Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.A high-quality GSM model for  GSM) models  is a Gram-positive, lactic acid bacterium, which not only asymptomatically colonizes the nasopharynx of humans, particularly of young children, but is also a major human pathogen, responsible for diseases such as otitis media, pneumonia, or meningitis. The S. pneumoniae R6 strain is one of the best studied strains in this species. It is a non-capsulated, highly competent derivative of a serotype 2 strain isolated from a child in the early 20th century. There is still a limited amount of biological information and literature available for this microorganism, although relatively recent studies have focused in reducing this gap  which contributes to the formation of H2O2 and acetate from pyruvate and also the expression of flavin-type NADH oxidases that reduce O2 to less toxic forms   . NeverthGSM model for S. pneumoniae strain R6, to perform comprehensive comparative studies between experimentally determined and computationally predicted phenotypes, under different environmental conditions and with various genetic alterations. This model will allow gaining insights into S. pneumoniae physiology and metabolism, beyond what the experimental data has been providing.The purpose of this work was constructing a high-quality Several databases were used throughout this work to aid in all stages of the study. A brief description of the information retrieved from each database is available in The genome sequence with the NCBI assembly accession number ASM704v1 was retrieved from the GenBank repository.GSM was supported by merlin  makes the annotation traceable, as each leaf of the workflow indicates a different decision regarding each gene’s annotation. Moreover, the labels can also be seen as an indicator of the confidence in a particular annotation.New gene functions assignments and corrections may have a significant impact in model performance. Therefore, genome-wide re-annotations should be carried out periodically, to retrieve the most up-to-date genomic information. For this purpose, a re-annotation workflow was developed and implemented, as shown in merlin, candidate metabolic genes were analyzed by verifying if an EC number was also identified in UniProt for that specific protein either in S. pneumoniae R6 or other S. pneumoniae strains. If true, an additional analysis was performed to verify if merlin’s classification matched the one in the UniProt database. Label A was assigned to genes encoding proteins with matching functions in UniProtKB/Swiss-Prot and label B to genes encoding proteins with matching functions in UniProtKB/TrEMBL.The analysis relied on the assignment of EC numbers to each enzyme or putative enzyme encoded in the genome. For the cases in which a complete EC number had been identified by merlin during genome annotation, was evaluated. If only one complete EC number with a classification score >0.2  was available, then the annotation was accepted. In cases where more than one complete EC number was present, priority was given to those that matched the homologous gene of another reference strain (Label D). S. pneumoniae strain D39 was selected as the first reference strain, while Lactococcus lactis strain NZ9000 was the alternative reference if no homologous genes existed in D39. For cases in which no matching gene function was ascertained from the reference organism, additional tasks, such as BLAST searching against proteins in UniProt/Swiss-Prot, were carried out in a final effort to identify a gene function. These annotations are identified with Label G.When there were conflicting EC numbers, priority was given to proteins in UniProt/Swiss-Prot when assigning gene functions (Label C). If no EC number was present in UniProt, then the existence of multiple complete EC numbers, identified by merlin. If available, the re-annotation was carried out in the same manner as in the cases in which no EC number was present on UniProt and therefore the gene was labeled G, F, or discarded, according to the workflow shown in If an incomplete EC number was identified, an alternative complete one was sought in In case of an absent complete EC number, a search across multiple databases such as BRENDA , UniProtAll cases that did not meet the minimum requirements stated in the re-annotation workflow were discarded from the model.Note that the presence of an EC number after annotation does not necessarily mean that the gene will be included in the metabolic model. Such are the cases of genes involved in DNA and RNA processes such as methylation or rRNA modification, as well as pseudo-genes among others.merlin these reactions are retrieved from KEGG. Such reactions should be spontaneous or promoted by enzymes encoded in the organism’s genome. Hence, the genome annotation determines which reactions will be included in the model. The algorithm used by merlin for the assembly of the metabolic network from the annotated genome is described in detail elsewhere (The assembly of the metabolic network involves collecting a set of reactions. In lsewhere .f G′0) and of reaction (Δr G′0), by analyzing manually curated GSM models of closely related organisms or by biochemical studies of the enzymes.The stoichiometry of reactions in the network should be verified to guarantee that all reactions are balanced. Likewise, the reversibility of the reactions should also be confirmed to avoid gaps and mispredictions of the model. The reversibility may be determined from the estimation of the standard Gibbs free energy of formation  is a repository of transport protein encoding genes, reported in the literature. s TRIAGE  for retrmerlin uses information retrieved from KEGG BRITE to implement these rules.The genes–proteins–reactions (GPR) associations are usually determined by searching biological databases and literature. However, determining if the genes encode subunits of a single protein, isoenzymes, or different proteins belonging to a protein complex may not be straightforward. Hence, Merlin searches for the structure of the protein complex modules, including their subunits, and the stoichiometry of every EC number available in the model. Subsequently, this tool parses the data, identifying the orthologs required by each GPR rule and searches for these sequences in the studied organism’s genome, thus identifying the rule and the subunits of the protein in the model  are rendered non-essential.L. lactis (iAO358) model  model  was usedmerlin , the growth ATP requirements are obtained. Although these requirements should be adjusted to different growth conditions, it was assumed, due to lack of additional data, that growth ATP requirements were the same for all genetic and environmental conditions, which is a common practice in GSM models. Therefore, this value was used for all simulations presented in this work. Likewise, 1 mmol.gbio−1 h−1 was used as the initial flux value (FVA) for maintenance ATP. However, this flux was adjusted to experimental data, both for anaerobic and aerobic conditions, as described in the section “Results”.The growth and maintenance ATP requirements were calculated with data retrieved from previous work . In anaeGSM models. Whenever experimental data are available, a comparison between simulations and experiments is easily performed, whereas when such information is not available model accuracy can be impaired. For this work, physiological data detailed in available references were used.The evaluation of the simulation performance under physiologically meaningful environmental conditions is essential for the validation of −1 at 1% to 0.47 h−1 at 3% of glucose), originating also a prolonged lag-phase and lower biomass production. Furthermore, growth and fermentation profiles were measured in semi-aerobic, anaerobic, and aerobic conditions, under pH controlled at 6.5. In semi-aerobic conditions, R6 grows until glucose is depleted from the medium (1% w/v) producing mainly lactate and minor amounts of acetate, formate, and ethanol. Under anaerobic conditions, strain R6 specific growth rate slightly increases (0.69 h−1 to 0.78 h−1) and by-products formation exhibits the same profile observed in semi-aerobic environment. The lower growth rate observed for semi-aerobic conditions can be explained by the higher pyruvate oxidase activity this strain exhibits, when compared with strain D39, whose enzymatic reaction has H2O2 as by-product, which is notably known to arrest growth in S. pneumoniae. Fully aerobic conditions drastically change growth and by-products accumulation in R6. For instance, specific growth rate increases to 1.07 h−1 for a short period of time (2 h), which entails a 16-fold decrease in biomass yield regarding semi-aerobic conditions. Metabolism shifts from lactate to acetate and H2O2 production, denoting also a high activity of pyruvate oxidase. Also, formate is no longer produced, revealing a total inhibition of pyruvate formate lyase (PFL). Upon growth arrest, lactate is consumed to produce acetate and H2O2, with generation of ATP. However, due to the impossibility of determining oxygen consumption rate in semi-aerobic conditions, simulations will only be performed for studies in anaerobic and aerobic conditions, henceforth referred to as study 1 and study 2, respectively.N-acetyl-D-glucosamine, mannose, and galactose. These sugars were chosen for their natural presence in the human nasopharynx. As stated above for strain R6, strain D39 produces mainly lactate in the presence of glucose as sole carbon source and in minor quantities acetate and ethanol. Formate is also formed but in values below the limit of quantification. The same profile of full homolactic fermentation is observed for growth in N-acetyl-D-glucosamine, although for this carbon source, formate and ethanol are produced in higher quantities. Curiously, the specific growth rate in N-acetyl-D-glucosamine decreases to 0.55 h−1 in comparison with 0.9 h−1 obtained in glucose and reaches a 1.3-fold lower optical density (OD 2.29 vs. OD 1.76). Consumption of mannose also denotes homolactic fermentation, although the products of mixed-acid fermentation increase 2.9-fold for acetate, 5.5-fold for ethanol, and 3-fold for formate in comparison with its production in N-acetyl-D-glucosamine. Growth rate and biomass production in this carbon source are similar with fermentation in N-acetyl-D-glucosamine. Lastly, strain D39 shows a mixed-acid fermentation profile on galactose, producing formate (in higher quantities), ethanol, and acetate in a proportion of 2:1:1. Lactate is detected as a minor product of fermentation. The specific growth rate for this carbon source is 0.48 h−1 and maximal OD is 2.16. This study henceforth referred to as study 3 will encompass the assessment of the model with all four carbon sources.Due to lack of information regarding carbon sources other than glucose for strain R6, the results published for parental strain D39 were also considered in this work. S. pneumoniae strain D39 in chemically defined medium supplemented with glucose. Briefly, one of the experiments performed in Härtel’s study aimed at determining which amino acids were essential for growth. S. pneumoniae D39 in CDM in which each amino acid was separately omitted. Analysis showed that pneumococci are auxotrophic for L-arginine, L-cysteine, L-glutamine, glycine, L-histidine, L-leucine, L-isoleucine, and L-valine. de novo biosynthesis of serine and have demonstrated the dual utilization of carbohydrates and amino acids by pneumococci. This work henceforth referred to as study 4 will be used to assess the impact of amino acids on organism growth.S. pneumoniae R6. Additionally, determining the function of genes identified as critical involved assessing information retrieved from several databases   was acceatabases  and litepfl (spr0415), pflF (spr0232), ackA (spr1854), adh (spr1866), pta (spr1007), lctO (spr0627), and activates ldh (spr1100). When galactose is used as the carbon source, the enzymes downstream of pyruvate exhibit alleviation of repression of the genes involved mixed-acid fermentation.For strain D39, it has been described previously  that wheOptflux , an openpfl genes was simulated by limiting the maximum flux through the reactions associated with these genes, to a fraction of the flux in the reference flux distribution (RFD). The RFD can be obtained by performing unconstrained simulations, for different environmental and genetic conditions. To assess anaerobic growth with study 1, two RFDs were determined: the first maximizing biomass with the defined conditions (RFD A.1) and the second by setting the specific growth rate to 0.78 h−1 (RFD A.2). The metabolism in different carbon sources involved determining one RFD for each. In this case, all RFDs were determined by maximizing biomass with the same medium, except for the carbon source. RFDs B.1, B.2, B.3, and B.4 refer to glucose, galactose, N-acetyl-D-glucosamine, and mannose, respectively.The repression of transcription of the pfl genes (spr0415 and spr0232) control the flux between pyruvate and acetyl-CoA, simulations using glucose as carbon source were performed by varying the levels of pfl between 0 and 100% (deactivated to fully active) of the respective RFDs, with 10% intervals. These simulations allowed assessing the behavior of the model, regarding the level of repression of the mixed-acid genes. Moreover, due to inhibition by CcpA (ackA (spr1854), adh (spr1866), and pta (spr1007) were severely restricted under anaerobic conditions, as shown in study 1, and were therefore set to 10% of the RDFs while lctO (spr0627) was reduced to 0%.Hence, for study 1, as  by CcpA  genes acpfl genes (spr0415 and spr0232), adh (spr1866), pta (spr1007), and lctO (spr0627) were restricted to zero, to simulate growth with glucose under aerobic conditions, as described in Likewise, for study 2, the flux of N-acetyl-D-glucosamine were performed by implementing the same limitations as for anaerobic conditions, as these genes are controlled by the regulator CcpA, which is active in the presence of these carbon sources   comparinin silico environmental conditions and by-product secretion. The consumption rates of all components of the environmental conditions for all studies were assumed to be the same as the ones calculated with anaerobic growth experimental data, except for the carbon sources and oxygen.The CDM composition utilized by GSM models simulate steady-state conditions, the experiments considered in this work were performed in batch conditions. Thus, only the exponential growth phase was considered.Although qS) and the specific rate of product formation (qp) are the coefficient of the linear regression of substrate [S] or product [P] versus biomass over specific growth rate X/μ. This relationship is linear when μ and qS are constant  against biomass X divided by the specific growth rate μ, which is approximately qS ≈ In batch cultures, the specific growth rates (μ) can be determined as the coefficient of the log-linear regression of the biomass concentration versus time, whereas the specific rate of substrate consumption (constant . For insqCS) were calculated using the linear regression approach, using data from the study performed by qGlc) for studies 1 and 2, summarized in qGlc was obtained by dividing the growth rate by the biomass yield and converting it to mmol, according to the following expression: qGlc ≈ In study 3, the carbon source consumption rates  were used to calculate the respective rates.2 flux to zero, whereas for aerobic growth (study 2), oxygen was left unbounded. Finally, the carbon sources assessment (study 3) was performed in anaerobic conditions.Anaerobic growth (study 1) was simulated restricting OThe CDM composition previously described  was usedin silico gene essentiality assessment, as genes considered essential in rich media should also be essential in CDM  two assessments were performed. In the first, the specific growth rate was maximized and in the second, it was fixed at 0.78 h to 100% , study 12O2 was simulated. Specific growth rate was fixed at 1.07 h−1, as reported in Regarding study 2, for aerobic growth  and ep is the relative production rate, regarding the product with the largest flux, in either the simulation or experimental data. The best pfl under-expression level was determined by the lowest distance to the experimental data.Where S. pneumoniae R6 exists regarding essential amino acids for growth, study 4, in S. pneumoniae R6 is auxotrophic.As, no experimental data for The comparison of data from study 5 with results obtained from OptFlux simulations was processed as explained below.For all genes considered essential using OptFlux with a match in OGEE, the reason(s) for essentiality was(were) sought in literature and biological data, and the gene was annotated as essential under those experimental conditions. For cases in which a gene identified as essential by OptFlux was considered non-essential by the OGEE, a first analysis that relied on verifying if the products of the associated reaction were required for biomass production and how many reactions were able to synthesize these products was performed. If the products were required for biomass production and all reactions that led to their production were dependent on that single gene then the gene was considered essential.in silico experimental conditions provided the compounds required for biomass production, rendering the reactions that led to their production non-essential. In this case, the simulation constraints were changed  to ascertain the need for the protein encoding gene(s). If under these new conditions, growth was inhibited, then the gene was classified as probably essential under specific conditions, otherwise it was labeled as probably non-essential.For cases in which genes were classified as essential by OGEE but not OptFlux, a first analysis relied on verifying if the reaction products were required for growth. Next, a search for multiple genes assigned the same function was performed. Whenever more than one gene was annotated with the same metabolic capability, a search across several databases and literature was carried out to determine if the annotation was correct. If all genes were correctly annotated, then GPR rules were analyzed. This step relied on analyzing biological data, as well as verifying orthologous genes from KEGG BRITE . If GPR in silico results did not match experimental data, model curation was performed. Merlin’s user-friendly graphical user interfaces allow performing re-annotations, correction of reactions’ directionality, and inclusion/exclusion of reactions from the model, as well as exporting the final model in Systems Biology Markup Language (SBML) (Whenever e (SBML)  format.merlin. This tool analyses the connectivity of all metabolites available in the model and determines which ones are either only products or reactants. Then, merlin identifies reactions in which these metabolites participate and highlights them. These reactions were analyzed against MetaCyc and KEGG pathways. This process involved identifying reactions in the vicinity of blocked reactions containing dead-end metabolites. Then, enzymes promoting the neighboring reactions were sought in merlin’s annotation to identify missing or misannotations. Also, literature on the main pathways in which blocked reactions participate was studied to assess any particularities of the organism, such as auxotrophies.The gap filling process relied on the analysis of the gaps highlighted by a feature available in Streptococcus species were closer to S. pneumoniae R6 than L. lactis and Bacillus subtilis. However, when no information for streptococci was found, specifically regarding reference strain S. pneumoniae D39, searches were first performed using homologous genes from L. lactis and secondly from B. subtilis. In addition to their close relatedness to S. pneumoniae R6, the metabolism of these species has been well described and both have manually curated GSM models available.As stated in the section “Materials and Methods”, a phylogenetic tree  was deveRe-annotation of the genome identified 1242 metabolic genes out of the 2046 candidates, representing 61% of the whole genome. Several reasons, such as the removal of pseudo and truncated genes, blocked reactions and corresponding encoding genes, removal of genes whose function could not be fully determined , manual curation of both annotation and model , decreased this number down to the final 372 (18.2%) genes that exist in the model. Examples of outcomes during the re-annotation phase, using the re-annotation workflow, are shown in merlin was able to classify it as a beta-lactamase encoding gene. The analysis of the protein sequence using CDD suggested that it belongs to the beta-lactamase superfamily, which led to the acceptance of the initial annotation and assignment of the respective EC number.Several annotation scenarios were faced, as seen in merlin’s assignment matched the information retrieved from reviewed genes in UniProt can be regarded with higher confidence. In these cases, information was often replicated throughout the remaining databases, such as the case of gene Spr0021.Gene annotations in which The annotation of genes with incomplete EC number, such as the cases of Spr0022 and Spr0064, required an in-depth analysis. Databases such as KEGG, UniProtKB, and CDD were consulted. In the case of Spr0064, the CDD classified the protein as a sugar isomerase. A search of the protein name assigned by BRENDA returned the EC number 5.3.1.26 which also belonged to the sugar isomerase superfamily. Due to the absence of literature support and information on other databases, the gene was annotated with this EC number and assigned the Label E. Regarding gene Spr0022, KEGG assigned it EC number 3.5.4.33 [tRNA(adenine34) deaminase], UniProtKB annotated the product as a hypothetical protein while BioCyc assigned the EC number 3.5.4.5 (cytidine deaminase) to the gene product. Analysis of the protein sequence on CDD revealed that the gene encoded a protein of the cytidine/deoxycytidilate deaminase superfamily and BLAST on UniProtKB revealed similarity to a gene that encodes a cytidine deaminase protein and search of this protein on BRENDA returned the EC number 3.5.4.5. Thus, this gene was assigned the Label E. Labels A, B, and C account for over 86% of the annotation, with approximately 33, 34, and 20% of the classifications, respectively.merlin increased reliability of the assigned function (uridine phosphorylase in this example).There were cases in which genes assigned with incomplete EC numbers  were ultimately annotated with complete ones. Despite the classification as hypothetical protein by KEGG and UniProtKB, the analysis of the conserved domains as well as the availability of complete EC numbers that matched protein function by merlin’s TRIAGE is available in Finally, a list of genes annotated as transporters by iAO358, is presented in DW) was provided in a personal communication by Mafalda Cavaleiro from Rute Neves lab. The cell wall contents were calculated from previous publications  represent about 0.26 g of cell wall per gram of biomass.The biomass macromolecular composition, mostly adapted from ications , accordiiAO358. As strain R6 lacks capsule, the capsular polysaccharide contents in iAO358 were distributed by the other macromolecules, maintaining the proportions for each component.The remaining macromolecular relative contents were obtained from merlin  in the SBML version 3 format is included in the The validation of this model involved using an abstraction of the CDM, developed with the aim of providing a high pneumococcal growth yield, by in silico environmental conditions, are shown in The results of the calculations performed according to −1), ΔX = 1.27 gBiomass L−1 and Δl–alanine = −0.24 g L−1. Thus, according to the calculations for determining the substrates specific consumption rates performed using linear regressions, the ql–alanine = 1.66 mmoll–alanine gBiomass−1 h−1. The linear regression approach was used to determine the qS for every metabolite in that table, except glucose and oxygen. Whereas the latter was left unbounded in aerobic conditions (and bounded to zero in anaerobic conditions), the former was retrieved from qGlucose = μ ×YX/S−1 × 1000 ≈ 31.71 molglucose−1 . Likewise, the aerobic maintenance ATP flux was set to 4.5 mmolATP−1. The remaining simulations, shown in the present work, were performed considering these estimates.Although using a flux of 1 mmoliDS372 model involved comparing simulation results to the work of S. pneumoniae R6 is depicted in The first validation performed to the pfl) and spr0232 (pflF), acetokinase spr1854 (ackA), alcohol dehydrogenase spr1866 (adh), and phosphate acetyltransferase spr1007 (pta) are repressed, and lactate dehydrogenase spr1100 (ldh) is activated, which confirms the prominent role of PFL in the by-product flux distribution. As discussed before, in anaerobic conditions using glucose as carbon source, S. pneumoniae strain R6 produces mainly lactic acid (93%) together with mixed-acid fermentation products, namely formate, acetate, and ethanol, which account for only 7% for the total of fermentation products as shown in It is well known that PFL is an intrinsic part of the mixed-acid fermentation process. After being synthesized, it has to be activated via a PFL-activating enzyme . Also, PS. pneumoniae R6 model in anaerobic conditions (study 1), FVA was performed for each of the by-products, while simultaneously varying the level of expression of pfl between 0 and 100%. This assessment involved restricting the flux of the reactions promoted by PFL to different ratios of the flux obtained in RFDs A.1 and A.2 for such reactions, while simulating the maximization and minimization of the production of each metabolite, and the results of these simulations are shown in Regarding the simulation using the −1), which was expected as this rate was used to determine the maintenance ATP requirements. However, in these conditions, the model cannot provide the observed by-products profile, as though silencing the pfl genes in the simulation allows generating enough lactate, formate, and acetate cannot be produced in sufficient amounts. When the flux of the reactions promoted by PFL was restricted to 10% of RFD A.1, the growth rate increases to ∼0.83 h−1, and the minimum amount of formate produced by this bacterium was 2.86 mmolformate−1, more than the one attained experimentally (1.9 mmolformate−1), not allowing a recapitulation of the by-products profile. However, in this case, the maximum production of lactate in the simulation is identical (58.27 mmollactate−1) to the one experimentally determined (58.98 mmollactate−1).As shown in the left panel of −1. The assessment of the influence of the genes encoding PFL demonstrates that, like before, when these genes are silenced the organism cannot produce enough formate. Instead, it redirects the metabolism to homolactic fermentation. Increasing PFL activity induces a shift from lactate to mixed acid fermentation products, namely formate that was found in minor amounts in the experimental conditions, which indicates that the pfl was not completely silenced. Restricting growth allows mimicking the experimental results for all levels of expression of genes encoding the pfl, except for the knockout (0%) as this restriction impairs the production of formate and acetate when compared to the experimental data. The experimental by-products profile can be reached in all simulations (except the case described before), when performing FVA analyses. Nevertheless, such profile is more robust when the expression of genes encoding PFL is closer to 0, as the lower the expression results in a lower the production of formate, as depicted in The next set of simulations  used for anaerobic simulations was also tested for aerobic growth.Simulations performed under aerobic environmental conditions (study 2) were rather different from the ones performed under the absence of oxygen, as shown in −1, while deactivating genes encoding the PFL enzyme and maximizing/minimizing acetate, H2O2, and lactate production, as described in the section “Materials and Methods”. Results show that, in these conditions, production of acetate and H2O2 is mandatory, whereas lactate’s FVA shows that only trace amounts of this metabolite can be produced  under anaerobic conditions. As seen before, it is possible to simulate different types of fermentation by varying N-acetyl-D-glucosamine simulation results are the farthest from the experimental results from all tested carbon sources, with a seemingly anomalous production of acetate. This result is explained by the fact that acetate is a by-product of N-acetyl-D-glucosamine degradation. In the model, genes spr0668 and spr1528 encoding phosphoenolpyruvate-dependent phosphotransferase sugar-specific systems transport this compound into the cell converting it to N-acetyl-D-glucosamine-6-phosphate and releasing pyruvate. Then, gene spr1867 encoding an N-acetyl-D-glucosamine-6-phosphate amidohydrolase converts the phosphorylated compound into acetate and D-glucosamine-6-phosphate, which explains the behavior shared between this carbon source and glucose. Therefore, study 3 assessment allowed determining the level of activation of pfl genes in different carbon sources, which until date has not been described.As expected, the results for galactose show the prevalence of mixed acid fermentation, while mainly the homolactic fermentation profile is exhibited for glucose and an increase in the fermentation behavior is seen in mannose, all as described in iDS372 model are shown in The results obtained by simulating the environmental conditions used in in silico using this model yields results showing a high degree of similarity (80%) to the results obtained by S. pneumoniae R6 although auxotrophic for L-arginine, L-cysteine, glycine, L-histidine, and L-valine contains all reactions required for de novo biosynthesis of this amino acids. Likewise, the unconventional pathway for the de novo biosynthesis of serine suggested by their study was also confirmed. The mismatching results were in respect to the amino acids isoleucine, leucine, valine, and glutamine. Härtel suggested that S. pneumoniae D39 was auxotrophic for these amino acids, as this strain did not grow in their absence, but in silico results performed in this study could not confirm this. The annotation of genes involved in the de novo synthesis of these amino acids and the lack of additional data to fine tune the pathways or determine how and when these genes are expressed prevent any attempt to further curate these pathways. Hence, these pathways either have regulatory mechanisms preventing the biosynthesis of these amino acids or some of the enzymes involved cannot sustain the flux required for biomass growth. The study from S. pneumoniae D39, in the absence of glutamate, proline, and methionine, presented a decreased growth rate. The independent omission of these amino acids from the medium, in silico, did not have any impact on the growth rate which was expected as this validation was qualitative and not quantitative. The fact that S. pneumoniae R6 could also explain the differences observed in these results.Overall, mimicking their experiment S. pneumoniae strain R6. Out of the 89 essential genes, 67 were not listed as essential by OGEE. Only 50 genes identified as essential by OGEE were present in our model and from these only 23 matched the results obtained by OptFlux’s gene essentiality tool. From this comparison, another nine genes could be considered essential if certain experimental conditions were met such as the removal of some amino acids , vitamins  from the medium composition or certain GPR rules were adjusted. The remaining 27 genes would never be classified as essential in this model due to the nature of stoichiometric models that do not take in account different catalytic activity of isoenzymes.As previously stated in the section “Materials and Methods”, study 5 involved performing the gene essentiality analysis with OptFlux’s gene essentiality tool using the anaerobic experimental conditions of iDS372 as the “real” results shown in OptFlux’s results were labeled as the “predicted” and the list of genes obtained from OGEE that exist in in vitro or in vivo gene knockout studies should be performed using the defined medium described in Overall, the results obtained clearly demonstrate that the model performs well in the discrimination of essential from non-essential genes with a high level of accuracy (75%). The model performs very well in predicting non-essential genes, shown by the high specificity (∼79%) and negative predictive value (∼90%). The prediction of essential genes is, in comparison to the non-essential, lower. This is shown by the relatively low sensitivity (46%) and precision (26%). A possible explanation for this relies on the fact that the genes identified as essential by OptFlux are directly linked to metabolism and influenced by the medium composition . On the other hand, the essential gene list obtained from OGEE, although generated using a complex medium, includes non-metabolic genes. Hence, differences in the number of essential genes from each approach were expected. For an improved validation of the results, In S. pneumoniae R6 strain, capable of predicting essential genes and simulating phenotypic behavior. This is the first manually curated GSM model to be reconstructed for any strain of S. pneumoniae and establishes the groundwork for a better understanding of the metabolism of this major pathogen.The main objective of this study was to reconstruct a GSM model for the S. pneumoniae proposed by Carvalho and colleagues was confirmed in this study. Also, this model was able to replicate S. pneumoniae’s behavior under different environmental conditions, including different carbon sources and oxygen availability. Considering the overall results obtained, iDS372 can be employed to provide reliable qualitative or quantitative simulations under different experimental conditions.A high level of manual curation, based on literature, experimental data, and biological databases, was performed when constructing the network, which should increase the reliability of the model. The prominent role of the PFL regulation in The medium used by iDS372’s predictions confirmed almost all essential amino acids under the conditions established by S. pneumoniae R6 possesses all the genes required to synthesize the amino acids whose results did not match suggests that other factors may regulate their expression in S. pneumoniae R6. Further studies on this topic can provide additional information on the differences between R6 and D39.iDS372 performed well on all five studies used to validate its phenotypical predictions to different genetic and environmental conditions.Finally, the model provided in the SBML level 3 version 2 was able to score over 97% on all consistency tests on the Memote  test suiThe lack of experimental data of quantitative nature limits the spectrum of application of this model. The elaboration of new studies using CDM and less rich media clearly defined environmental conditions, and quantification of both substrate consumption as well as by-product formation would yield more information, which could be used to further curate the network and enhance and extend its predicting capabilities.S. pneumoniae species, and the fact that iDS372 is the first curated GSM model for this species, it will be a pivotal model to study the impact of that genetic diversity in the metabolic capabilities of specific strains with potentially relevant clinical correlations.Considering the natural genetic diversity within the This manuscript contains previously unpublished data. The name of the repository and accession number are not available.OD and IR designed the research and provided guide throughout the investigation. OD, IR, and FP coordinated the project. JS and OD developed and refined the model. OD, JS, MR, and CF performed the model validation and evaluation. All the authors wrote the manuscript, read, and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Yet, a systematic 1H NMR based metabolomics characterisation of most tissues in this animal model has not been published. Here, we provide a systematic organ-specific metabolomics analysis of this widely employed model using NMR spectroscopy.The rapid expansion of Type 2 Diabetes (T2D), that currently affects 90% of people suffering from diabetes, urges us to develop a better understanding of the metabolic processes involved in the disease process in order to develop better therapies. The most commonly used model for T2D research is the db/db mice in 18 relevant biological matrices.The aim of this study was to characterise the metabolic modulations associated with T2D in 1H-NMR and 2D-NMR spectroscopy were applied to 18 biological matrices of 12 db/db mice  aged 22 weeks, when diabetes is fully established.High-resolution 61 metabolites associated with T2D were identified. Kidney, spleen, eye and plasma were the biological matrices carrying the largest metabolomics modulations observed in established T2D, based on the total number of metabolites that showed a statistical difference between the diabetic and control group in each tissue (16 in each case) and the strength of the O-PLS DA model for each tissue. Glucose and glutamate were the most commonly associated metabolites found significantly increased in nine biological matrices. Investigated sections where no increase of glucose was associated with T2D include all intestinal segments . Microbial co-metabolites such as acetate and butyrate, used as carbon sources by the host, were identified in excess in the colonic tissues of diabetic individuals.1H NMR-based metabolomics will represent a useful resource to explore metabolic pathways involved in T2D in the db/db mouse model.The metabolic biomarkers identified using The online version of this article (10.1007/s11306-019-1548-8) contains supplementary material, which is available to authorized users. T2D is also an increasing clinical issue among children and adolescents, who suffer more aggressive complications than adults or paediatric T1D, including hypertension, proteinuria, peripheral and autonomic neuropathy, renal disease and retinopathy  is a complex metabolic disorder characterised by insulin resistance and systemic hyperglycaemia  T2D mouse model: BKS.Cg-Dock7 < m > +/+ Lepr < db >/J is missing. Previous reports have characterised relevant biological matrices such as urine, plasma and kidneys, showing an increase in glucose levels and modulations in the tricarboxylic acid cycle (TCA cycle), branched-chain amino acids (BCAAs) levels, homocysteine-methionine metabolism and ketone and fatty acid metabolism at different stages of the disease. However, a systematic metabolomics characterisation of this animal model in a large number of biological matrices has never been published. Therefore, we herein provide a useful resource to progress in the understanding of organ-specific metabolic alterations in established T2D in the db/db mouse model  and bedding from each environment was mixed on weekly basis to minimise cage effect. After one week of acclimatisation, body weight was recorded on a weekly basis starting from week six. Animals were humanely euthanized by neck dislocation, according to the specifications of the United Kingdom Animals (Scientific Procedures) Act, 1986 (ASPA), when they were 22 weeks old. The procedure was performed first time in the morning.In order to characterise the metabolic fingerprint of T2D, twelve four-week-old mice  from the strain BKS.Cg-Dock7 < m > +/+ Lepr < db >/J and their corresponding WT controls were acquired from Charles River Laboratories, Italy. Animals were allocated into two different homogenous environments, diabetic and control, according to their genetic background , muscle, heart, intestinal sections , urine and blood were aseptically collected and immediately frozen in liquid nitrogen to be later on kept at − 80 °C until the day of the analysis. NMR sample preparation is detailed in S1.1H NMR spectra from all biofluids and extracts, except the liver, were acquired on a Bruker Avance HD 700 MHz  with a TCI Cryoprobe and equipped with a cooled SampleJet sample changer from the same manufacturer. For liver samples, NMR spectra were acquired on a Bruker Avance III 500 MHz NMR spectrometer  equipped with a High-Resolution Magic Angle Spinning 1H NMR probe from the same manufacturer at a rotational speed of 5000 Hz.2 filter was set at 39 ms. Additionally, one Correlation Spectroscopy (1H–1H COSY) was acquired on a selected representative sample from each bio-fluid and liver  NMR spectrum (for each tissue), a total of 64 scans were accumulated into 64 K data points with a spectral width of 13 ppm. Two types of 1D experiments were recorded, using standard pulse sequence: Carr–Purcell–Meiboom–Gill  (Meiboom and Gill i at time x − body weight i at time 0), liver and WAT relative weight (tissue weight i/body weight i) using RStudio .Descriptive statistics and one-way ANOVA (factor = genetic background) were carried out on body weight, body weight gain  using 7-fold cross-validation. Random permutation testing (300 randomisations) was then applied to validate the models and calculate a p value, which is the probability of obtaining such model purely by chance. Aliphatic and aromatic regions from urine datasets, where glucose signal is not present, were further studied applying a normalisation under total area , the Biological Magnetic Resonance data bank  and published literature  with manual phasing followed by automatic baseline correction using the Whittaker smoother algorithm and manual multipoint baseline correction when appropriate. Chemical shift calibrations were carried out relative to TSP (δ 0.00) for all tissues, except for liver and plasma where the glucose anomeric peak (δ 5.223) was used. NMR spectra were imported into Matlab version R2015b  and analysed using the statistic toolbox and algorithms provided by Korrigan Toolbox version 0.1 . In Matlab, residual water (δ 4.70–5.10) and noise (regions before δ 0.5 and after δ 9.5) were removed prior to matrix normalisation using a median-based probabilistic quotient method  , in particular during the first 6 weeks (p < 0.001), when animals were between 5 and 11 weeks old and increased body weight gain more rapidly and variability was smaller. During the last week, diabetic individuals had 210% higher body weight gain than controls. WAT weight was significantly higher (434.8%) in the diabetic group (p < 0.001) .The twelve animals of the study arrived at week four of age and were monitored until they were just over 22 weeks old, when they were euthanized. Body weight gain was significantly higher in diabetic animals . Leucine decrease was consistent with previous observations showing that ketogenesis is altered in the db/db mouse model. In addition, it has also been reported that BCAAs decreased in the late stages of the disease, which is consistent with the 22 weeks of age of the animals used in this work, effectively corresponding to a well-established disease . The O-PLS DA analysis of skeletal muscle identified higher levels of glucose, glycerol and lipids and lower levels of anserine, creatine and IMP in diabetic individuals   Fig. . Thus, aspleen O-PLS DA  identified that diabetic individuals had higher levels of choline, fumarate, glucose, glycerol, isobutyrate and NADH. Conversely, diabetic individuals had lower levels of aspartate, creatine, glutamate, hypoxanthine, lactate, O-phosphoethanolamine, serine, taurine, threonine and uracil  allowed the identification of metabolites differing between diabetic and control individuals , Gas Chromatography-Mass Spectroscopy (GC/MS)  is a leading cause of death and one of the major reasons of end stage renal disease  were driven by higher levels of triglycerides in diabetic individuals while minor changes in polar metabolites were also observed .cerebrum , it was still possible to identify some metabolites that increased amongst healthy individuals, including choline, glutamate, hypoxanthine and N-acetylglutamate. By contrast, diabetic individuals were associated with higher levels of lactate and sn-glycero-3-phosphocholine . Neurotransmission in the ventromedial hypothalamus is mediated by GABAergic neurotransmission. The suppression of GABAergic neurotransmission is necessary to activate the counter-regulatory responses to hypoglycemia  3-phosphoglycerate dehydrogenase deficiency, (ii) 3-phosphoserine phosphatase deficiency and (iii) phosphoserine aminotransferase deficiency. These enzyme defects result in severe psychomotor retardation and microcephaly . Diabetic retinopathy was previously linked to an increased activity of polyol synthesis pathway   is produced when In total, 61 distinct metabolites were identified associated with diabetic modulations. Glucose and glutamate were the most commonly associated metabolites, and they were significantly increased across nine biological matrices. Kidneys, spleen, eye and plasma, clustering all in the same super group in the heatmap (S6), were the organs and fluids that displayed the most varied metabolic changes. This clustering was partially due to a decrease in amino acids. The large heterogeneity in the metabolic response that is strongly organ-specific prevented further grouping of the organs.In total, 16 metabolites were found modulated in kidney and spleen, and 15 in eye and plasma. Table db/db mouse model BKS.Cg-Dock7 < m > +/+ Lepr < db >/J and their wild type control, identifying over 60 metabolites modulated between these two groups. This study represents the most comprehensive tissue-specific metabolic characterization of this model and is intended to be used as a reference for further research in this area. Kidney, spleen, eye and plasma were the organs that showed the most metabolic modulations between control and diabetic individuals. In total, across all the tissues and biofluids studied, 61 biomarkers were found associated with diabetes.The present study reports qualitative differences in 16 tissues between diabetic Some limitations of this study included a restricted coverage of some potentially important metabolites, such as bile acids and lipids, due to the nature of the methods employed and further studies are necessary to uncover these modulations. The use of a small number of mice of both genders, which impeded a study of gender specific changes is another limitation. Future studies, should also consider the impact of diet and environment on the metabolic modulations associated with diabetes. Hence, diabetic studies should be addressed as part of an integrative approach considering metabolomics along other ‘omics’ technologies such as metagenomics.Supplementary material 1 (DOCX 121 kb)Supplementary material 2 (DOCX 234 kb)Supplementary material 3 (DOCX 62161 kb)Supplementary material 4 (DOCX 13453 kb)Supplementary material 5 (XLSX 36 kb)Supplementary material 6 (DOCX 250 kb)Supplementary material 7 (ZIP 49544 kb)Below is the link to the electronic supplementary material.


Trypanosoma brucei parasite. The tsetse fly vector is of interest for its potential to prevent disease spread, as it is essential for T. brucei life cycle progression and transmission. The tsetse’s mutualistic endosymbiont Sodalis glossinidius has a link to trypanosome establishment, providing a disease control target. Here, we describe a new, experimentally verified model of S. glossinidius metabolism. This model has enabled the development of a defined growth medium that was used successfully to test aspects of S. glossinidius metabolism. We present S. glossinidius as uniquely adapted to life in the tsetse, through its reliance on the blood diet and host-derived sugars. Additionally, S. glossinidius has adapted to the tsetse’s obligate symbiont Wigglesworthia glossinidia by scavenging a vitamin it produces for the insect. This work highlights the use of metabolic modeling to design defined growth media for symbiotic bacteria and may provide novel inhibitory targets to block trypanosome transmission.Human African trypanosomiasis is caused by the  Trypanosoma brucei parasite, the causative agent of human African trypanosomiasis. The colonization and spread of the trypanosome correlate positively with the presence of a secondary symbiotic bacterium, Sodalis glossinidius. The metabolic requirements and interactions of the bacterium with its host are poorly understood, and herein we describe a metabolic model of S. glossinidius metabolism. The model enabled the design and experimental verification of a defined medium that supports S. glossinidius growth ex vivo. This has been used subsequently to analyze in vitro aspects of S. glossinidius metabolism, revealing multiple unique adaptations of the symbiont to its environment. Continued dependence on a sugar, and the importance of the chitin monomer N-acetyl-d-glucosamine as a carbon and energy source, suggests adaptation to host-derived molecules. Adaptation to the amino acid-rich blood diet is revealed by a strong dependence on l-glutamate as a source of carbon and nitrogen and by the ability to rescue a predicted l-arginine auxotrophy. Finally, the selective loss of thiamine biosynthesis, a vitamin provided to the host by the primary symbiont Wigglesworthia glossinidia, reveals an intersymbiont dependence. The reductive evolution of S. glossinidius to exploit environmentally derived metabolites has resulted in multiple weaknesses in the metabolic network. These weaknesses may become targets for reagents that inhibit S. glossinidius growth and aid the reduction of trypanosomal transmission.The tsetse fly is the insect vector for the  Paenibacillaceae and Serratia marcescens in the Asian malarial vector Anopheles stephensi (Aedes aegypti (Trypanosoma brucei parasites that cause human African trypanosomiasis (HAT)  .Glossina, also hosts a limited bacterial microbiome alongside the parasitic T. brucei. The microbiome consists of a primary, obligate symbiont Wigglesworthia glossinidia and typically, a secondary facultative symbiont Sodalis glossinidius  and flux balance analysis (FBA) to model S. glossinidius in silico. This is a powerful method when based on a well-annotated genome and the ability to test in silico hypotheses experimentally  carboxylase gene (ppc), preventing the conversion of PEP to oxaloacetate for the tricarboxylic acid (TCA) cycle. The pseudogenization of components of the l-arginine biosynthesis pathway indicate the requirement of an external source of l-arginine to supplement growth in silico. They concluded that exogenous l-arginine is required both as a biomass component and to form succinate via putrescine in order to supplement the TCA cycle in the absence of ppc. The common hexose sugar d-glucose is given as the sole carbon source. Importantly, this construction of iEB458 was limited by the lack of a well-annotated relative from the same genus, which is no longer an issue since the discovery of S. praecaptivus.To define an mentally . Analysia et al. , who desiLF517, and describe how it has enabled the development of an entirely defined medium that supports S. glossinidius growth in vitro (SGM11). Our data indicate the use of a carbon source lacking in the blood meal, namely, N-acetyl-d-glucosamine (GlcNAc). This suggests a complex nutritional interaction of S. glossinidius with the tsetse chitinous peritrophic matrix. Degradation of this by a microbe-derived chitinase might explain the increased persistence of the trypanosome when S. glossinidius is present , whereas the gene in S. glossinidius contains multiple frameshifts and premature stop codons  is split across three reading frames due to frameshift mutations and contains two premature stop codons. Download FIG S1, TIF file, 4.4 MB.Analysis of a portion of the Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the l-arginine to succinate is not supported in our analysis, with little or no evidence for these genes being present in S. glossinidius , aminobutyraldehyde dehydrogenase (ABUTD) and 4-aminobutyrate transaminase (ABTA) (S. glossinidius (patA gene in S. praecaptivus (Sant_1573). Similarly, there is no functional orthologue of patD for ABUTD, nor of puuE or gabT for ABTA. l-Arginine can still be converted to agmatine and then to putrescine via arginine decarboxylase  and agmatinase . This is the only route to the synthesis of this biomass component, meaning a source of l-arginine in the cell is still predicted to be essential. However, it is not supporting the proposed additional anapleurotic function.The proposed anapleurotic link from e (ABTA)  (Data See (ABTA) . PatA issinidius . There i10.1128/mBio.02106-18.7DATA SET S1iLF517 for Sodalis glossinidius. Compounds are listed in the first tab, reactions in the second tab, and the biomass composition in the third tab. Download Data Set S1, XLSX file, 0.1 MB.Metabolic model of Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the ppc. Removal of the ABTA reaction from iEB458, breaking the link of putrescine to succinate, results in zero biomass production. This can be rescued by the in silico addition of metabolites that can be introduced easily into the TCA cycle: the amino acid l-aspartate or l-glutamate or the organic acid succinate, fumarate, or α-ketoglutarate. The loss of ppc therefore requires the addition of a second organic substrate in addition to glucose for growth, suggesting an adaptation to an amino acid-rich environment that results from the tsetse blood diet. Important components, including l-aspartate and l-glutamate, are predicted to be present at high concentrations.An alternative organic compound must therefore serve the role of supplying TCA cycle intermediates in the absence of S. glossinidius genome enabled the construction of an independent metabolic model, iLF517. This model has significant differences to iEB458 . This model can be viewed and analyzed through a web-based FBA browser on DETOXbase (www.detoxbase.org/publications/iLF517).A systematic reanalysis of the o iEB458 . Growth a et al. . Full deiLF517 was analyzed via FBA to investigate metabolite essentiality and the presence of predicted auxotrophies. Eighty reactions are included in iLF517 that were absent in iEB458, and 32 have been removed . This value was selected originally for E. coli and indicates highly aerated growth conditions in a chemostat , to create SGM11, resulted in higher yields than with LB alone, although this difference was not statistically significant  for an OD650 value of 0.28  gene ptsG had been pseudogenized. It is intact in S. praecaptivus (Sant_2470), suggesting that there may be weak selection for its retention within the tsetse. While the nonspecific ManXYZ could substitute for this loss of function   is also retained −1 h−1 for d-glucose, GlcNAc, and mannitol, respectively.This investigation highlighted immediately the presence of a GlcNAc-specific PTS transporter gene, (SG0859)  Fig. 3)nagE, in nfection , 37, 38.retained  S. glossinidius was grown experimentally in LB and LB supplemented with a selection of carbon sources to test the hypothesis that GlcNAc and mannitol may be suitable alternatives. Of the saccharides tested, only d-glucose, GlcNAc, and mannitol increase growth significantly in comparison to LB alone (P < 0.01 by one-way ANOVA). Approximately two times greater endpoint growth is exhibited with an equimolar amount of GlcNAc compared to d-glucose (P < 0.05 by one-way ANOVA) . This demonstrates that the difference in growth between d-glucose and GlcNAc is not a result of the additional carbon. S. glossinidius grows significantly better on GlcNAc than mannitol (P < 0.05 by one-way ANOVA), reflecting the in silico results qualitatively.LB alone  (P < 0.0y ANOVA) . Normalil-arginine, l-glutamate, and a carbon source, namely, GlcNAc, does not produce a positive biomass output in iLF517. It is likely that S. glossinidius requires supplementation from certain vitamins that it cannot synthesize. During the transition to symbiosis, S. glossinidius may lose genes that encode components of the vitamin and cofactor biosynthetic pathways in favor of retaining transporters. There is also thought to be a connection between aspects of the tsetse microbiome in terms of vitamin biosynthesis  is used as a comparison. The loss of this pathway in S. glossinidius may be a specific adaptation to the tsetse. To investigate this further, three other Sodalis-allied symbionts were examined: “Candidatus Sodalis pierantonius” strain SOPE from the rice weevil Sitophilus oryzai   . A thiamy ANOVA) . This adr et al.  that S. S. glossinidius in SGM11 enables the thorough testing of amino acid usage in iLF517. Metabolites can be removed individually, and the effect on biomass production can be measured. iLF517 requires either l-glutamate or l-aspartate to produce a positive biomass output. Both l-glutamate and l-aspartate likely enter the cell through the GltP transporter SG2121  are dicarboxylate/amino acid:cation symporter (DAACS), amino acid-polyamine-organocation family (APC), branched-chain amino acid symporter (LIVSC), alanine or glycine:cation symporter (AGCS), and hydroxyl/aromatic amino acid permease (HAAAP). Download Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the l-aspartate or l-glutamate from SGM11 individually resulted in a significant decrease in the growth yield achieved by S. glossinidius after incubating for 72 h (P < 0.05 by one-way ANOVA) (l-aspartate and l-glutamate abolished growth completely (P < 0.001 by one-way ANOVA). This confirms that an exogenous source of one of these amino acids is essential for S. glossinidius growth. Examination of iLF517 reveals that l-glutamate feeds directly into the TCA cycle through deamination to α-ketoglutarate. The direct route to feed l-aspartate into the TCA cycle via fumarate (l-aspartase) is however missing in S. glossinidius , producing l-glutamate and oxaloacetate. The resulting biomass output was reduced from 0.35 to 0.31 g DW (mmol glucose)−1 h−1, demonstrating that S. glossinidius can use l-aspartate if l-glutamate is not available, although the latter may be preferred. l-Glutamate is therefore likely an important energy source in iLF517 both to form l-aspartate and to replenish the TCA cycle at α-ketoglutarate (Removing y ANOVA) . Removinsinidius . When iLlutarate .S. glossinidius appeared to confirm existing opinion  was grown in M9 medium alone (solid line) or with added l-arginine (dashed line), l-glutamate  or 5× concentration l-glutamate  for 24 hours in a microplate reader. This was compared to growth in the same medium combinations for argA (pink), argD (orange), and argG (green) gene deletion mutants from the Keio collection  to assess its completeness in comparison to S. praecaptivus. This revealed that argB, argE, argF/argI, and argH are full length and therefore likely functional  has a fragment (argG_2) that is almost full length in comparison to its S. praecaptivus orthologue. This indicates that argG is also likely functional in spite of its description as a pseudogene in the previous annotation.The new annotation suggests that the argA gene appears in two fragments: SGGMMB4_04654 (argA_1) and SGGMMB4_04655 (argA_2). It has been demonstrated in Pseudomonas aeruginosa that the two separate ArgA protein domains can be expressed individually (l-glutamate is provided (argA_2) fragment of this gene has a GTG start codon and may therefore be functional under the conditions shown in The vidually , 49. Theprovided . The SGGS. glossinidius argD orthologue also appears in two pieces: SGGMMB4_05438 (argD_1) and SGGMMB4_05439 (argD_2). Lal et al.  or hemL (SG0500), that may provide functional alternatives.The l et al.  showed tllection  were grollection  alone orarginine , confirmS. glossinidius can synthesize l-arginine, surviving when it is not supplied exogenously. Therefore, it is not a true auxotroph and could instead be described as a relic of a prototroph transitioning to auxotrophy.The data here suggest that under certain conditions, S. glossinidius metabolic network and demonstrates its application in designing defined growth media for the symbiont. The carbon source for iLF517 is GlcNAc, as opposed to d-glucose in iEB458. It has been verified empirically that S. glossinidius achieves a significantly better growth yield with GlcNAc than with d-glucose  medium subsequently removes any limitations caused by insufficient L-glutamate.rnithine . An excetabolite . The argnotation . S. glosripennis . The expe tissue , 60. SGMl-arginine biosynthesis pathway is undergoing the process of inactivation and will become entirely pseudogenized over evolutionary time. This is supported by the complete pathway for l-arginine biosynthesis in S. praecaptivus, suggesting that these genes may have been lost within the tsetse environment as a result of selection pressure or drift. It implies that l-glutamate is not limiting inside the tsetse, allowing relaxed selection pressure on the l-arginine biosynthesis genes. It also emphasizes strongly the importance of using in vitro experiments to test in silico assertions. This is particularly relevant in symbiotic bacteria where the functionality of broken or fragmented genes is not certain. Indeed, a recent report implies that some S. glossinidius genes thought to be pseudogenized are in fact under transcriptional control      and Aedeungunya) –75. Whilnfection –79, the prolixus , and Aceipalpis) .iLF517 and the S. glossinidius metabolic network has enabled the design of a defined growth medium that supports growth of the symbiont. While several FBA models for insect symbionts have been published  iEB458  was asse−1 h−1 in order to simulate a reduced oxygen environment. The uptake of ammonia, water, phosphate, sulfate, potassium, sodium, calcium, carbon dioxide, protons, and essential transition metals was unconstrained. Uptake of all other metabolites was set at zero, with the exception of those used in the analyses which have been set at 2 mmol g DW−1 h−1 GlcNAc and l-glutamate, 0.5 mmol g DW−1 h−1l-arginine, and 0.01 mmol g DW−1 h−1 thiamine. Cofactor constraints were implemented by introducing these metabolites to the biomass functions at small fluxes (0.00001 mmol g DW−1 h−1) (−1 g DW (mmol glucose)−1 h−1. Futile cycles, identified as reactions carrying biochemically unsustainable flux, were altered to the correct reaction stoichiometry where possible with guidance from EcoCyc and BiGG. The full description of the model is provided in The flux balance analysis (FBA) solutions were generated using the GNA linear programming kit (GLPK) integrated with custom software in Java. Oxygen uptake was constrained to 12 mmol g DWW−1 h−1) . The pheS. glossinidius strain GMM4 was obtained from the University of Liverpool. Working stocks were established by growing starter cultures on brain heart infusion (BHI) (Sigma-Aldrich) plates under microaerophilic conditions generated by Oxoid CampyGen sachets (Thermo Fisher Scientific) until growth was visible. Colonies were then transferred to liquid BHI medium and incubated for 4 to 7 days in cell culture flasks at room temperature until growth was visible. S. glossinidius from the working stock was then transferred to 5 ml fresh Luria-Bertani (LB) medium (Sigma-Aldrich) and supplemented with either d-glucose or GlcNAc (Sigma-Aldrich).iLF517 and the S. glossinidius metabolic network were used to design in silico an entirely defined medium, SGM11, in which to grow the bacterium. S. glossinidius from the working stock was transferred to 5 ml of M9 minimal medium . Metabolites were omitted from SGM11 individually and in combination to test the model predictions.l medium  containiS. glossinidius growth was measured at an optical density of 650 nm. Preliminary experiments using variable sampling times indicated that this was the most appropriate sampling time for S. glossinidius to reproducibly capture the final steady state but still retain a proxy for growth rate to guide the modeling results.The culture flasks were incubated for 48 h (LB) or 72 h (SGM11) at 25°C in a temperature-controlled water bath. Gentle agitation was achieved using magnetic stir bars to achieve a suitable balance between oxygenation, settling, and disturbance, and a stirring speed of 500 rpm was used. Intermediate time points were found to compromise the sterility of the cultures, and therefore, destructive sampling was the only reliable method of investigation, negating the possibility of higher-resolution temporal data. Endpoint increase in E. coli gene deletion mutants were obtained from the Keio collection . Error bars show standard errors of the means, and statistical significance was assessed using one-way analysis of variance (ANOVA).All statistical analyses were performed using SciPy in Python (version 2.7.10,


Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI ( Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints into genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid to understanding the interactions between different types of omics data.The recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in high-throughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between “data generation” and “data integration.”. In this study, we present a new method named REMI ( Genome-scale metabolic models (GEMs), which essentially associate an organism’s genotype with its phenotype, integrate genomic information with known information about metabolite levels to comprehensively describe an organism's metabolism [The turnover rates of metabolites through a pathway are called fluxes, and genome-wide intracellular metabolic fluxes are the ultimate regulator of cellular physiology. Perturbations on the normal physiology, such as those that occur in a disease state, directly influence the metabolic fluxes. The well-established experimental approach for determining these metabolic fluxes is tabolism . Due to tabolism .With the growing availability of high-throughput data for different organisms under a wide range of genetic or environmental perturbations, GEMs became popular because of their ability to incorporate omics data as additional regulatory constraints for FBA problems. Because GEMs associate a genotype with a phenotype, it is essential to understand that a single genome can result in thousands of different physiologies through different regulatory mechanisms. Therefore, the integration of static snapshots of the metabolism, obtained from transcriptomic and metabolomic data, provides more biologically relevant constraints for the system and helps to increase the precision of the flux prediction, therefore better deducing the observed physiology. However, despite the high number of methods that have been introduced in recent years for the integration of omics data into constraint-based metabolic models, the enhanced prediction of flux profiles using omics data, particularly in cases using multi-omics data, is still far from being resolved. Recently, these methods, their scopes, and limitations were extensively reviewed , and theThe existing methods for integrating gene-expression data into GEMs can be classified into two categories with the first relying on the integration of absolute gene-expression data into GEMs. This includes techniques such as gene inactivity moderated by metabolism and expression (GIMME)  and the et al. [The increasing availability and quality of metabolomic data have promoted the development of methods that can be integrated into GEMs to refine model reconstruction, to reduce the solution space of feasible fluxes, and to better predict the physiological state of a system. These methods, their scope, and their limitations have been reviewed by Töpfer et al. . One of et al. , 4. Anotet al. . Howeveret al. . Howeveret al. , and it For multi-omic data, methods have recently been introduced for integrating different layers of data, such as genomic, transcriptomic, proteomic, and fluxomic, into metabolic models  or multiRelative Expression and Metabolite Integration (REMI), to integrate relative expression and relative metabolite abundance data into thermodynamically curated GEMs. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We demonstrate that REMI’s ability to integrate different layers of constrictive data significantly reduces the solution space of feasible fluxes. REMI also extensively enumerates alternative optimal and sub-optimal solutions, bringing a robustness and flexibility to the flux distribution analysis. We applied REMI to a GEM of E. coli to estimate the central carbon metabolism flux measurements that were determined by 13C metabolic flux analysis (13C-MFA) and were provided by two independent experimental studies [To address this deficiency, we herein propose a novel method, termed  studies , 21. Alt studies ), we alsWe designed REMI as the first method to integrate relative gene-expression and metabolite abundance data into thermodynamically curated GEMs, reducing the solution space of optimal fluxes to provide results that are better at predicting cell physiologies closer to the experimental observations than can be reached using existing methods. The REMI workflow along with an illustration of the method performed on a toy model is presented in E. coli transcriptomic and metabolomic data obtained from two studies under 8 [E. coli GEM iJO1366 and to estimate the differential steady-state fluxes. We call the data and information from [E. coli GEM iJO1366 (REMI-Gex). The comparison of REMI-Gex and REMI-TGex highlights the significance of the thermodynamic constraints in reducing the solution space of flux analysis. We also performed some studies with only metabolite changes with thermodynamic constraints (REMI-TM) and without thermodynamic constraints (REMI-M).The REMI framework was applied to integrate the  under 8  and 3 [2 under 8  differenion from  “Datasetion from  “Datasetion from , though The underlying assumption of the REMI method is that the perturbation of gene-expression and metabolite levels influences the flux levels in the metabolic network. To this end, REMI maximizes the consistency between relative experimentally observed changes in gene expression and metabolite abundance with the flux levels (the objective function of the REMI constraint-based method). The maximum consistency is then calculated as an integer number, called the maximum consistency score (MCS). This represents the maximum number of constraints that can be incorporated into a FBA model from a given set of constraints (gene-expression or metabolite abundance levels) while ensuring that the model still achieves the required metabolic functionalities and remains feasible. MCS is a unique number, however, in that the complex nature and interconnectivity of metabolic networks can result in several alternative solutions for a given MCS, meaning that numerous combination of different constraints from the input data could result in the same MCS. The theoretical maximum consistency score (TMCS) indicates the number of genes (or metabolites or both) with available experimental data that can potentially be integrated into the model, and MCS indicates the number of these available constraints that could be consistently integrated into the model.et al. [E. coli model. After integrating the gene-expression data of each condition into the model and comparing it with the reference model, we computed TMCSs, MCSs, and the number of alternative solutions for the REMI-Gex method (without thermodynamic constraints) and the REMI-TGex method (with thermodynamic constraints). In contrast to other methods, REMI finds all possible alternative solutions of a given maximum consistency score, which involves all possible combinations of the given set of constraints that always result in a feasible model. These alternative solutions provide flexibility in the biological interpretation of the results as they are equally consistent with the provided experimental data (applied as constraints to the model). Note that in the GEM analysis, the alternate flux solutions are conventionally considered as equivalent phenotypic states [We first applied REMI to the integration of eight datasets from Ishii et al. , which ic states . In thisE. coli model using REMI-TGexM and obtained the MCS for the integrated metabolites as well as the global maximum consistency score (GMCS), which encompasses both genes and metabolites . Different conditions (mutations) alter the cell metabolism differently, leading to different levels of metabolic adaptations and metabolic flux rerouting. Hence, we speculate that the differences in flux rerouting across conditions results in differences in the numbers of alternative solutions across the seven relative conditions. Note that for REMI-TM and consequently for REMI-M, the constraints for all the deregulated metabolites were consistently integrated into the model, so we found only one solution for the REMI-TM models without any alternative solution.We further integrated the available metabolomics measurements into the abolites . Althougabolites . As Tablabolites , which svs Ref, gapC vs Ref, zwf vs Ref, wt5 vs Ref, and wt7 vs Ref comparisons. We observed that the number of alternative solutions for these five cases was identical between REMI-TGexM and REMI-TGex. This implies that the relative expression constraints and the relative metabolite constraints were not contradictory for these five cases. However, in rpe vs Ref and pgi vs Ref, all the metabolic data were integrated in the model, but the number of alternative solutions differed (and in the case of rpe vs Ref was noticeably reduced) between REMI-TGexM and REMI-TGex. To see if this indicated a contradiction, further investigation into the alternative solutions revealed that in the rpe vs Ref and wt5 vs Ref comparisons, REMI-TGexM and REMI-TGex have the same set of constraints, which means that the constraints from metabolomics and expression data were not contradictory. However, we found that the metabolomics integration resulted in a reduction in the number of alternative solutions  analysis.The results of this analysis indicate the core constraints that consistently operate in all the alternative solutions . Meaning that such core constraints certainly perturb fluxes within each pair of conditions. Therefore, these constraints could potentially be the indicators of the regulators of the condition-specific metabolism, which assist biologist in determining which metabolic subsystems to deregulate or to mutate. We believe that the capability to analyze and identify these regulators is a key advantage of REMI.E. coli because they were found to be deregulated in all mutant conditions.As shown in the To study the effect of thermodynamics on the model, we compared the reduction in solution space for the predicted flux profiles from the REMI-TGex and REMI-Gex methods when coupled with the gene-expression data . The MCSvs Ref as a case study, where we obtained MCS = 56 in REMI-Gex and MCS = 49 in REMI-TGex  that do not necessarily involve the same set of deregulated genes.To further benchmark REMI with the available experimental data, we used a second data set (2 overexpression compared to the ref condition) from an independent study where the role of metabolic cofactors, such as NADH and ATP in different aspect of metabolism is studied by overexpressing NADH oxidase (NOX) and the soluble F1-ATPase in E. coli . REMI in [et al.  into thevs. Ref case and the lowest BDR reduction for the NOX vs. Ref case, which therefore indicates more reduction in the feasible flux solution space in the rpe vs. Ref case compared to the NOX vs. Ref case. For the all comparisons, we found a further reduction in BDRs upon the integration of relative metabolomic data into the REMI-TGex model. In most of the cases, we found a similar decrease in BDRs, which means that the metabolomic data further constrained the solution space. Except for the wt7 vs Ref case, we observed a decrease in BDRs for all cases that were constrained by metabolites and expression data together (GexM) as compared to only expression (Gex) data. Unexpectedly and unlike all the other cases, by incorporating metabolomics data for the wt7 vs Ref case, we found an increase of one reaction in the BDRs. This suggests that for the wt7 vs Ref case the integration of gene expression and metabolites reroutes fluxes through the metabolic networks differently compared to other cases. As expected, we consistently find a reduction in BDRs for the REMI-TM model (thermodynamics and relative metabolomics) compared to without thermodynamics (the REMI-M model). This is in agreement with the fact that integrating thermodynamic constraints into a model eliminates infeasible reaction directionalities and consequently the flux feasible optimal space.To investigate the influence of thermodynamic constraints on flux ranges, we identified the overlapping constraints (HFCs) across all the alternative solutions and then enforced them to be active to build the most consistent model. An active HFC satisfies differential gene expression (or metabolite levels) between two conditions form a given experimental data. Thus, for each condition, we built the most consistent model despite having many alternatives. We next performed FVA on the REMI-Gex and REMI-TGex models. As REMI is based on pair-wise relative constraints (for two conditions) and builds two models that are then compared, as opposed to modifying one solution based on a given condition, we obtained two FVA solutions, i.e. one for each condition. We identified less bidirectional reactions (BDRs) in the REMI-TGex case compared to the REMI-Gex case , which mvs. Ref case as an example to demonstrate the average relative flexibility (ARF) reduction at a global  level as well as at the subsystem level.To further illustrate the positive influence of thermodynamic constraints in reducing the optimal solution space, we performed a relative flexibility  analysis using the REMI-TGex and REMI-Gex methods. To perform a relative flexibility analysis, a reference model is compared to a target model to investigate the relative flux reduction. For a reference, we used the iJ01366 model without integrating any data, meaning that the reference model implies only mass balance constraints. We took the pgi vs. Ref case, we found a 10%, 20%, 50%, 77%, and 80% reduction in the global ARF in REMI-M, REMI-Gex, REMI-TGex, REMI-GexM, and REMI-TGexM models compared to the reference model, respectively  the Pearson correlation between the predicted and measured intracellular fluxes, and 2) the average percentage error  showed a considerably improved flux prediction for the REMI-Gex, REMI-TGex, REMI-TGexM, and REMI-GexM models as compared to the GX-FBA method, indicated by Pearson correlation and average percentage error (vs. Ref and gapC vs. Ref cases (vs. Ref and ATPase vs. Ref), REMI-TGex predicted better correlation than REMI-Gex and GX-FBA, and the average percentage error of GX-FBA was higher than that of REMI-TGex and REMI-Gex  [et al. (3 datasets) [Eleven total sets of experimental data that had been previously integrated into the genome-scale model (GEM) of m et al.  and wereatasets)  and Holmatasets)  were useet al. [E. coli, cells overexpressing NADH oxidase (NOX), and cells overexpressing the soluble F1-ATPase (ATPase). The eight datasets from Ishii et al. [E. coli cells cultured at different growth rates of 0.2, 0.6, and 0.7 per hour along with single-gene knockout mutants of the glycolysis and pentose phosphate pathway .The three datasets from Holm et al.  comprisei et al.  include E. coli [https://github.com/EP-LCSB/remi. Mixed-integer linear programming (MILP) problems were solved using the CPLEX solver on an Intel 12-core desktop computer running Mac.All analyses were performed using IJO1366, the latest GEM of E. coli . The modIt has been previously shown that thermodynamic constraints not only effectively reduce the solution space of FBA by eliminating the thermodynamically infeasible fluxes from the solution space, but also allow the integration of metabolite concentrations. This provides important links between mass and energy balance and the phenotypic characteristics of the organism. The thermodynamic constraints, as depicted in Eq , were inn method .xj) of each metabolite j given the pH, the metabolite concentration range, and the ionic strength at the cellular compartment where the reaction occurs.For each reaction of a GEM, the Gibbs free energy of the reaction (E. coli GEM to translate the relative gene-expression levels (being relatively up- or downregulated) to the differential and relative flux values of corresponding reactions. GPRs are not mapped as one gene to one reaction, meaning there are many cases in which one gene is mapped to several reactions and multiple genes are mapped to a single reaction, which are depicted with “and” and “or” affiliations, respectively.We used the gene-protein-reaction (GPR) association rules acquired from the To capture this, we followed the same procedure for the mapping between GPRs and reactions fluxes introduced by Fang et al . In thatIn REMI, if the reaction R is associated with two genes (g1 “and” g2), the expression level ratios for genes g1 and g2 in the two corresponding conditions are calculated to obtain the geometric mean of the g1 and g2 ratios. Whereas, if the reaction R is associated with two genes (g1 “or” g2), the arithmetic mean of the obtained expression data ratios is calculated. Thus, from GPR associations, REMI computes the so-called tentative “reaction flux ratios” to further constrain the model. For the metabolomic data, the ratio of metabolite concentration for each metabolite (if available) is calculated for any two given conditions.To evaluate whether a reaction or metabolite was up- or downregulated, we sorted the ratios , and selected the top 5% as upregulated and the bottom 5% as downregulated. The fold changes greater than two are considered as significant in many studies. For comparison purposes, we used the two-fold change as the cut-off threshold to identify the significant gene expression and metabolite changes. We found that across all mutants, the set of significant changes identified with our threshold of the top and bottom 5% encloses the corresponding set identified with the two-fold change, meaning that the proposed cutoff criterion is conservative. However, this threshold is a user-defined parameter and one could use different threshold cutoff.R reactions and M metabolites, bidirectional reactions are decomposed into forward and backward reactions to allow all fluxes to have positive values. Assuming that S is a stoichiometry matrix, Smr is the stoichiometric coefficient associated with the metabolite m  in reaction r . Positive and negative stoichiometric coefficients of metabolites signify the substrate or products of a reaction. A binary variable zr was assigned to each reaction r to ensure a positive flux vr  throughusing Eq  to ensurIn REMI, two models are described for each given condition. For both models, we constrained the cellular growth rate to be at least 0.1 mmol g-1 DW-1 h-1, to ensure that the model is able to synthesize all the biomass building blocks required for the cellular growth. Throughout the manuscript, the terms “wildtype” and “mutant” are used to better differentiate between the two conditions (or models) when describing the REMI framework. REMI can, however, be used for any two given conditions and is not restricted to the wildtype and mutant labels. Eq  specifieu and d, respectively, where n is the total number of up- and downregulated reactions. For the upregulated reactions, a higher flux was enforced in the mutants as compared to the wildtype, while for downregulated reactions, a higher flux was enforced for the wildtype as compared to the mutant.The relative information about the gene-expression levels or metabolite levels between the two given experimental conditions was formulated as additional constraints and integrated into the two representative models of the conditions. To do this, binary variables for the up- and downregulated reactions were assigned as u upregulated and d downregulated reactions, a total of n binary variables were generated , where Bi = 1 indicates the up- or downregulation of a reaction. Next, n constraints  than a wildtype flux, where p is a reaction ratio between the wildtype and mutant (computed from gene-expression ratio). Constraints were added  w wu upregints (Eq ) were addded (Eq ) for d dx. In Eq , n constd in Eqs  and 9),,u upreguIn GEMs, gene-level perturbations can mediate both reactions and their subsequent metabolites. Available studies show a correlation between gene changes and metabolite changes and infer that perturbations at the metabolite level are formed from perturbations in genes or reaction levels , 28. Thuϕp in condition 2 is greater than the ϕp in condition 1 or the sum of consumption ϕc in condition 2 is less than the ϕc in condition 1  of up- and downregulated metabolites, where u’ and d’ are up- and downregulated metabolites, respectively. For an up-regulated metabolite i (i.e. in the mutant vs. wildtype), either more production or less consumption is enforced in the mutant compared to the wildtype using Eqs (Bi) is introduced, which switches to production if Bi = 1 and to consumption if Bi = 0. Similarly, for downregulated metabolites i, less production or more consumption is enforced in the mutant compared to the wildtype n’) of upsing Eqs , a binarype  was forAn aforementioned mathematical optimization model (objective function 15) subject to Eqs 5 subjectThe left-hand side of Eq  determinn and n’ represent a total number of up- and downregulated metabolites and up- and downregulated genes, respectively.To concurrently integrate both the relative gene-expression data and the relative metabolite levels, an integrated mathematical optimization model was built with a global objective function ). T). Tin sivi and vm are the in silico and measured vectors of the fluxes, respectively. The correlation coefficients +1 and -1 indicate a strong positive and negative linear relationship between vi and vm, and the 0 correlation coefficient indicates no linear relationship between vi and vm.In Eq , vi and r is used to measure the relative deviation between the two fluxes in two conditions, where x and y correspond to the flux of a given reaction in condition 1 and condition 2, respectively. Since |dr| lies between 0 and 1, one can consider dr as a percentage flux change from condition 1 to condition 2. The average (per reaction) percentage error, e, in the predicted in silico fluxes was calculated using Eq  for reaction i is calculated using the following equation:i at each of the two conditions, one condition is usually designated as a reference condition or reference state, such as when comparing the relative flexibility of a metabolic system with (condition 1) and without (condition2) thermodynamic constraints. The value of RF that is computed for each reaction i reflects the relative changes in the flux variability range of one condition compared to the other condition. The global relative flexibility change between two given condition is then computed by averaging the Fi values for each reaction i that carry flux in the reference state.The We developed the computational tool, REMI, which combines gene-expression, metabolomics, and thermodynamics constraints with the mass balance constraints imposed in metabolic models to predict phenotypic changes in an organism upon environmental or genetic perturbations. As the integration of these three additional physiological constraint results in a highly reduced flexibility of the predicted optimal flux profiles, REMI enhances the quality of the computationally predicted fluxes. REMI’s novel formulation permits the extensive enumeration of alternative solutions because there exist several alternative sets of pathway that result in the same phenotype due to the complexity and interconnectivity of metabolic networks, meaning that the results provided by REMI more accurately reflect natural biological states than previously existing methods. Within several examples, we showed the effectiveness of incorporating thermodynamic data with gene-expression and metabolomics in reducing the flexibility of predicted optimal flux profiles. This means that we can obtain manageable set of physiological consistent hypothesis and physiological interpretations which have a higher confidence as they are consistent with a larger set of data. Applying REMI to experimental data has shown that there is not always a full consistency between gene-expression and metabolomic data, which shows that there is still much to learn about how gene expression and metabolism are linked.E.coli or the Warburg effect in the cancer cells. Furthermore, REMI can also be used to investigate metabolism of diseased states compared to the healthy one, where numerous sets of omics data are available.The application of REMI goes beyond the study of physiology of a mutant versus a wild-type cell presented in this work. With a slight modification in the formulation, REMI can be employed for investigating the physiology of several mutants simultaneously against the wild type physiology within a single optimization. Although in this study we showcased REMI for constraining internal fluxes, REMI can also be applied to study the perturbation of external fluxes and metabolites whenever omics data are available. This has a potential application in studying the overflow metabolism, e.g., the acetate overflow in fast-growing Various REMI methods introduced in this work permit a wide range of applications depending on the type of available data . However, whenever gene-expression, metabolite abundance, and thermodynamic data are available, our results suggest that the most extensive data integration method, REMI-TGexM, provides the best results and the most reduced optimal solution space. As systematic multi-omics integration remains a challenge, REMI opens the possibility of not only multi-omics integration, but also the identification of the crosstalk between the various omics present in a system.S1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 FigThe bars represent the flux variability range, with a longer bar indicating greater flexibility of feasible flux ranges. Flux ranges are calculated based on the flux variability analysis of the REMI-TGex and REMI-Gex models at different consistency scores. For more clarification, two reactions out of the 45 are shown as examples, where it can easily be seen that there is a much greater variability in the Gex-10 and Gex-49 models by the length of the bar. This indicates that these two models are more flexible compared to the TGex-49 model. This means an equal consistency score (Gex-49) or a lower consistency score (Gex-10) in the absence of thermodynamic constraints (instead of Gex-56) provides a greater flexibility compared to TGex-49.(EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 Fig(A) The comparison between the mutant and wildtype for the pgm, pgi, gapC, zwf, and rpe mutants. (B) The comparison of NOX overexpression and ATPase overexpression against the wildtype.(EPS)Click here for additional data file.S4 FigComparison of the experimental values and predicted fluxes for (A) pgm and (B) zwf cases.(EPS)Click here for additional data file.