Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes ( “Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.” Metabolic knowledge gaps also hamper the advancement of bioengineering applications like the creation of sustainable cell factories for the green production of commodity chemicals and pharmaceuticals. Key examples of metabolic dark matter include underground metabolism resulting from promiscuous enzymatic activity5, undetected plant natural products and their uncharacterized biosynthesis pathways, and chemical damage of metabolites6.Metabolic “dark matter” designates biochemical processes where knowledge is still sparse, limiting our general understanding of metabolism, the discovery of key disease mechanisms8, and major gaps in biochemical and metabolic knowledge remain. As an example, 25% percent of proteins in E. coli, one of the best studied model organisms, do not have a function assigned9. In addition, almost 10,000 metabolites in the Kyoto Encyclopedia of Genes and Genomes (KEGG)10 are considered to be orphan, meaning that they are not integrated in any biochemical reaction within KEGG. An unbiased and global approach is needed to systematically explore the metabolic dark matter arising from the elasticity of enzymatic catalysis, which can be achieved by current computational approaches.Given the vastness of these and other unknown metabolic elements, it is essential to generate hypotheses on potential biochemical functions that guide the experimental discovery of enzymatic functions and natural products. While genomic, transcriptomic, proteomic, and metabolomic data have the potential to generate important hypotheses on metabolic dark matter, linking these data to metabolic functions remains difficult15. Identification of these biosynthetic routes is accomplished by biochemically “walking back” from the target to precursor metabolites that are produced by, or fed to, the host organism. This procedure is called retrobiosynthesis and is implemented in a range of tools such as BNICE.ch17, GEM-Path18, NovoPathFinder19, NovoStoic20, ReactPRED21, RetroPath23, and Transform-MinER24. Retrobiosynthetic methods rely on the concept of generalized enzymatic reaction rules. A reaction rule encodes the biochemistry of a substrate-promiscuous enzyme by describing the pattern of the reactive site recognized by the enzyme, as well as the bond rearrangement performed by the enzyme on the substrate. By applying the rule on a substrate that is non-native to the represented enzyme, the rule can predict if (i) the substrate can be recognized by the enzyme, (ii) if the biotransformation can occur, and (iii) the identity of the product molecule(s). While tools featuring reaction rules have the power to predict biochemical reactions, their application is usually limited to a given research or engineering question.Computational approaches to biological questions have attracted increasing interest over the past few decades. Diverse computational tools have emerged that can bridge knowledge gaps in metabolism through cheminformatic predictions of uncharacterized metabolic reactions, metabolites, and enzyme functions. Most of these tools have been developed for metabolic engineering applications, where the objective is to find biosynthetic routes that produce a desired target compound in a host organism10, which attempts to map dark matter in biochemistry by predicting reactions between metabolites from the KEGG database25. ATLAS contains ~150,000 hypothetical enzymatic reactions predicted by the retrobiosynthesis tool BNICE.ch and annotated with putative enzymes suggested by the enzyme prediction tool BridgIT26. In contrast to other tools that use automatic rule generation, the reaction rules in BNICE.ch are designed by experts based on biochemical knowledge and assigned the corresponding three-level Enzyme Commission (EC) number, which is a numerical coding used to classify enzyme-catalyzed reactions. The complementary tool BridgIT uses the knowledge of the reactive site encoded in the BNICE.ch rules to predict enzymes that can potentially catalyze hypothetical and orphan reactions. More than 100 reactions predicted by BNICE.ch and stored in ATLAS were validated in 2019 following their addition to KEGG, supporting the predictive utility of the ATLAS tool27. In addition, Yang et al. were able to experimentally validate predicted ATLAS reactions while designing one-carbon assimilation pathways28, further demonstrating the value of predictive ATLAS reactions in metabolic engineering. One major drawback of ATLAS is its limitation to KEGG compounds, which excludes many drugs and plant natural products with undefined or putative biological functions. Predicting enzymatic reactions from biochemical compounds retrieved from databases other than KEGG will help expand the scope of our predictions and enhance the application range and the predictive power of the database.One exception is the ATLAS of Biochemistry databasehttps://lcsb-databases.epfl.ch/Atlas2. The ATLASx platform can be readily used for the design of metabolic pathways, and for the exploration and expansion of biosynthesis pathways. ATLASx distinguishes itself from other tools and web services by providing a database of predicted reactions at an unprecedented scale, annotation quality, and user-friendliness   focus on metabolites and their enzyme-catalyzed interconversion within metabolic pathways and networks and contributed 22,447 (1.5%) of the total compounds within bioDB. The remaining 1,477,775 (98.5%) compounds were contributed by databases covering all compounds produced by, or known to interact with, biological systems . Remarkably, the lion’s share of these bioactive compounds  came from ChEMBL, suggesting that this database has the most comprehensive definition of bioactivity. The distinction between biological and bioactive compounds reflects the level of biochemical knowledge available for a given compound, i.e., the metabolic interactions of a compound present in both biological and bioactive databases tend to be better characterized than the interactions of a compound only found in a bioactive database. Based on our unification criteria, we found that the ratio of unique compounds varied across databases. The KEGG COMPOUND database had the highest number of unique compounds , while KEGG DRUGS had the lowest ratio , illustrating the heterogeneity of curation standards between different resources.36 . PubChem entries that could not be matched to any existing compound in bioDB were assigned to the chemical compound space (chemDB) in our database, regardless of their true origin . Though some of the 77,934,143 unique compounds from the chemical space may still be of biological origin, they may not be labeled as such, or may have the potential to be derived from biological compounds in a bioengineering setting. This artificial classification of biological and chemical compounds presents an opportunity to re-assign compounds from the chemical space to the biological space by integrating them into hypothetical biochemical reaction.To pave the way for integrating chemicals into biochemical pathways, we imported all compounds from the chemical database PubChem37, HMR38, Rhea39, BiGG models40, SEED41, MetaNetX32, MetaCyc31, Reactome42, and BKMS-react43, and merged into 56,087 unique bioDB entries  and other reactions that were not elementally balanced . We found that 56%  of total reactions were well-balanced . We found that 1,498,307 out of 1,500,222 (99.8%) of collected biological and biochemical compounds had at least one reactive site , or bioactive compounds45  with the capacity to be transformed by enzyme catalysis.To explore the hypothetical biochemical space and map metabolic dark matter, we used biological and bioactive compounds within bioDB as a seed for reaction prediction. Hypothetical reactions were predicted by applying 489 bidirectional reaction rules from BNICE.ch to the 1,498,307 compounds with at least one reactive site in bioDB. Reactions whose products were part of the assigned biological and bioactive compounds space were stored in the bioATLAS data collection, and reactions for which at least one product was only found in the chemical compound space were stored in chemATLAS. In total, we reconstructed 11,172 of the metabolic reactions in bioDB, and we predicted 5,236,833 hypothetical reactions from biological and bioactive compounds  to bioATLAS (27 steps), and increased again in chemATLAS (34-step diameter), suggesting expansion of the network towards uncharacterized chemistry and integration of previously disconnected components. The average path length increased monotonically from 7 in bioDB to 9 in bioATLAS, and to 12 in chemATLAS, reflecting the continuous growth of the main component  during the network expansion process.We created graph-representations of each network, where nodes represent compounds, and edges represent biotransformations between two compounds (detailed in the “Methods” section). For each of the three networks, we counted the number of connected components  Fig. , and we 48. While the steps (i) and (ii) are meant to expand the solution space, steps (iii) and (iv) allow to narrow it down to the number of pathways that can be tested experimentally . Steps (i) and (ii) are automated in ATLASx, and step (iii) can be performed online for a selected pathway by using the integrated BridgIT functionality, making it a valuable tool for the first three steps in pathway design. To support our claim, we validated the ATLASx pathway search on a set of well-characterized biosynthetic pathways obtained from the MetaCyc database.The quest for biochemical pathway alternatives is a key challenge in the bioproduction of natural and chemical compounds in chassis organisms, the elucidation of complex natural product biosynthesis, and the study of biodegradation routes. Standard pathway design pipelines include four main steps: (i) creation of the biochemical network; (ii) pathway search; (iii) enzyme assignment; (iv) pathway evaluation in the chassis model49 to search for pathways within the chemATLAS reaction network, and we examined whether ATLASx could recover the pathways from the MetaCyc validation set.For this benchmark, we applied the algorithm implemented in NICEpathWe found that out of the 3149 collected MetaCyc pathways, 1131 matched our curation standards (see “Methods”). For each MetaCyc pathway, we determined the precursor and the target compound. We then used the pathway search within ATLASx to extract biochemical routes connecting the precursor to the target, and we compared the extracted pathways to the original MetaCyc pathway. We were able to find pathways for 99% (1117 out of 1131) of precursor-target pairs within the whole ATLASx Fig. . We perfhttps://lcsb-databases.epfl.ch/Search2. Users can adjust the network scope as discussed above, as well as perform database-specific search scopes for all of the imported reaction databases.Our evaluation shows that known biochemical pathways can be diligently reproduced using ATLASx. The pathway search tool is available online at 50. According to KEGG, the biosynthesis of staurosporine from tryptophan involves 7 reaction steps, most of them being poorly characterized. To explore the biochemical vicinity of this pathway, we retrieved all compounds that were one step away from the original pathway intermediates. Out of 861 potential pathway derivatives, 60 were found exclusively in bioDB, 407 were contributed by bioATLAS, and the remaining 394 compounds were only integrated when chemATLAS reactions were considered. According to our analysis, most derivatives  were detected around tryptophan. Secondary hubs were found around K-252c and staurosporine, with each hub contributing 3% (24 compounds) and 3% (22 compounds) to the total number of pathway derivatives, respectively. Intrigued by the high number of potential staurosporine derivatives, we explored the network around this molecule and found 58 derivatives within a distance of four reaction steps , followed by 7-hydroxystaurosporine  and then K-252c , which is part of the staurosporine synthesis pathway. Midostaurin, a cancer therapeutic and protein kinase inhibitor commercially known as Rydapt52  and was one step away from staurosporine. While “popularity” estimation of chemicals is one of many possible ways to organize and rank compounds, other metrics such as molecular weight, toxicity, or structural similarity to known pharmaceuticals may be applied depending on the objective of the study. This example illustrates how to explore the biochemical vicinity of a compound or pathway within ATLASx, and to retrieve information  from external sources to rank derivatives based on their academic and industrial relevance.To characterize the potential staurosporine derivatives we identified, we retrieved the number of patents and citations associated with the compounds in the extracted network, and we ranked the compounds by the sum of patents plus citations. These metrics have been previously established to extract compounds with high industrial, pharmaceutical and academic interestWe further investigated the capability of ATLASx to detect and bridge knowledge gaps in the biosynthesis pathway of staurosporine. Out of the 7 reaction steps in the pathway obtained from KEGG, only one reaction is linked to an enzyme. The other 6 reactions are orphan , and half of these orphan reactions have unknown reaction mechanisms Table . To show53. Another orphan reaction in the staurosporine pathway is the conversion of 3′-demethylstaurosporine to O-demethyl-N-demethyl-staurosporine (step 6). For this reaction, BridgIT suggested that an N-formiminotransferase serves as a catalyzing enzyme (EC 2.1.2.5), although this prediction is accompanied by a relatively low BridgIT score of 0.34. Finally, the last step of the pathway is known to be catalyzed by an O-methyltransferase with EC number 2.1.1.139. In this case, BridgIT successfully mapped this reaction to itself and found the corresponding enzyme. Interestingly, we also found one reaction step in the pathway that could not be reconstructed by any suite of reaction rules. Although the reaction is known to be catalyzed by an enzyme of the cytochrome P450 class and assigned the partial EC number 1.13.12.-, no information on its reaction mechanism is available from public databases and scientific literature. We therefore hypothesize that the reaction mechanism of this enzyme either involves intermediate molecules that have not been characterized yet, or it harbors reaction mechanism that has not been observed before in nature.The first step of the pathway, the conversion of L-tryptophan to IPA imine, is identified with the partial EC number 1.4.3.- by KEGG. The computational tool BridgIT, which uses enzyme annotations of non-orphan reaction to suggest enzymes catalyzing similar orphan reactions, proposed the enzyme 7-chloro-L-tryptophan oxidase (EC 1.4.3.23) as the best candidate to catalyze this first step. This predication was bolstered by a high BridgIT score of 0.95, which indicates that both substrates have a similar reactive site and surrounding structure. While the native function of this proposed enzyme is to convert 7-chloro-L-tryptophan to 2-imino-3-(7-chloroindol-3-yl) propanoate, the activity of this candidate enzyme on L-tryptophan has been proven in a study by Nishizawa et al.https://lcsb-databases.epfl.ch/Atlas2) provides further query tools, such as the ability to identify all reactions associated to a query compound.This showcase exemplifies how BridgIT can be used on top of the BNICE.ch reaction prediction to find enzymes for hypothetical or orphan reactions and to fill gaps in metabolic pathways and networks. All of the presented analyses can be performed using the computational tools available online, in combination with open-source visualization software as detailed in the Methods section. We provide public access to our database through an online search interface, which includes a powerful pathway search algorithm that can be used for the design of metabolic pathways. The web access to ATLASx . We further predicted more than 3.6 million reactions that involved compounds from the chemical compound space, resulting in a total of almost 5.2 million in chemATLAS. From this wealth of information, we extracted insightful numbers on the reactivity and connectivity of biologically relevant molecules.Assessing the composition of metabolic “dark matter” is by definition difficult, since we lack a way to quantify the unknowns a priori. Fortunately, biochemical data collected and generated from our database allows us to answer a broad range of questions regarding the biochemical reactivity of compounds, the expansion of biochemical space from a graph-theoretical perspective, and the characteristics of our hypothetical reaction network. Potential applications of ATLASx include the prediction of bioproduction or biodegradation pathways involved in the transformation of commodity and specialty chemicals, pharmaceuticals, and plastics. ATLASx can also be used to discover the biosynthesis routes of poorly characterized secondary metabolites, and systematically fill in knowledge gaps surrounding metabolic models. For example, ATLASx can be used to expand the network around all compounds within a given metabolic model, remove dead-end metabolites, and then examine the expanded model for potential shortcuts, enhanced predictions, and enzymatic promiscuity.Since we successfully integrated tens of thousands of chemical compounds into a biochemical network, we hypothesize that many compounds are not yet part of any database, even though they potentially exist in nature or could be created by metabolic engineering. While the integration and accurate prediction of hypothetical compound structures remains an open challenge, ATLASx provides the necessary tools and conceptual framework to predict hypothetical compounds reliably in the future. In order to properly meet that future, ATLASx is designed as a dynamic database, and can be continuously expanded around biochemical pathways or compound classes of interest. We believe that predictive biochemistry is crucial for the advancement of synthetic biology and metabolic engineering, and hope that ATLASx can provide reliable reaction and pathway predictions for the scientific community.25, SEED29, HMDB30, MetaCyc31, MetaNetX32, DrugBank33, ChEBI34, ChEMBL35, BRENDA37, Rhea39, BiGG models40, Reactome42, and BKMS-react43 by January 2019. To efficiently store, retrieve, and analyze the increased amount of data, we created an SQL-based database where we imported the collected data (bioDB). The compounds were filtered to keep only those that had a defined, single molecular structure . Compounds were merged using their canonical SMILES as a unique identifier, which was calculated using OpenBabel54 version 2.4.0. As a result of the unification procedure, a unique compound entry in bioDB can contain different resonance forms, stereoisomers, as well as dissociated and charged states of a same compound. Since ATLASx does not distinguish between stereoisomers, users who wish to include stereochemical considerations are encouraged to post-process ATLASx output with external cheminformatic software  to expand the 2D structures to all possible 3D stereoisomers structures.We developed a unification algorithm based on canonical SMILES to generate consistent and duplicate-free data. Data was collected from KEGGReactions were unified based on the structure of their reactants and products. Transport and stereoisomeric reactions were filtered to only keep reactions that modify the connectivity of atoms within the molecule. Unified compounds and reactions were annotated with all available identifiers from different databases54 version 2.4.0. Reactions were annotated with enzymatic reaction mechanisms obtained from BNICE.ch. We further estimated the standard Gibbs free energy of the reactions in cellular conditions using the Group Contribution Method (GCM)55.Compounds were annotated with the following structural descriptors: chemical formula, molecular weight, InChI, InChIKey. For the annotation we used OpenBabelWe used BNICE.ch to determine the reactivity of compounds. For this, we applied all of the 489 bidirectional generalized reaction rules available in BNICE.ch to the unified set of compounds. The collection of BNICE.ch reaction rules has been continuously expanded in the past to account for a wider range of biochemical reaction mechanisms , or they define a set of cofactors to be excluded from the analysis to avoid the generation of hubs by currency metabolites. Here, we rely on NICEpath, a pathway search tool proposing a graph representation of metabolic reactions that weighs each substrate-product pair according the number of atoms conserved between the substrate and the product49. NICEpath has been previously shown to be able to retrieve relevant pathways from biochemical networks, and to help in understanding the inherent properties of biochemical networks .Most available methods for network analysis are based on manually derived reactant-product pairs  and with respect to the product (CARp), where nc is the number of conserved atoms between the reactant and the product, nr is the number of atoms in the reactant, and np is the number of atoms in the product. Hydrogen atoms are excluded from the calculation.For each pair, the conserved atom ratio (CAR) is calculated with respect to the reactant . If no pre-defined cofactor pair could be identified, we estimated the number of common atoms using the maximum common substructure algorithm (implemented as FMCS library in RDKit). For the remaining pairs, the number of conserved atoms was calculated based on the assumption that the maximum possible number of atoms is conserved between a given substrate-product pair . For the remaining reactant pairs, the atom conservation was assumed to be equal to 0.For bioDB reactions that could not be annotated with a BNICE.ch reaction mechanism, To create a weighted, searchable biochemical network, the CAR of a substrate-product pair is then transformed into a distance between the substrate node and the product node, and the two are connected by an edge representing the biotransformation.k-shortest loop-less path search algorithm to retrieve the top k pathways with the highest atom conservation from the source to the target compound58. Here, we analyze the atom-weighted networks to derive global properties of known and predicted biochemistry.The result is a reactant pair network where substrates and products that conserve more atoms lie closer to each other than reactants that conserve less atoms. Since a same biotransformation, or substrate-product pair, can occur in more than one reaction, each edge in the network represents all the reactions that transform the same substrate to the same product. The resulting atom-weighted biochemical networks can be searched for metabolic pathways using common graph-search algorithms. For example, NICEpath uses Yen’s 57. Hence, we removed edges with a CAR below 0.34, and we removed the weights from the remaining edges. The result is a set of unweighted graphs, which can be analyzed using standard graph analysis algorithms . The code used to perform the network analysis and to create the figures in the manuscript is available at https://github.com/EPFL-LCSB/ATLASxAnalyses, including documentation to reproduce the presented results59.We constructed three networks with different biochemical scopes: The 56,087 reactions in bioDB translated into 62,299 weighted edges connecting 14,914 bioDB compounds. The bioATLAS network connects 844,337 compounds in 1,590,057 reactions, represented by 2,503,627 edges in the network, and chemATLAS connects 1,876,992 compounds in 5,248,711 reactions, represented by 5,717,409 edges  of a given reaction, and we searched to connect the substrate of the pair to the product. To do this, we extracted the shortest path within the weighted reaction network of chemATLAS, considering only BNICE.ch curated reactions. If the substrate and the product could be connected in k reaction steps, and the product of the CARs along the path did not drop below the threshold of 0.34, we called the reactant pair https://metacyc.org/smarttables) on May 25, 2020. The pathways used for benchmarking had to pass the following criteria: The pathway (i) consists of a minimum of two reactions, (ii) it does not contain transport reactions and electron-transfer reactions, (iii) it does not contain reactions that are not listed in the MetaCyc reactions table, (iv) it does not contain compounds with undefined structure, non-carbon compounds, proteins and peptide polymers, RNA molecules, unknown compounds, (v) it is not circular (as we are comparing linear pathways), (vi) is not a polymerization pathway , (vii) is not a light-dependent pathway, (viii) is not a superpathway .MetaCyc pathways and reactions were downloaded from MetaCyc  from the pathway graph to minimize the possible number of shortcuts. We found the shortest loop-less linear path between every pair of compounds in this graph, and we considered the longest of these pathways (therefore the 2 more distant compounds) as the linear pathway of reference (e.g. longest branch of the branching pathway). The resulting dataset was further manually checked to exclude the pathways that were incorrectly extracted in the automatic procedure described above . For each intermediate, the LCSB ID was obtained using the online compound search interface, and the IDs were compiled into a compound list. The ID list was then used as input to extract all compounds one reaction step away from the native pathway for bioDB, bioATLAS, and chemATLAS scopes . The default values were applied . The resulting network file for chemATLAS was imported to the open-source graph visualization software Gephi (.csv file extension required for import). To assign the origin to each compound in the network, the list of nodes was obtained from the bioDB and bioATLAS networks, and each node in the chemATLAS network was assigned the corresponding scope. The node table was imported into the Gephi project, and nodes were colored in accordance to their scope. Finally, Gephi’s forceAtlas2 algorithm was applied iteratively to the network and the visualization was manually improved. This analysis can be reproduced by following the user guide available on our website (https://lcsb-databases.epfl.ch/pathways/downloads/ATLASx/userguide.pdf).The biosynthesis pathway of staurosporine was obtained from KEGG (https://pubchem.ncbi.nlm.nih.gov/) on the number of patents and citations associated to each compound. We further used the Entrez Programming Utilities (E-utilities) API service to search the PubMed database for citations by compound name. To find enzymes for the predicted reaction, the BridgIT tool was used26. BridgIT is available as a resource on our website (https://lcsb-databases.epfl.ch/Bridgit).To assess the “popularity” of the staurosporine derivatives, the number of publications was derived from PubChem and PubMed, while the number of patent annotations was extracted from PubChem. The PUG-REST service was used to retrieve information from the PubChem website (https://lcsb-databases.epfl.ch/Atlas2. The visualization of compounds is achieved by SMILES viewer, a light-weight JavaScript library developed by Probst and Reymond60 that visualizes molecular structures in runtime. The pathway search within known and predicted biochemical network uses the concepts and code developed in NICEpath49. The Python library NetworkX is used for the pathway search implementation, and the network statistics were obtained using the Python libraries NetworkX and SNAP61.The ATLASx search and analysis tools can be found at Further information on research design is available in the Supplementary informationReporting SummaryPeer Review File


Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC Breast cancer is one of the leading cancers in women, with around 2.26 million cases worldwide in 2020, representing an incidence of 11.7% and a mortality of 6.9% of all diagnosed cancers . Breast The multi-target and heterogenous pharmacological mechanisms of action of natural products—and thus, their more ubiquitous efficiency—help to counteract cancer heterogeneity and prevent drug resistance . Naturalhttps://github.com/sysbiolux/Herbal_drug_prediction accessed date 14 December 2021) tailored to the complex mode of action of natural products and the lack of the complete knowledge of their targets. The pipeline requires the gene expression data of cells treated with natural products and a genome-scale reconstruction such as Recon3D as an input in order to build context-specific metabolic models via FASTCORMICS or rFASTCORMICS [50 values, indicating the good performance of the developed drug prediction pipeline.In silico metabolic models allow the simulation of the metabolism of a whole cell or an organism , both foTCORMICS ,17. The TCORMICS  were useIn order to assess the potential effect of natural products on the metabolism of breast cancer cells, and to identify promising drug candidates, a microarray expression dataset composed of the expression data of MCF-7 cells treated with 101 natural products, one mixture of natural products , and the control—dimethyl sulfoxide (DMSO) —was usedAmong these 101 natural products, human targets for only 44 drugs were found in DrugBank V5 , PROMISCAs mentioned before, no metabolic target information could be found in DrugBank V5 , PROMISCWith a high dissimilarity score for a natural product model compared to the DMSO model does not necessarily imply that the associated natural product is efficient against breast cancer, but rather that the metabolic changes induced by the natural products are different from the ones induced by DMSO. However, selecting natural products that have a similar effect to known breast cancer drugs on the metabolic models increases the likelihood of the identification of efficient drug candidates.As a first step, and in order to quantify the effect of all 26 breast cancer drugs, the dissimilarity score between the DMSO model constrained by the cancer drug targets (cancer drug models) and the unconstrained DMSO model was computed based on flux ranges obtained using Flux Variability Analysis (FVA). Models that included reactions that maintained the same flux range between two models have a dissimilarity score of 0, whereas models that have very different flux ranges will receive a score close to 1. Methotrexate and capecitabine had the highest dissimilarity scores with the DMSO model A, which In order to determine which metabolic pathway should be preferably targeted in order to reduce the growth of breast cancer, and which pathways are targeted by natural products, the drug targets of natural products and breast cancer drugs were mapped to the reaction–gene matrix (rxnGeneMat field) of Recon3D , the inpIn total, 12 natural products were described to have targets in the androgen and estrogen synthesis and metabolism pathway. However, only ten out of these twelve showed a difference in the fraction of reactions present per pathway when compared to the DMSO model see : narciclIn total, 23 out of the 101 natural products and one drug mixture were predicted as the top candidates in at least one of the four in silico analysis steps: (i) the drug deletion prediction by the natural products’ targets, (ii) the dissimilarity to the DMSO model, (iii) the similarity to breast cancer drugs, and (iv) pathway analysis. This number was further reduced for the experimental validation by selecting only those natural products that popped up in more than one of the in silico steps , green. In order to experimentally validate the in silico predictions, 2D cell viability assays were performed to determine the anticancer activity of the nine selected natural product candidates: scutellarein, emodin, bruceine D, resveratrol, hydroxysafflor yellow A, salvianolic acid B, salvianic acid A sodium, ferulic acid, and glycyrrhizic acid. Ophiobolin A, which is a natural product that covalently inhibits calmodulin and has anticancer stemness properties , was cho3 values as determined using the DSS pipeline website, BREEZE (https://breeze.fimm.fi/ last accessed on 14 December 2021). DSS3 values essentially correspond to the normalized area under the curve of dose–response curves [3 values point to a higher potency for bruceine D than for the control compound ophiobolin A in invasive Hs 578T cells, and to a similar potency of bruceine D in MCF-7 cells compared to Hs 578T cells (50 (IC50 = 0.71 ± 0.05 μM) in Hs 578T, and low micromolar IC50 in MCF-7 cells (IC50 = 9.5 ± 7.7 μM)  , which iell line . The IC5terature . No dataterature , as thestermined . AltogetIn order to understand how emodin, bruceine D, and scutellarein affect the metabolism of MCF-7 cells, the difference in the reaction presence rates between the three respective natural product models and the DMSO model was calculated. The androgen and estrogen synthesis and metabolism pathway, as well as the pathways required for the biosynthesis of estrogens such as cholesterol, steroid and squalene, and cholesterol synthesis, had a lower presence rate in the three natural product models, suggesting that estrogen synthesis is downregulated by these three drugs . Furthery-axis by their absolute differences in reactions present per pathway for bruceine D. The differences in the reactions present per pathway between the natural product and the DMSO models were calculated. Only pathways that have at least an absolute difference of 0.1 in the fraction of present reactions for bruceine D, scutellarein and emodin are displayed. The pathways are ranked in the In order to ascertain which enzymes are most likely targeted by bruceine D, emodin, and scutellarein, the number of reactions impacted in each pathway was plotted against the known drug targets for each compound. The size of the dots indicates how many cancer drugs target the same gene. Bruceine D has no described targets in DrugBank V5  versionA. ScutelBesides the androgen and estrogen synthesis and metabolism pathway, emodin targets 12, 10, nine, six, and five genes in the drug, steroid, vitamin A, linoleic, and arachidonic metabolisms, respectively, whereas scutellarein targets three genes in the starch and sucrose metabolisms .The 26 cancer drugs mainly target CYP proteins, SLC carriers, ATP transporters, and UDP-glucuronosyltransferases, which are mainly found in the eight following pathways: androgen and estrogen synthesis and metabolism, arachidonic acid metabolism, bile acid synthesis, cytochrome metabolism, drug metabolism, linoleate metabolism, steroid metabolism, transport, and extracellular pathways. Some of these genes and pathways are also targeted by emodin. On the other hand, although scutellarein targets two CYP proteins and one UDP-glucuronosyltransferase, it does not target CYP3A1. Unlike emodin, which shares several targets with the breast cancer drugs, scutellarein has only a few targets in these pathways, suggesting a different model of action for this drug .50 in MCF-7 cells (IC50 = 9.5 ± 7.7 μM), and a submicromolar IC50 in Hs 578T cells (IC50 = 0.71 ± 0.05 μM).In the present work, a drug discovery workflow based on FASTCORMICS, Drug deletion, and Flux Variability Analysis was used to predict the best breast cancer drug candidates among 101 natural products, out of which nine were tested in a 2D cell viability assay using MCF-7 and Hs 578T cells. A dose-dependent decrease of cell viability was observed with the three natural products emodin, scutellarein, and bruceine D, with the latter showing a low micromolar ICMetabolic modelling was previously applied, with success, to the prediction of drug candidates and, more specifically, repurposable drugs ,16. HoweIn order to circumvent this issue, we identified natural products that, according to their expression profiles, affect the metabolism of cancer cells differently than the solvent DMSO alone. This approach does not necessarily imply that these products reduce growth, but at least it allows us to eliminate natural products with no or very small effects on the metabolism. We further selected products that have a similar effect to capecitabine and methotrexate on the metabolism of MCF-7 cells. These two breast cancer drugs had the strongest dissimilarity to the flux distribution of DMSO due to the shutdown of all of the reactions that are required for biomass production, suggesting that they have a strong impact on the metabolism. This assumption was further validated by the drug deletion that predicted these two drugs to reduce growth. Methotrexate has three metabolic targets that are predicted to reduce growth upon knockout: TYMS, DHFR, and ATIC. The knockout of TYMS was also predicted to stop growth in the capecitabine-constrained DMSO model. Because it is a rate-limiting enzyme of nucleotide synthesis, the knockdown of TYMS was previously described to reduce the proliferation of breast cancer cells . The inhThe knockout of the metabolic targets of the 24 remaining breast cancer drugs did not affect the growth in the MCF-7 models. Although the cancer drugs had around 82 combined metabolic targets, the targets responsible for the reduction in proliferation or cell death might be non-metabolic. These drug targets might, moreover, have an indirect effect on growth; hence, they would not be captured by constraining the DMSO models by the known targets. Furthermore, the metabolic impact might not be captured by the biomass formulation, i.e., by causing the accumulation of ROS and the death of the cell by apoptosis. Finally, all of the isozymes described to control a reaction in Recon3D might not be expressed in breast cancer cell lines, or were missing in the target list. Regarding the targets, ABCB1 and ABCG2—which are ATP-dependent drug efflux pumps for the xenobiotic compound, and are known to be linked to multidrug resistance —were tarAmong the natural product models, resveratrol and ferulic acid were predicted to decrease proliferation. However, unlike for methotrexate and capecitabine, the predicted growth reduction is not linked to the knockout of one gene, but to the combined effect of the knockout of multiple targets.3 values and lower IC50 values in Hs 578T than in MCF-7 cells , suggesting that this compound might be a valuable drug for triple-negative breast cancer treatment.In the cell viability assay, nine natural products—scutellarein, emodin, bruceine D, resveratrol, hydroxysafflor yellow A, salvianolic acid B, salvianic acid A sodium, ferulic acid and glycyrrhizic acid—were experimentally assessed for their effect on the 2D cell viability of MCF-7 and Hs 578T cells. Among them, emodin, bruceine D, and scutellarein dose-dependently decreased the 2D cell viability in both cell lines. Although similar assays have previously been performed in ER+ MCF-7 cells , this ce-7 cells , indicat50 values could not be determined. Resveratrol treatment decreased the Hs 578T cell viability only at a concentration of 160 μM, whereas it showed no effect on MCF-7 cells. This low effect was somewhat surprising, as previous studies have shown a decrease of MCF-7 or MDA-MB-231 survival or proliferation upon resveratrol treatment [Six of the nine predicted drugs did not show clear effects at the tested concentrations, and their ICreatment ,47,48. Ireatment , both streatment  have beereatment , and to reatment . In contreatment . Hydroxyreatment . Finallyreatment . EvidencEmodin is an anthraquinone isolated from different Chinese herbs , and is Scutellaria baicalensis, which induces mitochondria-mediated intrinsic apoptosis selectively on multiple myeloma [Brucea javanica. Bruceine D was reported to induce autophagy and apoptosis via the generation of ROS in lung cancer cells [Scutellarein is a flavone extracted from the herb  myeloma , and inh myeloma . Bruceiner cells  and pancer cells . A bruceer cells . In addier cells . More reer cells .Taken together, the targets of emodin, scutellarein, and bruceine D, as well as their exact mode of action, are far from being completely elucidated. However, it is nowadays commonly accepted that these three drugs display anti-proliferative and anti-apoptotic effects across different cancer types, and that they counteract EMT. They induce the accumulation of ROS and regulate several signalling pathways, including PI3K/Akt/NF-κB pathways ,68. PI3KIn order to obtain a deeper insight into the mode of action of bruceine D, scutellarein, and emodin, we had a closer look at their results in the four in silico experiments. Among the pathways that were differentially present in the bruceine D, scutellarein and emodin models were the androgen and estrogen synthesis and metabolism pathway, as well as pathways that are required for the synthesis of estrogens, such as cholesterol metabolism, squalene and cholesterol synthesis, and steroid metabolism. Notably, the ROS detoxification pathways (in the bruceine D model) and the pathways responsible for the production of antioxidants—such as folate metabolism (emodin and scutellarein models) and heme degradation —were downregulated, which is consistent with the described accumulation of ROS induced by the three natural products ,65,73,74Increased endogenous levels of androgen and estrogen are associated with a higher risk of prostate and breast cancer development, respectively . EstradiAndrogens have a dual and controversial effect on breast cancer development. At physiological concentrations, androgens can suppress breast cancer cell proliferation. However, at higher concentrations, androgens can promote breast cancer cell proliferation by augmenting free estrogen levels, or by regulating the transcription of genes, such as the mitotic gene Ki-67 .The androgen and estrogen synthesis and metabolism pathway is targeted by 24 out of the 26 breast cancer drugs. These drugs target four reactions of this pathway that are present in the DMSO model and absent in bruceine D, emodin, and scutellarein models. Notably, one of the reactions that causes the oxidation of estrone into 16α-hydroxy-estrone in the reticulum by a monooxygenase is under the control of 13 target genes, and notably of the gene CYP3A4. CYP3A4 is targeted by 21 out of 26 cancer drugs. Hydroxylated forms of estrogen, estradiol, and testosterone were described to be genotoxic, and hence to initiate tumour formation by inducing mutations. In addition, 16α-hydroxy-estrone has a strong affinity for ERs, and the ratio between 2-hydroxy-estrone and 16α-hydroxy-estrone in the urine has, for a long time, been regarded as a biomarker for breast cancer risk, although this assumption is currently under debate . Taken tTaken together, bruceine D, emodin, and scutellarein are predicted to reduce the growth of MCF-7 cells through the activation of ROS with the induction of oxidative stress, and through the downregulation of the androgen and estrogen synthesis and metabolism pathway.http://www.ncbi.nlm.nih.gov/geo/ accessed date 20 September 2020) [The microarray data published by Lv et al. (GEO ID: GSE85871)  was retrer 2020) . The datRStudio (R version 4.0.3) using the ArrayExpress package (version 1.50.0) (https://bioconductor.org/packages/release/bioc/html/ArrayExpress.html accessed date 3 May 2021) and the ReadAffy function from the Bioconductor repository. First, the data were log2-transformed and then normalized using the Frozen Robust Multiarray Analysis (fRMA) package (version 1.42.0) [fRMA package (version 1.42.0) was further run in order to compare the expression values of the dataset to a vector containing the median and standard variation of the lowest mode for each probe set for a collection of arrays for the same platform, and to compute the z-scores. The probe identifiers were converted to Entrez ID using an in-house pipeline described by [https://github.com/sysbiolux/rFASTCORMICS accessed date 3 May 2021) was used to build the context-specific models using the microarray gene expression data with the human metabolic model Recon3D [https://www.cytivalifesciences.com/en/us/shop/cell-culture-and-fermentation/media-and-feeds/classical-media/hyclone-minimal-essential-medium-mem-variations-liquid-p-05698# related-documents accessed date 1 May 2021), and were converted into the matching metabolites names via Metabolic Atlas (version 1.7) (https://metabolicatlas.org/ accessed date 1 May 2021) [Gene expression CEL files were read into  1.42.0) . The barribed by . In orderibed by   . AdditiodeleteModelGenes function of the COBRA Toolbox, which can be downloaded from https://github.com/sysbiolux/Herbal_Drug_Repurpusing/blob/main/data/constraining_models/DrugDeletionCombination.m accessed date 20 March 2021. In order to obtain a list of targets for each drug, drug–target interactions information was mined from three databases: DrugBank V5 [bioDBnet was the number 202004, released on 10 September 2020 and updated on 7 November 2020). The UniProt IDs were then converted to Entrez IDs using the db2db tool (bioDBnet:https://biodbnet-abcc.ncifcrf.gov/db/db2db.php accessed date 20 March 2021 ), from the biological DataBase network [deleteModelGenes function of the COBRA Toolbox V3 [In order to predict whether knocking out the natural product targets reduces cell proliferation, the context-specific models were constrained by setting the bounds of the natural product target reactions to zero using a modified version of the gBank V5  (version network  to matcholbox V3  was usedhttps://www.cancer.gov/about-cancer/treatment/drugs/breast) on 16 March 2021  by setting the targets of each natural product and cancer drug couple to zero. The growths obtained for the drug combinations and the natural product and cancer drug alone were compared, which was performed using the  toolbox . As somesingleGeneDeletion function from the COBRA Toolbox in order to determine whether the knockout of one of the target genes could explain the biomass reduction, or if the knockout of multiple genes was required to see an effect on growth.An in silico single-gene deletion was performed via the dist function inside the proxy package (version 4.0.3) in Rstudio (version 4.0.3), the Jaccard index was calculated between every natural product and the DMSO model, and then converted into the dissimilarity score. The natural products were then sorted by their dissimilarity to the DMSO model reactions and the top-five natural products were selected, as there was a larger drop of dissimilarity between the fifth and sixth natural product models.In order to assess the impact of the natural products on the metabolism of breast cancer cells, a dissimilarity score D was computed based on the Jaccard Similarity Index J (D = 1-J). The dissimilarity score is equal to 1 if the number of shared reactions between two models is above the total number of reactions. Using the fluxVariability function in the COBRA toolbox.In order to identify the natural products that have a similar effect on the MCF-7 metabolism as the breast cancer drugs, a Flux Variability Analysis (FVA)  was perf).A dissimilarity score was computed between the flux range of the cancer drug models and the DMSO model , then the dissimilarity score is equal to 0. If the reactions shared by both have very different ranges, the dissimilarity score will tend to 1. In case a reaction was not included in one of the two compared models, the bounds were considered to be equal to zero. Based on their dissimilarity scores, two breast cancer drugs were selected: capecitabine and methotrexate (see −4). Then, in a second step, the similarity score (si) was computed between the natural product and the capecitabine and methotrexate models . First, the rate of reactions present per pathway was calculated for each model and each pathway by dividing the number of active reactions in the context-specific models by the total number of reactions in this pathway in Recon3D using the subSystems field. Then, the difference rate of reaction presence was computed between each natural product model and the DMSO model. The pathways were then sorted in the function of the interquartile range. All of the pathways with three reactions or fewer were excluded.In order to identify the pathways that are more preferably targeted by breast cancer drugs, the drug targets were identified as described in Methods—Cancer Drugs. These target genes were mapped to the gene identifiers of Recon3D, and the target reactions were obtained using the https://clinicaltrials.gov accessed date 1 June 2021) [In total, 23 drugs were predicted by at least one of the four previous steps . The drugs predicted by two or more experiments were in general selected, unless they were toxic or had already been tested in MCF-7 cells , green. ne 2021)  was mine2O, and then stored in aliquots at −20 °C.Salvianic acid A sodium  was prepared in growth medium and ferulic acid (# HY-N0060), glycyrrhizic acid (# HY-N0184), scutellarein (# HY-N0752), hydroxysafflor yellow A (# HY-N0567), salvianolic acid B (# HY-N1362), emodin (# HY-14393), bruceine D (# HY-N3014) and resveratrol (# HY-16561)  were prepared in DMSO at an initial concentration of 100 mM, and then stored in aliquots at −80 °C. A 10 mM stock solution of ophiobolin A  was prepared in DMSO, and then stored in aliquots at −20 °C. A 100 mM stock solution of benzethonium chloride  was prepared in H2, H2O-saturated atmosphere.The MCF-7 (# HTB-22) and Hs 578T (# ACC 781) cells were obtained from American Type Culture Collection  and from Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH , respectively. Both cell lines were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM)  supplemented with 10% fetal bovine serum and 2 mM L-glutamine (complete DMEM) (Lonza Group). The cells were routinely passaged and grown at 37 °C in a 5% COTM cell viability reagent  according to the manufacturer’s protocol. Briefly, MCF-7 or Hs 578T cells were seeded at a density of 3000 cells in 100 μL complete DMEM medium per well in 96-well flat-bottom culture plates.The 2D cell viability was assessed using the alamarBlueAfter a 24-h incubation at 37 °C, the cells were treated with DMSO as a vehicle control, with 200 μM benzethonium chloride (BzCl) as a positive control for cell death, or with natural test products diluted in complete DMEM. The treatment with natural products was performed in a 1:2 dilution series ranging from 160 μM to 0.625 μM, or from 20 μM to 0.078 μM for bruceine D. Ophiobolin A, used here as a control compound for its anti-proliferative activities, was used for treatment in a 1:2 dilution series ranging from 20 μM to 0.078 μM.TM cell viability reagent was added to each well to a final concentration of 10%, and the plate reading was performed after a 2.5-h incubation in the dark at 37 °C. The fluorescence intensity (excitation at 530 ± 15 nm and emission at 590 ± 20 nm) was measured using a CLARIOstar  plate reader.After a 72 h treatment at 37 °C, alamarBluehttps://breeze.fimm.fi accessed date 14 December 2021) [50 and DSS3 values were derived from the data analysis, with DSS being a more robust parameter to quantitate drug sensitivity than IC50, corresponding essentially to the normalized area under the curve (AUC) of dose–response data [The raw fluorescence intensity values obtained from the cell viability assays were analysed using the DSS pipeline website BREEZE (er 2021) ,89. The nse data .Prism 9.2.0  was used to establish the dose–response curves. The results are presented as the mean ± SEM of at least three independent biological replicates, unless otherwise indicated. Each biological replicate was carried out in three technical replicates, out of which at least two were considered for evaluation.In order to gain insight into the possible mode of action of bruceine D, scutellarein and emodin, pathway analyses were performed on the three natural product models, which were then compared against the DMSO model, at a pathway level and a reaction level. Furthermore, targeted reactions by emodin and scutellarein were compared with reactions targeted with cancer drugs. First, the differences in the reaction presence rate between the three drugs and the DMSO model were computed. The pathways with absolute differences below 0.1 were not plotted in https://github.com/sysbiolux/Herbal_drug_prediction, accessed on 30 December 2021) enabled us to narrow down a list of 101 single natural products and one mixture of natural products to nine anticancer drug candidates. The subsequent experimental validation allowed us to confirm the potency of three out of these nine candidates, namely emodin, scutellarein, and bruceine D. Finally, our approach led to the identification of the pathways which are potentially involved in the anticancer activity of the three candidates.In conclusion, our in silico analysis pipeline (


Arabidopsis metabolic genes. A comprehensive set of protein–DNA interactions between Arabidopsis thaliana TFs and gene promoters in primary and specialized metabolic pathways were mapped. To demonstrate the utility of this resource, we identified and functionally validated regulators of the tricarboxylic acid (TCA) cycle. The resulting network suggests that plant metabolic design principles are distinct from those of microbes. Instead, metabolism appears to be transcriptionally coordinated via developmental‐ and stress‐conditional processes that can coordinate across primary and specialized metabolism. These data represent the most comprehensive resource of interactions between TFs and metabolic genes in plants.Plant metabolism is more complex relative to individual microbes. In single‐celled microbes, transcriptional regulation by single transcription factors (TFs) is sufficient to shift primary metabolism. Corresponding genome‐level transcriptional regulatory maps of metabolism reveal the underlying design principles responsible for these shifts as a model in which master regulators largely coordinate specific metabolic pathways. Plant primary and specialized metabolism occur within innumerable cell types, and their reactions shift depending on internal and external cues. Given the importance of plants and their metabolites in providing humanity with food, fiber, and medicine, we set out to develop a genome‐scale transcriptional regulatory map of  Arabidopsis thaliana reveals transcriptional coordination across multiple metabolic pathways that is developmental and stress condition‐dependent.A transcription factor‐promoter interaction network of primary and specialized metabolism in  Metabolism is the fundamental biological process underpinning all cellular functions. An organism’s metabolism comprises individual biochemical reactions organized into metabolic pathways where metabolites are sequentially transformed in increasing or decreasing complexity by enzymes. In conjunction, the primary metabolic pathways create the cellular building blocks and directly contribute to the interconversion of chemicals into energy currency. In plants, and most other organisms, primary metabolites serve as precursors to secondary, or specialized, metabolites crucial to the organism’s interaction with its environment. Plant specialized metabolites serve many functions, including defending plants from predators and pathogens, attracting symbiotic organisms, and promoting interactions with pollinators.Saccharomyces cerevisiae and Escherichia coli, these systems approaches integrate chromatin immunoprecipitation, transcriptomic experiments, and in silico models to elucidate transcription factor (TF)–enzyme promoter regulatory interactions and ultimately, investigation of genome‐scale regulatory networks of global metabolism  transcription to protein posttranslational modifications, with the best‐studied regulatory mechanisms being posttranslational modification and allosteric feedback of enzymes Nielsen, . Adding S. cerevisiae and E. coli, the genomes of multicellular organisms encode many more genes including TFs, enzymes and in plants, relative to animals, a further expansion of both enzyme and TF families linked to metabolism. The acquisition of multicellularity also enables the partitioning of function across cell types. Thus, multicellular organisms likely have more complex transcriptional and metabolic regulation in comparison with single‐celled microbes. However, few studies exist that systematically characterize the complexity of metabolic networks from the perspective of transcriptional regulation in multicellular organisms. Even fewer studies have explored the regulatory interconnection between the regulation of central and specialized metabolism in multicellular organisms, where specialized metabolism is a critical component of the organism’s response to the environment. Instead, the majority of studies on plant metabolism have focused on the transcriptional regulation of individual pathways  biosynthesis, a specialized metabolic pathway. This work showed that, as expected, these TFs not only regulate the GSL biosynthetic pathway, but also affect several primary metabolic pathways that synthesize precursors of GSLs, including methionine biosynthesis, tryptophan biosynthesis, the TCA cycle, sulfur metabolism, and folate metabolism , chromatin immunoprecipitation sequencing (ChIP‐Seq), DNA affinity purification sequencing (DAP‐Seq), and protein‐binding microarrays  of the TFs we examined. Additionally, we explored the combinatorial regulation of a single pathway, the TCA cycle, given its interconnection with other central metabolic pathways. This dataset provides a unique resource to extend our understanding of how metabolism is regulated in multicellular organisms.Arabidopsis primary metabolism, we conducted enhanced Y1H screens .To identify TFs that potentially regulate From the binding data obtained via the genome‐scale Y1H, we queried how promoters and metabolic pathways are organized. We detected 27,485 interactions between 1,930 TFs and 220 promoters across the 12 pathways surveyed Figs . Per proWe next examined how our representative specialized metabolic pathway, aliphatic GSL biosynthesis, is integrated with central carbon metabolism within the TF–DNA interaction network. Among the 974 TFs that bind to aliphatic GSL gene promoters, 933 of those TFs (˜95%) also bind to promoters from a central carbon pathway. In our pairwise comparison of pathways based on TF binding, the central carbon metabolic pathways that shared the most TFs with aliphatic GSL based on the level of enrichment using a hypergeometric test are the pathways that synthesize the sulfur‐containing precursors needed to synthesize aliphatic GSL (methionine and cysteine) Dataset . This siCHROMATIN REMODELING 19 (CHA19), EIN2 NUCLEAR ASSOCIATED PROTEIN 1 (ENAP1), LATERAL ORGAN BOUNDARIES‐DOMAIN 16 (LBD16), and WRINKLED 3 (WRI3) ‐controlled glucocorticoid receptor (GR). The gene expression was profiled by RNA‐sequencing 24 h after mock treatment or 10 μM dexamethasone induction in dark‐grown six‐day‐old Arabidopsis seedlings. Hundreds to thousands of genes were differentially expressed in response to TF induction across multiple insertion lines  for GR‐ENAP1; GR‐LBD16, and GR‐WRI3 .This global analysis of TF–metabolic promoter interactions suggests coordinate transcriptional regulation of central carbon and specialized metabolic pathways. However, genome‐scale assays like the Y1H suffer from both false positives and false negatives. We therefore tested the regulatory capacity of four TFs that bind to gene promoters from multiple metabolic pathways: 3) Table . We predines Fig . There wCHA19 and WRI3  . Additionally, we found other TCA cycle genes, totaling 28.5% of TCA cycle genes, to be enriched in the set of significant Dex‐dependent genes . These data collectively demonstrate that our TF–DNA interaction network was able to predict in planta regulatory interactions, in line with mapped networks for other biological processes  associated with glycolysis/gluconeogenesis, glycine catabolism, and the TCA cycle were overrepresented more than expected by chance in the significantly upregulated DEGs upon GR induction of WRI3 Fig . Cysteinsis) Fig . Pathwayet al, et al, WRI3, we inspected their subcellular localization. The TCA cycle DEGs of GR‐WRI3 are specifically localized in the mitochondria and plastids  and P = 0.001431 (plastids), respectively, two‐sided Fisher’s exact test, Methods). Overall, the inducible constructs support the Y1H‐binding results and the hypothesis of coordinated regulation across metabolic pathways.In most heterotrophic and autotrophic eukaryotes, the TCA cycle occurs predominantly in the mitochondria. Plants, however, have expanded this repertoire such that TCA cycle isoforms also function in the cytosol, peroxisomes, and plastids, thus allowing pathway interactions with photosynthesis, photorespiration, and nitrogen assimilation. These isoforms create the potential for localized bypasses, resulting in cyclic and noncyclic fluxes in the TCA cycle to optimize metabolism  synthesis. These functions are carried out by eight enzymes: citrate synthase (CSY), aconitase (ACO), isocitrate dehydrogenase (IDH), oxoglutarate dehydrogenase (OGD), succinyl‐CoA (coenzyme A) ligase (SCL), succinate dehydrogenase (SDH), fumarase (FUM), and malate dehydrogenase (MDH), many of whom have organelle‐specific isoforms Fig . PyruvatWe first mapped a TCA cycle subnetwork consisting of 4,684 interactions between 962 TFs and 55 TCA cycle genes Figs . Within et al, et al, et al, et al, et al, et al, et al, et al, r| > 0.8) in one or more microarray datasets  plant development . The expression of these 17 TFs was highly correlated with their TCA cycle gene targets in the salt and osmotic stress datasets, and was associated with primary metabolic GOs more than expected by chance Dataset . To firsgths Fig . These oHD2A and AtHB34 are the two TFs that have significant genotype‐dependent effects across the three measured traits. In the majority of the TF mutant alleles tested, hypocotyl lengths decreased across the TCA intermediate conditions, indicating that the majority of these TFs promote hypocotyl length. The direction of the effect on root length and the ratio of root to total seedling length was more varied among the TF mutant alleles across conditions  were measured and provide a comprehensive overview of the agriculturally relevant consequences of TCA cycle disruption over the continuum of plant growth in response to salt stress. TF‐dependent and TF by salt treatment‐dependent responses were evaluated using a two‐way ANOVA , COLD SHOCK DOMAIN PROTEIN 4 (ATCSP4), AT2G48100, and CHA19 for rosette area, LBD16 in dry shoot biomass, and WRI3 and AT2G48100 in seed yield  was observed between the number of significant phenotypes observed in the dark TCA intermediate‐feeding and salt stress response experiments and the number of significant C and N content phenotypes  underlies both context‐dependent phenotypes. A positive correlation  or strain Yα1867 (for TFs) was grown overnight at 30°C by taking streaking from a glycerol stock onto a YPDA (yeast peptone dextrose adenine) agar plate to make a lawn. A pea‐sized glob of yeast cells was resuspended in 1 ml of liquid YPDA and ˜100 μl was added to 50 ml of liquid YPDA in an Erlenmeyer flask to a starting OD600 of 0.15–0.20. The yeast culture was grown in a 30°C shaking incubator at 210 rpm for 2 h or until OD600 had reached 0.4–0.6.g) and resuspending in 1–1.5 ml of liquid SC–HIS–URA, and then 510 μl spread onto SC–HIS–URA agar plates with 15–20 glass beads. Promoter lawn plates were incubated for two nights at 30°C. The TF collection was cultured in deep 96‐well plates from glycerol stocks in 315 μl of liquid SC‐TRP for two nights at 30°C. TF culture plates were spun down for 5 min at 1,850 g and 150 μl of liquid media was removed. The pellets were resuspended in the remaining liquid media and 50 μl of the culture transferred to 384‐well plate and arrayed in duplicates, resulting in two 96‐well plates combined into one 394‐well plate.Five ml of SC–HIS–URA was inoculated with yeast strains of the promoters from glycerol stock and grown at 30°C and shaking at 210 rpm for 48 h. Cultures of promoter yeast strains were concentrated by pelleting down yeast cells , which were bulked together with another growth of the wild‐type reference. Arabidopsis plants were grown in Sunshine mix #1 in 2.23” × 1.94” × 2.23” pots. The chamber temperature was set to continuous 22°C and set to long day (16‐h light/8‐h dark) with light intensity of ˜120 μmol m−2 s−1 from T12 very high output (VHO) fluorescent light bulbs. Seeds were sieved for sizes between 250 and 300 μm and surface sterilized by soaking in 50% household bleach, 0.05% Tween 20 for 20 min and then rinsed with sterile water 5–7 times before stratifying in 0.1% sterile agar for 2 days in 4°C. Seeds were sown in Sunshine Mix #1 in traditional 1,020 trays with 18‐cell inserts (pot dimension 3.10” × 3.10” × 2.33”).Transfer DNA (T‐DNA) insertional mutant lines were ordered from the Arabidopsis Biological Resource Center at Ohio State University  agar pH 5.7 with potassium hydroxide (KOH), supplemented with 1 mM TCA metabolites or 0.21 mM sucrose based on theoretical ATP yield Rich, . The con−2 s−1 from T12 VHO fluorescent light bulbs. After germination, each pot was thinned to one seedling per pot. When the seedlings were 7 days old, flats were watered every 5–7 days with either deionized water (control) or 50 mM NaCl  to maintain soil moisture.Seeds were sieved for sizes between 250 and 300 μm and surface sterilized by soaking in 50% household bleach, 0.05% Tween 20 for 20 min and then rinsed with sterile water 5–7 times before stratifying in 0.1% sterile agar for 2 days in 4°C. The seeds were sown in Sunshine Mix #1 in traditional 1,020 trays with 18‐cell inserts (pot dimension 3.10” × 3.10” × 2.33”) and grown in a chamber with the temperature set to continuous 22°C and set to long day (16‐h light/8‐h dark) with light intensity of ˜120 μmol mArabidopsis thaliana Col‐0 seeds were sown in Sunshine Mix #1 in 3.5” × 3.5” × 5” pots. Pots were incubated at 4°C for 3 days before transferring into a growth chamber set to continuous 22°C and set to long day (16‐h light/8‐h dark) with light intensity of ˜120 μmol m−2 s−1 from T12 VHO fluorescent light bulb. Five days after seed germination, each pot was thinned to five seedlings per pot and grown to four to five weeks for Agrobacterium transformation.Ten to twelve T3 seeds from four independent lines were selected by the presence of red fluorescent protein (RFP) in the seed. Approximately 200–300 seeds of each line were surface sterilized with 50% bleach for 15 min, rinsed 5–7 times with sterile distilled water, and imbibed in the dark at 4°C for two days. The seeds were then sowed onto nylon mesh on Petri plates containing half‐strength MS agar plates pH 5.7 with KOH. The plates were double wrapped in foil and placed vertically in a dark chamber at ambient room temperature (20–22°C).et al, All networks are visualized using Cytoscape v3.7.1  of TFs in pENTR from .To determine the significance of association between two metabolic pathways based of their TF binding, we performed a one‐tailed Fisher’s exact test using fisher.test in R version 3.4.0. A 2 × 2 contingency matrix is calculated for all pairwise combinations of metabolic pathways based on the number of TFs detected in both pathways, the number of TFs detected in the first pathway, the number of TFs detected in the second pathway, and the number of TFs screened (2039 TFs). et al, Subcellular localization of TCA cycle targets was determined from the SUBA3 database . These datasets capture a range of experimental and biological perturbations and thus allow for the capture of conditional transcriptional correlations. We downloaded available.CEL files from the experiments only when the genotype corresponded to Arabidopsis development  loci identifiers were uploaded to the GO Term Enrichment tool powered by PANTHER released July 11, 2019 on For each experiment, 30 replicates of Col‐0 and 10 mutants were plated in a random block design. The TF mutant lines were divided into two blocks. Each plate (block) had six wild‐type seedlings and one seedling per mutant line for 15 or 16 TF mutant genotypes plated across three rows. The entire experiment was repeated twice, for a total of 20 biological replicates maximum per TF mutant lines and 60 biological replicates maximum of Col‐0 per TCA metabolite. Backlit images of plates on a light table were acquired using a Canon EOS T3i Rebel dSLR camera fixed on a camera stand. Roots and hypocotyls were traced manually using a Wacom Intuos drawing tablet in ImageJ.Arabidopsis Col‐0 seedlings  for determining the natural abundance of Carbon‐13 and Nitrogen‐15.post hoc comparisons between mutant alleles and Col‐0 conditioned on treatment were calculated using the emmeans  of the two alleles that were bulked at a different time were normalized by adding the difference between the two wild types to the raw values. Estimated marginal means (EMMs/least‐squares means) and s Lenth,  package CHA19, ENAP1, LBD16, and WRI3 in pENTR from the Arabdiopsis TF ORFeome collection  agar plates containing spectinomycin and rifampicin and then confirmed by genotyping polymerase chain. Four‐ to five‐week‐old, flowering Arabidopsis thaliana Col‐0 were transformed using the floral dip method  in the dark, assisted with a green headlamp. After 24 h of induction, the whole seedlings were collected, flash frozen in liquid nitrogen, and stored in −80°C until RNA‐Seq library constructions. Direct mRNA isolation was performed using biotinylated polyT oligonucleotide and streptavidin‐coated magnetic beads following the protocol from (Townsley https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), adapter and poly‐A trimmed using TrimGalore v0.6.0 using default settings  and then reassessed for quality using FastQC. Gene counts were obtained by mapping trimmed reads to the Arabidopsis thaliana genome (TAIR10) using STAR aligner v2.7.0f . Briefly, trimmed mean of M value normalization was performed using the calcNormFactors function from edgeR (Robinson et al, I + AL + TI × ALY = T where the main effects T and A are denoted as dexamethasone treatment and allele of TF, respectively. The false discovery rate was corrected using Benjamini–Hochberg with the p.adjust function. GO analysis was conducted on pantherdb.org by submitting the list of AGIs of genes significantly differentially expressed due to translocation of the GR–TFs’ chimeric protein into the nucleus. The complete biological process GO annotation dataset for Arabidopsis thaliana was used for testing statistical overrepresentation. Enrichment analysis was performed in R using the fisher.test function.FastQ file processing was performed on the University of California, Davis high performance bioinformatics cluster. FastQ files were quality checked using FastQC v.0.11.7 (DJK, SMB, MT, and BL conceived and designed the study. DJK and SMB secured funding for the project. MT, BL, XZ, TB, JJL, NC, AG, RN, and CC‐W performed the experiments and generated the data. MT conducted the formal analysis. MT, SMB, and DJK wrote the manuscript with contributions from all authors. DJK and SMB supervised the project.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Dataset EV9Click here for additional data file.Dataset EV10Click here for additional data file.Dataset EV11Click here for additional data file.Dataset EV12Click here for additional data file.Dataset EV13Click here for additional data file.


Wolbachia are endosymbiont bacteria known to infect arthropods causing different effects, such as cytoplasmic incompatibility and pathogen blocking in Aedes aegypti. Although several Wolbachia strains have been studied, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their obligate endosymbiont nature and its pathogen blocking ability. Motivated by the potential applications on disease control, we developed a genome-scale model of two Wolbachia strains: wMel and the strongest Dengue blocking strain known to date: wMelPop. The obtained metabolic reconstructions exhibit an energy metabolism relying mainly on amino acids and lipid transport to support cell growth that is consistent with altered lipid and cholesterol metabolism in Wolbachia-infected mosquitoes. The obtained metabolic reconstruction was then coupled with a reconstructed mosquito model to retrieve a symbiotic genome-scale model accounting for 1,636 genes and 6,408 reactions of the Aedes aegypti-Wolbachia interaction system. Simulation of an arboviral infection in the obtained novel symbiotic model represents a metabolic scenario characterized by pathogen blocking in higher titer Wolbachia strains, showing that pathogen blocking by Wolbachia infection is consistent with competition for lipid and amino acid resources between arbovirus and this endosymbiotic bacteria. Wolbachia are obligate intracellular symbionts, members of the Rickettsiales group, known to infect over 65% of all insects species, mainly arthropods, developing diverse interactions with their hosts, such as supplementation with vitamins . Genome-scale models have emerged as a powerful tool for studying cellular metabolism based on their genome annotation . This model includes 644 genes and 790 reactions, of which 220 are orphan reactions predicted to be present in the model despite the fact that some genes associated with this reaction are absent from Wolbachia genomes  cycle, an active amino acid metabolism, including transport of amino acids , and catabolism of glycine, glutamate, glutamine, proline, serine, and threonine; the inability to produce lipopolysaccharides; and the absence of an ADP-ATP exchanger protein  gene is lethal in Wolbachia wBm infecting C6/36 cells  by d-cycloserine, finding that it does not have a negative effect on lipid II biosynthesis in Wolbachia cells.We tested if  protein . Our ana36 cells . AdditioWolbachia reconstruction can replicate the lethal effect of phosphomycin in silico. The effect of high concentrations of this inhibitor is represented as a gene knockout, resulting in no cell growth in the performed simulations. iNJ644 knockout analysis predicts that inhibition of ddlA by d-cycloserine produces a nongrowth phenotype. ddlA is associated with the conversion of two alanines into one alanine dipeptide (Ala-Ala), which is a crucial step in lipid II synthesis. An additional blast search  showed that the Wolbachia genome includes genes with high homology to the Ala-Ala transporters found in Escherichia coli, suggesting that Wolbachia could get this metabolite from its host to be consistent with experimental data   –25. Over (SCP2) –, while i (SCP2) –. Additio (SCP2) –, 28.Additionally, a mosquito-specific pathway for urea disposal proposed by Scaraffia et al. , also knDiptera, thioredoxin, as opposed to glutathione, is used as a redox buffer. Particularly for Anopheles gambiae, it has been reported that alternative splicing of a thioredoxin reductase gene (trdR) leads to mitochondrial and cytoplasmic variants that keep redox homeostasis in this organism  knockdown in Aedes aegypti mosquitoes  of the tested conditions in which gene deletions were associated with metabolic functions that are represented in genome-scale models. The obtained model was subsequently coupled with our Wolbachia and Aedes aegypti metabolic model. Further curation was performed to connect Wolbachia precursors to the mosquito metabolic network  simulations showed that this endosymbiont requires the import of isopentenyl diphosphate (IPP) for biomass synthesis. These predictions are consistent with metabolite exchange predictions made for Rickettsia  synthesis, a compound known to be imported in Rickettsia , unlike what has been published previously for wMel WolbachiaWolbachia model was represented as a transport reaction that preserves the stoichiometric coefficients of the components present in the mosquito membrane. An analysis of A. aegypti and W. pipientis membrane composition stoichiometry showed that there is a surplus of cholesterol in Wolbachia that is not required for membrane synthesis. We speculate that this excess of cholesterol is drained in the form of the lipid droplets observed in Wolbachia-infected A. aegypti cells and that this imbalance is associated with the reported cholesterol-altered metabolism observed in mosquito-infected cells , where the optimal use of resources toward Wolbachia and mosquito biomass synthesis is presented. These results evidence that, in an optimal distribution of resources, there is a trade-off between the growth rates of both organisms. This can be derived from the negative slope of the optimal use of the resource curve (Pareto front) for cell growth in this symbiotic system. Since the value of this slope is −2.4 [μAedes aegypti/μWolbachia], producing a mosquito cell requires more nutrients than producing a Wolbachia cell. For instance, in the scenario of optimal use of resources, a 0.04 mosquito cell growth rate is associated with nearly double this value for Wolbachia growth (file S3 at the GitHub link above).Wolbachia growth rate are observed, given by Wolbachia riboflavin supplementation to A. aegypti. FBA simulations in a riboflavin-deprived environment show that although riboflavin supplementation is essential, higher growth rates of Wolbachia result in nutrient depletion and decreased mosquito biomass synthesis. The obtained results show that most of the feasible solutions were obtained around duplication times of 100 h for Wolbachia and between 0 and 400 h for Aedes aegypti. By analyzing duplication times of each organism , we estimate that there is a median of 3.45 Wolbachia cells per mosquito. Given that quantification of Wolbachia density inside the mosquito cell line Aa23 was estimated as between 300 and 1,200 bacteria per mosquito cell  , 65. Fluth rates . The obtoperties .Wolbachia's biomass has a higher influence on pathogen blocking. Different artificial Wolbachia biomass compositions, including only amino acids, nucleotides, or lipids, were simulated in order to test which component is critical for pathogen blocking  as a consequence of a diminished amino acid pool leads to viral replication arrest , which had negative effects on both bacterial growth and viral production.Recently, Koh et al.  comparedWolbachia pipientis, Aedes aegypti, and DENV lipid composition in our model shows that although Wolbachia and DENV share requirements for phosphatidylserine and phosphatidylethanolamine, most of their compositions differ. Even so, Wolbachia can synthesize both of these phospholipids from glycerol-3P . In Aedes aegypti, esterified cholesterol solubilization leads to a 100-fold increase in DENV genome copies in the presence of wMelPop  were generated using modelSEED (Wolbachia genomes for strains wMel and wMelPop with a coverage value above 85% and E values lower than 0.001 were considered metabolic functionalities preserved in this endosymbiont. Biomass determination was constructed using Escherichia coli (Rickettsia prowazekii (Wolbachia pipientis genome-scale model.Draft genome-scale models for two odelSEED . The obthia coli  compositowazekii , 18. Comowazekii . The curowazekii , preservWolbachia-mosquito model in which Wolbachia is represented as an intracellular compartment of Aedes aegypti. Wolbachia transport reactions were modified to move metabolites toward the cytoplasm instead of the extracellular compartment of the global model while exchange reactions were deleted, since Wolbachia should get all their resources from the mosquito metabolic network.Both models were combined to obtain a Wolbachia exchange reactions associated with metabolites absent in the Aedes aegypti network are conserved and considered to be delivered directly into Wolbachia. The obtained model was checked using memote (Aedes aegypti and Wolbachia pipientis, using the maximization of mosquito biomass as an objective function subjected to a minimum of Wolbachia growth as a constraint.g memote  and subjA viral production reaction that comprises nucleotide, amino acid, and lipid composition of the Dengue virus is included in the symbiotic model. Nucleotide and amino acid stoichiometric representation was obtained from Aller et al. , lipid cWolbachia organisms inside a mosquito cell was imposed in the symbiotic system (Aedes aegypti's biomass synthesis is the optimization objective of the whole system.FBA for each organism was achieved using cell growth as their optimization objective. An additional constraint derived from the reported number of c system , where mWolbachia's pathogen blocking properties, FBA was performed in a virus-infected Aedes aegypti cell with and without Wolbachia, imposing virus replication as maximization objective with a fixed Aedes aegypti cell replication.In order to study Wolbachia's metabolism without imposing an objective function.Sampling of the solution space was achieved by using the sampler included in COBRApy . This sahttps://github.com/natJimenez/symbioticModelAnalysis.Supporting information is available to download at