This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK ( The authors present a workflow integrating metabolic perturbations with protein structural analysis to identify drug off-targets, demonstrating how combining machine learning methods with mechanistic analyses can benefit off-target identification. For example, while antibiotic molecules are designed to selectively inhibit essential bacterial proteins2, even conventional antibiotic drugs are found to target multiple molecular targets inside bacterial cells. Identifying the full spectrum of drug targets is critical to understanding drug mechanisms of action as well as to exploit multivalency to tackle the problem of drug resistance.Pharmaceuticals are often used with incomplete knowledge of their intracellular drug-binding partners3. Large-scale measurement of drug perturbations, such as metabolomics or transcriptomics, coupled to machine learning has been a promising approach to understanding drug mechanism of action and targeting. These methods can identify trends that are uniquely associated with a drug effect. However, identifying specific targets from machine learning is challenging due to the difficulty in interpreting such models. By contrast, mechanistic models and targeted analyses have the advantage of greater interpretability but struggle to learn from large-scale datasets.Recent advances in systems biology tools are making systematic search for intracellular drug targets an increasingly accessible task4. In our previous report we showed that CD15-3 interacts with DHFR; however, overexpression of the DHFR-encoding gene folA was only able to partially rescue CD15-3-induced growth inhibition. The lack of complete growth rescue from overexpression of DHFR indicated the presence of an additional non-DHFR intracellular target of CD15-3, which could be responsible for the growth inhibitory effect of the compound5.Previously, we developed a novel antibiotic, the compound CD15-3, designed to interact with wild-type DHFR (dihydrofolate reductase) and its trimethoprim (TMP) resistant mutantsIn this work, we develop a multiscale drug target-finding workflow that integrates machine-learning analysis of metabolomics data with metabolic modelling and protein structural analysis. We apply this workflow to search for the off-target of CD15-3. First, we analyse untargeted global metabolomics with statistics and machine learning to unravel differences in the global metabolome upon antibiotic treatment and captured potential hotspots of metabolic perturbation. We then integrate metabolic supplementation growth rescue experiments with metabolic modelling to identify metabolic pathways whose inhibition is consistent with data. Finally, we perform protein structural analysis to identify likely targets within candidate pathways based on similarity to known targets and validate these candidates experimentally using a combination of gene overexpression, imaging experiments and in vitro enzyme assays.Antibiotic molecules often have unintended intracellular targets. A systems-wide analysis capturing the drug-induced intracellular perturbation can be insightful in unravelling the intracellular mechanism of action of the drug and its molecular target. We developed an untargeted global metabolomics-guided multi-layered analysis framework for antibiotic off-target identification to deploy to search for the unknown target of CD15-3  in CD15-3 for 12 hours and subsequently transferred them to M9 media supplemented with 0.8 g/L glucose. In a parallel control set, WT cells were grown for 12 hours in M9 medium with glucose at 0.8 g/L and subsequently regrown in the same media. Metabolite abundance levels were measured at four discrete time points  practical considerations such as availability of a transporter for the compound in E. coli and commercial availability of the substrate  cells in the absence of CD15-3.Supplementation with thymidine, NAG, serine, and IMP showed growth rescue, as reflected in improved growth rates Fig.  under th8. In our experimental condition a higher lag time in the presence of some external supplements and CD15-3 treatment reflects time of metabolic rewiring and adaptation in the supplement-enriched growing condition. It is interesting to note that NAG and serine significantly prolonged the lag time . To evaluate possible direct pathway inhibition, we computationally inhibited metabolic reactions one by one and calculated the ability of each metabolite in the model to rescue growth. To evaluate possible cofactor depletion, we generated cofactor draining reactions and calculated the ability of each metabolite in the model to generate additional cofactor charges. Finally, we correlated the model-calculated benefit of each metabolite to the experimental observed growth rescue potential of those metabolites.To rigorously evaluate the hypothetical effect of these supplements, we developed a constraint-based metabolic modelling workflow to assess whether inhibition of a particular pathway is consistent with observed growth rescue patterns of supplements Fig. , MethodsWe found that the experimental growth rescue pattern was most consistent with a folate cofactor drain mechanism of CD15-3 Fig. . Compari11. For example, the model initially predicted citrate to have a growth-rescuing effect; however, the citrate transporter is not expressed under normal conditions. Similarly, the model calculated only a minor benefit of IMP over AMP initially, but investigation revealed that the AMP incorporation pathway utilized by the model included both spontaneous reactions, which are not likely to occur at a high enough rate to sustain growth, and the cryptic gene adeD, encoding adenine deaminase. These cases were handled individually utilizing available expression data11, and all model corrections are included in the metabolic modelling code in the Supplementary Data 12 or through the use of gene-protein-reaction relationships to identify and shut off potentially problematic spontaneous reactions.We note that the metabolic model did not inherently incorporate the wild-type expression state under which supplements were administered. To correct for wild-type gene expression, we shut off flux through several reactions based on measured lack of expression under wild-type conditions4.To determine whether the observed agreement was specific to folate inhibition or was associated with growth more broadly, we additionally implemented random pathway inhibitions and compared the growth benefit under these conditions. While some metabolites, such as serine and glycine, had growth rescue behaviour that agreed more broadly with general growth benefit, other metabolites such as AMP/IMP and thymidine/uridine agreed substantially better with folate inhibition than inhibition of other pathways , DHPS (encoded by gene folP), DHFS (encoded by folC), MTHFC (encoded by gene folD), MTHFR (encoded by gene metF) and ADCL (encoded by gene pabC) using the pBAD promoter with 0.1% arabinose induction was carried out to determine whether any of the overexpressed genes show recovery from CD15-3 induced growth inhibition and thus may be the non-DHFR target of CD15-3. In our previous study4 we showed that overexpression of folA (encoding DHFR) partially rescued CD15-3-induced toxicity at lower concentrations of CD15-3.Utilizing the prioritized target list from structural analysis, we evaluated whether overexpression of any of these enzymes rescued growth inhibition by CD15-3. Regulated overexpression of candidate proteins viz., HPPK . HPPK is a key enzyme in the folate biosynthesis pathway catalyzing the pyrophosphoryl transfer from ATP to 6-hydroxymethyl-7,8-dihydropterin, is an attractive target for developing antimicrobial agents16. Upon folK overexpression cells did not show any change in growth rates in a broad range of concentrations of CD15-3  in the absence of CD15-3. On the other hand, overexpression of folA at 0.005% arabinose induction showed only partial rescue from CD15-3 inhibition    . Overexpression of metF did not show any rescue in CD15-3-induced growth inhibition , which is a precursor of tetrahydrofolate. ADCL converts 4-amino-4-deoxychorismate into 4-aminobenzoate (PABA) and pyruvate. Overexpression of pabC did not show any recovery of growth rates in CD15-3 treated cells  level of around 0.6 hour−1 at all concentrations of CD15-3. This indicates strongly that CD15-3 interacts with cellular HPPK as its non-DHFR molecular target. The complete growth recovery observed with folK overexpression indicates that the growth-limiting folate perturbation originates in the folK-mediated step, which in turn impacts rest of the folate pathway. In the Supporting Text (Supporting Information) we provide an explanation as to why folA overexpression leads only to partial rescue while overexpression of folK resulted in full recovery from CD15-3 induced inhibition. In short, the reason for the difference is in different expression levels of folA and folK in overexpression experiment. The Supporting Text presented a quantitative analysis of the effects of the inhibition of two proteins by a common inhibitor and the competing factors of inhibition of both enzymes and sequestration of the inhibitor by an overexpressed enzyme. Supplementary Fig. Of all the candidate genes, only 23. WT cells treated and untreated with CD15-3 were grown for 4 hours and subjected to DIC imaging. CD15-3 treated cells , as compared to untreated cells under control conditions (median cell length = 1.06 µm). This slightly higher median cell length could be attributed to the fact that remaining CD15-3 unsequestered by HPPK also targets cellular DHFR. Overexpression of folK although mostly reverses the effects of CD15-3 on cell shape; The overall pronounced rescue in cell length upon overexpressing folK further supports the conclusion that HPPK is the non-DHFR cellular target of CD15-3.Perturbation in the folate pathway leads to cellular filamentation and concomitant morphological changesTM assay to test for HPPK activity and its probable inhibition in presence of CD15-3. We induced the overexpression of HPPK using IPTG and purified the protein for the activity assay (described in the methods section). KinaseGloTM assay is based on chemiluminescence24. With higher ATP concentration in the assay buffer, luciferase leads to the conversion of beetle luciferin to oxy-luciferin with the emission of light. The HPPK-mediated reaction utilizes ATP leading to ATP depletion and hence drop in the chemiluminescence signal. Any potential inhibition of HPPK would retain the original concentration of ATP keeping the chemiluminescence signal intact or like the control (with no HPPK activity). We observed a marked drop in the absolute chemiluminescence signal intensity in the HPPK reaction set with no CD15-3 in the assay buffer  HPPK and inhibits its function, leaving unutilized ATP which contributes to the reported chemiluminescence.Next, we aimed to verify in an in vitro assay that CD15-3 indeed inhibits HPPK in a concentration dependent manner. To that end we performed a KinaseGlofer Fig.  inset. Ifer Fig.  inset sufer Fig. . CatalytIn our assay protocol, HPPK reaction was initiated with the introduction of the substrate, 6-Hydroxymethyl-7,8-dihydropterin in the assay buffer  . We observed that with an increasing substrate concentration CD15-3 induced inhibition is alleviated and chemiluminescence signal drops, suggesting the depletion of available ATP in the reaction mix  Fig.  showing 1 to anti-cancer formulations27. A systematic understanding of cellular targeting is critical in drug discovery programs as it provides mechanistic insights into intracellular drug action. This understanding in particular stands critical in the context of drug resistance, as drug-resistant cells can mount plethora of strategies to evade the drug action. For example, the bacterial “resistome” is a tight assembly of multi-layered highly orchestrated mechanisms28. In the current context of widespread antibiotic resistance, including the emergence of “multi-drug resistant” ESKAPE variants29, a mechanistic understanding of intracellular antibiotic targeting and what leads to bacterial death stands as an immensely pertinent problem.Intracellular drug target identification is a hard problem. Often candidate drugs interact with unintended proteins inside cells and the resultant phenotypic effect emerges from the off-target protein(s). This applies to drugs spanning from antibioticsE. coli traverses under Trimethoprim selection4. We hypothesized that CD15-3 is an interesting lead towards the development of the evolution drugs to overcome antibiotic resistance. However, our in-cell experiments showed that CD15-3 has an additional unidentified non-DHFR target; thus, blocking more than one cellular target makes the evolution of antibiotic-resistant E. coli phenotypes more difficult. It was important to determine the alternative target of CD15-3 in E. coli cells to better understand the mechanism of action of this prototypic “evolution-drug” inside bacterial cells. In the present study we developed an integrated multiscale framework utilizing global metabolomics interpreted through machine learning and metabolic modelling, gene overexpression assays, and growth recovery studies eventually analysing data in the context of the metabolic network to unravel the unknown intracellular antibiotic target of CD15-3.Previously we reported CD15-3 as a potential antibiotic that significantly constrains bacterial evolvability by blocking the evolutionary escape routes which 31. These datasets open the possibility of identifying the mechanisms of uncharacterized compounds through comparison to known profiles32. Also, machine learning methods have become increasingly popular for statistical analysis of the metabolomics data due to the inherent non-linear metabolomic data representation and the ability to process large and heterogeneous data rapidly34. While model interpretation has been a historical challenge in deploying machine learning for biological data analysis, interpretable ‘white box’ machine learning methods have come into focus as a viable area of development to empower drug discovery workflows35.Antimetabolite classes of antibiotics such as antifolates (for example Trimethoprim) target proteins at critical points in the bacterial metabolic scheme. Hence an investigation of the metabolic architecture provides essential clues tracing the potential points of metabolic perturbations under conditions of antibiotic action. Critical analysis of such points of metabolic perturbation and its comparison with untreated control sets provides mechanistic insights of the drug action inside cell. Recent advances in untargeted metabolomics have provided valuable insights into the global metabolome and helped to quantitatively identify the metabolic cascades impacted by perturbationsfolK, which codes for HPPK, showed complete rescue of growth rate under CD15-3 treatment conditions. Unlike DHFR overexpression which only partially rescued CD15-3-induced growth inhibition35, HPPK showed clear recovery at all concentrations of CD15-3. In the Supplementary Note Employing machine learning on the comparative metabolomics data and training a multi-class K-nearest neighbour model, we found that CD15-3-induced metabolomic perturbation has a typical antifolate signature, suggesting that the unknown target is located somewhere in the folate pathway. Focusing on folate pathway perturbation and performing metabolic supplementation experiments we observed that supplementation with a subset of metabolites lead to growth recovery. Our analysis in the context of the metabolic network utilizing constraint-based metabolic modelling confirmed that inhibition of folate metabolism was consistent with patterns of growth rescue. We utilized protein structural analysis to suggest targets upstream of the DHFR catalysed step within the folate pathway and performed gene over-expression studies for these target-genes to determine which candidate targets rescue growth inhibition by CD15-3. Among all the candidate genes The use of growth rescue experiments under various nutrient supplements was uniquely helpful in verifying the folate inhibition of CD15-3. We note that the choice of compounds to supplement in this study was based on a combination of identifying CD15-3 perturbed metabolites, metabolites close to the folate pathway, and metabolites that showed in in the machine learning analysis. We note that other metabolites could have been chosen that may have provided similar mechanistic information. For example, neighbouring compounds could be expected to provide similar information to the compounds we chose, provided that transporters are available for these compounds. Identifying metabolites ‘close’ to the folate pathway is a complex question as well due to the interconnected nature of the metabolic network. In the case of our study, we utilized a qualitative approach based on canonical pathway definitions established by manual curators, therefore matching a textbook understanding of proximity between metabolites.36 is making protein structure prediction an increasingly accessible task, this approach utilizing protein structural comparison could provide a new direction to rapidly demystify drug target identification (DTI). We note that in the case of CD15-3, we assumed a priori that the DHFR activity of the drug was important and could therefore analyse other likely targets based on similarity to this known enzyme target. For compounds with completely unknown targets, other approaches, such as docking and metabolite similarity assessment would need to be applied to generate an initial range of enzyme targets to analyse structurally. With no initial guess of where the initial target could be, a workflow still could be possible consisting of 1) metabolomics with comparison to previously characterized antibiotic compounds, 2) supplementation to probe growth inhibited pathways, and 3) overexpression of candidate targets to attempt a growth rescue. The structural analysis could still be deployed to computationally assess the possibility of multi-targeting of the drug.This work demonstrates a promising path towards white box machine learning workflows through coupling standard machine learning modelling with mechanistic metabolic modelling and protein structural analysis. As the release of AlphaFold37. Thus CD15-3 in principle can serve as a lead to a “monotherapy-analog” of “combination therapy” which blocks the emergence of antibiotic-resistant phenotypes by interacting with two targets making it difficult for bacteria to escape antibiotic stress. We note that, in principle, overexpression of any of the targets could sequester the drug, alleviating pathway inhibition as a resistance mechanism; however, this would come at a substantial proteomic cost itself.With HPPK as the non-DHFR target, CD15-3 can be considered as a multivalent drug, which can simultaneously block and inhibit two molecular targets (DHFR and HPPK) inside the bacterial cell. Interestingly, being an essential protein, bacterial HPPK has been an attractive target for designing antibiotics27. We believe our proposed framework would be relevant in addressing these off-target identification problems. Future works would involve using this multi-scale framework for off-target identifications in other cellular models and complementing it with relevant functional assays.We showed how integrating constraint-based metabolic modelling with machine learning in analysing large-scale metabolomics data can help capturing metabolic perturbation signatures and narrow down the search options for identifying intracellular drug off-target. We acknowledge that one limitation of the current proposed framework is this that it works with antimetabolite classes of antibiotics and has not been tested with antibiotics in general. Future work would aim at the development of a similar workflow for other classes of antibiotics where perturbations remain highly delocalized and impacts nonmetabolic cellular functions. Further, the problem of off-target activity is also highly relevant for drugs beyond antibiotics. Many anti-cancer drugs have been reported to have off-target toxicitiesBacterial cultures of WT Escherichia coli BW25113 in M9 medium with glucose at 0.8 g/L were grown overnight at 37 °C and were then normalized to an OD of 0.1 using fresh medium. A new normalization to an OD = 0.1 was conducted after additional secondary growth for ~4 hours. Thereafter the M9 medium and six different concentrations of the CD15-3 in the 96-well plates (1/5 dilution) were incubated. The incubation of the plates was performed at 37 °C and the orbital shacking and absorbance measurements at 600 nm were taken every 30 min during 15 h. Growth rate was calculated using logistic fitting on matlab. The M9 media pH under the conditions of metabolic supplementation were continuously monitored throughout the culture-time.Untargeted global metabolomics was performed to understand the global metabolome of the WT (WT Escherichia coli BW25113) treated and control sets under different experimental contexts. In all the experiments cells were grown in M9 medium with glucose at 0.8 g/L in a 250 mL flask and temperature of 37 °C was maintained. Cells were pelleted with a brief 2 minutes precentrifugation incubation step on dry ice. After pelleting, the cell pellets were mixed with 80% pre-chilled methanol. Samples were thereafter vortexed and incubated in dry ice for 10 min followed by centrifugation at 4 °C for 10 min at maximum speed. The supernatant was collected, and the pellet was repeatedly processed by resorting to the above-mentioned procedures. Samples were stored at −80 °C until analyzed by mass spectrometry.A Thermo q-Exactive Plus mass spectrometer coupled to a Thermo Ultimate 3000 HPLC was used to carry out the LC-MS analyses of metabolites in biological samples. The Electrospray source settings included a sheath gas flow rate and was set at 35, auxiliary gas flow rate at 5 L/min, a capillary temperature of 250 °C, and auxiliary gas temperature of 300 °C. Using the Thermo LC-MS Calibration mix immediately prior to the analysis calibration of the m/z range was performed. A scan range of 66.7–1000 m/z was used at a resolving power of 70,000 with alternating positive and negative ion mode scans. The chromatographic separation of metabolites was performed using hyrdophilic interaction liquid chromatography (HILIC) on a SeQuant ZIC-pHILIC column, 5 µm, polymer PEEK 150 mm×2.1 mm column (EMD Millipore) at a flow rate of 0.1 mL/min. Mobile phase A was 20 mM ammonium bicarbonate with 0.1% ammonium hydroxide, and mobile phase B was acetonitrile. The mobile phase composition was started at 100% B, and subsequently decreased to 40% B over 20 min. The column was then washed at 0% B for five minutes before re-equilibration to 100% B over fifteen minutes. The extracted ion currents were plotted using a mass accuracy window of 5 ppm around the predicted monoisotopic m/z value of the molecular ion of each metabolite. The integrated area of each peak was used to determine the response for each metabolite at their specific retention time as determined by chemical standards.38 and IDEOM39. In untargeted analysis for peak assignment, we used IDEOM and included both positive and negative peak M/Z values and predicted retention times calculated based on chemical descriptors. We followed the same method of analysis as we had in one of our earlier studies40. For putatively identified metabolites the retention times were found to correlate fairly well with the values included in IDEOM (R2 = 0.7) and published in other studies  . Based on this, additional metabolites from those sources closely matching IDEOM assignments were treated as standards in the identification routine. We also referred to KEGG and Ecocyc for additional chemical understanding of the metabolites.In all our experiments at least three independent biological replicates were analyzed. A list of 48 experimentally measured retention times was used for initial calibration of the retention time predictions. We performed data analysis for untargeted metabolomics using the software the packages MzMatch−1 glucose. Both the naïve (which were not exposed to CD15-3 treatment) and pre-exposed cells (cells treated with CD15-3) were subjected to growth in M9 medium with glucose at 0.8 g/L media and growth profiles were analyzed. The entire pre-exponential stage (lag phase) was grouped into equal time frames and both naïve and pre-exposed cells were harvested for metabolomic studies. Same process was also executed for harvesting both naïve and pre-exposed cells at late log phase.WT Escherichia coli BW25113 cells were grown both in absence and presence of prospective compound CD15-3 in M9 media supplemented with 0.8gLthyA, glyA, metF, purH, purC, folD, purD, adk and folK. The genes were under pBAD-promoter and the overexpression of the genes was induced using externally supplemented arabinose (0.1%).WT Escherichia coli BW25113 cells were transformed with blank vector plasmids (without inserts) as well as plasmids overexpressing genes viz. 6. The data was first filtered based on the criteria of having annotated, high confidence identities (annotation score > 50) and overlap (shared KEGG identifier) with the data generated in this study for CD15-3. Data was then averaged for each compound. Uniform Manifold Approximation and Projection (UMAP) was utilized to visualize the high-dimensional data in two dimensions, to inspect clustering behavior of the samples. A multi-class logistic regression model41 was trained to identify drug mechanism of action from metabolomics data. Five mechanisms of action were utilized from the original study: antifolates, cell wall synthesis, polymerase inhibition, translation inhibition, and oxidative stress. Mechanism labels were utilized as target values in a supervised learning approach. Zero time point data was excluded. UMAP and the LR algorithm were implemented using the scikit-learn Python package41. UMAP was implemented with 2 components, fixed random state of 42, and 14 neighbors, based on the number of samples for high and low concentration for a single drug. The LR algorithm was implemented with default hyperparameters, and performance was evaluated with leave-one-out cross-validation, utilizing different train-test splits and averaging performance of each run.Metabolomics data for ten antibiotics was taken from a published study10. We utilized MATLAB, the COBRA toolbox version 3.042 and the latest metabolic network reconstruction of E. coli, termed iML15159. Default options were used for the optimizeCbModel function.To analyze patterns in growth rescue data from metabolic supplementation, we utilized a flux balance analysis workflowTo enforce folate limitation, growth was first optimized under glucose growth at an arbitrary but realistic uptake rate of 10 mmol/gdW/hr. Then, folate dependent reactions nearby to each metabolite supplement were identified. Reactions that consumed folate were limited to 90% of their optimal flux, and it was confirmed that growth rate was correspondingly limited. Then, metabolites were supplemented in silico at a rate of 0.1 mmol/gDW/hr. The calculated growth rate following supplementation was compared to the calculated growth rate before supplementation to determine the growth benefit of the metabolite under folate limitation.Gene expression data was used to close flux through reactions that were not expressed under wild type conditions in a targeted manner around metabolites that were supplemented. Closing flux through all reactions that were not measured to not be expressed in the proteomics data was not possible due to lack of growth in the resulting simulations.The resulting scores were combined for different pathway specific inhibitions for each supplement, as detailed in the code available in the Supplementary Data For non-folate pathway comparisons, reactions were constrained to 0 flux, and the benefit of each metabolite supplement for each reaction inhibition was calculated.E. coli GEM-PRO for all available metabolic enzymes. These properties were calculated previously as part of the GEM-PRO development. These structural data was standard normalized and then correlated with Pearson correlation to examine global structural similarity.For the global structural analysis, general protein structural properties were taken from the 43 was used to pull different binding clefts from each of the selected proteins and the 5 largest of each protein binding clefts were kept. The similarity (Tanimoto coefficient) between the clefts of the folA protein structure (PDB ID: 1DRA) and each of the selected clefts of the proteins using isomif which was then plotted to view which binding clefts were most similar. FATCAT13 was used to compare the overall structural similarity between all of the selected proteins and the results were plotted using a clustermap to look for similar proteins. Visualizations of the proteins were created using nglview.In the targeted folate pathway structural analysis, proteins were selected that were nearby in metabolic maps in the pathway of interest, as well as some unrelated metabolic proteins as a control comparison. Isomif Statistical analyses of the data and their representation was carried out using Origin pro 8.1 package. Metabolomics data were processed using R based MS converting operation and IDEOM tool. For calibrating M/Z (mass to charge) values and retention times of the standard metabolites, XCalibur package was used. For the quantitative depictions of the metabolomics data statistically validated outputs were plotted using Python libraries of matplotlib and seaborn.44. We then selected the rotatable bonds in the ligand and prepared pdbqt file of the ligand molecule using Autdock Tool. Then we modified the candidate protein by adding polar hydrogen and calculated Kollman charges. We performed this docking analysis with exhaustiveness of 28, number modes of 10 and energy range of 4 by considering the candidate protein as rigid, whereas the ligand was flexible in nature.Using the AutoDock Vina 1.5.6 program, we performed the molecular docking study. We used the CD15-3 molecule in sdf format and converted it into pdb format using OpenBabel programE. coli (BL21 DE3 strain) after transforming the cells with pET 28 A plasmid which was obtained from Genescript. We induced the over-expression of HPPK with 1 M isopropyl-1-thio-β-D-galactopyranoside (IPTG). Followed by IPTG induction, the cells were grown for 4.5 hours. The cells were pelleted down by carrying out centrifugation at 6000 rpm for 20 minutes at 4 °C. This was followed by re-suspension of the pellet in pre-chilled lysis buffer . After thorough re-suspension in lysis buffer, the cells were sonicated . Unbroken cells and debris were removed by another round of centrifugation at 10,000 rpm for 10 min. The soluble fraction obtained thereafter was carefully removed and allowed to bind to Ni–NTA agarose resin. The Ni–NTA column was washed using 50 ml wash buffer  followed by elution with 20 mM Tris–HCl, 500 mM NaCl and 500 mM imidazole, pH 8.0. The eluted fractions were pooled according to their tentative protein content as per their absorbance at 280 nm. The post-elution fractions were subjected to dialysis in 20 mM Na-phosphate buffer pH 7.5. The activity assay performed with the purified protein showed that HPPK is catalytically active. We further overexpressed and purified three catalytically inactive mutants of HPPK viz. P43A, L45A and N55A. Catalytic sites were selected based on previously published works on pterin binding26. The pET28A plasmids overexpressing the inactive HPPK mutants were obtained from Genescript and overexpression was carried out using the above-mentioned protocol.Recombinant HPPK was over-expressed in TM assay kit. In this assay, firefly luciferase utilizes the ATP remaining after HPPK catalysis to produce a luminescence signal that is directly proportional to ATP concentration; from this, the HPPK activity can be derived. The enzyme activity calculation and selection of optimum concentration was done following previously published methods15. For kinetic measurements, an optimized HPPK concentration of 7 ng/50 µL assay volume was determined, which allowed for monitoring the first 10% of reactions turnover in a reasonable assay time period (20 min).The HPPK activity assay was conducted with the help of KinaseGlo2, pH 8.5, 0.01% (w/v) BSA, 0.01% (v/v) Tween 20 and 10 mM β-mercaptoethanol). Typically, 5 µl of test compound (dissolved in 50% DMSO) and 20 µl of enzyme were added to each well followed by 25 µl of assay buffer giving 18 µM pterin and 2 µM ATP in a total reaction volume 50 µl. After a 20-minute incubation at room temperature, the enzymatic reaction was stopped with 50 µl of KinaseGlo™ reagent. Luminescence was recorded after a further 10 min using the plate reader (Tecan Infinite M200 Pro). The inhibition constant Ki was derived using the relation Measurements were performed in 96-well plates using assay buffer  in absence and presence of CD15-3 at 42 °C for incubation and 300 rpm constant shaking. A drop in DHFR activity has been associated with cellular filamentation and a similar phenotype is observed under TMP treatment23. Since CD15-3 targets intracellular DHFR and soluble fraction of cellular DHFR is lower at 42 degrees C we chose this temperature for our imaging studies45.WT Escherichia coli BW25113 cells were grown in M9 media supplemented with 0.8gLfolK under conditions of absence and presence of CD15-3 compound. Intellesis Module was used for analyzing DIC images. On average, around 500 cells were analyzed for computing cell length. E. coli cell lengths in our imaging studies were not normally distributed. Nonparametric Mann-Whitney test was therefore used to determine if the cell length distributions were significantly different upon CD15-3 treatment.Aliquots were taken from the growing culture, and they were drop casted on agar bed/blocks. These blocks were taken further processed for differential inference contrast (DIC) imaging using Zeis Discovery imaging workstation. Multiple fields were observed and scanned for a single condition type and a minimum of three replicates were used for imaging studies. Similar methods for imaging were used for WT Escherichia coli BW25113 cell types overexpressing Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Phosphorus is an essential nutrient for plants. It is usually available in the form of inorganic phosphate in the plant environment. Yet, most environments contain extremely low amounts of phosphate, causing a major nutritional deficiency for plant life. During their evolution, plants have acquired a number of adaptations that help them to survive chronic phosphate deficit. Many of these adaptations are documented in the scientific literature and show changes in gene expression and modifications in the levels of enzymes and metabolites involved in plant carbon and respiratory metabolism. This research uses potato cells cultivated in vitro to measure major metabolic carbon fluxes  in response to two phosphate regimes . Our results show a remarkable stability of several metabolic fluxes in cells regardless of their phosphate regime. This is the case for the rate at which the carbon source is taken up from the medium. Two important metabolic fluxes used to fuel cell respiration are also not affected by phosphate. In all the experiments, cell age is the main factor affecting carbon metabolic fluxes. These findings lead us to conclude that potato cells maintain stable carbon fluxes during phosphate deficiency.Solanum tuberosum) cell cultures growing in control or limiting phosphate conditions. Sugar uptake was quantified using [U-14C]sucrose as precursor. Carbohydrate degradation through glycolysis and respiratory pathways was estimated using the catabolism of [U-14C]sucrose to 14CO2. Anaplerotic carbon flux was assessed by labeling with NaH14CO3. The data showed that these metabolic fluxes displayed distinct patterns over culture time. However, phosphate depletion had relatively little impact on the various fluxes. Sucrose uptake was higher during the first six days of culture, followed by a decline, which was steeper in Pi-sufficient cells. Anaplerotic pathway flux was more important at day three and decreased thereafter. In contrast, the flux between sucrose and CO2 was at a maximum in the mid-log phase of the culture, with a peak at Day 6. Metabolization of [U-14C]sucrose into neutral, basic and acidic fractions was also unaffected by phosphate nutrition. Hence, the well-documented changes in central metabolism enzymes activities in response to Pi deficiency do not drastically modify metabolic fluxes, but rather result in the maintenance of the carbon fluxes that support respiration.Nutritional phosphate deficiency is a major limitation to plant growth. Here, we monitored fluxes in pathways supporting respiratory metabolism in potato ( Phosphorus (P) is an essential macronutrient in plants. It is a crucial component of membrane phospholipids needed for cell and organelle integrity . As a coenolpyruvate carboxylase (PEPC) and/or phosphoenolpyruvate phosphatase activities under Pi-deficient conditions has been demonstrated in various systems glucose coupled to metabolic modeling) reported a small increase in anaplerotic C flux sucrose (Suc) and NaH14CO3 in order to test the hypothesis that changes in central metabolic fluxes occur in response to modifications in Pi nutrition. These two radioactive compounds can be used as tracers to quantify the metabolic fluxes that use them as substrates or precursors. Our data show that C fluxes in potato cell cultures are relatively stable under Pi deficiency. Our results suggest that the magnitude of carbon fluxes in potato cell cultures is more influenced by culture age than nutritional Pi status.Potato . Chromatographic resins (Dowex AG 50W-X8 (H+) and Dowex AG 1-X8 (formate)) were obtained from Bio-Rad Laboratories . Potato heterotrophic cell cultures established in our laboratory Suc, pelleted cells were resuspended in an MS medium adjusted to the concentrations of Suc and Pi present before centrifugation for each of the time points under study. For this, medium concentrations of Suc were determined by HPLC [14C]Suc tracer at a specific radioactivity of 2.64 × 103 Bq µmol−1. The center well of the flask received 2 mL of 1M KOH and the flask was immediately closed with a sleeve rubber stopper. Incubation was performed on a gyratory shaker at 120 rpm for 6 h. At the end of the experiment, the cell suspension was pipetted into the barrel of a disposable column  inserted in a 50 mL Falcon tube. This allowed quick separation of the cells from the medium by centrifugation (4500× g for 5 min). The cells, collected on the filter at the base of the column, were recovered by a second centrifugation with the column oriented top-down in another tube. The biological material was then subjected to ethanol extraction followed by chromatographic separation on two 3 mL columns (Dowex AG 50W-X8 (H+) and Dowex AG 1-X8 (formate)) arranged in tandem [14C]Suc metabolized to 14CO2 was determined from the radioactivity trapped in KOH Suc was used to calculate the molar fluxes of carbon d in KOH . Uptakenf carbon .14CO3 was adapted from a previous procedure Suc, 12-day-old cells were also used. Statistical analyses were performed using the R programming language (version 4.2.2) employing the doebioresearch package (version 0.1.0) Suc fed to cells was assessed in cultures growing under Pi-sufficient and Pi-deficient conditions  and cationic (basic) fractions is indicative of the labeling of pools of free sugars, organic acids and amino acids, respectively. For all treatments and time points investigated, radioactivity recovered in these different fractions was expressed as a percentage of the total recovered metabolized label (14C]Suc oxidized to CO2 (p < 0.05). At day 0, Suc oxidation to CO2 accounted for a little less than 40% of metabolized Suc. Over the next days, this proportion slowly increased to reach values above 50% at Day 9. The evolution of labeling of the neutral fraction (p < 0.05). The proportion of radioactivity present in this fraction was around 30% at Day 0. It increased slightly at Day 3, and decreased steadily thereafter. Labeling of the anionic compounds initially accounted for about 15% of the total radioactivity metabolized in the cells (p < 0.05). Finally, in both +Pi and −Pi cells, labeling of the cationic fraction was remarkably similar and stable throughout the culture cycle at around 15% of the total metabolized label (p < 0.05).Once the radioactive tracer was taken up by cells, its metabolic use could be quantified by accounting for radioactivity present in different fractions at the end of the experiment. Radioactivity present in COed label . In thesfraction b was alshe cells c. This p2 using the specific radioactivity of fed [U-14C]Suc .The calculated C flux values followed a very similar pattern in +Pi and −Pi cells between Days 0 and 6. Initially, the C flux between Suc and CO14CO3. At the end of the incubation, metabolism was stopped by the injection of an acidic ethanol solution into the culture medium. This killed the cells and volatilized any unincorporated labels. We determined acid-stable soluble radioactivity, which corresponds to the incorporation of the label in ethanol-soluble metabolites by anaplerotic pathways. The results of this experiment . However, beyond this time point, anaplerotic C assimilation decreased steadily under both Pi regimes, and no significant difference was detected between the two cultures.Cells growing in Pi-sufficient and Pi-deficient conditions were incubated with NaHperiment  showed, p < 0.05 (14C]Suc metabolized to cationic compounds. The effect of cell culture age was particularly strong in the cases of C flux between Suc and CO2 and anaplerotic CO2 assimilation (p < 0.001). Interestingly, no significant correlation could be observed between the Pi regime and the various metabolic parameters. The only significant interaction between phosphate regime and cell culture age was observed for anaplerotic CO2 fixation  and fructose (Fru) in the cell periplasm . Transpoure time . For thecultures . These rcultures . It is tcultures . This co14CO2, as well as 14C-labeled anionic and cationic compounds  abundance [It is interesting to compare this in vivo flux measurement with extractible PEPC activity values obtained in the same material . As mentbundance  and by ebundance . In vivobundance . Pi defibundance ,24,46,47The present research provided some insights in a previously understudied area of plant adaptation to nutritional Pi limitation. Here, we showed that in Pi-limited potato cell cultures, there is a general stability in important C fluxes such as sugar uptake from the medium, carbohydrate catabolism to fuel respiration and anaplerotic C fixation. Thus, despite the fact that large changes in metabolites and enzyme levels occur in this system , cells a


Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations. Secondary metabolites (a.k.a. idiolites) are small, structurally diverse, chemical compounds that are generated by plants and microbes. They are produced by secondary metabolic processes during the stationary phase (idiophase) of an organism’s lifecycle that follows its growth phase (trophophase). Although these compounds are not essential for microbial growth, they confer a selective advantage to the organisms in their environments and serve many diverse and important functions. These include roles in bacterial survival and ecological interactions. For example, it has been shown that certain diatoms excrete secondary metabolites to promote the growth of beneficial bacteria and encourage direct physical interaction with them, while dissuading attachment by opportunist organisms and hindering their growth .Secondary metabolites are also economically and medicinally important. Many herbicides, fungicides, bio-insecticides, antibiotics, immunosuppressants, antitumor drugs, and other high-value bioactive compounds are byproducts of microbial secondary metabolism. For example, certain alkaloid secondary metabolites  have anticancer functions and can be produced by a number of different fungi, but the yield significantly varies between the different organisms .Actinomycetaceae), sometimes—due to a desire for an increased growth rate or a need to couple the production of the compounds to the synthesis of other biochemicals—microbial chassis organisms such as E. coli are engineered to produce these compounds. The latter process could be riskier than using naturally producing organisms because at times the secondary metabolite or a byproduct of its production can end up being toxic to the chassis organism.Secondary metabolites are usually difficult to synthesize chemically. But, at times, this method is preferred over harvesting from biological sources. This is because chemical synthesis avoids problems like variable product quality that are associated with some natural systems . HoweverOften, the natural rates of production of secondary metabolites are too low to be industrially profitable. Thus, metabolic engineers spend a lot of time optimizing the rate of production of these compounds. One way of ensuring that a compound of interest is produced optimally is to accelerate the activity of primary metabolic pathways that produce the precursors and reducing the equivalents that are needed for its production . SimultaOur ability to manipulate complex biosystems is continually improving and the field of synthetic biology has made significant advances during the last decade. We are using heterologous gene expression to add functions to chassis organisms for the production of secondary metabolites . Most of the BGCs have characteristic catalytic domains that can be used to identify new gene clusters. This, along with the clustering characteristic of genes, has been used to develop computational tools , where m is the number of metabolites and n is the number of different reactions. FBA operates on assumptions of mass balance and metabolic steady state. Based on these assumptions, the following set of linear equations govern the system’s behavior:iX is the concentration of metabolite i, ijS is the stoichiometry of metabolite i in reaction j, and jν is the flux of reaction j. Other constraints that are imposed on the model based on experimental measurements limit the amount of nutrients that a system can import and byproducts that are exported. Also, if information regarding the average concentration of an enzyme and its catalytic turnover rate are available, flux through a reaction can also be constrained. Thus, in properly bound FBA simulations all reactions have a lower and upper boundary:ib is the export/import flux of species i; α, β are the lower and upper bound for internal fluxes; and χ and φ are the lower and upper limits for transport fluxes. For FBA simulations it is assumed that cells are at steady state. Balanced growth is assumed and a biomass reaction with fixed stoichiometric values is added to the model. This reaction quantifies the amounts of various metabolites that are needed to produce 1 g of biomass.Flux balance analysis (FBA) is a widely used mathematical method for using GEMs to study metabolism in biosystems. FBA finds optimal metabolic steady-state activity fluxes that satisfy constraints imposed by the metabolic network structure, mass balance, and the availability of nutrients see . FBA useOnce the GEM is appropriately constrained, FBA uses linear programming to solve for a feasible steady-state flux vector that optimizes an objective function. The most common objective function (cellular task to be optimized) is the production of biomass, i.e., cellular growth. However, other biological tasks can also be used as objective functions. There have been studies that have examined when and how such alternate objective functions might best be used ,42. AlsoFBA has been used to examine numerous biological topics that range from the fundamental nature of biological systems e.g., ,50,51) t,51 t50,5Streptomyces )OptStrain  is a majBilevel optimization methods have been some of the most popular tools for strain engineering during the past two decades and are still being used for designing microbial strains that optimize the production of valuable products e.g., ,92). Fig. Fig92])2,0) when the first objective operates at MultiMetEval  is an MOp1, value, the optimum value of the second objective  is calculated.For each ϖTwo-dimensional Pareto fronts provide a wealth of information about the nature of interactions between two processes (objectives) in a system. As can be seen from MultiMetEval also provides a platform to comparatively analyze metabolic differences between multiple GEMs. This can be used to identify organisms that are naturally adapted to overproducing secondary metabolites. The tool was used to examine the production capability of a large group of actinomycetes for different classes of secondary metabolite . The stuWhile FBA studies typically optimize growth as the cell’s primary objective , it has Rhodopseudomonas palustris [2) [2 production, carbon fixation, metabolism of aromatic compounds), we developed an MO analysis tool that we call multi-objective flux analysis (MOFA). Similar to Nagrath et al. [n-dimensional (n = number of objectives) Pareto front comprised of a set of evenly distributed Pareto-optimal points, regardless of differences in the magnitudes of the examined objectives.MO methods have been developed to study metabolic tradeoffs in systems that produce a variety of different high-value commodities such as biofuels  ,101,102.h et al. , MOFA ush et al.  method fR. palustris’ ability to produce H2 as a biofuel. As with the production of secondary metabolites, we found that the production of H2 under all conditions drastically reduces the organism’s growth rate [We developed MOFA because we wanted to examine the tradeoffs between more than a handful of objectives. Our high-dimensional (7 and 8 objectives) analyses provided us with novel insights into wth rate .We have since developed a Matlab version of MOFA that can be used with the COBRA toolbox . The COBGiven that secondary metabolites have a major role in how microbes interact with their surroundings, engineering microbial communities can be one way of inducing the production of secondary metabolites ,108,109.One MO-based tool developed for examining interactions in microbial communities is OptCom . OptCom OptCom has been used to examine different types of interactions  in microbial communities. This can be achieved by adjusting the inter-species flux constraints, i.e., varying the community-level optimization problem. A subsequent update to OptCom named d-OptCom  allowed CASINO  is anothGenome-scale models of metabolism and COBRA methods have become indispensable tools for system-level analyses and metabolic engineering. COBRA methods have been used widely to study the metabolism of microbes. They have provided novel insights that have been used to engineer new microbial strains that have high production rates for commercially, industrially, and medicinally important bioactive compounds. For example, FBA simulation methods have been used with high-performance computing and pathway-search algorithms to predict putative heterologous biosynthesis pathways for over 6000 compounds in 70 different microbes .While bilevel optimization tools and MO-based methods have been some of the most often-used tools for synthetic biology and metabolic engineering, other types of constraint-based modeling informed with different omics data could also contribute to these efforts. For example, it has been shown that metabolic flux changes associated with the transition of cellular metabolism from growth to idiophase closely correlate with gene expression dynamics. This provides a possible route for examining secondary metabolism in microbes by constraining GEMs with gene-expression data. Many tools have been developed for this purpose e.g., ,121,122),122121,1


Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the  The use of automated tools to reconstruct lipid metabolic pathways is not warranted in plants. Here, the authors construct Plant Lipid Module for Arabidopsis rosette using constraint-based modeling, demonstrate its integration in other plant metabolic models, and use it to dissect the genetic architecture of lipid metabolism. The structural diversity of plant lipids is generated by an underlying metabolic network whose fine-tuned regulation adjusts the lipidome to internal and exogenous cues4, relevant in agricultural and biotechnological applications6. A considerable part of our knowledge concerning the regulation of plant lipid metabolism has been obtained through reductionist approaches8. In contrast, approaches from the constraint-based modeling framework9 can be used with genome-scale models (GEMs), that include mechanistic descriptions of lipid metabolism, to: (i) characterize how lipid metabolism is interconnected with pathways of primary metabolism and (ii) design manipulation strategies for lipid-related traits11.Lipids comprise a class of structurally diverse molecules that facilitate compartmentalization of biochemical activities and are involved in critical cellular functions14 and microalgae17, however, that include a comprehensive description of lipid metabolism, with few notable exceptions21, most existing plants metabolic models do not consider reactions compartmentalization, pathway redundancy, and enzyme promiscuity in lipid metabolism. This results in a largely lumped representation of lipid metabolic pathways5 in existing plant models, reflecting the limited degree of annotation of plant genomes. Hence, despite numerous data sources25 and pathways databases28 the use of automated tools to reconstruct lipid metabolic pathways is not warranted even for the model plant Arabidopsis thaliana (Arabidopsis). This in turn limits the applications of constraint-based modeling to gain better understanding of plant lipid metabolism.In comparison to the GEMs of yeastTo capitalize from the constraint-based modeling framework, here we provide a reconstruction of a Plant Lipid Module (PLM), including a mechanistic description of lipid metabolism in the Arabidopsis rosette. The PLM can be readily integrated in a semi-automated fashion into a metabolic model of any size Fig. . We demoThe reconstructed PLM comprises 5956 reactions and 3108 metabolites in 16 compartments Fig. . It incl31 that: (i) have COBRA model structure32; (ii) include metabolites with molecular neutral and/or charged formulas, accompanied by the respective charges and identifiers (e.g. KEGG and/or ChEBI); and (iii) can be imported and exported with standard software packages/toolboxes (e.g. via COBRA Toolbox)33. The integration of the PLM was completed efficiently even for the largest model  compared to the template model itself activation of reactions for different pathways of lipid metabolism. For instance, we predicted: (i) the activity of chloroplast FAs synthesis and FAs elongation pathways in dark  higher activity displayed by reactions involved in chloroplast membrane lipid degradation mostly through hydrolysis  reduced activity of the de novo synthesis pathways of several precursors of neutral lipids (e.g. triacylglycerol—TG) such as lysophosphatidic acid (LPA), phosphatidic acid (PA) and diacylglycerol (DG) ark Fig. , but wit 47 Fig. ; (ii) entes Fig. , was acttes Fig. ; besidestes Fig.  in agreesis Fig. , consistDG) Fig. , in para 50 Fig. , and the 50 Fig. , in lineWe also extended the lethal gene analysis to identify if the results were accession- and condition-specific. We found that the knockout of genes from the single lethal set resulted in a consistent prediction of non-viable phenotypes for all accessions  . The remaining enzymes (up to 25%), corresponded to the so-called outliers, in which there was no agreement in the direction of change  rules. The latter are represented as logical rules that describe the relationships between genes, their protein products and the reactions they catalyzege Table  and 4. Tmentclass2pt{minim56 to obtain a list of the PTMs reported to date for the enzymes . To test the hypotheses we employed the FAT-PTM databasemes Fig.  which wemes Fig.  and outlmes Fig.  enzymes.mes Fig. . TherefoWRKY18 (AT4G31800) and TREE1 (AT4G35610), were identified as candidate genes in fluxes predicted for control (light) and extended darkness conditions    are related to various biological processes, for instance, embryogenesis and organogenesis, hormone signaling, DNA transcription, senescence, response to abiotic factors (light) and development phase transition  are related to transport of metabolites  map the orthologs that are included in the GPR rules of the PLM, making the PLM species-specific; and (iii) integrate the PLM in a condition-specific manner through the use of lipid profile measurements. These advances are expected to also improve knowledge-generation and characterization of gene functions following the proof of principle presented in this study.Through the creation of the PLM, we made a comprehensive compilation of the biochemical knowledge of lipid metabolism in vegetative tissues (for which there is sufficient evidence to date) for the model plant Arabidopsis. The functionality of the PLM was demonstrated through several case studies. For instance, we simulated the influence of environmental conditions (i.e. extended darkness) on the modulation of lipid metabolism and related pathways, where overall, we found a good agreement with the available experimental evidence. We also carried out gene essentiality analysis, where the integration of the PLM allowed the identification of a much larger number of lethal genes compared to the original model. Despite these advances, our results pointed at a series of discrepancies, such as the identity of the enzymatic steps that catalyze specific lipid remodeling events taking place under extended darkness. These discrepancies open possibilities to use the PLM to guide future efforts in filling knowledge gaps about lipid metabolism in vegetative plant tissues. Finally, the identification of candidate genes modulating leaf lipid metabolism via flux genome-wide association yielded a valuable resource of candidate genes for experimental validation. Of note, our findings confirmed up to 40% of regulators of lipid metabolic pathways in vegetative tissues and pave the way for model-driven discoveries in plant lipid metabolism. Together, these advances due to the refined representation of lipid metabolism in vegetative plant tissues in the PLM can be used to speed-up the engineering and selection of lines with improved lipid traits. The latter can further benefit from implementing the PLM for other species and conditions. This can be achieved by developing a pipeline to: (i) render the PLM organ-specific (e.g. seeds), by integrating organ-specific data  using well-established approaches for generating context-specific modelsArabidopsis thaliana, as well as different database resources65. The PLM includes all metabolic steps necessary for the synthesis and degradation of 26 classes of lipids . Further details of the functionalities of the software can be found in the Supplementary Fig. To test the functionality of the PLM, it was initially integrated into five different published models, the AraCore model67 and dark68 conditions, together with the total protein data for control69 and dark-treated70A. thaliana Col-0 plants. Since the ultimate amino acids fraction in the biomass is the combination of free and protein-bound amino acids, the absolute concentration of free amino acids for control plants was obtained from69. For dark-treated plants, the relative abundances of soluble metabolites were measured in a population of 284 Arabidopsis natural accessions of the HapMap panel42, then the absolute concentration of GC-MS data were obtained by multiplying the respective ratios calculated from the relative abundance data and the absolute values from control plants69; (ii) Starch and sugars: quantitative data for starch accumulation in dark-treated plants was obtained from70, as hexose equivalents. The data for organic acids, sugars and several intermediates of primary and secondary metabolism was obtained by GC-MS measurements and handled as explained above for free amino acids. In brief, the absolute concentration levels of Arabidopsis (Col-0) obtained from69 were adjusted by multiplying by the corresponding relative abundance ratios (dark- vs. light-grown plants); (iii) Cell wall and nucleic acids: to account for this biomass components, we followed the procedure described in Supplementary Method 71. Data for sphingolipid composition27, wax precursors72, cutin monomer composition73 and total chlorophyll content74, were obtained from the respective literature sources. In addition, a targeted lipid profiling analysis by LC–MS was performed for the previously described natural accessions. Analysis and processing of raw MS data were done with REFINER MS® 10.0 and XCalibur (Version 3.0) software. The absolute values for lipids were calculated as explained above for free amino acids using the ratios among dark- vs. light-grown plants, and the absolute content retrieved for control plants (see Supplementary Method The biomass components were incorporated via representative metabolites or precursors: (i) Proteins: the fractional amount of the protein-bound amino acids was calculated using proteomics data under controlFor the generation of biomass reactions, which are either condition- or accession-specific, we created scripts which retrieve the experimental data for the desired Arabidopsis accession and growth condition and calculates the stoichiometric coefficients for each biomass component  the stoichiometric coefficients for chain- and backbone-SLIME pseudo reactions were assigned according to14 by implementing the custom script mentioned above. Next, we constrained the model with the determined optimal relative growth and computed a reference flux distribution  when biomass flux was abolished upon its removal from the model75. The synthetic lethal and non-lethal gene sets obtained were further examined to calculate the accuracy of the results by using information on Arabidopsis mutants available34  values of each flux and the corresponding transcripts, measured under comparable environmental conditions. The calculation of the flux distributions via pFBA, and the construction of condition-specific biomass reactions were performed as described in Supplementary Method 54). Two sets of transcriptomics data for plants under 21h-XD were obtained from previous studies54, and processed first by calculating the mean of the biological replicates, and then assigning the abundance of the transcripts for each enzyme included in the model according to the GPR rules, considering the presence of isoenzymes and protein complexes77, followed by the calculation of log2 FC  panel were tested, after filtering with minor allele frequency >0.0579. The GWAS linear mixed model considering both kinship and population structure was applied. The first three principal components were considered to represent population structure. The significant threshold was set to 1/n, where n is the number of SNPs. The significant SNPs within an interval of 20 kb were considered as one and all genes in the interval of the most significant SNPs were considered as the candidate genes.For this analysis, we made use of the flux distributions calculated for Arabidopsis accessions under extended darkness (see Condition-specific flux distributions for Arabidopsis accessions), where the fluxes were regarded as traits. For each flux, the R package rMVP (R version 4.2.1.) was used for genome-wide association studies80, that was used to select the candidate genes coding for TFs. Since the list of TF-encoding candidates was substantially large, we selected only those obtained for lipid-related reactions. For each TF-encoding candidate, the list of genes with potential binding sites was obtained80. The latter was mapped to the GPR rules of the underlying reaction flux. We further examined the TF-encoding candidates with DNA-binding domains for the genes of the corresponding GPR rules, by reviewing their expression profiles. For this, we searched on TRAVA database59 the expression levels of genes in whole leaves. The read counts were normalized by applying the median-of-ratio method, followed by dividing by the maximum value of expression level, so all values vary from 0 to 159. The candidates were classified into four categories according to their level of expression.: (0) not expressed, (<0.3) low, (0.3 ≤ ≤ 0.6) intermediate, and (>0.6) high.The lists of candidates obtained were validated using information from GPR rules, and from transcription factors (TFs) and gene expression databases. Hence, from the GPR rules, the list of genes associated to each reaction flux was retrieved. Each gene was then mapped to the list of candidate genes obtained for the corresponding trait. The mapping process of the candidate genes was later extended to the GPR rules associated to the reactions immediately up- and down-stream of each of the traits for which a list of candidates was available. The next step consisted in building a list with the TFs contained in the AthaMap database61. To this end, the candidates were first grouped according to the type of protein they encoded (i.e. enzyme- or TF-coding gene). For the case of the enzyme-coding candidates, the metabolites associated to each reaction flux were mapped to the lipid profiles of the respective mutant, and the fold-change (mutants/wild-type) was calculated for the lipid species for which data were available (see Supplementary Method We validated the list of candidate genes by using the lipid profiles measured for a collection of 364 Arabidopsis T-DNA insertion linesFurther information on research design is available in the Supplementary InformationPeer Review fileDescription 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 Data 17Supplementary Data 18Reporting SummarySource Data


In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of  Arabidopsis  are indispensable tools for the systematic characterization of metabolic behaviors. Metabolome and transcriptome analyses could globally elucidate overall metabolic changes and the underlying regulatory mechanisms. Therefore, combination of genome-scale metabolic networks and omics analysis is an excellent method for systematically elucidating the impact of in vitro cultivation on plant metabolism. In plants, omics data were successfully integrated with the Arabidopsis GSMN and rice GSMN to illustrate their metabolite acclimation profiles under different conditions , Kyoto Encyclopedia of Genes and Genomes (KEGG), and other literature sources. Our tobacco GEM, named iJTC6240, accounts for 6,240 genes, 2,887 reactions, and 2,905 metabolites localized across five intracellular compartments, including cytosol, mitochondrion, plastid, vacuole, and peroxisome  for tobacco. To our best knowledge, this is the first reported tobacco model. The metabolic network was based on information from Ntabacum_Tn90Cyc P2), which is essential for maintaining vacuolar morphology and function  that are specialized metabolites produced by Solanaceae species.The model properties of iJTC6240 were compared with those of two plant GEMs, iHY3410  and Arabpsis GEM . Figurefunction . Consistfunction . A greatAfter the GEM reconstruction, we performed several tests to validate its predictive capabilities. Firstly, the model was evaluated by simulating known metabolic functions, including absorption of PSI and PSII photons during photosynthesis, as well as the biosynthesis and degradation of secondary metabolites, such as alkaloids and terpenoids. Secondly, the ability of iJTC6240 to predict tobacco growth rates was tested. We utilized experimental photosynthetic capacity  to constin-vitro cultivations on tobacco, we selected the most common conditions to cultivate tobacco plants in vitro and in soil (as described in the methods section). The seeds germinated approximately 1 week after sowing (WAS), and the germinated seedlings were transplanted into soil or in vitro. The in-vitro plants grew much slower than that in soil, but the true leaf number of both tobacco plants was almost the same during all stages of development . To further assess the differences in the photosynthetic efficiency, the effective quantum yield of photochemical energy conversion in PS II (Y(II)) and relative electron transport rate (ETR) were calculated for different light intensities (PAR). in-vitro tobacco plants than that in in-soi ones, and gradually decreased to nearly 0 µmol at 610 µmol photons m-2s-1 following light intensity increased. The ETR curve under in-vitro condition was also lower than that under soil-grown condition (in-vitro at 230 µmol photons m-2s-1 (PAR), once reached this level, ETR decreased. Collectively, these results indicated the suppressed photosynthetic capacity under in-vitro condition.We measured the leaf chlorophyll fluorescence parameters to explore the photosynthetic system of in-vitro cultivation inhibited the growth of tobacco compared to soil-grown cultivation. To study the impact of in-vitro cultivation on tobacco metabolism and globally detect the metabolic variations between in-vitro and soil-grown tobacco plants, we performed metabolome and transcriptome analyses for 10-WAS in-vitro and soil-grown tobacco plants.The above results showed that in-vitro tobacco and soil-grown tobacco plants, each with three biological replicates. After filtering out low-quality reads from the raw data, a total of 37.77 GB clean data was generated, and 5.64−6.74 GB for each sample was obtained. The GC content was ranging from 43.20% to 43.50%, and the percentages of Q30 bases were over 92.75%. The mapped efficiency of different samples with reference genome ranged from 94.64% to 95.12%  were identified from transcriptomic data, and their GO functional annotation is shown in We generated transcriptome sequencing data for o 95.12% Table S1in-vitro and soil-grown tobacco plants, a widely targeted metabolomics analysis was conducted in triplicates. From the overlapping map of total ion current (TIC) of the quality control (QC) sample, it could be observed that there was a high overlap in the curve of the total ion flow, and the retention time and peak intensity remained consistent. This suggested that the signal stability for the same sample was good when the mass spectrometry was detected at different times  and TCA cycle, as well as its closely related pathways, such as the metabolism of starch, sucrose, fructose, mannose, pentose, galactose and pyruvate were all downregulated. At the same time, many downregulated plant secondary metabolic pathways were enriched under in-vitro condition, including the phosphatidylinositol signaling system, flavonoid biosynthesis, and phenylpropanoid biosynthesis. Collectively, enrichment analysis of DEGs suggested that in in-vitro tobacco plants amino acid metabolism was up regulated, while lipid metabolism was attenuated, and the growth inhibition was likely due to the lower activity of primary carbon metabolism and related pathways. In addition, the secondary metabolism of in-vitro tobacco plants was also weakened compared to soil-grown ones.To further identify cultivation-condition-specific metabolic pathways, KEGG pathway enrichment analysis of DEGs was performed Figure 3DAMs were utilized for the enrichment analysis based on the metabolic classification. The upregulated amino acids and derivatives were enriched among the DAMs with the greatest enrichment degree , Asn (1.92-fold), Ser (1.21-fold), and Val (1.19-fold), but not Pro (0.82-fold). Among these, Ser, Val and Pro were also detected in the metabolomic data, and their metabolic peak areas were consistent with the measured absolute concentrations. Notably, the concentration of Gln, which was not detected in the metabolomic data, was much higher than that of the other amino acids and was primarily responsible for the significant upregulation of amino acids in in-vitro tobacco plants.To further quantitatively compare the common amino acids in plants from different culture conditions, automatic amino acid analyzer was utilized to determine the absolute concentrations of free common amino acids. The overall level of free amino acids under o plants Figure 4in-vitro condition, the pattern was consistent with the results of 10-WAS plants. These results indicated the metabolic variations we detected were mainly due to the different cultivation systems.In order to further exclude the influence of developmental status (such as flowering transition) on plant metabolism, 7-WAS tobacco metabolic variations between two cultivations were also tested except for 10-WAS tobacco. We measured the concentrations of amino acid, one of the most characteristic metabolic variations, for 7-WAS tobacco  and the glutamine synthetase (GS)/glutamine-2-oxoglutarate aminotransferase  cycle were upregulated under in-vitro condition, and the metabolomic data also showed that the concentration of Gln was higher than that under soil-grown. After normalized by the biomass rate, more ATP was produced by proton pumps (1.5-fold), while less NADH/NADPH  was produced under in-vitro condition.To further explore how different culture conditions influence growth, we examined the internal flux distributions under soil-grown and id cycle Figure 5+-oxidoreductase, is the most extensively used pathway (Photosynthetic organisms use various electron flow pathways to generate redox power from light energy in the form of NADPH. The linear electron flow (LEF) pathway, where the electrons flow from PSII to PSI through Cytochrome b6f complex and ferredoxin NADP pathway . Additio pathway .in-vitro conditions, suggesting that the CEF pathway is possibly always operational. Interestingly, the predicted rate of photosynthesis under in-vitro condition was much more dependent on the CEF pathway than under soil-grown condition. In fact, the corresponding energy generated by the CEF pathway under in-vitro condition was 42.9-fold higher than in soil-grown plants. The CEF pathway under in-vitro condition, rather than LEF, was used increasingly to generate more energy, that may response to the lower reducing power demand in the Calvin cycle because of low CO2 uptake, as well as the need to scavenge excess reducing power, which is cytotoxic. Additionally, considerable ATP was needed in a multitude of metabolic pathways, including Gln biosynthesis by glutamine synthetase and sucrose import via transmembrane transporters. Generally speaking, the CEF pathway carried the higher fluxes, highlighting its flexibility and also suggesting that the CEF pathway may contribute to the adaptation of energy demand under in-vitro condition.The simulations revealed that both the LEF and CEF pathways carried noticeable fluxes under soil-grown and in-vitro plants compared to soil-grown plants, which was attibuted to the lower CO2 uptake. In both simulations, the Calvin cycle was driven by the supply of CO2 from cytosol to plastid and was subsequently withdrawn in the form of 3-phosphoglycerate (3PG), that can be transported to cytosol. The 3PG in the cytosol was utilized for energy production through cytosolic glycolysis and the TCA cycle, in agreement with previous reports  instead of energy generation, with a decreased flux (by 45%). 13DPG was converted into glyceraldehyde 3-phosphate (G3P) that participated in PPP and gluconeogenesis. Under in-vitro condition, the plastidic dihydroxyacetone phosphate (DHAP) produced from G3P by fructose-bisphosphate aldolase was transported to cytosol to synthesize Fru-1,6-bisP, while under soil-grown condition it was utilized to synthesize plastidic G6P, which was transported to cytosol to enter Embden-Meyerhof-Parnas (EMP) pathway.The Calvin cycle exhibited appreciable lower fluxes (by 43%) in  reports . In thisPhaseolus vulgaris), which indicated that the TCA cycle was almost completely inhibited under light  acetyl-CoA from pyruvate and (2) succinate from cytosol. The TCA cycle of in-vitro tobacco plants was impaired (flux reduction by 77%), with an increase of malate production from fumarate (2.9-fold) and from oxaloacetate (by 68%). The malate highly produced by TCA cycle in mitochondria was then transported to plastids, where it was converted into oxaloacetate by malate dehydrogenase (NAD) to provide reducing power for the GS/GOGAT cycle, while the oxaloacetate was utilized to form Asp.In the simulation of soil-grown tobacco, no flux was observed from citrate to succinate, while under the in-vitro condition (by 92%) with a 90% flux of oxidative NADH as the first step of the respiratory chain. Additionally, succinate dehydrogenase  has a dual function as a component of the TCA cycle and the electron transport chain (ETC). The flux through SDH was comparable with its TCA cycle flux (76.7%).The fluxes within mitochondrial respiration were relative mild reduced under in-vitro condition, noticeable levels of ATP were consumed for this active transmembrane transport process. The sucrose invertase (INV) was predicted to degrade Suc into fructose and glucose, while that is not an energetically efficient way to break down Suc when compared to the cleavage of Suc by sucrose synthase (SUS) (Since sucrose (Suc) was imported from the surrounding medium under se (SUS) . Subsequ4+ and/or NO3- as nitrogen sources. After uptake, nitrate is firstly reduced into nitrite by nitrate reductase (NR) in cytoplasm, after which nitrite is transported into plastid and reduced to ammonium by plastidic nitrite reductase (NiR). In this regard, the flux analysis results indicated that this route exhibited reduced fluxes (by 75%) under in-vitro condition, and the resulting lower consumption of reducing power may be related to its lower supply from photosynthetic light reactions, and a greater proportion were shifted toward energy generation.In our model, we considered that tobacco can take up NHin-vitro condition. The ammonium assimilation in plastid is accomplished by the GS/GOGAT cycle, where GS catalyzes glutamine (Gln) synthesis by incorporating a molecule of ammonium into glutamate (Glu) in an ATP-dependent manner, while GOGAT produces two molecules of Glu by transferring the amide from Gln to 2-oxoglutarate (2-OG). Plants possess two isoforms of GOGAT that differ in their electron donor for catalysis, NADH-GOGAT (EC 1.4.1.1) and ferredoxin (Fd)-GOGAT (EC 1.4.7.1), both of which are located in the plastids (in-vitro than in soil-grown plants (4.91-fold). Collectively, it can therefore be seen that the higher activity of the GS/GOGAT cycle under in-vitro condition results in increased Gln synthesis at the expense of Glu, since the flux through GS was higher than through GOGAT. These findings may offer a reasonable explanation for the higher Gln but lower Glu content under in-vitro condition compared to soil-grown plants.Interestingly, ammonium assimilation, by which inorganic N is converted into organic N, was higher under plastids . Our modin-vitro cultivation. MS medium was originally designed for tobacco callus and is widely used for many plant species nowadays. MS medium contains macronutrients that include high levels of nitrate and organic additives, such as sugar, and vitamins. Sugar provides metabolic energy and carbon skeletons for the biosynthesis of the organic compounds required for cell growth and also act as an osmotic component in the culture medium. in-vitro plants is positively correlated with the sucrose content in the culture medium. However, high sucrose concentration added to the culture medium can cause starch and sucrose accumulation in the leaves, which can reduce the photosynthetic activity ] in the culture medium. The increasing nitrogen supplement could lead to an increase in the total nitrogen content in tissues. in-vitro tobacco. Furthermore, in silico simulations showed in-vitro tobacco had a higher activity of the GS/GOGAT cycle fixing the substantial amounts of ammonium at the expense of Glu to produce Gln, which may be attributed to an abundance supplement of ammonium salts in the culture medium.The composition of the culture medium is crucial for activity . In our in silico investigation, that resulted in lower growth rate and less biomass under in-vitro tobacco. The Calvin cycle was subjected to energy and reducing power. Although ATP production was abundant, the generation of reducing power (NADH/NADPH) is markedly limited indicated by our systematical combined omics-based analysis. Here we gave three suggestions on how to improve the situation under in-vitro condition. Firstly, our model prediction showed a surprisingly high level of cyclic photophosphorylation, resulting in the reduction power being utilized for the generation of abundance energy. We proposed suppressing cyclic photophosphorylation and boosting non-cyclic photophosphorylation activity for this purpose. Additionally, our model survey also predicted increased flux from fumarate to malate but decreased subsequently in the TCA cycle. To improve the flux towards oxaloacetate and consequently the overall TCA cycle, we suggested that supplement the culture medium with appropriate pyruvate or organic acid  salts, which could potentially elevate the concentration of TCA metabolites intermediates and thereby drive the TCA cycle to generate reducing power. Finally, addition of glycerin into medium was supposed to help to alleviate the deficiency in reductive power.Compared to soil-grown tobacco, although there was additional carbon (sucrose) uptake, we noticed diminished carbon fixation in the Calvin cycle in our in-vitro tobacco than soil-grown tobacco, we proposed: Firstly, since we provided sufficient detail in iJTC6240 to define all individual electron transport reactions including photosystem II (PSII), photosystem I (PSI), and the cyclic/non-cyclic electron transfer, both photons utilized in PSII and PSI exhibited lower level of flux  under in-vitro compared to soil-grown tobacco in our observations. We speculated that maybe an appropriately elevation of effective photons utilization that not inducing photoinhibition would confer improvements. Secondly, light quality serves as a pivotal factor affects various morphological processes in plants, it was reported that in blue light, photosynthesis was up-regulated (in-vitro.To improve the suppressed photosynthesis in egulated . In thatNicotiana tabacum cv. Petit Havana) seeds were sterilized with 75% ethanol for 1 min, and then sterilized with 10% sodium hypochlorite for 20 min, rinsed with sterile water three times, and finally germinated on Murashige and Skoog (MS) agar plates containing 3% (w/v) sucrose in a climate chamber at temperature of 25°C with a light/dark cycle of 16 h/8 h and photon fluence of 186 μmol m −2 s −1. The germinated seedling was transplanted into pots containing soil/vermiculite mixture (1:1) or into 500 mL glass culture vessels (covered with gas-permeable caps) containing solidified MS medium (100 mL per vessel) with 3% sucrose. The fresh weight of the tobacco aboveground part was measured using an electronic balance. The dry weight was recorded after drying the samples in an oven at 65°C to constant weight.Tobacco  and the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system , respectively. RNA concentration was measured using the Qubit® RNA Assay Kit in the Qubit®2.0 Flurometer .Genomic DNA was isolated using a Super Plant Genomic DNA Kit . Total RNA was extracted from 10-WAS plants using an RNAprep Pure Plant Plus Kit . DNA and RNA degradation and contamination were monitored on a 1% (w/v) agarose gel. RNA purity and integrity were assessed using the NanoPhotometerin-vitro and soil-grown tobacco plants. Qualified RNA from each samples were shipped on dry ice to the Biomarker Biotechnology Corporation  for cDNA library construction and RNA sequencing. The library was prepared using the VAHTS® Universal V8 RNA-seq Library Prep Kit for Illumina . Subsequently, the cDNA libraries were paired-end sequenced on an Illumina NovaSeq 6000 platform . Clean reads were obtained by removing reads containing adapters or ploy-N and low-quality reads from the original data. At the same time, the percentage of Q30 bases and GC-content of the clean data were calculated. All downstream analyses were based on clean data of high quality. After performing quality control, we obtained clean reads which were then mapped to the tobacco reference genome Ntab_TN90  and the public databases (Metlin and Massbank). The characteristic ions of each metabolite were obtained by triple four-stage rod screening, the signal intensity of the characteristic ions was obtained in the detector, and MultiQuant software was used to integrate and correct the chromatographic peaks. All integration data for the chromatographic peak areas were exported and saved. To detect the repeatability of the samples, QC samples (mixed sample extracts) were inserted in every 10 test samples during the analysis. The accuracy and reproducibility of metabolite detection could be determined by using the overlapping display analysis of mass spectrometry TIC of different QC samples. Combined FC of metabolite difference and the variable importance in projection (VIP) value of the orthogonal partial least squares discriminant analysis (OPLS-DA) were used to screen DAMs. The FC ≥ 2 or FC ≤ 0.5, and VIP ≥ 1 were considered as the standard of screening. KEGG pathway database was used to annotate the DAMs.Metabolite profiling was carried out using a widely targeted metabolomic method by Metware Biotechnology  for the same samples as transcriptome. Briefly, the freeze-dried plant samples were crushed using a mixer mill  and extracted with 70% methanol extraction solution. After centrifugation, the supernatant was filtrated and then analyzed using a UPLC-ESI-MS/MS system in conjunction with UPLC (Shim-pack UFLC Shimadzu CBM30A system) and MS (Applied Biosystems 4500 Q TRAP). The analytical parameters were set as the same as An automatic amino acid analyzer  was used for the determination of free amino acids. The samples were diluted with 10 mL of boiling distilled water and incubated in a water bath at 95°C for 10 min. The extraction was repeated three times and the supernatant was obtained after centrifugation. After adding 10% sulfosalicylic acid, the mixture was centrifuged at 13400 g for 10 min, then dissolved in 0.02 M HCl, and passed through a 0.22-µm pore-size filter membrane into a brown sample bottle for further testing. An autoanalyzer based on the ninhydrin method was used to quantify the free amino acids according to the method described by v/FmF) was calculated as (Fm – Fo)/Fm, where Fm is the maximum chlorophyll fluorescence, measured immediately after a saturating light pulse, while Fo, the steady-state chlorophyll fluorescence, was measured after dark acclimation before a saturating light pulse. The effective PS II quantum yield of the illuminated samples was calculated using the formula Y(II) = (Fm’-F)/Fm’. The trend of Y(II) and the chloroplast electron transport rate (ETR) parameter was determined on the basis of a rapid light curve (RLC) at PAR of 0, 10, 35, 80, 145, 230, 335, 460 and 610 µmol photons m−2 s−1.Chlorophyll fluorescence parameters were measured at room temperature using a pulse-amplitude-modulated (PAM) fluorometer . The expanded leaves were dark-adapted for 20 min before measurement. Six different areas of interest (AOI) were randomly selected on each leaf. The maximal quantum yield of photosystem II (Nicotiana tabacum) metabolism was obtained from Ntabacum_Tn90Cyc and KEGG  as inputs along with the exchange of CO2, oxygen, and water with the environment.An initial reaction set for tobacco  associations was utilized to localize the individual reactions in the draft model to appropriate subcellular compartments by querying tobacco gene sequences against UniProt database. If it was not available in UniProt, the compartmental information from the GEM  of a phycuration .Arabidopsis  genome-scale metabolic model   using thij is the stoichiometric coefficient of metabolite i in reaction j, n is the set of all reactions in the model, V is a vector of reaction flux, m is the transporter reaction of each biomass component, and t is the corresponding transporter flux of the biomass component.where Sin-vitro/soil-grown plants. E‐Flux utilizes gene expression data to set the upper and lower bounds of reactions using gene–protein–reaction (GPR) associations from the model. In detail, the transcriptome data were transformed by log2(FPKM+1) using the mean values of three duplicates. The mathematical operations utilized to calculate the numerical values were the sum of “OR” expressions. Finally, the GPR rule values were normalized, divided by the maximum value in each condition. The normalized values were used to establish the new lower and upper reaction bounds. Exchange reactions corresponding to each growth environment were set as −1/1. After that, the condition‐specific models were obtained with specific reaction bounds.The E‐Flux algorithm  was usedin-vitro condition was modeled by allowing photon influx with active photosynthesis and sucrose as the carbon and energy input (Sucrose_Cyto_tx), with all other constraints the same as the soil-grown one. Constraint-based FBA was run with the objective function of biomass maximization.The free amino acid data was incorporated into the model as the biomass component. Under soil-grown condition, photon influxes were allowed from the photon input reaction with active photosynthesis and with all organic sources of energy set to zero. The t-test in Microsoft Excel. A value of p< 0.05 was statistically significant.For quantitative analysis, values were expressed as the means ± standard errors of three independent biological replicates. Statistical significance analysis was performed using a Student’s https://www.ncbi.nlm.nih.gov/bioproject, PRJNA1014250; https://www.ebi.ac.uk/metabolights, MTBLS8557.The original contributions presented in the study are publicly available. This data can be found here: JY: Formal Analysis, Writing – original draft, Writing – review & editing. XW: Formal Analysis, Writing – original draft, Writing – review & editing. QY: Formal Analysis, Writing – original draft. JS: Formal Analysis. JC: Formal Analysis. ZL: Conceptualization, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition. HM: Conceptualization, Writing – review & editing, Supervision, Funding acquisition.