGTGAGGTCAACCACTGCGACAT-3′) and for the β-subunit od nrdB gene from CLas, RNR (RNRf 5′-CATGCTCCATGAAGCTACCC-3′ and RNRr 5′-GGAGCATTTAACCCCACGAA-3′)66. The reactions were carried out under the following conditions: Initial denaturation 95 °C for 3 min, followed by 95 °C for 15 s and 55 °C for 30 s for 40 cycles, and a final extension at 65 °C for 5 s. Relative CLas titers were estimated using the ΔΔCt method67. Briefly, the CLas Ct was first normalized to the housekeeping gene (GAPC2) to account for DNA template differences, and then to the 0 days Ct, which was set to 1 (or 100%). Growth rates at each time point were calculated using the initial CLas titer as a reference point. The minimal and maximal data were discarded before the analysis of variance (ANOVA) analysis. Calculations were performed using The Preprocessing Data and The Statistics and Machine Learning Toolboxes of MATLAB (The MathWorks Inc.).All control and treated CLas models. The samples were obtained from the phloem-enriched samples from different citrus cultivars and from Asian citrus psyllid (ACP) alimentary canals as described below. For each growth condition, the storage and consumption of starch, calculated using experimental data, were taken into account  on Swingle citrumelo (C. paradisi Macf. X Poncirus trifoliate [L.] Raf.) rootstock, Tango mandarin (Citrus reticulata Blanco) on Sour orange rootstock, and Washington navel orange (Citrus sinensis [L.] Osbeck) on Sour orange rootstock  to prevent surface contamination. After drying for three minutes, the bark was peeled from each piece and the inside surface located away from the cut ends was quickly scraped with a razor blade twice—first to remove surface contamination and potential xylem and second to collect a phloem-enriched sample. Samples were immediately placed in PowerBead Tubes filled with Solution MBL and the Phenolic Separation Solution from an RNeasy PowerPlant Kit , which were held in a CoolRack  on dry ice. RNA was extracted using the RNeasy PowerPlant Kit following the kit protocol with two minutes of bead beating, eluted in 50 µL of RNase-free water, and stored at −80 °C for library preparation.CLas-exposed colonies maintained at the U.S. Horticultural Research Facility in Fort Pierce, Florida using an aspirator. Alimentary canals were dissected from ACP in a weight-boat containing Solution PM1 from an RNeasy PowerMicrobiome Kit  placed on ice, and deposited in two PowerBead Tubes containing Solution PM1  held in a CoolRack (BioCision) on dry ice. RNA was extracted using the RNeasy PowerMicrobiome Kit following the kit protocol with one minute of bead beating, eluted in 50 µL of RNase-free water, and stored at −80 °C for library preparation.Approximately 100 adult ACP were collected from C. maxima (Burm.) Merr. genome (NCBI_Assembly: GCA_002006925.1) and bacterial 16S rRNA sequences (SILVA database: https://www.arb-silva.de/) were chosen as reference templates. Valid reads were aligned to reference templates using bowtie2 (version 2.3.4.1) with parameters set by the flag very-sensitive. Unmatched reads were picked out and converted to fastq format using samtools (version 1.8) and bam2fastq , respectively. To count the FPKM (fragment per kilobase per million mapped reads), reads were mapped to 7 Liberibacter strains: CLas strains A4 (GCF_000590865.2), FL17 (GCF_000820625.1), psy62 (GCF_000023765.2), YCPsy (GCF_001296945.1), gxpsy (GCF_000346595.1), Ishi-1 (GCF_000829355.1) and L. crescens BT-1 (GCF_000325745.1).Alimentary canal sample complementary DNA (cDNA) libraries were prepared using the ScriptSeq Complete Gold Kit (Yeast) , following kit protocols and performing ribo-depletion. Citrus sample cDNA libraries were prepared using the ScriptSeq Complete Kit (Plant Leaf) , again using provided protocols and ribo-depletion. In both cases, ScriptSeq Index PCR Primers  were used for barcoding samples. RNA Sequencing was performed using Illumina’s HiSeq2500 platform. Raw RNA reads were trimmed using TrimGalore (version 0.4.4) including adapter removal and quality control: low-quality ends from reads (Phred score < 20) were trimmed and reads less than 20 bp were discarded. Next, read quality was checked using FastQC (version 0.11.7). To discard host and 16S rRNA reads, Supplementary InformationDataset 1