Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions.  Our study presents a comprehensive metabolic model of Quercus suber, also known as the cork oak tree, which is a keystone species of the Mediterranean forests. We used a genome-scale approach to build a model (iEC7871) that integrates transcriptomics data and accounts for the metabolic interactions among different tissues of the tree. Specifically, we derived tissue-specific models for the leaf, inner bark, and phellogen, which are the main tissues involved in cork production. We then combined these models into a diel multi-tissue model that simulates the metabolic behaviour of the tree under light and dark conditions. Our model includes the pathways associated with the biosynthesis of suberin monomers, which are the main components of cork. By analysing these pathways, we identified potential targets for improving cork quality and yield in Q. suber. Our work provides a valuable resource for studying the metabolism of cork oak trees and highlights the importance of integrating multi-omics data to build predictive models of plant metabolism. Quercus suber L., is a characteristic tree of the Mediterranean/Iberian landscape ecosystem. The tree forms a thick bark of cork (phellem) containing high levels of aliphatic and aromatic suberin, extractives (waxes and tannins), and polysaccharides [https://www.treeoftheyear.org/Previous-Years/2018).The cork oak, charides . Cork prcharides ,3. Only Phellem is produced by the phellogen (cork cambium), involving the proliferation of phellogen derivates cells which undergo differentiation into cork cells through cell expansion, deposition of suberin and waxes, and an irreversible senescence program ending with cell death. The first debarking occurs when the tree has 18–25 years old. This first harvested cork, known as “virgin” cork, produced from the original phellogen, yields poor-quality cork. The original phellogen is then replaced by a traumatic phellogen that proliferates to originate a new cork layer. From the third debarking onwards, a higher quality cork with high economic value is obtained—reproduction cork (or “amadia”) . Cork grThe cork oak tree represents one of the most relevant broadleaved forest resources in the Mediterranean basin , having a substantial socio-economic and ecological impact in these countries. This species can generally live over 200 years, which implies a capacity to bear with biotic stresses like bacteria, fungi and insects, as well as abiotic stresses like drought, floods and fires ,6.Quercus suber [Due to its economic interest in Portugal, a national consortium was created to first sequence the transcriptome  and receus suber . The genArabidopsis thaliana [Zea mays (maize) [Oryza sativa (rice) [Populus trichocarpa (black cottonwood), was published [Q. suber would be useful to provide insights into the metabolic behaviour of this species in its different tissues through different development stages, as well as the tree’s response to stress conditions.Genome-scale metabolic models aim at depicting the whole metabolic network of an organism. Such models have been widely used for metabolic engineering purposes, mainly with prokaryotes and yeasts. Among other applications, GSM models can be used to analyse an organism’s metabolic traits in different environmental and genetic conditions, including the effect of gene knock-outs and over/under-expression . High-quthaliana ,16, Zea  (maize) ,18, and a (rice) ,20. In 2ublished . This maQ. suber at the multi-tissue level.This paper describes the reconstruction of iEC7871, a genome-scale metabolic model for the cork oak tree. Besides the generic GSM, we present tissue-specific models and a multi-tissue metabolic model that can be used to study the metabolic behaviour of Q. suber based on an up-to-date genome annotation was reconstructed in this work.A GSM model for Q. suber was functionally annotated using the automatic workflow available in merlin. A detailed analysis of the genome annotation is available in A Basic Local Alignment Search Tool (BLAST)  search aCO2 (phototrophic), or sucrose (heterotrophic) as the sole energy and carbon sources, respectively. Additionally, the model requires H2O, NH3 or HNO3, H3PO4, H2SO4, Fe2+, and Mg2+ to produce biomass.The metabolic model is mass balanced and can predict growth in phototrophic and heterotrophic conditions. These conditions were defined by setting the photon and A. thaliana [Z. mays [Glycine max (soybean) [et al. (2015) [A. thaliana and Z. mays. The full models were developed by integrating data retrieved from KEGG, BioCyC databases, and published GSM models. These models were manually refined by evaluating the gene-reaction associations, originating evidence-based models.The general properties of the iEC7871 and five published plant models–thaliana ,24, Z. m[Z. mays ,18, and soybean) —are presmerlin automatically retrieves spontaneous reactions from KEGG, of which 80 were kept in the model. The source of all reactions is available in Table N in The iEC7871 comprises 7,871 genes, 6,232 reactions , of which 3,260 are blocked. Additionally, there are 708 exchange reactions, and 6,482 metabolites distributed across eight subcellular compartments–the cytoplasm, mitochondria, plastid, endoplasmic reticulum, Golgi apparatus, vacuole, peroxisome, and the extracellular space. Most of the metabolic reactions included in the model were derived from homology searches , while 987 transport reactions were automatically added by TranSyT. During the manual curation stage, 638 reactions were manually added to the model, including 293 KEGG reactions not identified when generating the draft network either due to missing and wrong gene annotations, as well as a lack of information regarding some KEGG reactions . From the 264 transport reactions manually added, 225 have no gene associated. These included several simple diffusion transport reactions for molecules like oxygen across the different compartments, as well as transporters required to ensure network connectivity. In addition, A. thaliana and Z. mays models developed by Seaver et al. (2015) [et al. (2015) [et al. (2013) [et al. (2011) [Only the full models of . (2015)  have a s. (2015) . As thes. (2015)  identifi. (2015)  identifi. (2013)  and Saha. (2011) , which pA. thaliana model. The maize and A. thaliana models share 560 reactions, and 536 were found in the three models. The metabolites analysis shows similar behaviour.A total of 3,477 unique reactions and 2,967 unique metabolites were identified across the three models. The iEC7871 includes 1,721 reactions that are not present in the other two—A. thaliana genes’ annotations. The remaining 18% were annotated by gene records of organisms not accounted for in the annotation workflow.The reactions available only in the iEC7871  were further analysed to assess the annotation of the genes and pathways associated with them . This procedure allowed identifying 52 reactions with cumulative effect on suberin and biomass production. The top 10 enzymatic reaction targets and associated metabolic pathways are listed in Suberin is one of the major components of the phellogen. It was reported that reproduction cork presents a higher percentage of suberin than virgin cork. Thus, the flux scanning based on enforced objective flux (FSEOF) method  was applThe analysis of the top reaction targets highlighted the importance of specific reactions and metabolic pathways that affect suberin production. The reaction “R00842__cyto”, catalysed by glycerol-3-phosphate dehydrogenase, was identified as the reaction with the most impact on suberin biosynthesis. This enzyme is responsible for the conversion between glycerone phosphate and glycerol-3-phosphate, a component of suberin. Similarly, caffeate O-methyltransferase and p-coumarate 3-hydroxylase, involved in the biosynthesis of another suberin component (ferulate) were recognized as targets. This analysis also identified several reactions associated with fatty acid metabolism , as well as the cytochrome P-450 reductase. Several other reactions associated with fatty acid biosynthesis, elongation, and modification were identified  is associated with a reaction responsible for producing it from its precursors .A. thaliana models [merlin. The fatty acid, lipid, and carbohydrate compositions were determined using experimental data for Q. suber or closely related organisms when species-specific data was not available  and carbohydrates (40.7%). Carbohydrates have a significant representation in the composition of the three tissues, being the major component of the inner bark (46.9%), together with lignin (38.20%). The phellogen is mainly composed of suberin (36.2%), with a considerable representation of carbohydrates (17.6%) and lignin (25.2%).troppo operates at the reaction level  [The number of metabolic reactions is quite similar among the four tissue-specific models. Nevertheless, the number of reactions in the leaf model’s plastid is slightly higher than in the other models, while in the endoplasmic reticulum, the opposite behaviour can be observed. The reproduction phellogen model presents a higher number of transport reactions than the remaining ones. To assess potential differences in the metabolism of the reproduction and virgin phellogen, s (pFBA)  and fluxs (pFBA)  were perThe number of metabolic reactions associated with each pathway is available in Tables H-I in MODEL2205040001, MODEL2205040002, MODEL2205040003, MODEL2205040004, MODEL2205040005, and MODEL2205040006, together with the respective MEMOTE [www.ebi.ac.uk/biomodels/curation/fbc) reports.The models developed in this work were deposited in BioModels  and assie MEMOTE , and FROin silico simulations in photoautotrophic, heterotrophic, and photorespiratory conditions. The exchange fluxes obtained in such simulations were not directly compared with measured rates due to the unavailability of this data. The results for the model’s validation are available in detail in The models were tested to guarantee that no biomass or energy is produced without energy input in each condition. The general and tissue-specific metabolic models were validated by analysing the fluxes of CO2 by RubisCO using light as the energy source through a C3 photosynthetic pathway. The predicted photon and CO2 uptake fluxes are 53.68 mmol∙gDW−1∙h−1 and 4.24 mmol∙gDW−1∙h−1, respectively, for a biomass production fixed at 0.11 h−1  [As expected, the photoautotrophic growth is supported by the assimilation of  h−1 see . These v. (2012)  for AraGCO2 fixed per mol of photons) and assimilation quotient (CO2 fixed per O2 evolved through photosynthesis) are common measures of photosynthetic efficiency. Thus, these parameters were determined for the leaf model. In photoautotrophic conditions, the photon yield was calculated as 0.079 Q. suber  and dark (night). Common pools were created to connect the different tissues for the light and dark phases. The diel multi-tissue GSM model comprises 28,430 reactions and 29,489 metabolites. This model allows the uptake of minerals, such as M models . The excA. thaliana and Z. mays are reference organisms for C3 and C4 plants, with several metabolic models available. Nevertheless, such models for less studied plants have been becoming available in the last decade [Q. suber, implies the utilization of this data to reconstruct metabolic models of less-studied organisms. Despite the manual curation efforts to assure network connectivity, especially for secondary metabolism pathways, the model still presents a high number of blocked reactions. This is associated with the lack of knowledge regarding enzyme specificity, and replication of reactions in multiple compartments.t decade ,50–53. TA. thaliana and Z. mays models developed by Seaver et al. (2015) [A. thaliana’s homologs. The information available in Swiss-Prot for such species is exceptionally high, while it is scarce for woody plants. A taxonomic comparison between A. thaliana and Q. suber reveals a substantial phylogenetic divergence, as they belong to distinct orders: A. thaliana belongs to the order Brassicales, while Q. suber falls within the order Fagales. This divergence in evolutionary lineage may result in inaccurate or incomplete gene annotations, potentially leading to erroneous or missing information. Another obstacle is the presence of incomplete EC numbers. For instance, over 300 genes were initially annotated with incomplete EC numbers associated with Cytochromes P450.The iEC7871 was compared with evidence-based . (2015) . The iECThe KEGG Pathways associated with these reactions were also analysed. The pathways with a higher number of unique reactions are associated with the metabolism of fatty acids, xenobiotics, terpenoids, and other secondary metabolites. Also, the “Tryptophan metabolism” and “Tyrosine metabolism” pathways include the secondary metabolism of these amino acids, such as the quinolinic acid production, dopamine, and its derivates.When classified according to the main areas of the metabolism in KEGG, the pathways representing “Lipid metabolism”, “aminoacid metabolism and carbohydrate metabolism”, and “Xenobiotics biodegradation metabolism” were the most represented regarding reactions that are only present in iEC7871.The high number of reactions and metabolites only available in the iEC7871 is most likely associated with three different reasons: the approach followed to develop the GSM model; the different databases used for the reconstruction of the models, which has been pointed to have a major impact on the reconstruction of metabolic models ; the metet al. (2015) [A. thaliana and maize models, leading to a remarkably high number of metabolites and reactions. These models were screened by the authors, and only genes for which evidence in PlantSEED or PlantCyc was found were kept in the evidence-based models. Thus, these models are mostly based on PlantCyc identifiers, which increases the number of mismatches between the models, as mapping identifiers between this database and KEGG is not straightforward.Seaver . (2015)  used botA. thaliana, O. sativa, and Z. mays. These pathways are not always complete or available in biological databases, implying an additional effort to find information regarding genes, reactions, and metabolites. Although there is still much to learn about these compounds and their respective biosynthetic pathways, genome-scale modelling can provide insights into a given organism’s potential to produce secondary metabolites. The model reconstructed in this work includes reactions associated with the biosynthesis and metabolism of several secondary metabolites, steroids, and drugs.Plants produce a wide range of compounds through their secondary metabolism, whose function includes defence against abiotic and biotic stress or beneficial interactions with other organisms . Many seA. thaliana and Z. mays models considered in the Results section. This pathway represents a set of reactions, mostly associated with the cytochrome P450, responsible for the response to the presence of toxic xenobiotics. The cytochrome P450 family has been described as being highly upregulated in developing phellem tissues [Q. suber [The “Metabolism of xenobiotics by cytochrome P450” includes 56 reactions not found in the  tissues . Neverth tissues , and proQ. suber ,60. FurtQ. suber. Although these hormones are usually synthesized by animals, they were already identified in a few plants [The model presents all the necessary reactions to produce jasmonic acid and its derivates, usually called jasmonates, through the “alpha-Linoleic acid metabolism” pathway. These plant hormones are associated with the regulation of growth and developmental processes, stomatal opening, inhibition of Rubisco biosynthesis, and nitrogen and phosphorus uptake ,62. The w plants –67. The Although this model contains a considerable amount of information regarding secondary metabolism, further curation using species-specific knowledge would improve the connectivity of the model and reduce the existing gaps in these pathways.As mentioned before, suberin is the major component of phellogen, while its abundance is residual in the leaf and inner bark. The phellogen also contains significant amounts of lignin and waxes. The biosynthesis pathway of the monomers of these cork components in the model is represented in The synthesis of aliphatic suberin monomers is associated with the KEGG’s “Cutin, suberine, and wax biosynthesis” pathway. Nevertheless, the reactions of this pathway in the database are associated only with palmitic, oleic, and docosanoic acids and some of their derivates. Hence, additional reactions and metabolites were added to allow the production of the components of suberin, and waxes identified in the biomass formulation stage.Fatty acids are exported from the plastid and imported into the endoplasmic reticulum, where these can be elongated to originate long-chain and very-long-chain fatty acids. Cytochromes P450, especially homologues of the CYP86A family, are responsible for catalysing several reactions that synthesize suberin monomers. The proteins with NCBI accessions XP_023927575 (CYP86A1) and XP_023893592, XP_023920300, XP_023893496, XP_023896997, XP_023896998, and XP_023871406 (CYP704B1) are responsible for the ω-hydroxylation of long-chain fatty acids, while XP_023897881, XP_023929457, XP_023923098, and XP_023874080 (CYP86B1) act on very-long-chain fatty acids.Epoxide hydrolases (EC: 3.3.2.10), ω-oxo-fatty acid dehydrogenases (EC: 1.2.1.-), ω-hydroxy-fatty acid dehydrogenases (EC: 1.1.1.-), and peroxygenases (EC: 1.11.2.3) can act in the ω-hydroxy acid in different combinations, to originate the diverse α,ω-dicarboxylic, poly α,ω-dicarboxylic, and polyhydroxy-fatty acids. The genome annotation and the model manual curation allowed identifying 38 genes encoding epoxide hydrolase, five encoding ω-hydroxy-fatty acid dehydrogenase, and six encoding peroxygenases. Nevertheless, no genes encoding ω-oxo-fatty acid dehydrogenase were available in known biological databases; thus, the reactions catalysed by this enzyme (“R09458” and “R09459”) were added without gene association.The polymerization process is still not completely understood but it likely involves esterification reactions through glycerol-3-phosphate acyltransferases (GPAT), producing acylglycerol esters ,69. ThisOne of the major differences between virgin and reproduction cork is the suberin content . Thus, tWaxes are also an important cork component and are composed essentially of sterols and terpenoids . These aThe phellogen contains a considerable amount of lignin, although this polymer is present in other tissues. The synthesis of the respective monomers  is described in the KEGG’s “Phenylpropanoid biosynthesis” pathway. Phenylalanine is the main precursor of this pathway that occurs mostly in the cytoplasm. It is converted to cinnamic acid by phenylalanine ammonia-ligase (EC: 4.3.1.24). Cinnamate monooxygenase, caffeate methyltransferase, and ferulate-5-hydroxylase are responsible for the successive conversion of cinnamic acid to coumarate, ferulate, hydroxyferulate, and sinapic acid, in the cytosolic surface of the endoplasmic reticulum. These metabolites can be converted into lignin monomers in a three-step process catalysed by coumarate-CoA ligase (EC: 6.2.1.12), cinnamoyl-CoA reductase (EC: 1.2.1.44), and cinnamyl-alcohol dehydrogenase (EC: 1.1.1.195). Through the polymer assembly in the cell wall, peroxidases (EC: 1.11.1.7) convert the guaiacyl, hydroxyphenyl, and syringyl alcohols into guaiacyl, hydroxyphenyl, and syringyl lignins, respectively. The linkages between aliphatic and aromatic suberin were not included in the model since these macromolecules are represented in the biomass separately.These findings pave the way for further research and engineering approaches to enhance suberin production. By overexpressing the identified target reactions or manipulating the associated metabolic pathways, it becomes feasible to fine-tune the biosynthesis of suberin and potentially achieve higher yields. This has implications not only for cork quality and properties but also for various applications that rely on suberin, such as sustainable packaging, biodegradable materials, and agricultural protection. The identified reactions serve as valuable starting points for experimental investigations and provide a foundation for understanding the underlying metabolic mechanisms involved in suberin production.in vivo phenotype of the respective tissue or condition [A generic metabolic model comprises all the reactions catalysed by all the enzymes encoded in an organism’s genome. However, this approach does not account for the regulatory network present in each organ, tissue, or cell in different environmental conditions. The integration of transcriptomics data in a GSM model allows obtaining metabolic models closer to the ondition .Q. suber would be useful to increase the model’s reliability and improve predictions. The composition of the inner bark and phellogen was based on experimental data available for Q. suber [Transcriptomics data for the leaf, inner bark, and phellogen were integrated into the generic model, originating three tissue-specific models with different biomass compositions. The leaf macromolecular composition was mostly based on previously published GSM models ,32,74. QQ. suber ,75. WhilQ. suber . Waxes aQ. suber , these cin silico simulations did not allow identifying significant differences between them. The cork oak phellogen’s transcriptional profile allows inferring that the flavonoid route is favoured in bad quality cork, while the lignin and suberin production pathways are preferred in good quality cork production [The number of transport reactions in the Reproduction Phellogen is visibly higher than in the other tissues, including the Virgin Phellogen. These reactions are essentially associated with the transport of steroids and hormones. Based on BLAST searches against TCDB , the genoduction . A recenoduction . Althougoduction ,76. HencTissues and organs of multicellular organisms do not perform their metabolic functions individually. Instead, they interact with each other by exchanging sugars, amino acids, hormones, and others. In plants, the simplest example is the sucrose formation in the leaves and its transport across all other organism tissues. Multi-tissue GSM models allow analysing the dependencies between tissues in terms of biomass precursors, carbon skeletons, nitrogen, sulphur and phosphorus sources, and the energy required for their translocation and proton balance ,81. The The leaf, inner bark, and phellogen models were merged into a multi-tissue GSM model. Other tissues, such as the root and xylem, could also be considered and included in the multi-tissue model. Nevertheless, this work’s scope is related to the metabolism of cork precursors and including more tissues would increase the complexity and computational requirements of the model unnecessarily.The most significant differences between the light and dark phases of metabolism were identified in the leaf  is stored and used as energy source when needed. The storage of starch is more efficient in the model than sucrose, glucose, or fructose. Whereas starch is mobilized in plastids, soluble sugars are accumulated in the vacuole, implying an energetic cost for their transport. The model produces malate in the dark (light-independent reactions), which is used during the day (light-dependent) by the malic enzyme to produce pyruvate and obtain reducing power (NADPH). Citrate was also produced at night (light-independent reaction) through the tricarboxylic cycle (TCA) and used to feed the diurnal TCA and provide carbon skeletons for the amino acid metabolism. This behaviour is in agreement with the previously published diel GSM model for thaliana .For the inner bark and phellogen, the metabolic profile was very similar between day and night. This is associated with the metabolic constraints applied to the model: for both phases, the leaf produces and transports sucrose and amino acids to the inner bark, and then to the phellogen. Thus, as the nutrient supply is very similar, and the objective function is the same (biomass production), the simulations applied here are not able to distinguish daytime and nighttime metabolism. Nitrate provided by the inner bark is transported into the leaf and is then converted into ammonia. In the plastids, ammonia is used to incorporate nitrogen through the glutamine synthetase-glutamate synthase pathway.Sucrose and amino acids are exported from the leaf and then imported by proton symport by the inner bark. The proton balance of the common pool is maintained by plasma membrane ATPases. In the inner bark, sucrose is used as an energy and carbon source to produce biomass components. As mentioned before, the inner bark biomass is mainly composed of carbohydrates, lignin, and protein. Hence, pathways associated with the synthesis of cell wall precursors  are the most relevant in this tissue.Q. suber at a global level. It can be used to study the biosynthetic pathways of suberin, lignin, waxes, and several plant secondary compounds. The introduction of additional species-specific knowledge would improve the overall network connectivity, allowing obtaining a model with increased reliability.The remaining sucrose and amino acids produced in the leaves are transported to the phellogen. The reversible sucrose synthase activity allows sucrose conversion into UDP-glucose, which is used to produce other cell wall components, and fructose that follows the glycolytic pathway to produce pyruvate. A fraction of it is transported into the plastid and converted by pyruvate dehydrogenase into acetyl-CoA, the fatty acids precursor. As mentioned above, fatty acids are elongated and suffer a series of hydroxylation, epoxidation, and peroxidation reactions in the endoplasmic reticulum. Ferulate, another precursor of suberin, is produced through the “Phenylpropanoid biosynthesis” pathway, with phenylalanine produced in the leaf serving as a precursor. The glycerol 3-phosphate used for the esterification reactions is produced from glycerone phosphate produced through glycolysis.The diel multi-tissue GSM model developed in this work is a useful framework capable of providing a systematic overview of the metabolism of In this work, we present the first multi-tissue diel genome-scale metabolic model of a woody plant. The iEC7871 was based on knowledge retrieved from genomic information, biological databases, and literature. It can simulate the Cork Oak tree’s metabolic behaviour in phototrophic and heterotrophic conditions, as well as photorespiration. This model comprises the pathways of the central metabolism and several pathways associated with the secondary metabolism reproducing the formation of the major components of cork.The integration of transcriptomics data was used to obtain tissue-specific models for the leaf, inner bark, and phellogen. These models were merged to obtain a multi-tissue GSM model that comprises the diel cycle’s dark and light phases.This GSM model comprehends the four main secondary metabolic pathways participating in cork production: acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids. The lipid biosynthesis pathway is required for the biosynthesis of the linear long-chain compounds forming the aliphatic suberin domain, which share upstream reactions with wax biosynthesis. The phenylpropanoid metabolism is needed for the biosynthesis of the cork aromatic components, which share reactions with wood lignin.The metabolic models developed in this work can be used as a tool to analyse and predict the metabolic behaviour of the tree and evaluate its metabolic potential. Metabolic modelling methods can be applied, including dynamic approaches, to study the changes in this tree’s metabolism over time and environment.merlin v4 was used to support the reconstruction process, as this software provides a user-friendly interface that conjugates automatic and manual procedures. COBRApy v0.20.0 [ v0.20.0  was usedtroppo [The troppo  python phttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/), Sickle [FastQC  [et al. (2013) [et al. (2011) models were directly compared after removing the compartments since they present KEGG IDs.The evidence-based models developed by Seaver . (2015)  were com. (2013)  and Sahatroppo, together with the generic GSM model, to generate tissue-specific models. Troppo was executed using the CPLEX solver and the Fastcore [troppo’s integration strategy was developed to guarantee that the tissue-specific models can produce biomass. The median of each dataset was used as threshold, and the remaining parameters were kept as default. Troppo identifies the reactions that should be removed from the generic model, based on the expression of the genes included in the gene-protein-reaction association of each reaction. Nevertheless, the reactions that were maintained in the model within this approach kept all their respective genes. The carotenoid biosynthesis has two branches, one associated with trans-Phytofluene and the other with 15,9-dicis-Phytofluene. The enzymes present in the two branches are similar (phytoene desaturase and zeta-carotene desaturase). However, one of them is dependent on photosynthesis since it unbalances the plastoquinone/plastoquinol ratio. The other one is stoichiometrically balanced since it involves successive oxidation and reduction of these quinones. Hence, the presence of the second branch was guaranteed in the leaf model since it is essential for carotenoid production through the dark phase.Transcriptomics data from different tissues were used to obtain tissue-specific models. The quality of the transcriptomics data was analysed using the FastQC software. After trimming the data with Sickle, Bowtie2 was used to align the trimmed FASTQ files against the reference genome. FeatureCounts was used to count the mapped reads to each gene. After filtering and normalizing the data with the edgeR library, datasets with the normalized counts of each gene were obtained. These files were used as input for Fastcore  reconstrtroppo were adapted to account for the light and dark conditions, by duplicating all reactions and metabolites. Reactions converting sugars , 18 amino acids, and nitrate between light and dark were added to the model, based on a previously published approach [The tissue-specific models obtained using approach .To build the multi-tissue model, the diel tissue-specific models were merged, and common pools between leaf and inner bark (common pool 1), and inner bark and phellogen (common pool 2) were created. Since the root was not accounted for, the uptake of water, nitrogen, sulphur, phosphorus, and magnesium sources was included in the inner bark.Q. suber was found, simulations were carried out with arbitrary values, and only flux ratios were evaluated. The first approach was to fix the biomass formation to 0.11 h−1, and set the minimization of the photon uptake as the objective function. In the second approach, the photon uptake was fixed to 100 mmolphoton. gDW−1. h−1, maximizing biomass production. Photorespiration was simulated by constraining the carboxylation/oxygenation flux ratio (Vc/Vo) of Rubisco (reactions R00024 and R03140) to 3:1 [mmolphoton. gDW−1. h−1.Two different approaches were used to perform simulations with pFBA. Since no experimental data regarding exchange fluxes for ) to 3:1 . SimulatThe pFBA and FVA simulations with the diel multi-tissue GSM model were performed using the restrictions applied in photorespiration, with the additional constraint of the nitrate light/dark uptake ratio, which was settled to 3:2 ,49.vsuberin) was maximized to obtain the theoretical maximum production of this compound. The production of suberin was enforced from 5% to 95% of the value calculated previously, using the growth rate (vbiomass) as the objective function for FBA simulations. The reaction’s fluxes were analysed, to identify those that increased with higher suberin production rates. The identified targets were evaluated using FVA, and only those that satisfy fph, was used to rank the identified targets, and was calculated as:To explore differences between virgin and reproduction cork through suberin production, the FSEOF approach was applied to the generic metabolic model . First, Quercus suber was retrieved from the NCBI database with the assembly accession number GCF_002906115.1. The transcriptomics data was retrieved from the EBI database [The genome sequence of database  (AccessiS1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 FileData collected from publications, and respective processing to include it in the GSM models.(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 FileAnalysis of the quantum yield for different carboxylation/oxygenation ratio of Rubisco . Summary of the simulations detailed in (PDF)Click here for additional data file.


Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function . Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model  were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system’s individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in  In recent years, computational modeling of biological systems has acquired a significant role in understanding and predicting cellular performance. Computational modeling for explaining cell behavior has been accomplished by numerous discrete, continuous, and spatially explicit models describing subcellular systems and processes that biological systems need to survive . Indeed,Escherichia coli and biofilm formation by Pseudomonas aeruginosa, respectively [Following the development of next-generation sequencing techniques, a key objective has been to relate annotated genome sequences to cellular physiological functions. Therefore, systems biology has been designing new computational strategies to reconstruct biological networks (genomic and metabolic) and to model and dynamically simulate biological systems in order to study, from a holistic perspective, the regulation of mechanisms leading to the expression of diverse phenotypes. Thus, the extensive dataset of measurements of all biomolecules at the system level favors integration from the molecular to the whole organism level. In recent decades, several computational studies have made developing multiscale models of some biological systems possible. For example, Thiele et al. and Biggs and Papin have shown the advantages of multiscale models for understanding metabolic and macromolecular synthesis in ectively ,5.P. aeruginosa [pvd genes, which encode enzymes involved in PVD synthesis and which are regulated by the quorum-sensing (QS) phenomenon [-in this case, induced under conditions of iron deficiency, and responsible for synchronizing phenotype expression in a bacterial community—in this case, the factor virulence PVD expression) [Following a holistic approach, this work aimed to explain the synthesis of the siderophore pyoverdine (PVD), a virulence factor in ruginosa . The PVDenomenon  (a mecharession) . The secP. aeruginosa has two QS systems, LasI/LasR and RhlI/RhIR. The signaling molecule of the LasI/LasR system is N-(3-Oxododecanoyl)-L-homoserine lactone (3O-C12-HSL), which is synthesized by LasI and detected by the LasR receptor. The RhlI/RhIR system is regulated by N-butanoyl-L-homoserine lactone (C4-HSL), synthesized by RhII, and detected by the RhIR receptor. Both QS systems are hierarchically organized, such that the LasI/LasR system regulates the function of the RhlI/RhIR system, allowing further fine-tuning of the QS feedback on specific genes [Pseudomonas Quinolone Signal (PQS) system. This system encodes the synthesis of 2-heptyl-3-hydroxy-4-quinolone (2C7-3OH-4(1H)Q) that forms a complex with the available ferric ion (Fe3+) and plays an essential role in the pvd genes expression. At the cellular level, the quinolone complex positively controls the expression of the Pvd-type proteins, which is responsible for PVD maturation, synthesis, and exportation [ic genes . These Qortation .pvdL, pvdI, pvdJ, and pvdD genes [pvdA, pvdF, and pvdH genes. According to the model proposed by Visca et al. [3+ complex, which is transported back into the intracellular space by the FpvA carrier. Iron is then released from PVD to the periplasm by a mechanism involving iron reduction (Fe3+ to Fe2+), and PVD is then transported out of the cell via a specialized carrier of the siderophore recycling process [Most PVD biosynthesis is carried out in the bacterial cytosol by four non-ribosomal peptide synthase (NRPS) proteins encoded by the dD genes . Togethea et al. , the Pvda et al. ,11,12 th process ,14,15,16P. aeruginosa is proposed here for the first time to describe and understand the influence of the QS phenomenon on PVD biosynthesis in P. aeruginosa from a systemic perspective. That model involves the process of bacterial communication mediated by the QS phenomenon for synthesizing signal molecules that regulate the expression of pvd genes to generate enzymes that metabolically catalyze the production of the virulence factor. Furthermore, this integrative model combined two biological network models: (i) The QS gene regulatory network model, based on deterministic approaches and whose simulation results were the concentration of chemical species and the fluxes of chemical reactions; and (ii) the model of the P. aeruginosa metabolic network based on the flux balance analysis (FBA) approximation. This approximation addressed the biomass maximization problem constrained by mass balance and thermodynamic conditions to obtain the optimal distributions of reaction fluxes through the metabolic network [P. aeruginosa metabolic networks. Then, the fluxes of these shared reactions, obtained from the QS network, were used as a constraint system to model the P. aeruginosa metabolic network by dynamic flux balance analysis (DFBA) [P. aeruginosa strain PAO1 metabolism to PVD biosynthesis as a function of variations in QS signal intensity.An integrative  model that combines the QS gene regulatory network and the metabolic network of  network ,19,20,21s (DFBA) ,23. The P. aeruginosa QS network as a gene regulatory network for the expression of pvd genes encoding enzymes for PVD synthesis and maturation; (ii) construction of the P. aeruginosa genome-scale metabolic network, including PVD biosynthetic reactions using the FBA approximation; and (iii) integration of these two network models into a multiscale model using the DFBA approximation  construction of a deterministic model of the ximation .The following elements were used to construct and model the QS gene regulatory network, (i) bibliomic information about the genes involved in the QS process, (ii) a language/platform for the biological representation of the retrieved bibliomic data, and (iii) a language/platform for the mathematical modeling of the biological representation.Bibliomic information was retrieved from generic and specialized databases, such as the Scopus and Web of Science bibliographic indexes and specific biological databases. In addition, several search queries were used to find relevant documents and database entries that provided the maximum number of chemical species for modeling the biological process.https://www.genome.jp, accessed on 5 March 2016), KEGG [The QS network model was built using the standard System Biology Markup Language (SBML) format. The CellDesigner 4.4 software  was used6), KEGG ,26, BioC6), KEGG ,28, UniP6), KEGG  and PubC6), KEGG ,31 datab3+ were fixed . In addition, each reaction incorporated the kinetic constant (k) values  and the links of each interaction between related molecules. The corresponding ODEs for mRNAs and proteins in the transcription and translation processes are exemplified in Equations (1) and (2), respectively [For QS modeling, the initial concentrations of genes, QS regulatory proteins, and available cytosolic Fex(t) for a series of time points [To construct the deterministic QS model, elementary kinetics, based on the law of mass action for all chemical species, was assumed for all reactions in the system. QS model simulations were run using SOSlib  in the Ce points .P. aeruginosa. When the microbial population density increases in these cultures, the concentration of QS signal molecules that regulate the expression of bacterial phenotypes, such as PVD, also increases [P. aeruginosa metabolic network.After testing the stability of the QS network by the behavior of signal molecules, intracellular ferribactin production, and intracellular and extracellular PVD production, a single condition was set for the different simulation scenarios, namely, the initial concentration of the extracellular signal molecule PQS (E-PQS). This ranged from 0.01 μM to 0.1 μM with 0.01-unit intervals and from 0.01 μM to 0.1 μM with 0.1-unit intervals for a total of 20 simulation scenarios . These sncreases ,35,36. IP. aeruginosa metabolic network required a previously published generic cell model of the bacterium, the specific metabolic pathway of PVD synthesis, and software for modeling the hundreds of reaction equations representing the metabolic pathways. The in silico construction of the P. aeruginosa published in the literature; (ii) the P. aeruginosa genome annotation in the PseudoCAP database; and (iii) the metabolic pathway of PVD synthesis in the MetaCyc database (PseudoCyc), which was extended by bibliomics. As in the IMO1056 model, each reaction was manually mapped against data in biological databases .The computational modeling was based on three data sources: (i) The genome-scale metabolic network model iMO1056 , which w, BioCyc , ModelSE, BioCyc , Metanet, BioCyc , Transpo, BioCyc , TCDB [4, BioCyc ) for preCuring a metabolic process refers to making the network mathematically feasible by removing pathologies such as metabolites that are not produced or consumed by any reaction in the model, eliminating thermodynamically infeasible cycles, and validating the production of metabolites in the biomass reaction.P. aeruginosa metabolic network, the list of reactions in the network was represented as a stoichiometric matrix Before curating the ineering , (ii) adineering ,44, and ineering . In thisineering , Prigentineering , and Kimineering , in whicP. aeruginosa metabolic network modeling using a steady-state FBA approximation [https://www.gams.com/, accessed on 20 March 2023).The ximation  compriseh−1 (K3) .P. aeruginosa metabolic network represent two types of biological processes; both are part of a living cell but differ in time scale, spatial location, control mechanisms, and chemical species that usually work separately. However, both networks share chemical species, such as the enzymes encoded by the QS network genes that catalyze some biochemical reactions in the P. aeruginosa metabolic network. The challenge was to combine these two networks to function as an integrated multiscale model. The work of Mallmann and collaborators inspired the methodological proposal to solve the challenge [Pseudomonas aeruginosa metabolic network model, and (ii) using the fluxes of the reactions shared by both model as constraints in the multi-stage FBA .Under the multi-stage FBA approximation, both network models were combined by fixing in the metabolic model, as equality constraints, the fluxes of the shared reactions, obtained from the QS network simulations according to each simulation scenario where E-PQS concentration was changed . The unigDW−1h−1 . SimulatQS and P. aeruginosa metabolic networks, Subject to:scenario . FinallyP. aeruginosa metabolic network and the interaction between both networks over time was possible. Furthermore, the DFBA approximation proposed by Mahadevan et al. [The behavior of the integrated model over time was observed by dynamic simulation using the DFBA approximation. Thus, observing the influence of the QS network on the n et al.  was usedQS and P. aeruginosa metabolic networks. Simulations were run to obtain (i) the flux profile of each reaction, (ii) the growth rate profile, (iii) the biomass concentration profile, and (iv) the concentration profiles over time of the metabolites of interest, i.e., the QS signal molecules, glucose, and PVD. The optimization problem was solved as a nonlinear programming problem (NLP) using the CONOPT v3.15N solver in GAMS software . This model includes the equality and inequality constraints of the system (thermodynamics) and the fluxes of the QS network reactions fixed in the P. aeruginosa metabolic network (according to scenarios 4+ = 104.127 mM), phosphate (Pi = 3.88 mM), sulfate (SO42− = 0.286 mM), biomass (X = 0.1 g/L) and oxygen (O2 = 1 mM). The initial concentrations of glucose, nitrogen, phosphate, and sulfate were calculated according to the approximate composition of the Luria Broth (LB) culture medium reported in the technical specifications of the commercial culture medium.Subject to:P. aeruginosa strain PAO1  was grown in Luria Broth medium  in a 1-L BioStat®A bioreactor , equipped with a disc-type impeller, a ring-type diffused bubble aeration system, pH and pO2 probes, a chiller for temperature control, and an aeration system for continuous and automatic control of the laboratory airflow. Cultures were grown in triplicate batches under controlled pH = 7, Tº = 37 °C, agitation = 200 rpm, oxygen saturation = 20%, and air flow = 2 L*min−1 (2 vvm). Cultures were incubated for 24 h, and aliquots were collected at 1-h intervals.The integrated model, named CCBM1146, was evaluated qualitatively by comparing the behavior pattern of PVD production and biomass production between data obtained from in silico simulations and those obtained from in vitro cultures of P. aeruginosa cultures were taken to measure the absorbance at 600 nm by spectrophotometry. The mean values were analyzed by logistic regression using the StatSoft statistical software to fit the data according to Equation (6) and obtain the following specific growth equation for strain PAO1.Aliquots of 1 mL of the P. aeruginosa were taken and centrifuged at 14,000 rpm for 5 m at 4 °C. The pellets were discarded, and the supernatants were stored at −80 °C for subsequent determination of the PVD profile following the protocol of Meyer and Abdallah [After incubation was complete, three aliquots (1 mL) of the in vitro cultures of Abdallah ,54,55. Thttps://doi.org/10.17632/2xzzkmnpfx.1). In addition, this QS network model comprised 103 biochemical reactions, including DNA transcription, mRNA translation, protein complex formation, inhibition reactions, and molecule-to-molecule interactions with positive, negative, or unknown effects (k) values reported in the literature. The values of k not reported in the literature were assumed to be within the range of published k values for similar reactions . For model construction, a system of 114 ODE was generated using a total of 103 kinetic parameters and represented using the SBGN graphical notation of the SBML models.The QS network model described here consists of 114 subcellular chemical species, including proteins, small molecules, genes and their corresponding mRNAs, and nine complexes containing different molecules . In all simulation scenarios, the concentration of the E-PQS signal molecule was modified to emulate the behavior of in vitro cultures of P. aeruginosa, where population growth increases as a function of time (more cells per unit volume) (The QS network was modeled in the twenty simulation scenarios described in  volume) . The per volume) . https://doi.org/10.17632/2xzzkmnpfx.1). In this simulation, increasing concentrations of QS signal molecules were not considered because the purpose was to assess the basal capacity of the model to reproduce the QS circuits and produce QS signal molecules intracellularly. In silico intracellular production of the chemical species of interest corresponded to simulated concentrations of 0.0234 μM for 3O-C12-HSL, 0.0102 μM for C4-HSL, 0.00099 μM for PQS; 0.0158 μM for ferribactin, and 0.0635 μM for PVD . Subsequently, simulations run according to the scenarios in The low concentration of these chemical species showed that the system on its own produced a basal amount of signal molecules, ferribactin, and PVD in response to the concentration of Fewhich depends directly on the QS phenomenon [The highest PVD yield (0.122 μM) was achieved with 0.6 μM E-PQS  established by the BioCyc database (https://doi.org/10.17632/y9htx3fcjm.1).The curation process was complemented by (i) evaluation of metabolite production and consumption in the biomass reaction by reverse engineering; (ii) detection and resolution of pathologies in the 880 metabolites of the network: 29.51% had pathologies, 17.25% were resolved and 12.26% with unresolved pathologies remained in the model; (iii) detection and resolution of TICs: 23 TICs were identified and entirely resolved in the network. Finally, the proposed model, CCBM1146, comprised 1123 reactions, 880 metabolites, 136 protein complexes and transport proteins, and 1146 genes coding for 826 enzymes. Each reaction was manually assigned a metabolic system and its corresponding labeled subsystems according to the ontology pathway  obtained for the objective function of the model was 0.55 h−1 . After simulation, 352 reactions with active fluxes were obtained, including those for synthesis, transport, and exchange of the QS signal molecules  and PVD. This relatively small number of reactions is principally due to the structure of the biomass equation. Also, it could be related to the addition of PVD to the biomass reaction, which triggers the activation of a small set of essential reactions of the central metabolism (including biomass metabolites and PVD synthesis reactions). Furthermore, since PVD is a secondary metabolite, these reactions result in PVD production upon activating necessary intermediary metabolism and secondary metabolism reactions in the model.The definitive CCBM1146 model and the steady-state FBA approximation were used to obtain the flux distribution of reactions in the P. aeruginosa metabolic network model. Since these networks have different types of biological language, the flux values of nine reactions shared by both networks were considered to be useful for merging the behavior of chemical species in both types of network models. These nine shared reactions were involved in the synthesis and transport of QS signal molecules, ferribactin synthesis, and PVD synthesis and transport. After applying the multi-stage FBA approximation in six simulation scenarios of the integrated model  the QS signaling pathway, (ii) a regulatory pathway for pvd gene expression, and (iii) the effect of these two processes on the P. aeruginosa metabolic network for PVD biosynthesis. These three processes were encompassed here in a multiscale model to explain the influence of the QS phenomenon on the metabolic pathway of PVD biosynthesis in P. aeruginosa.A thorough understanding of complex biological systems’ behavior requires studying and comprehending the interplay of several processes that often occur at different spatial and temporal scales in the biological system as a whole ,2,61. ThP. aeruginosa proposed in this work is the first approximation that includes the three QS systems reported for this microorganism. It also incorporates the autoregulatory mechanisms of these three QS systems as well as the mechanisms responsible for the expression of pvd genes, the transport of PVD to the extracellular space, and the chelation and entry of iron into the bacterium through the PVD/Fe3+ complex. The data obtained from QS model simulations are supported by the fact that any system in dynamic equilibrium tends to reach a steady state, which translates into an equilibrium that resists external forces of change (perturbations). When the system is perturbed, the regulatory systems—in this case, the QS systems—respond to the signals emitted to establish a new equilibrium; this function is executed by the QS systems themselves and is known as feedback control. All biological processes that integrate and coordinate the functioning of living organisms are examples of the homeostatic regulation required by the system to ensure cell survival. Thus, the biological system is driven to the homeostatic state expected from its behavior, which is likely to maintain stability while adapting to the optimal conditions for survival. This argument is supported by the work of Gonzalez et al. [The deterministic in silico model of the QS gene regulatory network in z et al. , which sz et al. .P. aeruginosa in culture, i.e., as the bacterial population increased (more cells per unit volume), the extracellular concentration of QS signal molecules per unit volume also increased. It was also evident that the signal molecules of all three QS systems were synthesized intracellularly and that as their concentration decreased, PVD production increased; that is, the QS network model reproduced known biological behavior and worked according to the expected cellular dynamics concerning the QS systems  forms a complex with the Fur protein, Fur/Fe2+, that binds to the promoter regions of pvd genes (iron-repressible or iron-regulated genes), thus inhibiting their transcription. Under limiting intracellular iron concentrations, the Fur-mediated repression decreases, and a positive transcriptional regulation of pvd genes ensues [3+ complex at the extracellular level, its subsequent entry into the cell via an ABC-type transport system, and finally, to the release of Fe2+ into the cytosolic space.The proposed deterministic QS network model emulated in silico the natural behavior of  systems . It is is ensues ,64,65. TP. aeruginosa [On the other hand, it should be noted that the simulated extracellular PVD B behavedruginosa . Thus, tP. aeruginosa [In addition, the model acquired the emerging property of resilience, characteristic of adaptive biological systems. Although this property was not modeled, it became apparent when the model was disturbed as the QS regulatory system responded to outputs to establish a new dynamic equilibrium that reached a steady state. The generated equilibrium resisted disturbances, understood as external forces of change. The in silico results were similar to those reported on the natural behavior of QS mechanisms in ruginosa ,71.P. aeruginosa by combining two networks with different temporal and spatial scales, namely, the QS communication network responsible for regulating the expression of pvd genes and the bacterial metabolic network of PVD biosynthesis.This study proposed a systemic approach to design an integrative multiscale model to understand the influence of the QS phenomenon on the expression of the PVD metabolic phenotype of The CCBM1146 metabolic network model is an improved version of the genome-scale metabolic network iMO1056 developed by Oberhardt et al. . The CCBPseudomonas aeruginosa in cultures incubated under controlled conditions—intended to validate some of the results of the CCBM1146 model—showed that the stationary phase was reached between 14 h and 15 h of culture , which, like PVD, has a high iron affinity, and the newly discovered narrow-spectrum metallophore Pseudopaline (PSP), that acts as a virulent factor too and is involved in nickel and zinc uptake [The methodological strategy employed in this study and the results indicate that the proposed multiscale CCBM1146 model offers valuable insights into the influence of the QS phenomenon on the metabolism and subsequent behavior of c uptake ,76. ThisP. aeruginosa. In the future, this integrative model could help to infer the different metabolic phenotypes that may arise among other microorganisms when exposed to different concentrations of the signal molecules of their own QS circuits responsible for regulating the synthesis of their virulence factors. Furthermore, the DFBA approximation applied to the CCBM1146 model evidenced its descriptive capability for the profiles of biomass and PVD, showing a similar behavioral pattern of an in vitro culture of P. aeruginosa; this makes it more valuable and suitable for evaluating, in the future and from holistic and dynamic perspectives, how different conditions can affect the behavior of P. aeruginosa for the synthesis of different virulence factors regulated by QS. From a systemic perspective, this work presents a methodological strategy to design a novel multi-scale and multi-class model proposal by combining two classes of models with different scales. The CCBM1146 model helped to characterize and explain the complex and emerging behavior derived from the interactions between these two models, which would have been impossible by studying each model or scale separately. Moreover, the CCBM1146 model served to understand and infer the natural influence of PQS intensity on the metabolic PVD biosynthesis in P. aeruginosa metabolic network for PVD biosynthesis. Combining these two network models was possible by fixing the fluxes of reactions shared by both models as system constraints in the multi-stage FBA and DFBA approximation. Thus, it was possible to model the influence of the QS phenomenon on the P. aeruginosa metabolism to PVD biosynthesis as a function of QS signal intensity.Moreover, the model could be used to test experimental conditions in vitro. Finally, this work proposes, for the first time, the integration of the quorum-sensing gene regulatory network with the However, though in this work a sensitivity analysis was performed for the QS network model, for further modeling works under the steady-state and dynamic-state flux balance analysis approach of the CCBM1146 integrated model, it is recommended that a sensitivity analysis be performed.


Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC–MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-β-d-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.Tomato ( In pathogenic invasion, a cascade of complex but integrated metabolic and molecular networks are triggered, leading to the real time activation of plant responses to influence sensing and cross talk between the interacting partners3. Progression of the disease activates different metabolic pathways in plants for the biosynthesis and accumulation of primary and secondary metabolites that either help them in instigating their survival ability by maintaining minimal growth or reducing pathogen invasion by organizing immune responses to combat the disease5. The biosynthesis and metabolism of chemically diversified plant-derived metabolites entwined with their diverse biological functions are typically linked with plant health, defense and survival in the environment7. Therefore, identifying chemical diversity, mapping the pathways with biological functions and understanding integrated responses at metabolic level in pathogen challenged plants can help prediction of the outcome of such interactions in system biology perspectives8. Such studies are important to pin point biological properties of multiscale network connections and significant biomarker metabolite signatures in plant pathogenic interactions9. Emerging metabolomics technologies entwined with bioinformatics tools and statistical modules are unequivocally becoming important to understand reprogramming of metabolites and their metabolic pathway in plants under plant-pathogenic interaction11. LC–MS is an indispensable tool not only for revealing biotic stress-linked significant biomarker metabolites that hold prospects of becoming mQTLs to assist stress breeding programs, but for exploring intrinsic cellular mechanisms that regulate plant responses in the environment also13.Pathogenic interactions in plants initiate cellular metabolic reprogramming and by doing so, host plant counteracts with the damage caused due to the progression of infection14. Tomato, as a major horticultural crop for nutrition and human health is considered as model to decipher plant responses against abiotic and biotic stress challenges16. Worldwide, tomato is highly prone to > 200 diseases, probably because of the low genetic diversity and intensive selection during domestication and evolution17. Early blight disease caused by A. solani is a devastating necrotrophic disease of tomato causing extensive damage to foliar tissues18, due to which the crop usually suffers > 50% loses under field conditions17. Pathogenic interactions with susceptible plants impair growth and development by downsizing primary metabolism, even in the conditions where the pathogen could not cause disease or death of the plants19. Aside, consequent activation of bioenergetics in secondary metabolism in stressed plants also becomes high burden at the cost of growth and productivity20. Critical perturbations in the metabolism of susceptible plants post pathogen invasion drive key outcomes of the attempted infection19. This could be observed in substantial metabolic reprogramming in key biosynthetic pathways that allow plants to synthesize and accumulate diverse metabolic capabilities to overcome diseases21. Hence, uncovering metabolic responses in pathogen-challenged and non-challenged susceptible plants could reveal key changes in the pathways of defense, signaling, hormones and primary and secondary metabolites that determine pathogen progression2. It, therefore, became imperative to explore global metabolomic profiles of plant tissues to explore cellular metabolic reprogramming for ascertaining chemical readouts of plant defense22.Throughout the world, tomato is the second most cultivated crop globally with the production of 189 million tons from 5 mha of land as per FAOSTAT 20222, our current understanding of susceptible plant responses against pathogens in terms of their intrinsic chemical diversity and metabolic pathway modulations is rather limited. We hypothesize that a comparative mapping of the global metabolome profile in susceptible variety of tomato grown under pathogen-challenged and non-challenged condition could reveal metabolomics response in plant tissues in unbiased manner. Accordingly, we compared the metabolome profiles between diseased and control plant leaf tissues and identified biomarkers that not only significantly discriminated the two contrasting samples but were also linked with key metabolic pathways which influenced plant growth, development and defense under pathogenic challenged conditions. Results from this study could pave the way for developing screening methods for resistance and susceptible crop varieties based on metabolite profiling, designing tools for disease diagnostics based on key biomarker metabolites and targeting mQTL development based on signature markers, defense-linked metabolic pathways and metabolite traits for their implications in stress breeding programs in the crop against biotic challenges.Although plant pathogen interactions were studied at biochemical and molecular levelsA. solani pathogen-challenged (KATR) and non-challenged (KANTR) tomato leaves was made to understand intrinsic metabolic changes. The disease incidence score of 67.6% indicated severity of the invasion in susceptible plants after 3 days of pathogen inoculation , peak intensity and p values] elucidated signature biomarker metabolites having critical impacts against pathogenic invasion in the samples.A comparative metabolite profiling of ion Fig. a, followion Fig. b and subion Fig. c using Lt-test at p ≤ 0.05  analysis suggested 481 significantly up- and 548 down regulated metabolite features  and 17.5% at component 2 (y-axis)  (x axis) having a value between + 1 to −1 showing features of unique m/z and rt with high covariance to depict discriminatory biomarker features  was the key annotated metabolite with maximum FC (11.84) followed by glucose 6-phosphate , sphingosine ,zeatin-7-β-d-glucoside  and serotonin  , 4-aminobutyraldehyde , propanoate , tyramine  and linalool  along with other metabolites , uridine (m/z 236.6783), homocyctine (m/z 207.6843), adenosyl-homocysteine (m/z 383.18347), guanosine cyclic monophosphate (cGMP) (m/z 344.18148), tyrosine (m/z 152.05662), pantothenic acid (m/z 220.11954), adenosine (m/z 268.10376), adenosine diphosphate (m/z 428.30032), azmaline (m/z 327.27134) and riboflavin (m/z 377.13184) out of 25 top selected features. Among these biomarker metabolites, apigenin-7-O-glucoside, uridine, adenosyl homocysteine, cGMP, tyrosine, pantothenic acid and riboflavin were up-regulated  database. Two major pathways having maximum metabolite hits were ‘metabolic pathways’ (n = 530) and ‘biosynthesis of secondary metabolites’ (n = 416)  high impact in pathogen challenged tomato plants included those involved in the biosynthesis of diterpenoids (PI 1.0001), brassinosteroids (PI 1.0), sesquiterpenoid and triperpenoids (PI 1.0), steroids (PI 0.925), secondary metabolites (PI 1.0), carotenoids (PI 0.837), ubiquinone/other terpenoids (PI 0.891), valine, leucine/isoleucine (PI 0.817) and monoterpenoids (PI 0.375)   was grouped with significantly high enrichment to include isoprenoids (n = 52), fatty acids and conjugates (n = 42), sterols (n = 39), amino acids and peptides (n = 30), monosaccharides (n = 31), prenol lipids (n = 21), quinols and hydroquinones (10), cholines (n = 2), phenylpropanoids and phenols (n = 5), sphingonoid bases (n = 6) and phenylacetaldehydes (n = 3) all showing statistically significant S. lycopersicum database was shown to the relatedness of metabolites with specified set of pathways. The ‘cellular overview’ of the pathway collage diagram uniquely helped in showing specific pathways of the up and down regulated annotated metabolites  and revealed significantly up (481) and down (548) regulated metabolite features at FC threshold (≥ 2.0). Functional analysis further linked enriched metabolite features with diverse metabolic pathways and putative annotation of metabolites was also obtained. The diversity in metabolite features and their differential regulation reflected dynamic and influential metabolic reprogramming in leaf tissues post pathogen inoculation, as has been observed in other plant-pathogen systems26.We examined metabolome of a less explored plant pathogen system of susceptible tomato-24 after proper cross validation based on R2Y and Q2 values, where positive Q2 signifies non-overfitting25. Cross model validation in metabolomics data has largely been advocated in the experiments with low numbers of samples with large variables for the accurate inference of class differentiation27. Top 25 metabolite features having VIP score > 1.0 from both the models were believed to be the predominantly abundant discriminant metabolite features supposed to be involved in prominent biological role against biotic stress in tomato plants.PLS-DA and OPLS-DA results not only showed clear discrimination between the groups, but also showed predictive ability to reveal critical importance of potentially annotated differentiating discriminant metabolites with VIP score > 1.0 exerting high discriminatory power Figs. , 4. Thes30. Terpenoids, steroids, alkaloids, porphyrins, polyphenols, folate derivatives and indoles were typical differential secondary metabolic groups accumulated in plants as an impact of disease challenge. Melatonin and serotonin derived from catalysis of amino acids in tomato are reported to regulate plant growth under stress31. These two indoleamines are being reported for the first time in tomato in response to early blight (A. solani). They possess antioxidative and growth-inducing properties and provide beneficial opportunities for stress acclimatization32. Porphyrins and other metabolites pyropheophorbide a and porphobilinogen are the precursors of chlorophyll and vitamins that regulate stress-responsive genes and ROS-mediated complex metabolic networks33. Other antioxidants showing differential response included vitamins , are potentially known to minimize ROS induced cell damage under biotic stress34.Among the up-regulated 41 discriminant metabolite signatures, lipids were the major class followed by carboxylic acids, amino acids and fatty acids. These class of metabolites are supposed to mediate signaling and modulate effector-triggered systemic immunity in plants35. Metabolites like palmitic acid, linoleic acid, 2,3-Dihydroxy-3-methylpentanoate and others including pantothenate and gibberellin A9 which are known antioxidants, metabolic signals, mediator of biosynthetic pathways, antimicrobials, resistance inducers and are structural cellular components38 were also down regulated. A shift from primary to secondary metabolism and compromise in production of primary metabolites over secondary metabolites under stress conditions has been observed in plants39. Also, for a favorable energy balance required for defense, up regulation of defense-linked metabolic pathways seems compensated by the down regulation of genes for other pathways19. In such circumstances, down regulated accumulation of secondary metabolites in susceptible tomato plants may be considered as become pathogen-mediated cellular response of susceptibility that may influence disease progression in plants40.Majority of the nitrogenous metabolites including tyramine, histidinol, phenylethylamine, betaine aldehyde, pyroglutamic acid and N-acetylserotonin were down regulated in pathogen-challenged plants. Plant responses to biotic and abiotic stressors are controlled by the interaction between nitrogen (N) and polyamines (PA), which is connected to secondary metabolic pathways41 based on the potential discriminant performance of metabolite features42 as shown by the AUC being 1  was shown to represent metabolic disorder in plants under challenged conditions46. Enrichment of cysteine and methionine metabolism has been reflected by our results 48. Identification of cGMP as biomarker reflected its important role during pathogenic stress response as has been suggested49. Tyrosine was found to be up regulated biomarker in the results and tyrosine metabolism, like phenylalanine and tryptophan metabolism are interlinked with phenylpropanoid pathway which further relates to plant defense50. Pantothenic acid (vitamin B5) being the core of coenzyme A, is essential molecule in plant metabolism51 and its up-regulated accumulation as well as enrichment of pantothenate/CoA biosynthesis   susceptible tomato (S. lycopersicum var. Kashi Aman) was grown in glass house conditions and inoculated with the pathogen72. Briefly, seeds were surface sterilized by using 2% sodium hypochlorite solution for 5 min and after drying, were seeded in earthen pots (20 × 20 × 14 cm) having 5 kg sterilized mixture of soil, cocopeat, perlite and vermiculite . After 3 weeks, seedlings were transplanted in 10 separate pots and allowed to grow in glass house conditions. Lower leaves were inoculated with the pathogen inoculum (2.1 × 104 conidia mL−1) at early flowering stage (60 days) using sterilized bruise73. Pots were kept for disease development under 86–90% relative humidity. After 3 days of inoculation, upper leaves were collected from diseased and control (non-inoculated) plants for metabolome extraction. The disease incidence (%) was obtained at the time of sampling as reported earlier72. Plants (either cultivated or wild), including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislations.Early blight  and dried on rotary evaporator (50 °C). Dried extracts from replicated samples were reconstituted in 1 mL HPLC-grade methanol and filtered through 0.22 µM syringe filter. Samples were pooled together and 200µL of the mixed and vortexed solution was transferred to capped dark glass vials at 4 °C. For each sample 3 biological replicates from two independent experiments were analyzed by LC–MS/MS. The analysis included quality samples comprising blanks and technical replicates as described by Mhlongo et al. (2021)74.For capturing comprehensive number of both polar and non-polar metabolites from plant leaf samples, 2 g of frozen leaf tissues were pulverized and extracted by sonication in triplicate with HPLC grade methanol (5 mL). Samples were centrifuged at 6000−1 for 31 min. The elution conditions were initially 5% solvent B for 0 to 2 min, further increased from 5 up to 95% (solvent B) for 2.01–22.0 min, then 95% for 22.01–27.0 min and finally 5% for 27.01–31.0 min.Leaf extract samples from pathogen-challenged and non-challenged plants were separated using Dionex Ultimate 3000 HPLC system 15µL per sample as injection volume was analyzed on Hypersil Gold C-18  column at 35 °C using mobile phase (A: 0.05% formic acid in water and B: 0.05% formic acid in acetonitrile) at a flow rate of 350 µl minm/z) was obtained in negative ionization mode by Q Exactive mass spectrometer  coupled to HPLC system at the first order resolution. The ion source parameters were sheath gas flow rate 60 au (arbitrary unit), aux gas flow rate 20au, sweep gas flow rate 10 au, capillary voltage (+) 3.2 kV and capillary temperature 275 °C. The S-Lens level was kept at 55 rf and probe and aux gas heat temperatures remained at 250 and 350 °C, respectively. Sample run was performed in three technical replicates along with quality control blank samples.Data acquisition on full scan MS range  25-45 eV, maximum IT 50 ms, loop count 5, MSX count 1, resolution 35,000, microscans 1 and isolation window m/z 2.0. Further, Data Dependent Acquisition (DDA) and automated precursor ion selection methods were described by Singh et al. (2023)75. For MS2 spectra, two blanks and three quality checks (QCs) were ensured during system conditioning.Tandem mass spectrometry (MS/MS) parameters improve mass fragmentation patterns. For MS/MS, scan range was 100–2000 75. The raw files were pre-processed and analyzed to yield peak intensity, retention time (rt), abundance, missing values and adjoint ion combinations after appropriate normalization of metabolite features (m/z). During analysis, data normalization was performed with the combined preset parameters grouped into three categories in MetaboAnalyst 5.0  by selecting median, log transformation (base10) and Pareto scaling. The overall annotation of metabolite features was performed by Functional Analysis module using Pathway Enrichment and Network Analysis sub-modules. Statistical (one factor) and chemometric analysis was performed to establish distinction within the groups and identify discriminant metabolite biomarkers.The raw data generated from LC–MS/MS was subjected to Compound Discoverer 3.3  for data analysis as per the parameters described previouslym/z, p value, t score and rt as input file and generated annotated compounds having m/z, rt, KEGG identifiers (ids) of the matched metabolites and ion forms based on Arabidopsis thaliana plant database. The KEGG ids when submitted to PLANTCYC PMN using S. lycopersicum database (http://plantcyc.org) resulted in the annotation of the metabolites as per the Metabolomics Standards Initiative (PI-level 2 annotation)76 reporting minimum standards for chemical analysis78. Pathway mapping was obtained using KEGG Mapper. Putatively annotated biomarker metabolites were validated for their robustness using Multivariate Exploratory Receiver Operating Characteristic (ROC) Curve analysis that was based on three multivariate algorithms, support vector machines (SVM), partial least squares discriminant analysis (PLS-DA) and random forests in MetaboAnalyst 5.0. Pathway Collage, a diagram containing user-specified set of pathways of the annotated putative metabolites was prepared using PMN (Pathway Collages (plantcyc.org) while metabolite-metabolite interaction was performed by MetaboAnalyst 5.0.Functional annotation of the metabolite features was obtained from MetaboAnalyst 5.0  which required 79, which comprised nodes as input metabolites and edges as the association measures, correlation network was developed. For other purposes, SPSS 16.0 was applied for one-way ANOVA  using Student t-test.Univariate and multivariate data analysis was performed by Statistical Analysis (one factor) module of MetaboAnalyst 5.0. By the means of Debiased Sparse Partial Correlation (DSPC) networkThe study explored metabolomic responses of susceptible tomato variety Kashi Aman challenged with early blight pathogen. Out of 8487 infection-exclusive and 8742 non-infection exclusive metabolite features, 481 significantly up- regulated and 548 down regulated features were identified. OPLS-DA model based VIP score ≥ 1.0 coupled with FC threshold ≥ 2.0 suggested 43 up regulated and 23 down regulated annotated discriminant metabolites which belonged to different group of compounds arising from various metabolic pathways. Pathway mapping revealed that the annotated features with KEGG ids were distributed across 106 biosynthetic pathways of primary and secondary metabolism. Secondary metabolism pathways predominantly included secondary metabolites (other antibiotics), steroids, brassinosteroids, mono-, sesqui-, di- and triterpenoids, carotenoids and ubiquinone/other terpenoids. Network impact analysis indicated dominance of those pathways which aid to modulate plant biological responses towards defense, resistance, and/or survival. The ROC-based annotated biomarker metabolites apigenin-7-glucoside, uridine, homocyctine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, adenosine, riboflavin and azmaline could be considered as potential diagnostic biomarker biomolecules in tomato leaves in response to pathogen challenge. Deciphering pathogen-driven key metabolic changes in tomato leaf tissues, altered metabolic pathways influencing plant responses to pathogen and potential biomarkers in challenged plants using metabolomics could help in designing diagnostic tools. The identified strong discriminant metabolites may help in developing metabolite-QTLs (mQTLs) having implications in molecular breeding for resistance in crop plants against biotic stresses. Results from the pathway mapping may also be implicated for targeted modifications in key pathways through biotechnological tools including genome editing coupled with the classical breeding for improving crop performance against pathogens in the field.Supplementary Figure S1.Supplementary Figure S2.