S. aureus. Genome-scale gene expression data of S. aureus strain NewHG (sigB+) elucidated their effect on physiological functions. Moreover, metabolic modelling from these data inferred metabolic adaptations. We compared wild-type to deletion strains lacking pknB, stp or both. Ser/Thr phosphorylation of target proteins by PknB switched amino acid catabolism off and gluconeogenesis on to provide the cell with sufficient components. We revealed a significant impact of PknB and Stp on peptidoglycan, nucleotide and aromatic amino acid synthesis, as well as catabolism involving aspartate transaminase. Moreover, pyrimidine synthesis was dramatically impaired by stp deletion but only slightly by functional loss of PknB. In double knockouts, higher activity concerned genes involved in peptidoglycan, purine and aromatic amino acid synthesis from glucose but lower activity of pyrimidine synthesis from glucose compared to the wild type. A second transcriptome dataset from S. aureus NCTC 8325 (sigB−) validated the predictions. For this metabolic adaptation, PknB was found to interact with CdaA and the yvcK/glmR regulon. The involved GlmR structure and the GlmS riboswitch were modelled. Furthermore, PknB phosphorylation lowered the expression of many virulence factors, and the study shed light on S. aureus infection processes.Serine/threonine kinase PknB and its corresponding phosphatase Stp are important regulators of many cell functions in the pathogen  Bacteria adapt to different environments to survive changing conditions, and accordingly, they possess an enormous variety of protein kinases involved in signal sensing and transduction. For pathogenic bacteria, infection is ‘growth despite stress’, meaning adaptation to an often-poor metabolic substrate pool the host provides. The most well-described signalling cascades in bacteria are two-component systems (TCSs), which in general consist of a membrane histidine kinase that senses an extracellular signal, autophosphorylates a histidine residue and transfers the phosphate group to an aspartate residue of a response regulator or transcription factor . Ser/ThrS. aureus . One study. HS. aureus, as well as regulating its metabolism by allowing growth under a wide range of different substrates. We inferred by bioinformatics a detailed metabolic model for this metabolic adaptation that was quantitatively confirmed by an independent experimental transcriptome dataset from a different S. aureus strain. This model suggests that S. aureus uses all available metabolic pathways, including combinations of basic pathways to achieve concerted up- or downregulation of biosynthesis and central metabolic pathways. PknB and its intimate connection to the glmR regulon and the cdaA operon achieve a stable glucose flux, improved aromatic amino acid and nucleotide synthesis and, importantly, peptidoglycan synthesis with cell wall homeostasis, activating central virulence regulators  with implications for virulence and antibiotic resistance. There is no doubt that the activity of kinase PknB versus phosphatase Stp has an important impact on S. aureus cell physiology and also virulence. However, the exact role has to be defined depending on growth conditions. Moreover, the characterisation of individual mutants allows predictions of their physiological role. Their regulation is complex  and may differ depending on the activity state of both partners.Transcriptome data and comprehensive metabolic modelling show that serine/threonine phosphorylation is involved in the virulence capacity of S. aureus NewHG mutant strains lacking pknB/stk, stp or both pknB and stp genes were constructed, as recently described [pknB, stp and pknB/stk were amplified by PCR and cloned together with an erythromycin resistance cassette (ermB) into the temperature-sensitive shuttle pBT2. The vector was electroporated into S. aureus RN4220, transduced to S. aureus NCTC 8325 via phage Φ85 and chromosomally integrated after a temperature shift. Following verification of the correct deletion of the target genes, the gene knockouts were subsequently transduced into strain NewHG via phage Φ85, and deletions were verified by PCR using gene-specific primers.The escribed .: BrieflS. aureus encodes pathways to catabolise multiple amino acids, including those that generate pyruvate, 2-oxoglutarate and oxaloacetate  were cultivated overnight at 37 °C in B-medium  [We used B-medium, a modified LB medium, which was developed for cultivation of staphylococci . by addial. 2017 ). The aug, and the bacterial pellet was resuspended in 800 µL of RLT buffer  and mechanically disrupted with glass beads  in Fastprep®-24 . The cell lysate was centrifuged for 2 min at 18,000× g, and the supernatant was used for RNA isolation. RNA was isolated with a RNeasy Mini kit  according to the instructions of the manufacturer. To remove the DNA template, RNA was treated with RNase-free DNase I . In contrast to our previous publication, in which we analysed the gene expression of a pknB mutant compared to its corresponding wild-type strain NCTC 8325 during mid-logarithmic growth (OD600: 1.0), we now determined the gene expression of ΔpknB, Δstp and ΔstkΔstp and the wild type in the strain NewHG background.The next day, the strains were diluted in 100 mL B-medium  and again incubated until the cells reached the late exponential growth phase (OD600 = 5.0). Afterwards, RNA was isolated from 7 mL of the bacterial cultures, which were harvested by centrifugation for 10 min at 10,600× S. aureus RNA were labelled by Cy3-dCTP using SuperScript II  following the manufacturer’s instructions. Labelled products were then purified onto QiaQuick columns . Purified genomic DNA from the different sequenced strains used for the design of the microarray was extracted , labelled with Cy5 dCTP using the Klenow fragment of DNA polymerase I  and used for the normalisation process [http://www.ncbi.nlm.nih.gov/geo; accession nos. GPL10537 for the platform and GSE122362 for the original dataset; see also analysis in The microarray was manufactured by in situ synthesis of 60-base-long oligonucleotide probes , selected as previously described . The arr process . Cy5-lab process  using Gestp and NewHG ∆pknB were inoculated from separate overnight cultures (1:200) into 15 mL of fresh B-medium each. We incubated three independent cultures of each strain at 37 °C until the respective OD600 reached 5 ± 0.1, as previously described [To validate the transcriptome data obtained with DNA microarrays, a qRT-PCR approach was applied. NewHG ∆escribed .Immediately, the bacterial pellets were lysed, and the total RNA was extracted and purified using the RNeasy Mini Kit from Qiagen  according to the manufacturer’s instructions. Using the RapidOut DNA Removal Kit from Thermo Fisher Scientific , the DNA was digested from total RNAs. For confirming the absence of DNA traces, qPCRs were performed with assays specific for 16S rRNA.gyrB as a housekeeping gene with the method of Pfaffl [cDNA synthesis was performed with SuperScript IV from Thermo Fisher Scientific and random primers. We used the SsoAdvanced Universal SYBR Green Supermix  for qRT-PCR according to the manufacturer’s instructions. Five sets of primers  were desf Pfaffl  to correS. aureus analysis were generated, as described above, and retrieved from the NCBI Gene Expression Omnibus (GEO) dataset (GEO accession no. GSE15346) [Transcriptome data of SE15346) .Proteome data and inferred metabolic adaptations by flux modelling of changes after diauxic shift  were useProtein interaction data were generated before , as descS. aureus metabolic network models were previously reconstructed using annotations of KEGG and TIGR [S. aureus COL, as it is one of the most referenced and investigated strains. The second network model was based on S. aureus NewHG [S. aureus COL model developed previously, as mentioned before [and TIGR . The firus NewHG , a highld before . The reaExtreme pathways are defined as a unique, minimal set of enzymes and enzyme complexes (participating reactions) to support the steady-state operation of a metabolic network with irreversible reactions to proceed in appropriate directions . Together these extreme pathways limit the solution space of metabolic possibilities for the metabolism of the organism modelled . These pS. aureus extended model, a list of extreme pathways and their mathematical representations (the null space) were obtained for further analysis.Extreme pathway analysis was performed using YANAsquare software . To calcpknB/WT, Δstp/WT and ΔpknBΔstp/WT. Transcriptional data were measured, and the statistical analysis is given accurately in the GEO data (accession nos. GPL10537 for the platform and GSE122362). However, regarding the metabolic flux inference, these results are mathematical flux calculations. There is no statistical error, and it is a mathematical calculation. We tested the accuracy of flux calculations from gene expression data measuring directly metabolite concentrations. We found the limit of sensitivity is at least a 10% difference, as proven by direct metabolite measurements .S. aureus. Zeroes as entries indicated no known fluxes or flux not detectable; 50 was the starting value, and the maximum value was 100. Convergence criteria were successfully met for all the simulations with different numbers, i.e., result values are as follows: for the WT: iter 310, final value 55.847131; for ΔpknB: iter 280, final value 55.141834; for Δstp: iter 300, final value 53.315411; and for ΔpknBΔstp: iter 330, final value 55.627214 . Convergence is achieved for values below 100, as validated by metabolite measurements [Each pathway is a subset of enzymes or reactions that transform specific substrates into products with no accumulation of internal metabolites. The activity associated with each pathway is based on the best fit of transcriptomic data, and it was used in this study to discover the differences in metabolic fluxes between the WT and three mutants of S. aureus protein–protein interactions (PPIs) were studied using the STRING database tool, version 9.1 [The sion 9.1 . This apThe interaction network of Stk, Stp and FemX/A/B proteins among cell wall synthesis and cell division proteins relied on experimental data by bacterial two-hybrid analysis . PhosphoyvcK/glmR homologues. Multiple sequence alignment (MSA) was then performed using MUSCLE [The protein sequence BLAST was performed online using the default parameters and certain species or phyla desired to look for g MUSCLE , and resg MUSCLE . The evoglmS element was predicted using the Riboswitch finder (http://riboswitch.bioapps.biozentrum.uni-wuerzburg.de) accessed on July, 2020 and the RNAfold web server (http://rna.tbi.univie.ac.at) based on the latest ViennaRNA package (version 2.4.11).The sequence-based identification of the metabolite-dependent glmR/yvcK of B. subtilis is involved in survival under different gluconeogenic conditions, it might be more similar between microorganisms that share specific niches, and thus, they are adapted to use the same substrates for energy production and cell components synthesis. For that reason, a phylogenetic tree of different bacterial species was created with MEGAX using the maximum likelihood method based on the sequence homology of the GlmR/YvcK protein.Because S. aureus, an attempt was made to build a 3D structure model using available sequences of the proteins and by structurally comparing it with alternative bacterial templates and the binding residues.5.To validate the residues of binding sites, 3D structure prediction was performed using the online platform SWISS-MODEL . Relativ


In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for  Recent years have seen several advances in the usage of bioprocessing to produce a wide range of chemicals . MicroorClostridium beijerinckii   of sd.edu/) . The simsd.edu/) . These cThe reliability of the potential intervention targets is analyzed by comparing the flux values of biomass and product in the mutant with those of the wild-type organism. The mutant model is obtained by deleting the reaction in case of knock-out targets or by fixing the flux bounds of the amplification target to its theoretical maximum. FVA is performed on this mutant model with biomass as objective to obtain the range of flux values for the products and biomass. Any reaction with more than a 5% increase in the maximum product flux and less than 75% decrease in biomass flux is considered a promising intervention strategy. FVA was performed using fastFVA to reduce the computational time .To obtain higher-order intervention strategies, the potential targets obtained earlier for a given set of metabolites were combined, and all possible combinations of intervention strategies of a certain size (up to three) were evaluated using FVA. The score for each product box v3.0  and IBM E. coli and S. cerevisiae were obtained using co-FSEOF as described in Metabolic engineering strategies for the co-production of all pairs of secreted metabolites in E. coli is one of the well-studied model organisms and has high-quality GSMMs available. The latest GSMM, iML1515  for co-production of isobutanol and succinate in Chemical processes based on fossil fuels are cheaper when compared to bioprocesses, which leads to reluctance in the adoption of sustainable bioprocesses in industries. To improve the economic feasibility of a bioprocess, we can optimize the process variables and/or genetically engineer the microbes . Even thIn this study, we present co-FSEOF, by adapting the effective FSEOF algorithm to study the co-optimization of a set of metabolites. FSEOF is a well-established constraint-based modelling algorithm, which has been used to reliably predict metabolic engineering strategies for a variety of systems . It has E. coli and S. cerevisiae under both aerobic and anaerobic conditions. Anaerobic fermentation enabled the co-production of a higher number of metabolites when compared to aerobic fermentation in both organisms. This could be due to the incomplete respiration in the absence of oxygen that leads to the formation of multiple by-products. Also, S. cerevisiae produces more industrially significant metabolites when compared to E. coli. Some of these proposed intervention strategies have been verified experimentally by other studies in literature, as mentioned in Using co-FSEOF, we examined the co-production of multiple pairs of metabolites, and both deletion and amplification targets were obtained in E. coli under aerobic conditions, and around 1,000 pairs of metabolites could be co-optimized in the other cases. An important class of metabolites observed in the analysis are alcohols such as ethanol, isobutanol, and 2,3-butanediol, which can be co-produced using various interventions. Co-optimization can thus enable the efficient production of biofuels (The co-optimization analysis for all possible pairs of metabolites in the network is intended to be exploratory in order to give a larger picture of the metabolic capabilities of the organism. This analysis showed that around 200 pairs could be co-optimized in biofuels . We can The evaluation of the results is carried out using FVA, which ensures the robustness of the targets obtained. While FBA provides one optimal solution from the solution space, FVA gives the entire range of values the flux can take up. This is a significant difference that sets co-FSEOF apart from other existing algorithms like OptKnock  and OptRectively . HoweverCo-production can open new avenues for the sustainable production of chemicals. Designing bioprocesses for co-production using laboratory experiments alone is cumbersome and can result in sub-optimal strategies. co-FSEOF empowers us to explore and exploit microbial systems in a better fashion. It can be used to computationally study and optimize co-production by identifying intervention strategies for multiple metabolites and thereby improve the efficiency of bioprocesses. It should be noted that the co-optimization analysis was limited to pairs of metabolites to reduce the computational time. Nevertheless, co-FSEOF can be easily extended to co-optimize a more extensive set of metabolites. To conclude, this study can be used to identify various genetic manipulations that can co-optimize a set of products, which might be challenging to achieve through pure experimentation. It provides a novel and critical approach to study co-production computationally. We hope this study will aid the design and development of more sustainable bioprocesses.


Saccharomyces cerevisiae. The relative importance of the strain-specific metabolic capabilities for the production of volatile organic compounds (VOCs) remains unclear. We investigated which amino acids contribute to VOC production and whether amino acid-VOC relations are conserved among yeast strains. Amino acid consumption and production of VOCs during grape juice fermentation was investigated using four commercial wine yeast strains: Elixir, Opale, R2, and Uvaferm. Principal component analysis of the VOC data demonstrated that Uvaferm correlated with ethyl acetate and ethyl hexanoate production, R2 negatively correlated with the acetate esters, and Opale positively correlated with fusel alcohols. Biomass formation was similar for all strains, pointing to metabolic differences in the utilization of nutrients to form VOCs. Partial least-squares linear regression showed that total aroma production is a function of nitrogen utilization (R2 = 0.87). We found that glycine, tyrosine, leucine, and lysine utilization were positively correlated with fusel alcohols and acetate esters. Mechanistic modeling of the yeast metabolic network via parsimonious flux balance analysis and flux enrichment analysis revealed enzymes with crucial roles, such as transaminases and decarboxylases. Our work provides insights in VOC production in wine yeasts.Genetic background and environmental conditions affect the production of sensory impact compounds by IMPORTANCESaccharomyces cerevisiae is widely used in grape juice fermentation to produce wines. Along with the genetic background, the nitrogen in the environment in which S. cerevisiae grows impacts its regulation of metabolism. Also, commercial S. cerevisiae strains exhibit immense diversity in their formation of aromas, and a desirable aroma bouquet is an essential characteristic for wines. Since nitrogen affects aroma formation in wines, it is essential to know the extent of this connection and how it leads to strain-dependent aroma profiles in wines. We evaluated the differences in the production of key aroma compounds among four commercial wine strains. Moreover, we analyzed the role of nitrogen utilization on the formation of various aroma compounds. This work illustrates the unique aroma-producing differences among industrial yeast strains and suggests more intricate, nitrogen-associated routes influencing those aroma-producing differences. It has been widely recognized that yeast cell growth and overall wine fermentation performance are regulated by initial nitrogen levels within the grape must. Consequently, nitrogen limitation can induce sluggish or stuck fermentations –4. Ammon–7–S. cerevisiae that amino acids are directly involved in the formation of higher alcohols, esters, and fatty acids, and that these volatile organic compounds subsequently influence aroma attributes of wines  and fusel alcohol concentrations , 13–16. 13–of wines , 17. Nonof wines –20. Althf wines –, 21, 22.f wines –, 23. Mort aromas –26. ThusS. cerevisiae strains that are selected for winemaking differ immensely in their aroma production profiles  regression. Moreover, since mechanistic understanding of the relation between amino acid consumption and VOCs production is currently lacking  production capabilities across strains growing in the same enological medium. All fermentations were conducted under atmospheric conditions at a temperature of 20°C and were performed until completion (t = 404 h). Throughout the fermentation, cell biomass  and degrees Brix (°Bx) levels were monitored at 11 time points. Biomass growth, sugar utilization, and ethanol formation curves are shown (Fermentations were carried out for each of the re shown .P = 0.974). All cultures were fermented to completion  (P < 0.05). In contrast, concentrations of the other six major metabolites (P > 0.05).All strains reached maximum biomass at approximately 120 h. Furthermore, all strains did not significantly differ in biomass concentrations  . More spl sugar) . These vl sugar) . For alll sugar) . Acetic l sugar) . Succinil sugar) . Final cl sugar) . One-wayabolites  did not P < 0.05). However, these and most variations in amino acid concentrations were observed at 20 and 28 h. As noticed , and Thr (Uvaferm only) occurs, with 95% of their concentrations being drastically depleted by 28 h. Group II contains the subsequently preferred compounds, where 95% of the compound was consumed by 44 elapsed hours. These group II compounds are Arg, Gln, Ile, Leu, Phe, Ser, Thr, His, Asp, and Val. Group III consists of compounds such as Ala, Trp, and Tyr, which all show steady consumption until 95% of their concentrations were utilized by 72 h. A remaining group, group IV, was taken up after some initial delay (not consumed within the first 20 h) and did not have 95% utilization until after 96 h. This sole amino acid was Gly.Many amino acids were mainly consumed during the exponential cell growth stage of fermentation. Only some small amounts of Ala, Asn, Gly, and Met remained in the medium after 100 h, though all were utilized shortly after that. The consumption data reveal preferences of yeast for particular amino acids during utilization over the exponential growth phase. The consumption of the nitrogenous compounds can be separated into four groups based on the time at which 95% utilization occurs from the compound. Group I consists of the earliest-consumed compounds, where strain-dependent consumption of NHP < 0.05). At 168 h of fermentation, the VOC concentrations among the strains were significantly different for eight VOCs, while two were the same. These compounds that did not differ significantly at the end of fermentation were 2-methyl-1-propanol and 2-methyl-1-propyl acetate.To study the relationship between amino acid consumption and VOC formation during grape juice fermentation, 10 VOCs (aromas) were measured using headspace solid-phase microextraction gas chromatography mass spectrometry (HS SPME GC-MS) from various compound classes pertaining to different aroma properties throughout the fermentation process . The comAll VOCs begin to form early in the fermentation process. Some compounds, such as 3-methyl 1-butanol and methionol, were even generated before 20 h, though nearly all VOC formation characteristically corresponds with yeast growth. Moreover, the maximum production rate of fusel alcohols and acetate esters occurred from 28 h to 96 h. Fusel alcohols were produced at the highest concentrations, with isoamyl alcohol, isobutanol, and 1-propanol showing the most rapid production earliest during fermentation. The acetate ester production rate began to decline for all of the strains after 96 h and became relatively stagnant after 168 h. Profiles for ethyl esters were similar to those observed for acetate esters. However, initial ethyl ester formation proceeded at a lower rate than acetate esters, and the maximum concentrations were much less than acetate esters. Overall, most VOCs showed stagnated production after 96 h, which indicates growth-dependent behavior. Some VOCs, in particular 2-methyl-1-propyl acetate, showed a decrease in concentration toward the end of fermentation, most likely due to volatilization. All final VOCs concentrations were found within the typical range found in wines  of the VOCs was performed. From the PCA, 76.26% of the variance was explained by the first two principal components (PC) (PC1 = 51.25% and PC2 = 24.98%). As depicted, separation of the samples was achieved according to the yeast strains . PC1 sep acetate .S. cerevisiae culture. Therefore, enhancing growth of yeast cultures leads to the overall increase of production of VOCs and VOC precursors. Second, it is known that specific amino acids are utilized and degraded which contribute to the synthesis, via the Ehrlich pathway, of fusel alcohols and subsequently to the production of acetate esters. However, other pathways related to lysine and glycine degradation, which have been shown to play a role in VOC formation in other types of fermentations, may be essential to enological fermentations as well. To evaluate this hypothesis and understand the causes of strain-dependent variation, the ammonia and amino acid utilization for all four yeast strains were measured and correlated with biomass formation and VOC production for 11 different aroma compounds during the exponential growth phase , and total VOC concentration were the response variables that comprised the Y-block in 16 different PLS regressions. The response variables contained concentration measurements corresponding to the same time points as the predictor variables. From the initial duration of the fermentation until 96 h, the ammonia and amino acid utilization data were input as the predictor variables used to develop the X-block in each of the 16 PLS regressions. Subsequently, the PLS regression analysis determined which variables in both the Y-block and the X-block contributed most to the variation in the data. Lastly, the PLS regression analysis indicated how the variables were correlated and then lumped the variables into a new latent variable (LV). Our summarized results show that nitrogen utilization is strongly correlated with biomass formation and production of each of the aroma compounds, except methionol, for which only moderate correlation was found  linear regression was employed to correlate the contributions of ammonia and individual amino acid utilization to the production of biomass and VOCs by the yeast strains during fermentation. Statistical determination of the nitrogenous variables that contribute most to the variation in the fermentation kinetic data regarding VOCs was performed using interval-partial least squared (iPLS) regression analysis. iPLS was done using the ammonia and amino acid utilization of all four as found . The reaR2 = 0.96, which signified a robust linear relationship between the measured biomass dry cell concentration versus the predicted biomass dry cell concentration according to the nutrient utilization of these yeast strains . Cross-validation (CV) was performed in order to prevent the model from overfitting the data  and to evaluate how the model would operate using a new set of data. CV correlation coefficients (Q2) were generated to illustrate the predictive strength of the model .PLS regression was first conducted to determine how nitrogen utilization contributed to yeast growth (biomass formation) during the exponential growth phase in the fermentation. he model . The rooR2 value for each model for VOCs achieved an R2 value greater than 0.77, indicating nitrogen utilization was correlated with the production of each VOC. The only aroma compound that the PLS regression analysis showed to have a more modest correlation with nitrogen utilization was methionol. Partial least-squares regression modeling yielded eight LVs and generated a coefficient of determination (R2) of 0.61. The model cross-validation results indicate a Q2 of 0.5 and RMSEC and RMSECV values of 0.64 mg/liter and 0.73 mg/liter of methionol, respectively. Overall, these data point to a modest correlation between the methionol concentration and nitrogen utilization during fermentation. As a result, it is suggested that not only nitrogen utilization determines the production of methionol during fermentation and that there may be other processes that play a role in methionol production, such as sulfur utilization.A separate series of PLS regression analyses were performed subsequently for each of the VOCs, as well as classes of compounds, to investigate a correlation between the production of VOCs during the fermentations and nitrogen utilization of the yeast strains. Each of the models captured at least 99.5% of the variation in the nitrogen utilization data and at least 77% of the variation in the aroma concentration data were captured by each of the models except methionol . In addiOne of the core goals of this work was to improve understanding of the impacts of nitrogen utilization on VOC profile differences among four commercial wine yeast strains. By applying PLS to the data sets mentioned above, information about how specific nitrogen utilization variables correlate with each of the 16 response variable sets was obtained. This facilitated understanding of which nitrogenous compounds are responsible for aroma production and might offer clues about metabolic variations among the strains. The PLS models were able to explain the biomass formation and the production of each of the aroma compounds, excluding methionol, which was moderately predicted. Regression vector plots were created to assess the statistical weights of the original nitrogen predictor variables on the PLS models. In other words, regression vector plots were used to determine the degree to which the 18 original nitrogen variables were positively or negatively correlated with the response variable. These regression vector plots for PLS models predicting individual aroma compounds and classes of aroma compounds are provided in the supplemental material , while a summary is provided in The regression vector plots for all the yeast strains illustrate the degree to which the 18 original nitrogen predictor variables are correlated with the response variables (VOC and biomass concentrations) . Furthermore, it is worth noting that the magnitude of the correlation coefficients for each nitrogen predictor variable was different across the PLS regression models . The model for biomass growth indicated that asparagine, serine, glycine, lysine, isoleucine, and tryptophan were all positively correlated with biomass concentration, whereas, histidine, threonine, and valine are negatively correlated with biomass concentration . AmmoniaA summary of the results from all the regression vector plots highlights correlative behavior of the nitrogen variables for predicting individual VOC production . The regTo test which metabolic pathways are crucial in VOC production, we applied parsimonious flux balance analysis (pFBA) and flux enrichment analysis (FEA) with fermentation data used as constraints. The pFBA analysis revealed many essential genes/reactions for nutrient uptake and turnover, including amino acids, and VOC production . After eP < 1 E−10). This result could indicate substantial influence coming from lipid metabolism and sulfur metabolism. Despite potential influence from other parts of metabolism on VOC formation, our statistical and metabolic modeling results firmly indicated that amino acid utilization routes such as leucine, glycine, lysine, and tyrosine induce VOC production  by chance, we employed flux enrichment analysis (FEA) to identify statistically significantly enriched subsystems with essential reactions . FEA higoduction . We alsooduction . InteresS. cerevisiae strains with diverse aroma bouquet-producing potential and growth characteristics under identical experimental conditions to analyze the nitrogenous compounds that correlated with yeast cell growth and aroma production. In agreement with previous studies  on the one hand, and methionol concentration with nitrogen utilization (R2 = 0.61) on the other hand, it appears likely that nitrogen utilization is not the only significant factor influencing methionol production. The relatively low correlation for methionol formation could be due to the absence of causal relationships between the metabolic pathways of methionol biosynthesis and nitrogen metabolism. Methionol biosynthesis is also intricately linked to the sulfate reduction pathway and, thus, the relationship of sulfur uptake during fermentation . The strains were stored at −80°C in a 25% (vol/vol) glycerol solution according to the method of Amberg et al. , Lalvin R2 (R2), Lalvin ICV Opale , and Vitilevure Elixir Yseo (Elixir), used in this study are Lallemand  commercial yeast strains. Uvaferm and R2 are g et al. . The str600 of ∼0.1 (∼15 ml) was added to 400 ml of MMM medium. The pH of the MMM medium was 3.25 and the fermentation temperature was maintained at 20°C. Cultures were stagnantly cultivated at 20°C for 17 days. Initial cell concentration (OD600) and °Bx measurements were performed at the beginning of fermentation and subsequently 10 more times over the course of the fermentation until °Bx fell below one or remained constant between two consecutive measurements. °Bx measurements were performed with a refractometer and OD600 measurements were performed with a spectrophotometer. Samples (10 ml) were taken at regular intervals and transferred to a 15-ml Greiner tube, sealed, and frozen at −20°C until analysis. Experiments were performed as biological triplicates.MMM synthetic grape juice medium , with a 1:1 mixture of glucose (110 g/liter) and fructose (110 g/liter), 123 mg/liter of YAN, and 11 mg/liter of ammonium, was prepared according to the method of Giudici and Kunkee . MMM med600 measurements using a calibration curve for each yeast strain. Total cell dry weights were determined in triplicates.The total cell dry weight (biomass) of the samples was determined by taking a sample directly from the culture medium and passing through dried and preweighed membrane filters with a pore size of 0.2 μm  by a vacuum filtration unit, as described by van Mastrigt and coworkers . Total bg for 10 min at 4°C), and the supernatant was stored at −20°C until analysis. Supernatants were deproteinated by adding 0.25 ml Carrez A (0.1 M potassium ferrocyanide trihydrate) and 0.25 ml Carrez B (0.2 M zinc sulfate heptahydrate) to a 0.5-ml sample followed by centrifugation for 10 min at 13,000 × g. Glucose, fructose, lactate, acetate, malate, citrate, ethanol, 1-propanol, and 1,2-propanediol were quantified by high-performance liquid chromatography (HPLC) on an Ultimate 3000  equipped with an Aminex HPX-87H column (300 × 7.8 mm) with precolumn (Bio-Rad), as described by van Mastrigt and coworkers  for quantification and UV measurements at 220, 250, and 280 nm for identification. All analysis was performed in duplicate.After taking a sample from the Erlenmeyer flask, cells were immediately removed by centrifugation  for 10 min. at 4°C. Amino acids were derivatized using the AccQ-Tag Ultra Derivatization kit (Waters), for which 20 μl of the deproteinated sample solution supernatant or standard amino acids mixture was mixed with 60 μl of a modified AccQ-Tag Ultra Borate buffer . Next, 20 μl of an AccQ-Tag reagent previously dissolved in 2.0 ml AccQ-Tag Ultra reagent diluent was added and vortexed for 10 s. Then, the sample solution was capped and warmed at 55°C in a heatblock for 10 min. Amino acids and ammonium were quantified by ultraperformance liquid chromatography (UPLC) on an Ultimate 3000  equipped with an AccQ-Tag Ultra BEH C18 column   with a BEH C18 guard column  . The column temperature was set at 55°C, and the mobile phase flow rate was maintained at 0.7 ml/min. Eluent A was 5% AccQ-Tag Ultra concentrate solvent A and Eluent B was 100% AccQ-Tag Ultra solvent B. The separation gradient was 0 to 0.04 min 99.9% A; 5.24 min 90.9% A; 7.24 min 78.8% A; 8.54 min 57.8% A; 8.55 to 10.14 min 10% A; and 10.23 to 17 min 99.9% A. One microliter of sample was injected for analysis. Compounds were detected by UV measurement at 260 nm.An aliquot of 40 μl of 5-fold-diluted samples was mixed with 50 μl of 0.1 M HCl solution containing 250 μM norvaline as the internal standard. Then, 10 μl of chilled 30% sulfosalicylic acid (SSA) was added, and the solution was mixed and centrifuged  according to the manufacturer’s procedures. The ammonia levels were verified to exhaust after 28 h of fermentation. This method was also used to correct quantification of ammonium detected from the UPLC. Norvaline was used as an internal standard (IS) for the UPLC amino acid analysis. All analysis was performed with a single replicate.m/z 33 to 250 in full scan mode with 3.0030 scans/second. VOC profiles were analyzed with Chromeleon 7.3 software. The ICIS algorithm was used for peak integration and the NIST main library was used for identification by matching mass spectral profiles with the profiles in NIST. One quantifying peak  was used per compound for quantification, while one or two confirming peaks were used for confirmation.To determine the volatile organic compounds (VOCs), 2 ml of sample was transferred to a 5-ml gas chromatography (GC) vial. Samples were stored frozen (−20°C) until analysis by headspace solid-phase microextraction gas chromatography mass spectrometry (HS SPME GC-MS) . SamplesThe 11 aroma-associated compounds studied were propan-1-ol, 3-methylbutan-1-ol (isoamylol), 2-methylpropan-1-ol (isobutanol), 2-phenylethan-1-ol, methionol, ethyl ethanoate (ethyl acetate), ethyl butanoate, ethyl hexanoate, 3-methylbutyl acetate (isoamyl acetate), 2-methylpropyl ethanoate (isobutyl acetate), and 2-phenylethyl acetate . All of The yeast strain-specific impact on the kinetics of amino acid consumption and aroma compound formation for the 11 volatile aromas was determined by plotting the compound concentrations throughout the fermentation. Furthermore, several performance metrics were accessed from the dynamic consumption and production profiles, such as the final concentration of volatile aroma compounds and utilization of nitrogenous compounds. In this study, nitrogen (ammonia/amino acids) utilization was determined as the difference in the ammonia/amino acid concentration from beginning to sample time point of interest throughout the fermentation . The finP < 0.05) was performed in R (version 3.6.2) using the statistics package applying post hoc Tukey’s honest significant difference (HSD) test (significance level of P < 0.05), as well as in SPSS .One-way analysis of variance (ANOVA)  regression was employed to determine a relationship between biomass and VOC production data (Y-Block) and nitrogen (ammonia and amino acid) utilization data (X-Block). The utilization was calculated as the difference in concentration at the beginning of the fermentation to concentration at the sampled time point. These two data blocks were imported into MATLAB  for PLS regression analysis and interval-PLS (iPLS) variable selection using the PLS Toolbox . Parameters for iPLS were selected according to the method of Wise et al.  and AndeR2), cross-validated correlation coefficient (Q2), root mean squared error of correlation (RSMEC), and RMSECV. The impact of selected nutrient utilization variables on the PLS model was assessed using scores, loadings, and regression vector plots. Overall, the PLS model was used to determine the nutrient utilization (ammonia and amino acids) which contribute to the biomass formation and VOCs production during fermentation.Subsequently, once iPLS was completed, the selected variables were added to PLS Toolbox for structured equation modeling. The approach used was adapted based on an approach by Henderson et al.  using thS. cerevisiae, Yeast 8.4.2   of ast-GEM) . To adeqast-GEM) , constraz et al. , 71  database convention  to evaluate which reactions and pathways are essential under the given system conditions and eliminate blocked reactions . It uses a bilevel optimization in which the growth rate (biomass) is optimized using FBA, followed by the minimization of total flux through all gene-associated reactions at the maximum growth rate calculated. The underlying assumption is that, under growth pressure, there is a selection for strains that can reach the highest growth yield while using the minimum amount of enzyme. Then, genes/reactions are classified into six categories based on Lewis et al. , i.e.,  using the Cobra Toolbox 3.0 . For insAll data generated or analyzed during this study are included in this published article and its supplemental material.


Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota. The interplay between human diet and the gut microbiome is complex. Here, the authors present a model of human-microbiome interaction that can predict how phenolic compounds are metabolized by the human gut microbiome, identifying diet-specific metabolites in children of varied clinical conditions. Compounds derived from the diet affect the abundance of different species present in the gut microbiome, which, on the other hand, release key metabolites and signals that regulate host health. The relevance of this interaction is supported by an increasing body of literature showing that the beneficial effect of dietary interventions in different clinical conditions is associated with specific signatures of the gut microbiota3.Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition5. In particular, the integration and analysis of genome-scale metabolic models of different bacterial species that are present in the human gut microbiota have received much attention6. Thanks to the tremendous effort in the last years to generate high-quality computational platforms for metabolic reconstruction10, extensive microbial community models of the human gut microbiome are now available. In particular, AGORA constituted the first large effort in the literature, involving 818 species present in the human gut microbiota11.Given the complex molecular events implied in this question, the development of computational models, driven by meta-omics data, constitutes a major task in systems biology14. This approach is promising in personalized nutrition and could help in elucidating how different microbial species in the human gut exploit and transform nutrients derived from the diet and in systematically designing effective dietary strategies when the gut microbiome is dysregulated. For example, AGORA has been already applied to predict dietary supplements for Crohn’s disease15. Using a similar approach, we predicted the effect of solid diet on the gut microbiota metabolism of infants16. Despite these early attempts, genome-scale metabolic models of the gut microbiota are still in their infancy and further developments are required to make them into a practical tool in personalized nutrition.These network-based community models, which integrate the metabolic capabilities of different bacterial species in the gut microbiome, can be analyzed via constraint-based modeling (CBM)http://www.i-diet.es). A similar result was found for CarveMe8, a more recent metabolic reconstruction platform, which involves 92 dietary compounds from i-Diet in the 5587 bacterial metabolic models reported. In addition, universal metabolic databases, such as the Model SEED7, on which reconstruction platforms rely for gap filling, are incomplete and include metabolic capabilities of species that are not present in the human gut. Overall, these limitations restrict the scope of CBM approaches to predict the interaction between diet and gut microbiota.A major issue of current metabolic reconstruction platforms is the limited information about metabolic pathways of dietary compounds that are transformed by the gut microbiota. AGORA only includes 99 out of 650 dietary compounds included in i-Diet, a commercial software for personalized nutrition , is thus more amenable to analyze the role of the human gut microbiota in diet metabolism.In this article, using a combination of bioinformatic tools, metabolic databases, and literature, we extend AGORA and substantially improve the coverage of gut microbial metabolism of dietary compounds. Particularly, we include degradation pathways of 209 dietary compounds (not present in AGORA), from which 179 are phenolic compounds, a family of metabolites highly relevant for human health and nutrition that are mainly transformed by the gut microbiotaTo illustrate our contribution, we first show that AGREDA provides a more complete connection than AGORA to the nutritional composition of 20 typical recipes of the Mediterranean diet. In addition, using 16S rRNA gene sequencing data, we apply AGREDA to predict output microbial metabolites from in vitro fermentation of lentils with feces of Spanish children representing different conditions: normal weight, obesity, allergy to cow’s milk, and celiac disease. We provide experimental validation and compare AGREDA with AGORA using both targeted and untargeted metabolomic strategies. Finally, for the same children, we assess the metabolic interaction between the 20 recipes mentioned above and the gut microbiota, finding a substantially higher number of significant associations in AGREDA than in AGORA. In conclusion, AGREDA addresses the necessary intersection between human nutrition, metagenomics, and computational modeling to advance towards personalized nutrition.19 (see “Methods” section), where reactions from different organisms are merged into a single compartment. This strategy reduces the size of reconstruction and computation time and has been proved accurate and effective for analyzing the metabolic capabilities of the gut microbiota as a whole21, which is the aim of our study, focused on predicting output metabolites from the gut microbiota in different conditions. However, we store for each reaction its taxonomic assignment , we removed the boundaries between the different species and deleted duplicated reactions, obtaining 2473 metabolites and 5312 different reactions, together with their taxonomic assignments. Henceforth, this summarized network is referred to as AGORA.We started from AGORA7 database and manually curated literature knowledge. Through their Enzyme Commission (EC) numbers (if available), reactions were annotated to species present in AGORA using different bioinformatics tools and metabolic databases . All the reactions extracted from the universal database to fill existing gaps included taxonomic assignment to species in AGORA, which provides support for the predicted pathways.Next, we applied a gap-filling algorithm to include in our reconstruction the maximum number of dietary compounds and their degradation pathways. This step was based on FastCoreWeighted, included in the COBRA ToolboxFinally, a single-species analysis was applied to the resulting metabolic model in order to identify blocked reactions and discard pathways requiring transport reactions with limited evidence in public databases (see “Methods” section). We also conducted similar quality checks to those originally performed with AGORA: aerobic and anaerobic growth in different growth media, as well as carbon source uptakes and fermentation product secretions for different species  to the more complex (proanthocyanidins), with all families represented Fig. . Overall28. In the case of Spanish children, our group has estimated an average intake of phenolic compounds of 2079 mg/day29. However, the phenolic compounds are barely absorbed in the small intestine and reach the gut microbiota where they are metabolized by organisms belonging to different phyla, usually into smaller molecules that are more easily absorbed in the large intestine30. Therefore, the benefits of most phenolic compounds are actually exerted by their output metabolites, hence the importance of being able to define their microbial metabolization31. Figure The daily intake of phenolic compounds is high in comparison to that of most micronutrients, since they are especially abundant in highly consumed food items such as tea or coffee  and fruits, vegetables, and legumes (wide range of different flavonoids)In addition to phenolic compounds, we improved the coverage of other relevant families of metabolites present in the diet, ranging from carbohydrates, carotenoids, fats, minerals, phytosterols, vitamins, and xanthines Fig. . OverallIn order to assess the improvement that AGREDA represents over AGORA for the purpose of assessing the effects of the different diets on the gut microbiota metabolism, we selected 20 representative recipes and employed i-Diet to calculate the nutrients present in each of them .32, we determined the potential list of output microbial metabolites (by-products) that can be derived from the fermentation of lentils. On average, for each sample, AGREDA predicted 98 output metabolites that were not captured by AGORA.In order to assess the role of the gut microbiota as a whole during the fermentation of lentils for each of the different conditions detailed above, we contextualized the reference AGREDA and AGORA models with the nutritional information of the lentils recipe from i-Diet and the taxonomic composition of the fecal inocula . We repeated the same analysis for CarveMe; however, it was not able to predict most of the output metabolites and the results are poorer than those of AGORA . In particular, we focused on output microbial metabolites with a different predicted outcome (presence/absence) between AGREDA and AGORA in at least one of the samples analyzed. This subset comprised 135 output metabolites; however, we discarded those not identified in any of the 24 samples analyzed, possibly due to the lack of sensitivity of the metabolomic approach adopted. Thus, we reduced the analysis to 105 output metabolites, among which 19 took part both in AGREDA and AGORA and 86 were only predicted by AGREDA.p value: 1.876 × 10−5 vs 0.1324, respectively; Supplementary Table p value: 7.205 × 10−6; Supplementary Table The binary outcome obtained from metabolomic data for these 105 metabolites in the 24 samples analyzed can be found in Supplementary Data We conducted a similar in silico analysis as the one presented above for the 20 recipes considered in Fig. Overall, we derived 27 context-specific AGREDA models for 21 different conditions, namely, 20 recipes and the minimal medium, which makes a total of 567 context-specific metabolic models. The same analysis was also accomplished with AGORA. As done in the lentils study discussed above, using FVA, we determined the potential list of output microbial metabolites that can be obtained from each of the different scenarios considered , among which 49 metabolites depend on the clinical conditions, 87 metabolites depend on diet (recipes), and 15 metabolites depend on both factors. The rest of the output microbial metabolites did not show any significant pattern for clinical conditions and diet.Based on the results of FVA, we analyzed the relevance of different recipes and clinical conditions in the production of output microbial metabolites. To that end, we built a logistic regression model for each output metabolite using each recipe and clinical condition as independent (explanatory) variables (see “Methods” section). We identified in AGREDA 151 output metabolites whose potential production is significantly affected by different recipes or clinical conditions . Nevertheless, all the reactions include their corresponding taxonomic assignation, namely, for each reaction, the bacterial species that can carry it out are annotated. Altogether, we obtained a metabolic model including 2473 metabolites and 5312 reactions that explains the overall metabolic capacity of the microbial community. Note here that, due to the inclusion of the species interactions in the first step and of the taxonomic assignations of the reactions in the second one, the information of the interactions at the species level is not lost.7, as well as with other metabolic databases and literature knowledge of gut microbiota metabolism, as we detail below.In order to extend the metabolism of dietary compounds by the gut microbiota, we integrated the information provided by AGORA with the Model SEED databasehttps://modelseed.org/), which involves 20,133 metabolites and 34,655 reactions. To minimize the inclusion of reactions from species not active in the human gut microbiota, we decided to annotate the EC numbers present in the Model SEED with the species present in AGORA. Note here that Model SEED does not incorporate Gene-Protein-Reaction rules, as available in AGORA; instead, it presents a broad functional annotation of reactions through EC numbers. Therefore, the integration of Model SEED with AGORA can be done through the taxonomic annotation of its EC numbers. We describe below the different strategies followed to carry out this task with existing genomic annotation tools and relevant metabolic databases.We first downloaded Model SEED from the online portal  associated EC numbers. This information was incorporated into the reactions present in Model SEED. In addition, from the KEGG database35, we downloaded the list of EC numbers for 500 species present in AGORA. With this information, we could further annotate reactions in Model SEED without taxonomic information.Genome FASTA files from different species in AGORA were downloaded from GenBank40. Based on them, we extracted more reactions with enzymatic information and used again the myRAST software for taxonomic annotation. For the remaining EC numbers without taxonomic information, we manually looked for additional information in the KEGG, BRENDA41, and UniprotKB42 databases. After this process, we obtained a list of 3577 different EC numbers and 14,021 reactions in Model SEED that are related to at least one of the species in AGORA.We also performed a manual annotation of reactions and EC numbers present in Model SEED. We found that several reactions that did not contain any EC number information in Model SEED were annotated in public databases, such as KEGG or MetaCyc43, the Human Metabolome Database44, KEGG, and the RetroRules database45. We then conducted a similarity analysis with the RDKit package46 and the Morgan (circular) fingerprint with radius 247. Fingerprints with similarity 1 were obtained and manually checked. We removed 703 repeated metabolites and 1054 reactions from Model SEED.We noticed that some metabolites in Model SEED were involved in reactions under different names. Using both manual curation and chemoinformatic tools, we identified and deleted metabolites and reactions that were duplicated. In particular, we first extracted the InChI identifier for the metabolites in Model SEED , based on PubChemIn order to integrate AGORA and Model SEED, we performed an automatic search of the compound names in both sources and identified duplicated metabolites and reactions. Model SEED added to AGORA 17,820 metabolites and 32,409 reactions, including 12,459 reactions with taxonomic assignment.In addition, we manually identified the list of metabolites from i-Diet present in Model SEED and found 221 that were not present in AGORA. We created an exchange reaction for each of these compounds and included them in our metabolic database. We also added 221 reactions and 19 metabolites from the existing literature on metabolism of phenolic compounds in the gut microbiota, including their taxonomic annotation , 100 for reactions without taxonomic and functional annotation, and 1000 for reactions manually assigned to plant metabolism. The logic of these weights is to penalize reactions for which there is no taxonomic or functional evidence in the species reported in AGORA, i.e., gap filling is preferably done through reactions annotated to species found in AGORA and therefore in the human gut.The universal biochemical reaction database described in the previous subsection was used to extend AGORA and connect input dietary compounds to output microbial metabolites or intermediate metabolites for biomass production. To that end, we used the implementation of FastCoreWeighted available in the COBRA ToolboxAs we found dependencies between different nutrients from i-Diet, namely, some of them are interconnected as inputs and outputs, we ran FastCoreWeighted sequentially, updating the core at each iteration. In the first iteration (Iteration 1), the core included the reactions from literature and AGORA. Note here that the different biomass reactions available in AGORA were included as part of the core in order to guarantee biomass production in the resulting model. In the second iteration, the core comprised the resulting network from Iteration 1 and the input exchange associated with the first nutrient from i-Diet. In the third iteration, the core comprised the resulting network from Iteration 1 and the input exchange associated with the second nutrient from i-Diet. This process was repeated for the 221 nutrients from i-Diet. Reactions obtained at each iteration were included in the final model.Note here that, in order to include each input exchange reaction as part of the core in the different iterations, we split reversible reactions in our universal database into two irreversible steps. In addition, when we added the input exchanges of nutrients from i-Diet as part of the core in the different iterations, we penalized the inclusion of their associated output exchanges to avoid artifacts in the resulting network (weight of 1e5). The same approach was employed for the output exchanges of i-Diet metabolites.48, obtaining a metabolic model involving 2742 metabolites and 6122 reactions. As a result, all the reactions extracted from the universal database to fill the gaps in the degradation of input dietary compounds included taxonomic assignment to species in AGORA, which provides a higher reliability to the outcome of the process. Note here that, for the transport reactions associated with i-Diet compounds, we assumed their presence in those species that encoded their degradation reactions and gave them the corresponding taxonomic annotation. An illustration of the gap-filling algorithm can be found in Supplementary Note We integrated the reactions selected in the different iterations of the gap-filling process, obtaining an active network made of 2920 metabolites and 6277 reactions. At this stage, we still had 51 reactions without taxonomic assignment to species in AGORA. To avoid the inclusion of metabolic pathways with low evidence in the human gut microbiota, we deleted this subset of reactions and ran fastFVA23. Inconsistent reactions were manually corrected using available metabolic databases, such as KEGG or MetaCyc40, or deleted in the case the level of accuracy or annotation in Model SEED was insufficient. Reversibility of reactions was defined according to the information provided in AGORA, Model SEED, and other metabolic databases. After corrections and removals, we extracted a metabolic model with 2720 metabolites and 6088 reactions.We then checked mass and stoichiometry balance of reactions in AGREDA with the function checkBalance from the COBRA ToolboxNote here that each reaction in the derived metabolic model includes its associated Taxonomy-Reaction (TR) rules, which defines the OR Boolean rule with the species supporting this reaction, in analogy with Gene-Protein-Reaction rules typically used in the field of CBM. TR rules permit a straightforward contextualization of the overall model with data of species abundance as well as conducting single-species analysis. A more detailed description of TR rules can be found in Supplementary Note After the gap-filling process, due to the mixed-bag network strategy adopted, we may have pathways requiring transport reactions that are not defined in our metabolic model, which may lead to incorrect predictions. To overcome this issue, we conducted single-species analysis and identified for each organism metabolites involving exclusively consumption or production reactions (dead-end metabolites). The flux through this subset of metabolites is therefore blocked in their corresponding species. We identified 289 metabolites presenting this problem in at least 1 of the 818 species. Then we searched these metabolites in the Human Metabolome Database and included an exchange reaction in the following cases: (1) they were identified in both feces and biofluids in the entry “Biospecimen Locations”; (2) they were identified in feces in the entry “Biospecimen Locations” and located in the extracellular compartment in the entry “Cellular Locations.” This criterion was satisfied for 37 metabolites, and therefore an exchange reaction was included in our model for each of them  dietary compounds from i-Diet, respectively. Full details can be found in Supplementary Data After including these exchange reactions, we ran fastFVAFrom the list of metabolites and reactions involved in AGREDA, we derived the resulting stoichiometric matrix, Equation  represenReaction fluxes are limited by irreversibility constraints, growth medium conditions (diet), and species abundances (16S rRNA gene sequencing data). Lower bounds for irreversible fluxes were made equal to zero. In addition, uptake fluxes of compounds that are not present in the diet were fixed to zero. Finally, reactions whose annotated species are not present in the sample considered were blocked, i.e., both lower and upper bounds were set to zero. For this last step, we made use of the TR rules introduced in the previous subsection.M (M = 1000) for the uptake fluxes of active nutrients in the different recipes.In the analysis reported in the “Results” section, for each sample, we assessed which output metabolites can be potentially produced from input nutrients available in the different recipes. For this study, which is essentially structural, we fixed a sufficiently large bound 49. However, in our structural analysis, we did not force any constraint related with producing a minimum of biomass for each species in the sample. Our qualitative study is not affected by the inclusion of these constraints.AGREDA included a different biomass reaction for each species, which was directly extracted from AGORA. We checked that biomass production was possible for all species in AGREDA in different reported growth media. We could potentially apply to AGREDA different approaches in the literature, ranging from multi-objective approaches to models where all species grow at the same rate32. As a result, we obtained blocked reactions  and potentially active reactions (their minimum or/and maximum fluxes are different from zero). As we particularly focus on output exchange reactions, we developed our own implementation of FVA in the MATLAB environment using IBM ILOG CPLEX as the optimization engine. This analysis was done for all recipes and samples considered in the “Results” section. The same methodology was applied to AGORA.Once each context-specific model was defined, based on diet and species abundance, we applied FVAp values for different output metabolites analyzed and applied the false discovery rate (FDR) approach. Note here that output metabolites presenting no variation in the different cases were not considered in this analysis.In the “Results” section, we discussed the relevance of different recipes and clinical conditions in the production of output microbial metabolites in the children considered. In particular, we built a logistic regression model for each output microbial metabolite using recipes and clinical conditions as (independent) explanatory variables. For each output metabolite, the binary response is a vector of dimension 84 , being 1 if the metabolite can be produced, 0 otherwise. In order to avoid multicollinearity, we took as reference (intercept) lean children under minimal growth medium conditions and kept the other 23 independent binary variables . For multiple hypothesis correction, we integrated all the For the in vitro digestion and fermentation, the following reagents were used: potassium di-hydrogen phosphate, potassium chloride, magnesium chloride hexahydrate, sodium chloride, calcium chloride dihydrate, sodium mono-hydrogen carbonate, ammonium carbonate, and hydrochloric acid, all obtained from Sigma-Aldrich (Germany). The enzymes—salivary alpha-amylase, porcine pepsin, and bile acids (porcine bile extract)—were purchased from Sigma-Aldrich, and porcine pancreatin was from Alfa Aesar (United Kingdom). The fermentation reagents  were obtained from Sigma-Aldrich (Germany).50. Briefly, in the oral phase, 5 mL of salivary solution with alpha-amylase (75 U/mL) and 25 μL of 0.3 M CaCl2 were added to 5 g of lentils and the mix was incubated at 37 °C for 2 min. Then 10 mL of gastric solution with pepsin (2000 U/mL) and 5 μL of 0.3 M CaCl2 were added and the pH was lowered to 3.0 by adding 1 N HCl; the mix was then incubated at 37 °C for 2 h. Finally, 20 mL of intestinal solution with pancreatin (100 U/mL), bile salts (10 mM), and 40 μL of 0.3 M CaCl2 were added and the pH was raised to 7.0 with 1 N NaOH, after which the mix was incubated at 37 °C for 2 h. The enzymatic reactions were halted by immersing the tubes in iced water. The samples were then centrifuged at 6800 × g for 10 min at 4 °C and the supernatants were separated from the solid residue or pellet.The in vitro digestion method was carried out according to the protocol described by Brodkorb and colleagues52. Feces were collected from three children (9–11 years old) from each of the groups studied: allergic to cow’s milk, celiac, obese (body mass index (BMI) ≥ 30) and lean (BMI ≤ 25). Feces from children belonging to the same group were pooled together to account for inter-individual variability. Additionally, seven different inocula were prepared from the celiac-, lean-, and obese-derived pools, respectively, and six different inocula from the allergic-derived one, yielding therefore a total of 27 fermentation experiments. Right after collection, feces were mixed with glycerol (50:50 w/v) and frozen at −80 °C. Briefly, 500 mg of digested wet-solid residue were placed in a screw-cap tube. In all, 10% of the digestion supernatant was added to the solid residue in order to mimic the fraction that is not readily absorbed after digestion. Then 7.5 mL of fermentation medium  and 2 mL of inoculum  were added, to reach a final volume of 10 mL + digestion supernatant volume. Nitrogen was bubbled through the mix to produce an anaerobic atmosphere and the mix was then incubated at 37 °C for 20 h under oscillation. Immediately afterwards, the samples were immersed in ice, to stop microbial activity, and centrifuged at 6800 × g for 10 min. The supernatant, representing the soluble fraction potentially absorbed after fermentation, was collected and stored at −80 °C.The in vitro fermentation was carried out according to the protocol described by Pérez-Burillo et al.Genomic DNA from the solid residues of the fermentation reactions was extracted using the MagNaPure LC JE379 platform (ROCHE) and the DNA Isolation Kit III  Ref. 03264785001, following the manufacturer’s instructions, with a previous lysozyme lysis. DNA was quality-checked by agarose gel electrophoresis (0.8% wt/vol agarose in Tris-acetate-EDTA buffer) and quantified using the Qubit 3.0 Fluorometer (Invitrogen) and the Qubit dsDNA HS Assay Kit.53  was used for the Illumina protocol for the small subunit ribosomal RNA gene 16S rRNA) Metagenomic Sequencing Library Preparation (Cod 15044223 Rev. A). PCR primers targeting the V3–V4 hypervariable region of the 16S rRNA gene were designed as described by Klindworth et al.S rRNA Me54. Once we obtained the ASV table, we assigned species-level taxonomic identifications to each ASV with DADA2, based on exact matching (100% identity) between ASVs and the reference sequences in the Silva database (version 132)55.16S rRNA gene raw sequence reads were processed, trimmed, and clustered into amplicon sequence variants (ASVs) using DADA256. Here we required at least 97% identity for the species-level assignment; however, as MegaBLAST does not take into account the previously assigned genus level, we only considered ASVs for which MegaBLAST and the DADA2 classifier method assigned the same genus. Finally, ASVs with <0.01% of the total number of counts were removed, and rarefaction was applied up to the smallest library size across samples  for further analysis.In addition, for those ASVs that were identified with DADA2 at the genus level but not at the species level, we applied the MegaBLAST module from BLASTFinally, we linked identified taxa to the species present in AGORA. Due to the metabolic diversity between organisms within each genus, we only considered ASVs that reached the species level. Given that the taxonomic assignment methods typically provide information at the species level but not at the strain level, identified taxa could be related to different strains present in AGORA. To overcome this issue when contextualizing data in AGREDA, we considered reactions common for all the strains within the species. We followed this strict strategy to avoid including reactions with limited taxonomic evidence.For individual phenolic quantification, the following standards were used: phloroglucinol, phenol, 3-(3-hydroxy-phenyl)propionate, 4-hydroxyphenylacetate, acetate, dihydrocaffeic acid, cianidanol, myricetin, and quercetin  and 5--gamma-valerolactone . Diethyl ether for extraction was purchased from Sigma-Aldrich (Germany).57, slightly modified to adapt it to UHPLC. In brief, 1 mL of fermentation supernatant was mixed with 1 mL of diethyl ether and kept in the dark at 4 °C for 24 h. The organic phase was then collected and two more extractions with diethyl ether were performed. These 3 mL of diethyl ether were dried in a rotary evaporator set at 30 °C, and the solid residue was resuspended in 1 mL of methanol:water (50:50 v/v) mix. The mixture was then ready to be injected into the UHPLC system (Agilent 1290 Infinity II), which is equipped with a quaternary pump, an autosampler kept at 5 °C, and a diode array detector set at 255 nm. The column used was an InfinityLab Poroshell 120 Sb-Aq 2.1 × 150 mm and 1.9 microns. The flow rate was set at 0.250 mL/min for 46 min. Two mobile phases were used: milli-Q water with 0.1% of formic acid (A) and acetonitrile (B) with the following gradient: 0 to 28 min from 95 to 60% of A and from 5 to 40% of B; 28–36 min from 60 to 0% of A and from 40 to 100% of B; 36–41 min from 0 to 95% of A and from 100 to 5% of B; these last conditions were kept for 5 min. Identification and quantification were carried out by comparing retention times obtained from pure standards (listed in reagents section). A calibration curve for each of the compounds was performed in the range of 0.1–25 p.p.m.Phenolic compounds were analyzed through ultraviolet ultra-high-performance liquid chromatography (UHPLC) as described by Pérez-Burillo et al.58. Briefly, fermented extracts were filtered through nylon filters of 0.20 µm before UPLC injection (2.5 μL). A quality-control sample was prepared by mixing equal volumes from each fermented extract. The quality-control sample was randomly injected during the sample analysis to monitor the system stability and attenuate the analytical variation resulting from system instability.The untargeted analysis of the metabolites produced after in vitro fermentation of lentils was performed following the method of Rocchetti et al.59: 0 min, 5% B; 12.5 min, 20% B; 17.5 min, 60% B; 22 min, 5% B. The initial conditions were maintained for 5 min. The flow rate was set at 0.6 mL/min throughout the gradient. Separation was carried out at 25 °C. The mass spectrometric (MS) analyses were carried out in full-scan mode (range m/z 50–1000) using an electrospray (electrospray ionization) interface and the following conditions: drying gas flow, 9.0 L/min; nebulizer pressure, 35 psig; gas drying temperature, 350 °C; capillary voltage, 3000 V; and fragmentor voltage, 80 V.2.5. All MS data were acquired using the LockSpray to ensure reproducibility and mass accuracy. The molecular masses of the precursor ion and of product ions were accurately determined with leucine enkephalin (m/z 556.2771) in negative and leucine enkephalin (m/z 554.2615) in positive modes at the concentration of 1 ng/μL at an infusion flow rate of 5 μL/min.A LC apparatus ACQUITY UPLC M-Class System  was coupled to a time of flight–mass spectrometer detector (SYNAP G2 from Waters). The mass spectrometer and UPLC system were controlled by the MassLynx v4.1 software. The UPLC column was a fused-core Poroshell 120, SB-C18 . The mass spectrometer was operated in both negative and positive ion modes. A gradient elution was programmed using as a mobile phase A acidified water (1% acetic acid) and as mobile phase B acetonitrile. A linear gradient was applied as described in Gómez-Caravaca et al.http://cosmos-fp7.eu/msi). Potential metabolites that passed the mass accuracy detection threshold, that had plausible chromatogram peak features, and that showed significantly different trends from the control  were considered as potential fermentation markers of the different conditions.For data analysis, features were selected based on their coefficient of variation with the quality-control sample (features with a coefficient of variation >30% were eliminated). Raw data were processed using the MassLynx v4.1 software  according to the targeted “find-by-formula” algorithm. Accurate mass information was used together with the spectral isotope pattern (isotopic spacing and isotopic ratio) to achieve a higher confidence in metabolite identification. Data from Human Metabolome Database and Phenol-Explorer 3.6 were used as references for compound identification, adopting a 5 p.p.m. tolerance for mass accuracy. Thus, identification was carried out according to Level 2  as set out by the COSMOS Metabolomics Standards Initiative .Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary