Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants  essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments. Antimicrobial resistance is a major threat to global health. Worryingly, a growing number of pathogens exhibit an extraordinary capacity for acquiring new antibiotic resistance traits in the bacterial population worldwide . New mulEscherichia coli plays a major role in the antimicrobial resistance (AMR) global health crisis. First, the ability of E. coli to acquire resistance via single nucleotide polymorphisms (SNPs) in its existing genome , have led to invaluable results in the identification of specific genomic markers which could be used to effectively predict resistant strains and to detect AMR genes –30. Most–E. coli has been able to adopt a highly flexible carbon and energy metabolism for adaptation against stresses in niche environments  offers a way of mechanistically evaluating the genetic determinants identified using ML. A GSM is a computational model of metabolism, which includes all known biochemical reactions and their corresponding gene-protein-reaction (GPR) rules. The GPR rules provide important information linking genes to the reactions that are catalyzed by the enzymes they encode and provide a means of simulating the metabolic system-level behavior of the bacteria to perturbations in the gene. While GSMs have proven invaluable tools for predicting genotype-phenotype relationships , they laE. coli. Using our approach, first we were able to accurately predict AMR resistant and susceptible phenotypes against 11 out of 12 different antibiotics, as well as identifying 225 (35 of which matched the specific antibiotic class reported in AMR-related databases) known AMR-conferring genes in 3,616 E. coli strains. Second, by elucidating the effects of genetic discriminants on bacterial growth, metabolite yields, and biochemical fluxes using the GSM, we were able to relate genetic determinants to a number of metabolic adaptation mechanisms, including reduced growth, alternative carbon source utilization, changes to energy metabolism, iron metabolism, nucleotide metabolism, and modifications to cell wall metabolism.In this study, we developed a computational solution integrating the discriminant power of ML with GSM models to reveal the systemic relationships connecting the genetic determinants of AMR to important metabolic evolutionary adaptations in E. coli genomes for which AMR testing and metadata were available from public databases was selected. To efficiently analyze the AMR phenotypic variability that is likely to arise from a combination of SNPs and changes in gene content, we used an integrated k-mer and SNP-based ML approach. A gradient boosting classifier (GBC)  , 45 was er (GBC) . Flux ba10.1128/mSystems.00913-20.1FIG S1FIG S1, PDF file, 0.04 MB.Flow chart of the integrated machine learning/genome-scale metabolic modeling pipeline. The different sections of the pipeline are highlighted in different colors. The actions relating to the k-mers are shown in dark blue. The actions performed on the SNPs are shown in purple. The steps to build and annotate the pan-genome are shown in red. The machine learning performed on the k-mers and SNPs is shown in orange. Finally, the genome-scale metabolic modelling analysis is shown in green. Download Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli strains. We selected a set of 3,616 unique E. coli genomes for which AMR testing and metadata were available from the Pathosystems Resource Integration Centre (PATRIC)  .Importantly, the genome sequences of these strains were all listed in PATRIC as “good” quality assemblies, had less than 250 contigs, and were labeled as either “WGS” (for whole genome sequenced) or “Complete” as the genome status in PATRIC. The genomes have experimentally measured AMR phenotypes, which are annotated as either “susceptible” or “resistant.”These isolates included a wide variety of geographic locations see  and AMR 10.1128/mSystems.00913-20.2FIG S2E. coli strains were selected based on the availability of metadata provided in Pathosystems Resource Integration Centre (PATRIC). (a) Geographic locations of the E. coli strains. The color on the map scales logarithmically with the number of resistant strains found in that area. (b) Bar chart with 12 antibiotics on the x axis and the number of genomes resistant (red) or susceptible (blue) to each antibiotic on the y axis. The counts are stacked on top of each other. Download FIG S2, PDF file, 0.1 MB.The 3,616 unique Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Next, the pan-genome was extracted for the selected strains using the default parameters in Roary version 3.13.0 , which cThe next goal of this analysis was to identify features in the genome sequence of each isolate which strongly correlated with resistance or susceptibility to each of the 12 antibiotics described above. To this aim, we implemented a gradient boosting classifier model for each antibiotic studied. DNA segments (k-mers) of 13 bp long were used as features in the classifiers, with the AMR phenotype used as the model labels (resistant or susceptible). For each classifier, 10,000 features were selected based on the chi-square test. We used the performance metrics accuracy, area under the receiver operator characteristic curve (AUC), precision, and recall to evaluate each model. A synthetic minority oversampling technique (SMOTE) was used to reduce the impact of unbalanced classes in the antimicrobial label groups and achieve robust classification results. The performance metrics were calculated as the mean of 50 simulations . The perThe maximum importance in the 50 runs was captured for each k-mer. To identify important genes, the k-mers with a maximum importance score greater than 0 for each antibiotic model (as assigned by the GBC), were cross-referenced to the pan-genome of the 3,616 genomes. The identified k-mers, their corresponding genes, and maximum importance scores obtained by the GBC are shown in 10.1128/mSystems.00913-20.3FIG S3FIG S3, PDF file, 2 MB.Number of features or genes identified in each step of our novel pipeline for (a) the k-mer-based approach and (b) the SNP-based approach. Results are shown for the antibiotics with an AUC of  >0.95 only. Download Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00913-20.8TABLE S1Table S1, XLS file, 0.4 MB.The identified k-mers, their corresponding genes, and importance scores obtained by the GBC. Download Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the www.github.com/sanger-pathogens/snp-sites) and used as the features in the GBC model for fitting AMR labels. A synthetic minority oversampling technique was applied to oversample data of the minority class, compensating for unbalanced classes. The performance metrics were calculated as the mean of 50 simulations  in the protein-coding genes of the core genome of the pan-genome were identified using the SNPsites tool , we integrated the genetic determinants with the GSM iML1515  and 123 (ciprofloxacin), with a small number of genes overlapping between antibiotic AMR models . These 210.1128/mSystems.00913-20.10TABLE S3Table S3, XLS file, 2.4 MB.The GSM results for the 289 genes corresponding to the top 10% of k-mer and SNP-based AMR models. Download Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli (E. coli (E. coli (We found genes enriched in amino acid metabolism (histidine and arginine), the pyrimidine salvage pathway, putrescine biosynthesis pathway, and transport metabolism. Importantly, histidine metabolism has been found to play an important role in stress resistance in E. coli , 54, whi and evaluated the metabolic processes that were affected. In doing so, we can infer potential metabolic adaptation mechanisms that can be linked to a change in gene function .Next, to investigate further the metabolic processes involved in adaptation to antibiotic stresses, we considered the effects of the 289 genes on bacterial growth. The ability of bacteria to adjust their metabolism to slow down growth has for example been found to be advantageous for reducing the damage that occurs as a result of being the primary target of antibiotics –62. Iden–E. coli with mutations in the 289 genes. Single gene deletions under rich environmental conditions were carried out in iML1515 to mimic the effect of a “loss of function” mutation on the entire system . Importantly, we found a total of 20 gene knockouts that were lethal to the bacteria. These genes show a high level of agreement with in vivo gene essentiality results , peptidoglycan metabolism , lipolysaccharide biosynthesis (lptG), S-adenosyl-l-methionine metabolism (metK), and folate metabolism (folP)  . Importain vivo results , and a variety of nucleosides (ciprofloxacin).Additionally, gene modifications that affect the utilization of alternative carbon sources was also investigated. Alternative carbon source utilization has been found advantageous for pathogenic survival of bacteria, including r jejuni –68. To t jejuni –. The carNext, the GSM was used to investigate whether the genetic determinants could be linked to additional metabolic adaptation mechanisms, beyond those affecting the growth rate and alternative carbon utilization. For this analysis, we examined the effect of each gene on metabolite reproducibility and reaction fluxes. More specifically, we simulated single gene knockouts as before, however this time, we captured the effect on metabolite yields and flux spans  for all metabolites and reactions in the iML1515 model. The output of this analysis is twofold: (i) to identify clusters of genes that have similar metabolic phenotypes and (ii) to elucidate the metabolic adaptations that are most important in providing bacteria with possible resistance to antibiotic stress. Genes that confer a similar phenotype could give rise to higher variation of strains, while providing similar advantages for resistance . DetermiThe lethality of each genetic determinant on all metabolites in the iML1515 model was determined using FBA. A gene knockout was considered lethal to the production of a specific metabolite if it results in blocking the biosynthesis of the metabolite . The results are represented as a bipartite graph of 98 genes and 508 metabolites. A gene is connected to a metabolite via an edge if its knockout results in preventing the metabolite’s production. Using the Clauset-Newman-Moore greedy modularity maximization algorithm, we clustered the genes and metabolites into groups of similar phenotypes . The lar10.1128/mSystems.00913-20.4FIG S4FIG S4, TIF file, 2.7 MB.(a) Network diagram showing the significantly enriched pathways in each cluster in Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the murC, ftsI, dapE, glmM, murB, murG, mraY, and mpl in cluster 2 had a significant effect on the production of the metabolites involved in peptidoglycan (PG) metabolism. Peptidoglycan is a mesh-like structure that provides the strength and shape of the outer cell membrane, as well as providing protection against osmotic pressure. Modifications to PG can prevent the release of cell wall components, which initiate the host immune response  Heatmap showing the different subsystems that are affected by the genes in the gene-metabolite network in Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli strains  biosynthesis in particular. Importantly, point mutations in folP have been identified to prevent sulfonamides from inhibiting THF production  cycle. The concentration of acetyl-CoA, an important derivative of CoA, has also been found to play a key role in assessing the cell metabolic state, which, in turn, determines the fate of either cell growth, survival, or death  . Importa strains . The genresponse –79. Impoesponse –, 81. E. floxacin . Allantofloxacin . Furtheroduction . We idenpicillin . The proor death .menD, entS, aroC, dxr, pheA, menC, menE, ispA, and entD in cluster 2 all affect enterobactin biosynthesis, either directly or via the chorismate biosynthesis pathway. Enterobactin is an iron-scavenging siderophore and has been found important for pathogen virulence . Importantly, previous work has found reduced respiration via the ETC resulted in mutant strains highly resistant against ampicillin and gentamicin (–The production of metabolites relating to iron metabolism were affected by genes in both clusters 2 and 6. The four genes in cluster 6 affect metabolites involved in heme biosynthesis. The capability (or improved capability) for heme synthesis may provide pathogens with a competitive advantage for colonization, since heme is the largest source of iron for the cell. Excess heme, however, increases the level of ROS and therefore is extremely toxic to the cell, so the regulation of heme concentration is essential . The genirulence –88. An irulence –, 90. Chantamicin . The ETCntamicin –62.cysJ, metK, asd, gss, cysH, cydC, metF, and gshB for instance are all affecting metabolites involved in glutathionylspermidine (GSP) biosynthesis. Importantly, GSP can be recycled back to glutathione and spermidine. Glutathionine is an important antioxidant metabolite required for detoxifying ROS  strains, due to its involvement in the regulation of the locus of enterocyte enfacement (LEE). The LEE system is essential to these pathogenic bacteria in order to attach and infect host epithelium cells (E. coli (EHEC) infections in mice  was used to identify the biochemical reactions whose flux span was affected by mutations in the genetic determinants. The results are represented as a bipartite graph of 145 genes and 861 affected reactions . A gene 10.1128/mSystems.00913-20.6FIG S6FIG S6, TIF file, 2.7 MB.(a) Network diagram showing the significantly enriched pathways in each cluster in Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the gmhB, waaC, waaP, accA, lptG, lptF, waaA, hldD, fabG, lpxL, hldE, fabD, and glmM are affecting the biosynthesis of nucleotide sugars. These sugars are incorporated into the O-antigen region of LPS, which is located in immunodominant part of LPS , nucleotide metabolism , amino acid metabolism (histidine and methionine), and iron metabolism (heme). The main differences between networks involve the set of genes in cluster 1. Unlike before, this analysis reveals the genes t of LPS . Furtherasd, gcvP, gcvT, serA, metF, cysH, cysJ, and serB, were found to affect amino acid metabolism , all of which are involved in folate transformation of E. coli. As previously discussed, folate metabolism can affect persistence to antibiotic exposure. Alternatively, however, sulfur amino acid residues in proteins, including methionine and cysteine, are found to be extremely reactive with ROS; therefore, changes to the genes specifically affecting these amino acids may play a role in ROS detoxification , specifically, 8% higher for aztreonam, 17% higher for ampicillin, 2% higher for cefepime, 5% higher for cefoxitin, 6% higher for gentamicin, 28% higher for trimethoprim, 10% higher for tobramycin, and 21% higher for tetracycline. A possible reason for this is due to the inclusion or exclusion of accessory genes in the two approaches. That is, the k-mer-based approach allows for discriminating between resistance and susceptibility according to both the core and accessory genome, whereas the SNP-based approach is restricted to the core genome only. The antibiotics that performed well only via the k-mer-based approach may therefore be highly dependent on acquired resistance genes, such as the highly discriminant beta-lactamases. The SNP-based approach, however, successfully predicted AMR resistance for the two fluoroquinolone antibiotics ciprofloxacin and levofloxacin, and for the beta-lactam antibiotic meropenem. Importantly, the SNP-based approach performed extremely well (AUC > 0.98) for ciprofloxacin, suggesting that the antibiotic induces mutations, which is consistent with the literature . ImportaE. coli . More specifically, we predicted metabolic phenotypes of genetic variants via gene knockouts and identified the metabolic processes that were being affected. Importantly, using our new ML-FBA integrated approach, we could reveal interesting links between genes and potential metabolic adaptation mechanisms that, importantly, were not identified using standard gene pathway enrichment analysis.accA and metK for ciprofloxacin; fabD and fabG for levofloxacin; murG, lptG, and mraY for meropenem; folP for ampicillin; and glmM for gentamicin. Modifications to these genes may result in slower growth, which has previously been found advantageous to pathogenic bacteria, including E. coli and Salmonella, for reducing the damage that occurs as a result of being the primary target of antibiotics , however, are again important for nucleotide biosynthesis and have in fact been found important for persistence in E. coli cells exposed to ampicillin  for the entire set of genes in the model is possible, which could be readily available for future AMR studies to draw insights on potential new AMR genes. Future efforts may precompute all of these deeper metabolic effects for each gene in a given GSM(s). Such future endeavors will offer the possibility to future AMR genome-wide association studies (GWAS) to readily draw insights from potential AMR gene metabolic effects as predicted by these methods without needing to set-up and solve all the GSM problems independently.eis and rrs −three-dimensional (3D) structure-function prediction analysis may then enable us to determine whether the SNPs result in a loss or gain of function, which is directly integrated as constraints into models such as GEM-PRO. The effects of the SNPs on the genes  is not determined in our approach and if considered would allow further insights into the biological interpretation. Finally, our approach was applied to a GSM model of the E. coli K-12 MG1655 strain, and therefore was limited to the genes in this genome. Developing a GSM of the pan-genome of the 3,616 strains used may reveal additional metabolic genes that are important for resistance. Furthermore, an extended version of iML1515 has been developed that includes ROS-specific reactions . We selected genomes that were annotated as either “susceptible” or “resistant” to a single antibiotic. These AMR phenotypes were derived from laboratory analyses only and included a mixture of both Clinical and Laboratory Standard Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) AMR standards. The list of the laboratory method standards used to determine the AMR phenotypes is detailed for each isolate in the supplemental Excel file “Ecoli_genomes_metadata.xlsx,” which is provided on https://github.com/tan0101/GSM_mSystems_2021. All the genome sequences of the isolates that were used in this study were listed in PATRIC as “good” quality assemblies. Isolates labeled “good” quality in PATRIC meet the criteria set by Parrello et al. . These k-mers were then counted in each individual genome. All the counts were compiled into an n samples × n-k-mers + 1 matrix with the additional column for the resistance phenotype .Lists of k-mers of length 13 which occurred in at least one of the genome files were generated for each antibiotic using all the genomes for each antibiotic with GenomeTester4  based on homologous gene groups produced by Roary (https://github.com/sanger-pathogens/Roary). Each strain was processed according to its AMR phenotype for each antibiotic as follows. (i) First, the genome sequences were used to obtain the core gene sets, which are present in ≥99% of each set, ranging from 1,627 to 2,903 depending on the strain sets in each antibiotic. (ii) Each core gene nucleotide sequence was further aligned, and single nucleotide variants were identified. The position of a SNP in a gene was selected as a feature in the machine learning if the nucleotide varied in more than 5% of strains . (iii) The data set of SNPs was assigned  for each allele of the strain as the matrix for machine learning.The variant sites (SNPs) in the core genome alignment were extracted using a SNP sites tool  = TP/P•Precision  = TP/(TP + FP)•Accuracy (ACC) = (TP + TN)/(P + N)•Area under the receiver operator characteristic curve (AUC)The k-mers and SNPs (features) were analyzed using the gradient boosting classifier (GBC) model in scikit-learn  v0.19.1 was applThe mean of these 50 iterations was then used as the result statistic for the performance. Boxplots from the Seaborn  package P > 0.05) difference between the number of times a k-mer occurred in the susceptible or resistant condition. The k-mers found by the GBC were searched against the pan-genome of all our genomes using BLAST. The parameters for the search were as follows: E-value, 1,000; word size, 13 (same size as the k-mers); gap opening penalty, 5; and gap extension penalty, 2. The search hits were annotated by searching a gene transfer format (GTF) file corresponding to the pan-genome and by retrieving the information about each hit.The GBC assigns an importance score between 0 and 1. This does not indicate which phenotype the k-mer is more associated with. To work out which phenotype the important k-mers were associated with, we compared the number of times a k-mer occurred in the susceptible condition to the number of times it occurred in the resistant condition. Next, we performed a chi-square test on these counts to determine whether there was a significant . We considered the essentiality of a gene under both rich medium conditions and M9 minimal medium conditions. To mimic rich medium conditions, the model was constrained to allow all carbon sources into the system, with a fixed uptake rate of 1 mmol/gDCW/h. If a feasible solution exists, while maximizing the biomass equation as the objective function, then the knockout of the gene was not essential. To mimic M9 minimal medium conditions, the model was constrained so one individual carbon source had a maximum uptake of 10 mmol/gDCW/h. This simulation  was repeated for each carbon source in the model. The genes whose corresponding knockout model achieved a growth rate of 0.0001 hi for the wild type (wt) and the jth gene knockout model is denoted as i,wty and i,jy, respectively.A drain reaction was added to iML1515 for each metabolite in the model . The maximum theoretical yield of each metabolite was calculated by setting its corresponding drain reaction as the objective function, with glucose as the only carbon source in aerobic minimal M9 medium conditions. The simulations were carried out for the wild-type model and each gene knockout model. The maximum theoretical yield of metabolite G = , such that the nodes, N, represent genes (U) and metabolites (V), and the edges, E, connect a gene in U to a metabolite in V. The ith metabolite is connected by an edge to the jth gene, if i,jy = 0, given that i,wty>0. Networkx’s greedy modularity algorithm was applied to the network to assign genes and metabolites to clusters that were densely connected. The algorithm minimizes the number of interconnections between clusters, while maximizing the number of intraconnections. Cytoscape v3.7.1 was used for visualization of the clusters in the bipartite network  was constructed using networkx, such that the nodes represent genes (U) and reactions (W), and the edges (E) connect a gene in U to a reaction in W. The ith gene is connected by an edge to the jth reaction, if the knockout of the ith gene reduces the flux span by at least 10% compared to the wild type. As before, networkx’s greedy modularity algorithm was applied to assign genes and metabolites to a cluster in order to identify groups of genes that have a similar impact on the metabolic fluxes. As with the metabolite yields, Cytoscape v3.7.1 was used to then visualize the clusters in the bipartite network.Similar to before with metabolite yields, a bipartite graph E. coli , are available on https://github.com/tan0101/GSM_mSystems_2021.The accession numbers of the 3,616 10.1128/mSystems.00913-20.7FIG S7x axis according to the nine clusters shown in FIG S7, PDF file, 0.1 MB.(a) Heatmap showing the different subsystems that are affected by the genes in the gene-reaction network in Copyright © 2021 Pearcy et al.2021Pearcy et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Inflammatory bowel diseases, such as Crohn’s Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn’s Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis. Changes in microbiome composition have been linked to complex multifactorial diseases, e.g., type 2 diabetes, metabolic syndrome, and non-alcoholic fatty liver syndrome3, as well as inflammatory bowel disease (IBD)4. IBD can be separated into two subtypes, Crohn’s Disease and ulcerative colitis4. Factors contributing to the IBD pathogenesis include genetics, diet, lifestyle, and the gut microbiome5. There is an urgent need for a mechanistic understanding of the role of these complex host-microbiome–environment interactions in IBD6. Ultimately, personalised treatment approaches targeting the diet-host-microbiome axis are needed6. A number of studies have reported differences in the abundances of certain taxa between IBD patients and healthy controls, identified through 16S rRNA sequencing or metagenomic approaches8. However, metagenomic approaches alone are insufficient to infer the functional metabolic activity of the microbiome6. Thus, functional, pathway-based analyses are required to elucidate not only the changes in composition in the gut microbiomes of IBD patients but also the metabolic changes that could serve as a target for therapeutic interventions.The human gut microbiome plays an important role in human health and disease. It performs important functions, such as maturation of the host immune system, digestion of food, synthesis of short-chain fatty acids, vitamins, and amino acids, and protection against pathogens4. Changes in microbial metabolites, such as short-chain fatty acids, secondary bile acids, and tryptophan, have been especially implicated in IBD4. To gain insight into changes in microbiome structure and function, metabolomic and metagenomic analyses for the same faecal samples are commonly performed, and subsequently, positive and negative correlations between species abundances and specific metabolites are inferred for cohorts of IBD patients and controls10. However, the mechanisms underlying these correlations remain unclear9 and it is difficult to disentangle the contributions of microbial and host metabolism to altered metabolite levels. Mechanism-based computational models that integrate omics data , as well as dietary information could mechanistically link changes in microbe abundances and metabolite levels and, ultimately, propose potential disease mechanisms, biomarkers, and personalised therapies5.Several studies have shown differences in the blood and/or faecal metabolome between IBD patients and cohorts11. Briefly, COBRA relies on a manually curated genome-scale reconstruction of metabolism of a target organism, which can be converted into a mathematical model and subsequently interrogated through simulations, using established methods, such as flux balance analysis12. COBRA models13 can be readily contextualised by implementing different types of data as constraints, e.g., metagenomics14, metabolomics15, proteomics16, or dietary information17. To enable constraint-based modelling that captures the diversity of the human gut microbiome, we have assembled a resource of 818 curated genome-scale reconstructions of human gut microbes, AGORA18. The reconstructions have been built on the strain level from refined genome annotations and experimental data18. AGORA enables the creation of personalised microbiome models from metagenomics data that freely allow host–microbe and microbe–microbe metabolic interactions14. Thus, these microbiome models have valuable applications in studying microbe–microbe and host–microbe interactions19. Several studies have already successfully applied AGORA to predict microbiome metabolism in IBD, such as predicting personalised dietary supplements20, predict the potential for metabolic cross-feeding were predicted in patients and controls21, stratify subtypes according to their metabolic networks22, and predict treatment efficacy23.One such mechanistic modelling approach is constraint-based reconstruction and analysis (COBRA)8 have performed metagenomic sequencing of the microbiomes of paediatric Crohn’s Disease patients and healthy control children and found that the Crohn’s Disease microbiomes stratified into two clusters, i.e., a “near cluster”, which resembled the healthy microbiomes in composition and a “far cluster” characterised by microbial dysbiosis that was distinct in composition from both healthy controls and the near cluster microbiomes. Based on these results, they separated the patients into two groups, “non-dysbiotic” and “dysbiotic” IBD8. We have previously constructed personalised models for a subset of 20 dysbiotic Crohn’s Disease and 25 healthy control microbiomes from this cohort24. We have then applied the COBRA approach to predict the bile acid deconjugation and biotransformation potential of each sample24. The computational modelling stratified the Crohn’s Disease microbiomes and healthy microbiomes by their bile acid metabolism profiles24. Here, we substantially expanded the computation of metabolic profiles to a wide variety of metabolic subsystems. We systematically predicted the potential of each microbiome to secrete and take up all metabolites, for which biosynthesis pathways and transport reactions were present in the community models and identified the metabolites that best stratified the dysbiotic and non-dysbiotic individuals. For all secreted metabolites, we computed the respective contributing strains in each microbiome. Finally, we validated the predictions for amino acid metabolites against published metabolomic data from the same cohort. Taken together, we present a computational systems biology approach that bridges the gap between metagenomic and metabolomic data and provides mechanistic, testable hypotheses for metabolite–microbe associations.Previously, Lewis et al.8 . Each personalised microbiome model was further contextualised by simulating the intake of an Average European diet (Methods). The metabolic profile of each microbiome was then computed as follows: All dietary, faecal, and strain-specific exchange reactions present in the model were retrieved, and the minimal and maximal fluxes through these exchange reactions were computed using distributed flux balance analysis25 (Methods). This approach enabled a systematic evaluation of secretion potential, uptake potential, and strain-specific contributions for each metabolite that could be transported by at least one microbiome model. The modelling framework captured the range of metabolic capabilities encoded by the human gut microbiome, and the variation in metabolic potential as a function of microbiome composition.First, we developed a large-scale, computationally efficient constraint-based modelling approach Fig. . BrieflyOverall, the 108 microbiome models accounted for exchange reactions for 419 metabolites, for which the minimal and maximal fluxes were computed. We calculated the microbiomes’ theoretical potential to take up and secrete metabolites  of the predicted uptake and secretion fluxes reported in Supplementary Tables Since the microbial composition of the dysbiotic and non-dysbiotic individuals clearly differed9 and putrescine26 have been reported for Crohn’s Disease patients. Hydrogen sulfide has been proposed to both worsen27 and protect against28 gastrointestinal inflammation. On the other hand, a reduced secretion potential was predicted for branched-chain fatty acids, nicotinamide (vitamin B3), and degradation products of mucins and other glycans  error of 0.0648. Among the secreted metabolites that best stratified the non-dysbiotic and dysbiotic microbiomes were chorismate, d-ribose, l-lactate, and phenol  between species and metabolites were found for 66 secreted metabolites Fig. . For inss32 Fig. . Secondas32 Fig.  as obsere34 Fig. . The remTo explain the observed changes in metabolite secretion potential, we calculated and inspected the absolute presence of reactions in the microbiomes, as well as the quantitative reaction abundances on the whole community, class, and genus level. The absolute presence of 84 and 393 reactions was distinct between healthy and IBD and between the dysbiotic and non-dysbiotic IBD cluster, respectively Fig. . Thus, tThe abundances of 460 reactions on the total community level, 868 reactions on the phylum level, and 37,059 reactions on the genus level differed significantly between healthy controls and IBD patients , t(105) = −2.06, p = 0.042)  Fig. .Fig. 4Re36. While taurine, hydrogen sulfide, methionine, and cysteine could be secreted by all microbiomes, the capability to produce other sulfur species, such as thiosulfate, sulfite, and sulfate, differed strongly between the three study groups , revealing that also non-dysbiotic IBD microbiomes displayed an altered sulfur metabolism pattern. Consequently, when analysing the pattern of final breakdown products prevalent in secretion profiles, we found different distributions for these patterns across the three study groups  have been repeatedly implicated in the pathophysiology of IBDups Fig. .z = 4.29, p = 1.79e-05). Therefore, over 50% of the difference in number of secreted sulfur compounds between healthy and dysbiotic IBD communities could be attributed to the lack of heparan and chondroitin degrading species in IBD. Consequently, IBD microbiomes containing any heparan and chondroitin degrading species were more similar to healthy controls in terms of diversity in sulfur metabolism , and future studies with bigger sample size are needed to corroborate the effectiveness of COBRA modelling to predict metabolomic changes in IBD. In agreements with metabolomic findings, we predicted an increased potential to synthesise betaine, glutamate, glycine, leucine, phenylalanine, and tryptophan in the dysbiotic IBD cluster . In the case of IBD, this includes particularly microbial sulfur metabolism with potential clinical consequences. Following a stoichiometric argument, all sulfur consumed by microbial communities must be released either by death or secretion. Assuming that the uptake of sulfur is relatively constant across microbiomes, reduced numbers in secreted sulfur metabolites must result in higher secretion potentials for the few secreted compounds. In accordance, the ubiquitously secreted hydrogen sulfide showed higher secretion potentials in dysbiotic IBD microbial communities, which displayed drastically reduced numbers in secreted sulfur compounds. Hydrogen sulfide is considered to be pro-inflammatory in the gastrointestinal environmentBacteroides spp.32, the abundances of which strongly correlated with glycan degradation product secretion in our study . A previous metabolic modelling approach already predicted dietary metabolites that could improve short-chain fatty acid secretion profiles in IBD20. Here, we additionally propose a mechanistic explanation for potentially beneficial dietary interventions. In conclusion, by computational modelling of microbial communities, we identified a potentially beneficial intervention on gut microbiota in IBD, which could be applied in a personalised manner in IBD cases specifically lacking glycan-degrading species. This underlines the utility of analysing the metabolic capabilities of microbial communities with mechanistic, strain-and molecule-resolved modelling tools.Importantly, the loss in diversity in sulfur metabolism was mediated by the loss of glycan-degrading species. Host glycan degradation is mainly attributed to udy Fig. . In this39 from the same cohort. While one cannot compare fluxes and concentrations44, the trends of upregulated and downregulated metabolites in the dysbiotic cluster can nonetheless be compared. Indeed, for 16 of 23 amino acid metabolites, in silico fluxes agreed with measured faecal concentrations , and cluster 2 (dysbiotic IBD) was adapted as defined by the original authors8.Paired end Illumina raw reads of 83 IBD patients in the PLEASE cohorthttps://www.vmh.life) of the AGORA resource18. To build the personalised models, the COBRA Toolbox52 extension Microbiome Modelling Toolbox14 was used. Personalised microbiome models were created in MATLAB  version R2018b using the mgPipe module, as described previously24. Each personalised model contained a community biomass reaction, which was parameterised by applying the strain-level abundances as stoichiometric values for each microbe biomass reaction in the community biomass reaction. These constraints enforced that all strains grew at the experimentally measured ratios. The models were further contextualised as follows: To simulate a realistic intake of dietary nutrients in mmol per g dry weight per hour in the 108 microbiome models, an Average European diet was retrieved from the Diet Designer resource on the Virtual Metabolic Human17 website (https://www.vmh.life). The diet was converted to uptake fluxes through a dedicated Microbiome Modelling Toolbox function (convertVMHDiet2AGORA.m). Moreover, to account for host metabolism, the uptake of metabolites of host origin known to be present in the intestine  were allowed using standard COBRA Toolbox functions52. Finally, to simulate a realistic turnover of microbial biomass, the allowed flux through the community biomass reaction was set to be between 0.4 and 1 (mmol/person/day), corresponding to a faecal emptying of once every three days to once a day.Personalised models for the 108 samples were created using Version 1.03 . The computation of the total community metabolite production potential, total community metabolite uptake potential, and the contribution of each strain to each metabolite was performed in Julia v0.6.4  using the Julia implementation of flux balance analysis, COBRA.jl53. COBRA.jl was performed on a high-performance cluster using the IBM CPLEX solver  through the CPLEX interface for Julia. A customised Julia script was used that retrieved all dietary exchange reactions, faecal secretion reactions, and strain-specific internal exchange reactions for each microbiome model. This resulted on average in 13,677 exchange reactions per microbiome model, which were then each minimised and maximised using distributed flux balance analysis53. The fluxes were exported from Julia using the customised Julia script and further analysed in MATLAB.Absolute reaction presence and reaction abundances on the total community, phylum, and genus level were calculated in MATLAB using dedicated Microbiome Modelling Toolbox functions (correlateFluxWithTaxonAbundance.m).Wilcoxon rank sum test, and correction for false discovery rate (FDR) were performed in MATLAB using the ranksum and mafdr functions, respectively. Correlations between computed fluxes and species abundances were calculated using a dedicated Microbiome Modelling Toolbox function (p < 0.01) than a linear function and was therefore used. Next, we tested the secretion (binary: yes vs. no) on association with the study group using Fisher’s exact tests controlling for the FDR as before. The number of tests to control for was determined by counting the number of independent dichotomised secretions.The number of secreted metabolites was calculated from the computed net production fluxes .In a third step, we counted the number of sulfur-containing metabolites independently secreted for each microbiome. We tested this number on association with the study group in linear regressions. Furthermore, we explored the classification of healthy microbiomes vs. dysbiotic IBD microbiomes in logistic regression by the number and calculated the area under the curve (AUC) as metric of classification accuracy. Then, we analysed the pattern of final breakdown products of sulfur metabolism  across the three study groups via Fisher’s exact test. Finally, we checked whether the presence of any glycan-degrading species statistically mediated the effects of the study group variable via the Sobel Goodman test, deriving the confidence intervals by non-parametric bootstrapping with 1000 replications31 , which relies on the MetaboAnalystR55 package. Briefly, the random forests classifier is built using a customisable number of trees with one-third of the data left out of the bootstrap sampling process. The left-out data is then used to estimate the out-of-bag (OOB) error. Metabolite secretion fluxes were imported through the Statistical Analysis module in the MetaboAnalyst interface without performing normalisation, transformation, or scaling of the data. The classifier was built with 5000 trees and the resulting visualisation of features ranked by their contributions to classification accuracy was exported.Random forests analysis was performed using the online implementation of MetaboAnalyst 5.056 (http://circos.ca). All other data was visualised in MATLAB version R2018b and in R version 3.5.357 (https://www.r-project.org).Phylum to amino acid and subsystem contributions were visualised using the online implementation of CircosFurther information on research design is available in the Supplementary InformationSupplementary Data 1Reporting Summary


Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance.IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown. We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes. We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients. The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants. Rhizophagus irregularis  . AMF arearadices ) is one t growth –8, fruit growth –, 10, and growth ––14. Furtnditions –22. ThesR. irregularis grow into a network of coenocytic hyphae, which can be separated into three major structures: the extraradical mycelium (ERM), the intraradical mycelium (IRM), and ARB (R. irregularis mainly provides inorganic phosphate (Pi) and nitrogen (N) to the host plant as its extensive hypha network bridges the nutrient depletion zone surrounding the roots , which is translocated through the ERM toward IRM  and nitrate (NO3−) as well as amino acids and small peptides via designated transporters. Three NH4+ transporters, GintAMT1 to -3, and a NO3− transporter, GiNT, have been identified in R. irregularis  and, thus, depends on fatty acids provided by the host plant  reactions via gene-protein-reaction (GPR) rules  rules . FurtherR) rules . The depglycerol , 33) wasR. irregularis, we relied on the gene annotations supported by the literature  cycle and pyruvate metabolism, the model of A. terreus displays particular differences in purine metabolism, steroid biosynthesis, sphingolipid, and pyrimidine metabolism, and lipid metabolism, and the model of P. chrysogenum differed in sphingolipid metabolism and fatty acid elongation  Characteristics of published fungal models selected for comparison with iRi1574. (B) FBA solution and feasible ranges and of all reactions in the iRi1574 model determined by FVA. (C) eMOMENT solution and feasible ranges and of all reactions in the enzyme-constraint iRi1574 model. (D) Experimentally measured hyphal dry weight and protein content as well as growth predictions by FBA and eMOMENT with different saccharides and their concentrations . (E) CoeCopyright © 2022 Wendering and Nikoloski.2022Wendering and Nikoloski.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the vbio, through a biomass reaction that integrates the biomass precursors. This results in a linear optimization problem that imposes metabolic steady state and physiologically relevant bounds on reaction fluxes, i.e.,S represents the stoichiometric matrix, including the molarity with which each substrate and product enter a reaction of the metabolic model, v stands for the flux distribution, and R denotes the set of reactions in the model. Since it is well-known that there are often multiple steady-state flux distributions, v, that achieve the same growth  , 79, whie growth , to chare growth .10.1128/mSystems.01216-21.9TEXT S2Text S2, DOCX file, 0.03 MB.Supplemental methods. Download Copyright © 2022 Wendering and Nikoloski.2022Wendering and Nikoloski.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. chrysogenum, iAL1006, and the iRi1574 model showed narrower ranges than the remaining models, except for fatty acid metabolism. The reason for this observation is the higher number of soft- and hard-coupled reactions to the biomass reaction in the iAL1006 and iRi1574 models compared to the others , and (iv) uncoupled if the range was equal to 2,000. All classes were defined with a tolerance of 10−3 while guaranteeing for nonoverlapping classes. Download FIG S3, TIF file, 2.5 MB.Reaction coupling to the biomass reaction. Coupling types were assessed by performing flux variability analysis at 90% of the optimal growth rate. The resulting flux ranges of flux-carrying reactions were categorized as (i) hard-coupled to the objective if the range of a reaction was equal to the range of the biomass reaction, (ii) soft-coupled if the range was below the biomass reaction flux range, (iii) partially coupled if the range was between the biomass reaction range and the maximum . As a result, we simulated growth on 11 carbon sources by using FBA (described above), resulting in different growth rates . Further, we quantified the ATP production for each of these carbon sources under the same conditions while guaranteeing 0% or 50% of the optimal predicted growth  The growth rates with 11 carbon sources, considered individually, were predicted by FBA using the default medium with only a minimal uptake of palmitate and no additional carbon source uptake allowed. Using the same setup, ATP production (B) and yield (C) were assessed by maximizing the flux through a sink reaction while allowing for a reduction of optimal growth by 100% or 50%, respectively. Download Copyright © 2022 Wendering and Nikoloski.2022Wendering and Nikoloski.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. irregularis  are allowed, with uptake rates restricted to their minimal fluxes at optimal growth, this increase in growth amounts to 9.7% (see below for the predictions from the enzyme-constrained model).Moreover, it has been reported that the addition of myristate to the medium leads to enhanced growth of egularis . We founn source . Therefoi from fungus to the host plant  and the abundance of the respective enzyme(s) . We then employed an extension of MOMENT  ; these mnzyme(s) , 88. An aint see , 88, simaint see , we intraint see , resultii. Without additional constraints in the enzyme-constrained iRi1574 model, the positive effect of myristate uptake on growth could not be reproduced, since myristate is catabolized via peroxisomal β-oxidation and the expression of the required enzymes is not outweighed by the benefit of generating acetyl-CoA from myristate. However, when the allocation of total protein is shifted from the optimal ratio toward increased abundances of peroxisomal proteins  was used as a reference. The ratio Zα was then calculated by dividing the predicted growth rate when myristate uptake is allowed (orange line) by the respective reference value. Values greater than 1 for Zα indicate that there are values for the ratio α for which uptake of myristate can slightly increase growth. Download FIG S5, TIF file, 1.6 MB.Effect of myristate uptake on predicted growth of the enzyme constraint iRi1574 model upon differential allocation of protein pools. The eMOMENT approach was used to predict growth rates of the iRi1574 model with additional enzyme-constraints. As the β-oxidation enzyme machinery is not utilized when growth is optimized , the total protein content was split into a peroxisomal and a pool of remaining enzymes, at ratio Copyright © 2022 Wendering and Nikoloski.2022Wendering and Nikoloski.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the d-glucose, d-fructose, raffinose, and melibiose) at three different concentrations  . Like in the findings based on FBA described above, palmitate was present in the default medium since growth without palmitate is not possible, irrespective of additional supply of carbohydrates  . These din Sy167 , which ihydrates , 47. To d-glucose and raffinose, which is broken down to sucrose and d-galactose extracellularly. Hence, the iRi1574 model can reliably predict growth based on different carbon sources when protein content and protein-reaction associations are considered. A reason for the significant negative correlation with FBA could be the altered biomass composition after rescaling by the changing protein content as well as the lack of protein constraints, which are present in the enzyme-constrained model.We next compared the predictions of growth from the eMOMENT approach with those from FBA , with the same restrictions on palmitate uptake . We obseThe applied approach to integrate total protein content allowed us to predict not only optimal growth but also abundances of individual proteins for the 12 combinations of carbon source and concentrations considered. Since multiple allocations of proteins to enzyme complexes and reactions can lead to optimal growth, we sampled the set of feasible enzyme abundances  at 99% of the respective optima. The resulting predictions on alternative enzyme allocation at optimal growth were used to investigate the plasticity of enzyme allocation under the different conditions. We quantified the plasticity in the abundance of each protein by the CV across the simulated conditions. The CV was calculated for predicted protein abundance and reaction flux across the 12 growth scenarios E and F. To assess whether the plasticity in flux is dependent on the variability in enzyme abundance of the catalyzing enzymes, we compared the respective sets at the extreme ends of CV distribution (10% and 90% quantiles) between protein abundance and reaction fluxes . Among tAmong the 78 reactions with highly variable fluxes (CV ≥ 39.79), the majority lie within the lipid and fatty acid metabolism  and tranR. irregularis. To this end, we used published expression data   to find differentially activated reactions between each pair of structures. By using 0.6 as a threshold, we found that mainly reactions of the amino acid metabolism exhibited differential fluxes between each pair of structures, followed by reactions in metabolism of cofactors and vitamins, carbohydrate, lipid, and nucleotide metabolism   (Table SAw) . We usedtabolism . Upon intabolism . MoreoveR. irregularis is one of the most extensively studied AMF that forms symbioses with major crops, insights from the annotation of its enzymatic genes, the extensive body of evidence about its physiological and molecular responses to different environmental stimuli, and mutual effects on plants with which it interacts have not yet been systematically investigated in the context of metabolic modeling. The constraint-based modeling framework allows us to dissect the molecular mechanisms that underpin these responses and also to suggest targets for future metabolic engineering to boost the beneficial effects of this AMF. However, achieving this aim requires the assembly of a high-quality large-scale model that leads to accurate quantitative predictions of multiple traits in different scenarios.Although R. irregularis based on the KBase fungal reconstruction pipeline followed by consideration and inclusion of exhaustive literature research as well as manual curation for consistency and mass and charge balance. One possible caveat of using fungal reconstruction pipelines is that the resulting model may be very similar to the employed templates. Nevertheless, by conducting comparative analyses of the enzyme set of iRi1574 and that of published fungal models, we demonstrated the specificity of iRi1574 and its ability to capture the particularities of R. irregularis metabolism. More importantly, validation tests demonstrated that iRi1574 can (i) accurately predict increased growth on myristate with minimal medium with the FBA model as well as under additional constrains on enzyme distribution in the enzyme-constrained model, (ii) predict growth that is highly correlated with hyphal dry weight measured in a close relative  when considering enzyme constraints, and (iii) increase growth rate with tighter association with the host plant based on integration of relative transcriptomics data. The extensively validated model was used to show that the transition from IRM to ARB could be linked with changes in amino acid and cofactor biosynthesis.Here, we presented the enzyme-constrained iRi1574 GEM of R. irregularis, study the effect of the modifications on plant performance by coupling metabolic models of the symbionts, and further refine the model based on integration of heterogeneous molecular data. Altogether, these modeling efforts can guide future reverse genetics tools used to understand the functional relevance of metabolic genes in R. irregularis in shaping plant traits.This first model of an AMF can be coupled with root-specific models of model plants to investigate the effects of symbiosis. Further, a two-dimensional (2D) growth simulation approach  can be eRhizophagus irregularis DAOM 181602  (The genome of 39145.3) , 51 serv39145.3)  using th39145.3)  using fu39145.3) .To enhance connectivity between the cellular compartments, 198 transport reactions were added from the yeast iMM904 metabolic model . The imphttp://www.chemaxon.com). With elemental compositions and metabolite charges available, the model was manually mass and charge balanced.Metabolite formulas were added from PubChem and adapted to the net charge at the average cytosolic pH of 6.2  using ChAfter these steps, additional reactions were added from various literature sources. Most of the lipid metabolism is based on the results from reference −1  were then removed by repeatedly applying flux variability analysis (FVA) and correcting reaction reversibility or adding additional reactions as suggested previously . For theSynthesis reactions for LCOs were added by first modeling the synthesis of COs with chain lengths of 3 to 6 with subsequent acetylation reactions adding 16:0,  species , 71.GSE99655) (R. irregularis genome using STAR 2.7.3a (Structure-dependent transcriptome sequencing (RNA-seq) data were obtained as raw sequence reads (GenBank accession no. SE99655)  and mappR 2.7.3a . The reaR 2.7.3a . The aveR 2.7.3a , 108 witkcat values to reactions, an approach similar to that for GECKO  were added to the minimal medium  or not (y=0). This was done to enforce a lower bound, α, for the abundance of expressed genes to avoid numerical problems. The value for The following MILP, which we termed eMOMENT, imposes constraints that were adopted from the MOMENT approach , which wC, was determined by the experimentally measured protein contents at the given concentrations .Further, the total protein content, trations . To accok is smaller than or equal to the sum of enzyme abundances across all reactions with which it is associated.We extended the constraints we borrowed from the MOMENT approach by one additional constraint , which tThe feasible abundance ranges for all proteins were determined by individual minimization and maximization for S. cerevisiae, considering only the maximum concentration of 1 M. Across the calculated values, the maximum possible influx for each monosaccharide was selected. For this experiment, palmitate was also retained in the medium with the same upper limit as that described above. For each of the three structures , the abundance of each protein, For the metabolic change experiment, all four carbon sources that were used in the analysis described above were added to the same minimal medium. Similarly, the upper bounds on monosaccharide import were calculated using transporter kinetics from The total protein content was set to the maximum value measured across all growth conditions used in the experiment before (i, was defined as the product of estimated enzyme abundance and the respective turnover value:To conduct FBA, the transformed transcript count, Growth was predicted for each of the three structures by FBA using the adapted reaction limits. After this, FVA was used to determine the feasible ranges for each reaction while keeping the growth at 99% of the optimum. These ranges were used as the limits for the sampling procedure which attempts to find an optimal solution with minimal distance to a random flux vector Like this, 5,000 points were sampled and used for a reaction-wise comparison between the three structures. To this end, the nonparametric estimate for common language, Aw , was usep and q represent the vectors of sampled fluxes for the same reaction at two different structures.The variables https://github.com/pwendering/RhiirGEM.All procedures, data, and approaches used are available at


Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations.Escherichia coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E.coli’s metabolic capabilities.We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of https://gitlab.com/csb.ethz/pta.Python and MATLAB packages available at Bioinformatics online. Different -omics data from high-throughput measurement technologies can in principle be used to derive additional constraints, but data integration is challenging and the resulting constraints are condition-specific  with Gibbs reaction energies R the gas constant, T the temperature and activity term) maps the contribution of metabolite concentrations to the reactions they participate in. Reaction energies constrain fluxes via the second law of thermodynamics, which implies that positive (negative) net flux can only occur if the free energy of a reaction is negative (positive). This ensures that biochemical processes result in net energy dissipation.In a metabolic model, thermodynamics quantifies the favorability of a set Γ of How well thermodynamics constrain a model depends on the precision of the estimates of the variables in (2). Standard reaction energies can be estimated from measured equilibrium constants . MetabolThermodynamics-based Metabolic Flux Analysis (TMFA) , a framework in which the uncertainty of free energies and concentrations is modeled with a joint probability distribution , it follows that the reaction energy estimates are also normally distributed aswhere t to values that are compatible with the estimated concentrations and standard free energies up to a confidence level α, such thatα-quantile of the q degrees of freedom. We exploit that q is much smaller than the dimension of t by using a q-dimensional standard normal distribution to express t asWe use this distribution to constrain i, Lastly, we enforce the second law of thermodynamics and require that all reactions in Γ have a well-defined direction, that is, non-zero flux. For each Note that this requires reactions that are structurally blocked (e.g. by involving dead-end metabolites) to be removed in advance; they would conflict with (9).steady-state thermodynamic space (or short: thermodynamic space) t that fulfill (7) and satisfy the steady-state constraints (1), (9). In other words, We define the big-M formulation  . Next, wQuantitative assessment of a model aims to find features in PMO predictions indicative of knowledge gaps or model inaccuracies. First, we construct a condition-specific model with available flux and concentration constraints from a base model and use PMO to estimate iz-score z for each predicted value iμ in units of standard deviation  and a set of linear inequalities constraining the signs of free energies of irreversible reactions. Without a close-form description of the boundary of the space, finding the orthants that allow steady-state flux solutions is challenging. Despite the exponential number of orthants, a ray in an n-dimensional space only crosses at most n + 1 of them. Thus, at each step, we identify all orthants intersected by the ray and discard those that do not allow a steady-state flux distribution. We verify the steady-state condition with a linear program that checks the existence of a flux distribution satisfying (1) and the directionality constraints of the orthant. For each feasible orthant, we add its intersection with the ray to I.Sampling from I. We sample the next point along a ray proportionally to the target probability distribution, which is a Multivariate Normal (MVN). First, we restrict the MVN to a normal distribution along the 1-dimensional ray. We then compute the Cumulative Distribution Functions (CDFs) for each valid segment of I to determine its weight, sample a valid segment according to these weights and finally sample a point in the segments according to the associated truncated normal distribution on the segment. We optionally store the sample and the samples count for the selected orthant to estimate the orthant’s probability.Reducing the dimensionality. Our method for sampling t conditioned on Schur complement of Convergence. Since each sampled orthant is full-dimensional and all orthants are reachable, asymptotic convergence to arbitrary target distributions is guaranteed  We used We implemented the TFS and CHRR  samplers in C++ and validated them against established approaches see . We provE.coli model iML1515  and one gluconeogenic (actetate) growth condition and applied PMO to the corresponding models constrained by the measured concentrations.substrate channeling. Thermodynamics-based modeling assumes a well-mixed system. If two or more enzymes catalyzing consecutive steps of a pathway assemble in a complex, reaction products may find themselves close to the binding pockets of the subsequent enzymes, which increases the reaction probability. In such cases, the intermediate is said to be channeled between the two enzymes, breaking the assumption of homogeneity. As a result, reactions that would require unusually high or low concentrations of a reactant can become feasible even at unfavorable intracellular concentrations. An example highlighted by PMO are the first two steps of proline synthesis, ProB and ProA   . Physiolirection . Conversreaction . In mostr = 0.32, r = 0.63), showing that their concentration is thermodynamically constrained. When we focused on the 95% Confidence Interval (CI) of the samples and compared them to the ranges predicted by TMFA (with 95% CI on the distributions of Finally, we compared the metabolite concentrations predicted by TFS and TMFA to the experimentally measured values in the four hosphate . The mear ranges , with andistance . In 66% E.coli, it allows to combine metabolic models with experimental data to discover quantitative and structural model inaccuracies and generate hypotheses on mechanisms such as substrate channeling. We expect this capability to be particularly important for less well-characterized organisms. Conversely, we showed that the stoichiometry of a network, coupled with thermodynamic constraints and few physiological observations can significantly reduce the space of feasible metabolic modes and metabolite concentrations.Realistic models must obey the laws of thermodynamics: quantitative observations inform on the model structure, and mechanistic knowledge constrains parameters. Here, we show how this connection can be exploited in both directions to analyze metabolism. Our PTA framework for thermodynamic-based modeling includes optimization and sampling methods for the evaluation and exploration of metabolic networks. As demonstrated for In contrast to other methods, PTA interprets uncertainties in the thermodynamic parameters probabilistically to account for correlations between uncertainties of multiple estimates. Moreover, we require all reactions to have a well-defined direction. The model cannot silently resolve thermodynamic inconsistencies by setting a flux to zero as in TMFA—problems are visible to the modeler, guiding model curation and the formulation of novel hypotheses. Using the thermodynamic space as prior for sampling the flux space changed the exploration principle from the one used by US. Instead of using the volume of the flux space only, TFS determines the probability of a mode based on the probability of having a set of reaction energies allowing it. This excludes thermodynamically infeasible cycles from the solution. It also prevents distributed thermodynamic bottlenecks  and highHowever, PTA comes at the cost of higher computational complexity. Enforcing non-zero flux for each reaction makes optimization problems more difficult to solve, and sampling the non-convex thermodynamic space requires long simulation times; the asymptotic complexity is potentially exponential. Here, we successfully analyzed models with up to 880 reactions. For each condition, enumeration of internal EFMs and solution of PMO required less than five minutes, while sampling took approximately one day. To make larger models tractable, PTA can trade speed for coverage by limiting thermodynamic constraints to compartments or subsystems of interest.E.coli or other organisms, and we also obtained novel hypotheses. That gluconeogenesis appears unfavorable is puzzling: several observations suggest channeling of glycolytic compounds. Yet, this observation could be attributed to inaccurate estimates of standard reaction energies, for example, because magnesium ions, which affect the thermodynamics of glycolysis (To discover new biology, PMO proved effective at identifying occurrences of substrate channeling. While TMFA suggested such occurrences (ycolysis , are insFinally, while TFS predicts significantly fewer modes than contained in the flux space, these modes cover a wide range of behaviors. Thermodynamics alone does not determine what a cell does, but it restricts what options are available to achieve a certain phenotype. The final choice probably follows from other constraints or principles such as enzyme cost and minimal resource usage, whose integration could further increase the predictive power of thermodynamically constrained models.btab194_supplementary_dataClick here for additional data file.


However, more fundamental forms of metabolic heterogeneity are seen in clonal Saccharomyces cerevisiae communities growing in nutrient-limited conditions. Cells within such communities exhibit contrasting, specialized metabolic states, and are arranged in distinct, spatially organized groups. In this study, we explain how such an organization can stem from self-organizing biochemical reactions that depend on special metabolites. These metabolites exhibit plasticity in function, wherein the same metabolites are metabolized and utilized for distinct purposes by different cells. This in turn allows cell groups to function as specialized, interdependent cross-feeding systems which support distinct metabolic processes. Exemplifying a system where cells exhibit either gluconeogenic or glycolytic states, we highlight how available metabolites can drive favored biochemical pathways to produce new, limiting resources. These new resources can themselves be consumed or utilized distinctly by cells in different metabolic states. This thereby enables cell groups to sustain contrasting, even apparently impossible metabolic states with stable transcriptional and metabolic signatures for a given environment, and divide labor in order to increase community fitness or survival. We speculate on possible evolutionary implications of such metabolic specialization and division of labor in isogenic microbial communities.In fluctuating nutrient environments, isogenic microbial cells transition into “multicellular” communities composed of phenotypically heterogeneous cells, showing functional specialization. In fungi (such as budding yeast), phenotypic heterogeneity is often described in the context of cells switching between different morphotypes ( Unicellular organisms seldom naturally exist as individuals. Rather, they live in communities with varying degrees of complexity , differences between individual hyphae of the same mycelium, or phenotypic variation between distinct, isogenic mycelia  heterogeneity arise within this clonal community, where the distinct states retain spatial organization? Emerging evidence suggests that the nature and organization of biochemical networks within these cells can explain how cells in heterogeneous states organize and function within the community.Recent studies now address how this occurs . In yeasThe explanation for the appearance and maintenance of these glycolytic cells came from emergent, self-organizing biochemical principles. Initially, within a colony, cells were highly gluconeogenic, and an outcome of gluconeogenesis is the production and accumulation of an originally limiting resource, the disaccharide trehalose - which is made of two glucose molecules. As trehalose amounts increase, this newly available resource becomes abundantly available to all cells. Some cells  increase trehalose uptake and switch to consuming and breaking-down trehalose for glucose, and this results in global, stable transcriptional changes which allow them to sustain high rates of glycolysis . SimulatFor reproductive success and survival, cellular communities must perform multiple complex biological tasks. The metabolic burden on any single cell can be large if it is required to carry out all necessary biochemical reactions to enable growth and manage survival alone, and this can lead to fitness trade-offs. Therefore, a strategy deployed by many cell communities to overcome this problem is to break down complex processes into discrete sets of steps and divide tasks among different individuals of the community . Due to i.e., the same cell cannot perform these reactions simultaneously. For example, mass-action driven metabolic fluxes ensure that the same cell cannot sustain high rates of glycolysis and gluconeogenesis at the same time. Therefore, dividing metabolic labor can become a strategy that allows microbial communities to survive fluctuating nutrient environments  and this allows the community as a whole to grow in a detoxified environment  leads to the production and accumulation of trehalose. This new (formerly limiting) resource allows some cells to stochastically switch to a trehalose-consuming state, which is glycolytic, and these glycolytic cells effectively function as auxotrophs  . This isThis highlights the concept of biochemically versatile metabolites and how their metabolic flexibility can drive, enable, or control biochemical networks that set up cross-feeding systems of specialized cell states in clonal cell communities.+), acyl transfers (acetyl-CoA and carbamoyl phosphate), and glucosyl transfers (UDP-glucose), which in turn drive multiple transcriptional and metabolic transformations across primary biochemical pathways H, and acetyl-CoA  change the availability of some of the sentinel metabolites described above. Studies from the organized, metabolically specialized cell groups within yeast colonies illustrate how this might be so. Here, the two molecules central to setting up the cross-feeding system are the disaccharide trehalose, and the amino acid aspartate . Both moWhat these studies describe are cross-feeding systems that are largely formed by self-organizing biochemical networks, which themselves have metabolically flexible molecules as the driving entities behind them. What this more broadly entails is that while sentinel metabolites power cell metabolism  that drives metabolic heterogeneity in isogenic communities is sustained over very long periods (which yeast and most microbes experience in the wild), it could potentially lead to mutagenic events where subsets of the clonal population locked into a particular metabolic state evolve auxotrophies and subsequently perform restricted biochemical tasks. Such auxotrophs will exist only as long as they are supported by the complementary population that forms the cross-feeding system. This is of course seen in other syntrophic microbial populations which have lost their ability to facultatively switch back to any alternate metabolic state. In other words, could prolonged metabolic heterogeneity as a consequence of constant environmental selective pressure therefore be imagined as an evolutionary precursor of sympatric speciation events in isogenic communities?Saccharomyces pastorianus, the source of lager beer, which is a result of a hybridization event between Saccharomyces cerevisiae and Saccharomyces eubayanus. This newly formed hybrid inherited properties from both parental strains, making it optimal for lager beer brewing  produce hybrids with reduced fitness in either parental habitat , 2010. WIt, therefore, is a plausible speculation that metabolic heterogeneity that occurs in isogenic communities as a consequence of sustained selective pressure may result in ecologically driven speciation events. Could multispecies communities that exhibit metabolic cross-feeding have originally started as isogenic groups of cells that carried out metabolic division of labor as an adaptation strategy to a specific environmental niche?


Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin–Benson–Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide–harvesting potential in plants with an AP3 bypass and CETCH–AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some  The ever-increasing per capita CO₂ emissions are further limiting our options of mitigating the adverse effects of climate change. Biological processes such as photosynthesis and respiration are the two main attributes that reciprocally regulate net CO₂ concentrations in the atmosphere, but the anthropogenic emissions disturb the balance between CO₂ release and CO2 fixation by the Earth vegetation  cl cycles . Here, wArabidopsis biomass through photorespiratory bypassing in the chloroplast has already been attempted and has thus paved the way for crop improvements  by the enzyme ribulose-1,5-biphosphate carboxylase-oxygenase (Rubisco) . C4 and ovements . Followiovements  synthetieductase . Likewisectively . Besidesectively . In compin vitro implementation of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle  cycle . This arH) cycle . The CETH) cycle . One limH) cycle  when itsttention . Despitettention . We reco2-harvesting alternatives  bypass developed by 2 in the chloroplast. It would be interesting to see which of the bypasses (AP3 vs GOC) would be more energy efficient when it comes to their co-integration with the CETCH cycle. Likewise, MOG  cycles developed by 3- instead of CO2, and thus sequester CO2 in an indirect manner. A comparison in terms of energetics and efficiency between CETCH and MOG cycles would be very intriguing for the in planta realization of these various synthetic pathways.Besides the artificial CETCH and AP3 pathways, there are also other COrnatives  which ca2 harvesting pathways into predictive models, we used structural metabolic network modeling. This is a common approach based on steady states in cellular metabolism. In contrast to dynamic metabolic modeling, the structural network modeling does not require knowledge of detailed kinetic parameters. With the structural approach, a metabolic model can be analyzed by knowing which enzymes are present and considering initial constraints. We used elementary mode analysis (EMA), a metabolic pathway analysis (MPA) method. EMA calculates the “minimal set of enzymes that can generate valid steady states”  diminishes the photorespiratory flux while increasing photosynthetic enzyme activities in the chloroplast and thereby confirming the metabolic phenotype  associatThe reconstruction of the native metabolic model was achieved by integrating different KEGG  modules Elementary modes (EM) are the non-decomposable paths in a metabolic network that can keep the network at a balanced state, in which internal metabolites are neither accumulated nor consumed, and their concentrations stay constant. In summation, they are groups of enzymes that can generate a steady state and cannot be broken down to smaller flux modes ; however, it represents the photorespiration in addition. It includes both the oxygenase and carboxylase functions of Rubisco. In this long elementary mode  revealed a new elementary mode (A-EM5). A-EM5 starts with the oxygenase activity of Rubisco to produce 3-PGA that is then used in the CBB cycle  from BB cycle . The eleBB cycle .in vitro carbon fixation cycle developed by 2 to generate glyoxylate in 14 reaction steps , which is phylogenetically closer than E. coli MAS. Elementary mode analysis resulted in eight joint EMs (J-EMs). Interestingly, J-EM8 consisting of 13 reactions of the CETCH cycle and 1 reaction from AP3 represents the connection between the two synthetic pathways. We also obtained the same EM (A-EM5) with AP3 connecting the CBB cycle and the first part of the chloroplast pathway of photorespiration to AP3 . Likewise, the CBB cycle activities increased with the integration of AP3 and CETCH combination (6.5%) as well as the integration of AP3 alone (7.5%). The integration of the CETCH cycle alone caused a slight decrease (0.8%) in the enzyme activities of the CBB cycle with a notion that the presence of CETCH caused repression in the activities of CBB enzymes. However, highest activities for all CBB cycle enzymes were observed when addition of the CETCH cycle was combined with PLGG1 deletion . In summOn the contrary, the enzyme activities of the photorespiratory pathway are repressed when PLGG1 is fully suppressed irrespective of the model tested in our analysis. The exception was the central redox buffering enzyme catalase which increased in activity. Even in the presence of PLGG1, integration of AP3 and/or CETCH decreased the activities of all photorespiration enzymes except those in the chloroplast, which increased upon the integration of the AP3 bypass. AP3 relies on the production of glycolate through the first two steps of the photorespiratory pathway . It is nInter-pathway Compatibility, CO2Harvesting Efficiency, and Prospects for In Planta Realization). Thus, these in silico results suggest that the integration of CETCH and AP3 along with a PLGG1 deletion has stimulating effects on photosynthetic reactions and diminishing effects on photorespiratorion. Furthermore, the combination of the CETCH cycle with the AP3 bypass increased the activity of malate synthase pointing to higher malate production with implications for higher biomass formation.Overall, modeling the effect of the topological changes on enzyme activities demonstrated a high stimulation of photosynthesis and elimination of photorespiration in peroxisome when CETCH integration is combined with PLGG1 deletion. However, effects of a 100% PLGG1 suppression should also be considered for possible toxicity that may arise from glycolate production in the chloroplast  or is directly released to the environment (see Supplementary text). Overall, we argue that AP3 is more suitable for co-integration with CETCH since it can deal with the glyoxlate produced in the CETCH cycle unlike the GOC cycle that does not consume glyoxylate; consequently, AP3 attains higher energy efficiency than GOC. Nevertheless, these aspects merit detailed investigation for in vivo realization.We next evaluated how robust this change is concerning the alternative pathways investigated. We integrated another recently designed alternative pathway: the GOC  bypass , which iin vitro realization of the CETCH cycle by Schwander et al., reported enzyme-specific activity kinetics. Their analysis showed highest activities for catalase  and mesaconyl-CoA hydratase , while 4-hydroxybutyryl-CoA synthetase  and methylsuccinyl-CoA oxidase (MCO. 0.1 Umg−1) proved to be less active. One may tempt to speculate that lower specific enzyme activities in the CETCH may limit the efficiency of the whole CETCH because our analysis  or longer but more energetically feasible MOG  cycles. The simplest cycles found by analysis  suggestsanalysis  from theanalysis  can be m2-fixing CCR enzyme (crotonoyl-coA carboxylase/reductase) than Rubisco  CO Rubisco . To furt Rubisco . These rin planta realization of synthetic cycles may demand the repression of endogenous cycles in order to achieve prominent CO2 fixation potential of the synthetic cycles. The artificial pathways change the elementary modes in the model and hence the distribution of fluxes across the network. CBB cycle enzymes have higher estimated activity as they are present in more EMs  than CETCH cycle enzymes (J-EM8). It is because of the fact that CBB cycle enzymes have higher connectivity with other pathways in the cellular system than the artificial CETCH cycle. It is noteworthy to mention that actual kinetic data such as enzyme and metabolite concentrations or at least gene expression data  and enzyme activity estimates. Our study utilizes a well-known method in metabolic modeling, EMA (in planta realization of these CO2-harvesting modifications, conveniently achieved in the model plant Arabidopsis thaliana. Likewise, these models can also be implemented in crop plants by exploiting conventional molecular biology tools as well as new genome-editing techniques. Besides crop plants, all our results are readily transferable to the highly conserved photorespiratory and photosynthetic cycles found in algae (2 harvesters even without engineering, they are often aimed for potential CO2-sequestration applications. Future works will integrate more experimental data and then can simulate diverse scenarios more accurately with actual kinetics and better resolution.We investigate here ing, EMA , in a noin algae , and bluin algae . As algain algae  are fast