Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the  The Arabidopsis thaliana REIL proteins are involved in the late cytosolic steps of 60S ribosome subunit maturation and are required for growth under low temperature2. The reil1-1 reil2-1 double mutant is deficient for both REIL paralogs and, unlike Arabidopsis Col-0 wild type, does not resume growth after cold shift, even to moderate 10 Ribosomes are the workplaces of protein biosynthesis, and defects in the pathway of ribosome biogenesis have an effect on many cellular processes, like metabolism, which critically depend on enzymatic proteins. While metabolism is known to affect ribosome function via the target of rapamycin (TOR) signalling pathway, little is known about how defects in ribosome biogenesis feed back on metabolism3. They also depend on the enzymatic setup of a cell, which is in turn governed by gene regulatory and signalling networks that affect protein activity4. However, determination of metabolic fluxes is a tedious and labour-intensive task7. A targeted analysis that predicts relevant fluxes for hypothesis generation based on integration of available high-throughput data sets from systems biology studies may streamline the planning of such time-consuming experimental flux studies.One possibility to investigate the feedback of ribosome biogenesis defects on metabolism is the characterization of reaction fluxes. Metabolic fluxes depend, in part, on the metabolite pools8. In general, metabolic fluxes of a system are predicted under the assumption that this system operates in steady-state and optimizes an objective (e.g. biomass yield). If feasible, the resulting mathematical approach often results in a non-unique flux distribution. To this end, constraints defined through integration of high-throughput data can reduce the solution space of feasible flux distributions11. Such approaches have been shown to result in a more accurate prediction which is closer to the actual physiological state12. Despite the availability of methods that integrate high-throughput data, their full potential has yet to be realized13.In this regard, constraint-based approaches have proved as a valuable tool for hypotheses generation regarding flux distributions and their differential behaviour. For instance, the simplest of these approaches, flux balance analysis (FBA), can predict steady-state fluxes in bacteria at exponential growth14 as well as approaches that use time-series data . iReMet-flux18 is the only constraint-based approach to date that can integrate relative metabolite levels to investigate differential flux behaviour between two scenarios. It relies on a mass-action-like description of reaction rates (i.e. flux). In contrast to uFBA, iReMet-Flux does not require data on absolute quantification of metabolite levels and therefore allows for a broader application due to the availability of relative metabolomics data. In contrast to TREM-Flux, it does not assume a linear scaling with the change of metabolite levels between two time points. In addition, iReMet-Flux differs from a recent approach in which the relative metabolomics data are integrated on a qualitative level (i.e. increases or decreases)14. Similar to the objective on which MOMA is based19, iReMet-flux minimizes the flux differences between two scenarios, but does not rely on pre-calculated flux distributions for a reference scenario. Additionally, iReMet-flux allows for the integration of relative enzyme levels either by direct usage of quantitative or qualitative proteomics data, or via gene expression ratio that can serve as a proxy21. However, if employed to time-series data, it does not account for the magnitude of possible flux changes between time steps. To address this problem, we extended iReMet-flux to account for temporal changes, while keeping the possibility of multi-level high-throughput data integration.Of particular interest are approaches which allow integration of relative metabolite levels, since these datasets are easier to obtain in contrast to absolute metabolite concentrations used in thermodynamic flux balance analysisA. thaliana Col-0 wild type and reil1-1 reil2-1 double mutant plants before and after cold shift. Finally, we provided directly testable hypotheses about the impact of REIL-mediated deficiency in ribosome biogenesis on metabolism.Here, we aimed to develop a novel constraint-based approach, termed TC-iReMet2, that facilitates the integration of relative metabolite and transcript levels while accounting for temporal change of physiological parameters. We used TC-iReMet2 to investigate differential flux behaviour of A. thaliana. Unlike genome-scale metabolic networks22, we refer to large-scale models as those reconstructed following a bottom-up approach23.We propose Time Course Integration of Relative Metabolite and Transcript levels (TC-iReMet2) that estimates fluxes based on the integration of time-course data on relative metabolite and transcript levels. The key feature of TC-iReMet2 is that it accounts for the possible magnitude of flux changes between time points and thus could provide a more accurate explanation of flux rerouting over time. We show that TC-iReMet2 can be applied to study flux redistributions in pathways in a large-scale metabolic network of S of the considered metabolic model. The rows of the stoichiometric matrix correspond to metabolites, and columns stand for reactions. The integer entries denote the molarity of a product (positive entry) or a substrate (negative entry) in a reaction, ensuring mass and charge conservation. In the following, we assume that the investigated metabolic network contains P reactions and n metabolites, and that its functioning is compared between two experimental scenarios, denoted by A and B (e.g. mutant and wild type) over to time points t. Furthermore, we denote by Similar to other constraint-based approaches, TC-iReMet2 uses a stoichiometric matrix i, j, j enters a reaction i as a substrate, A: B: Under mass action kinetics, a flux through an irreversible reaction j as i as i as To simplify the notation, we will refer to the ratio of metabolite levels of 25. For metabolite ratios, only a small portion of the metabolome, and hence metabolite ratios, can be quantified. To account for the case that a metabolite ratio cannot be measured, general upper and lower boundaries for metabolite ratios are introduced. If the ratio of metabolite j is experimentally quantified, it is indicated by Determining the entirety of metabolite and enzyme concentrations is not possible with the existing technologiesi is defined, it is indicated by In the absence of enzyme ratios, we use the Gene Protein Reaction (GPR) rules of metabolic models to approximate enzyme ratios using transcriptomic data. The GPR roles are defined by a set of Boolean expressions that describe which genes encode an enzyme. For example, gene products encoding for isoenzymes or isoforms are linked by an OR operator. Conversely, protein subunits that must be present simultaneously to form an active enzyme are linked by an AND operator. In case of an enzyme encoded by one gene, the enzyme concentration is approximated by its expression value. For each reaction that is catalyzed by a complex requiring multiple genes, the enzyme concentration is set to the minimum expression value of gene products connected by the AND operator. For the OR operator, the sum of expression values for the respective genes is used. These rules were applied to each reaction in both scenarios, fractioned and assigned as the corresponding enzyme ratio. Therefore, an enzyme ratio is represented by a ratio of gene expression levels following the GPR rules. Equivalently to metabolite ratios, if a GPR rule for reaction A. thaliana. This has been verified by performing flux variability analysis at a fixed flux through the biomass reaction, to specify that 80% of reactions operate in only one direction18. A ratio constraint for reaction i is included if not only the enzyme ratio, but also at least one of the substrate ratios corresponding to that reaction is available. For metabolites or enzymes whose ratios could not be determined we use the extremal values found at that specific time point. Let F(i) denote the set of substrates of reaction i. Additionally, let the set of irreversible reactions with at least one experimentally quantified metabolite ratio and approximated enzyme ratio be denoted by j and transcript ratio of i are indicated by In this setup, we only consider constraints for irreversible reactions, since more than 80% of reactions that are assumed to follow mass–action-like kinetics  are irreversible in the analyzed model of opt, is calculated for both scenarios via FBA. A biomass fraction B to its respective value derived from FBA. In contrast, biomass flux in scenario A is fixed to a fraction Similarly, a ratio constraint for the biomass reaction can be formulated. To this end, a time-point specific biomass fraction, denoted by A and B respectively. (ii) the biological system aims to maintain an optimal state given by the enzymatic setup. This assumption is captured by making sure that the flux distributions between the two scenarios at a given time point t. We model this assumption by accounting for the magnitude of possible physiological changes by assuring that the difference of flux distributions between time points is as small as possible, i.e. A, weighted by B, weighted by Furthermore, we assume that: (i) the metabolic network to operate in quasi-steady state at every time point. Hence, In summary, the TC-iReMet2 approach is cast as a quadratic program (QP) as follows:A. thaliana REIL deficiency. To this end, we compared predicted flux differences between Col-0 wild type and reil1-1 reil2-1 double mutant with deficiency in cytosolic 60S ribosome biogenesis. The REIL proteins are required for growth when plants are shifted to cold . Along with the morphometric data, the relative changes of metabolite pools and transcripts were profiled30  and non-growth-associated maintenance functions (NGAM22).The experimental setup and the availability of transcriptomics data and data on relative metabolite levels allowed the application of TC-iReMet2The overall flux distance of wild type compared to mutant across all predicted reactions differed before cold shift, with the wild type having a higher overall flux Fig. A. This pWe next considered the flux differences for each reaction in the metabolic model. Additionally, we investigated reactions displaying large changes in flux differences at early time points, as the most interesting to understand the changes in the metabolic network functionality in response to the cold shift.We focussed on differential behaviour of all reactions between mutant and wild type, excluding transport reactions and artificial reactions to avoid bias due to lack of gene association for these reactions. To this end, we applied K-means clustering to group reactions . In contrast, cluster 2, consisting of 46 reactions, exhibited mainly decrease of relative flux changes (DDD). This inverse behaviour is best captured by the function of RuBisCO as it exhibits strong flux changes for its carboxylation function (cluster 5) and oxygenation function (cluster 2). Reactions in cluster 3 mainly exhibited no changes (NNN). Similarly, cluster 6 summarizes reactions that exhibit constant positive flux change over all time points. The remaining clusters 1, 4 and 7 group reactions that exhibit an inverse shift in behaviour at day 21.If we consider the top 10 reactions  did not find a solution after 1000 trials. Therefore, this analysis indicates that the findings are specific, in the sense that alternative optima are unlikely, and significant as there are no other possible flux distributions in optimal solution.14. With the advances in the proteomics profiling, TC-iReMet2 has the potential to provide further applications closer to the assumptions of the approach.Here, we proposed a computational approach, termed TC-iReMet2, and showed that it provides the means for time-resolved predictions of fluxes while keeping the simplicity of the constraint-based modelling framework and allowing for the integration of relative metabolomic, transcriptomic, and morphometric data. The findings of this study indicate that TC-iReMet2, a differential flux profiling method, can be used to identify differential fluxes between wild type and mutants over time. It is important to note that TC-iReMet2 uses the ratio of transcripts as a proxy for the ratio of enzyme abundance (following GPR rules). This is a strong assumption, knowing that post-translational modifications and translational efficiency have a large effect on both the abundances and ratios of proteins. However, since transcript ratios are used as one component of the constraints, such an approach provides a better coverage of metabolic networks than proteomics data33. One possible approach to overcome this issues is to use a power-law formalism34, which would allow the constraints to remain linear, at the cost of making assumptions about which regulators affect a reaction rate and with what strength. For this reasons we have decided that TC-iReMet2 is formulated based on mass-action-like kinetic, while allowing for discrepancy to model possible effects due to the mentioned saturation and regulation.Moreover, the enzyme kinetic assumed in TC-iReMet2 does not consider saturation effects no presence of regulators (e.g. activators or inhibitors) of enzyme activity. Inclusion of a saturation effect, like in Michaelis-Menten kinetic, would not allow casting the problem with only linear constraints, rendering application to large-scale networks computationally challenging. Similar problem arises when considering the inclusion of regulation, which is additionally problematic due to the lack of information on how the effect of the regulator is captured in the enzyme kinetic form used, particularly for plantsreil1-1 reil2-1 double mutant to A. thaliana Col-0 wild type before and after cold shift strongly supported for the previously suggested theory stating that REIL mediated ribosome biogenesis deficiency feeds back into metabolism. Overall, we find that flux differences are more similar during hibernation phase, with strong flux redistributions occurring at day 21. This is evident from the data from the morphometric analysis  as a cofactor. These changes may indicate either an altered redox state of these cofactors or more likely differential use of NAD and NADP after cold shift in the mutant. For example, when considering the predicted inverse flux changes of the mitochondrial iCit dehydrogenase isozyme reactions, iCitDHNAD_m and iCitDHNADP_m, we can deduce in agreement with our metabolic model that the mutant switches to preferential use of NAD rather than NADP for this reaction. Inversely, a preferential use of NADP is predicted for 6-phosphogluconic dehydrogenase reactions, 6PGDHNADP_h and 6PGDHNAD_h, and for the H-serine dehydrogenase isozyme-reactions, HserDHNADP_h and HserDHNAD_h. Taken together with the additional indicated flux changes of glutamate synthetase (GluSNAD_h), and of the mitochondrial malate dehydrogenase , or glutamate dehydrogenases, GluDH1NADP_m and GluDH2NAD_m, we hypothesize that the Generation of this hypothesis would not have been possible by stand-alone analysis of the transcriptome data alone. When we compare the results of TC-iReMet2 with a differential analysis of the transcriptomics data reaction per reaction following GPR rules, only the increase of isocitrate dehydrogenase (iCitDHNAD_m) flux in the mutant can be found overlapping with TC-iReMet2’s predictions .z denotes the value of TC-iReMet2’s objective function and A linear program with a single quadratic constraint is defined to find possible alternative solutions. For this, the solution space has to be defined. In addition to the defining constraints given by TC-iReMet2, a single quadratic constraint, due to quadratic objective function TC-iReMet2 is based on, has to be introduced. It fixes the value of the objective function to be the same as in optimal solution, forcing the optimization to find alternative optima. Here, 2. Select a random direction 3. Find the extreme point in solution space described by 4. If there is a solution for the optimization program at step 3, a new point at the edge of the solution space can be determined to form a line segment with the initial point.5. Randomly choose a point on this line segment to create a sample, which is in turn updated as a new initial point 6. Repeat steps 1. to 5. until the defined number of samples is collected.The multiplication of relative metabolite levels, substitution of unmeasured metabolite ratios with their respective minimum and maximum ratio value together with approximated enzyme ratios can lead to immense ratio constraints, which in turn could lead to numerical instabilities. Determining a maximum considered ratio constraint is therefore crucial to ensure numerical stability. To this end, we calculated the flux distributions allowing for a maximum ratio constraint ranging from 10We used R statistical programming languages implementation of the K-means algorithm with seven assumed clusters . The morphometric parameters apparent planar leaf area and perimeter underestimate biomass accumulation, since rosette leaves could slightly overlap. We contrasted these estimates by using the sum of diameter 1 and 2 as proxies of biomass accumulation. The diameter may slightly overestimate biomass because only the longest leaves are considered. Accordingly, we integrated the four biomass proxy parameters by giving equal weight to each one of them (Supplementary Table To model the fraction of biomass yield in scenario mentclass2pt{minimhttps://www.ncbi.nlm.nih.gov/geo/) and are available through accession number GSE101111. The metabolomics data are obtained from the Supplemental Table 30. The morphometric data are obtained from Schmidt et al., 20132. All data are included in the provided GitHub repository (https://github.com/tciremet2/TC-iReMet2) as well as in the Supplementary Tables to ensure easy access and reuse of the provided implementation.The transcriptomics data are already published and are uploaded to the Gene Expression Omnibus (36 in conjunction with the Tomlab optimization environment37. Statistical analysis and creation of figures was done with R38 programming language and R’s ggplot2 library39 and “MATLAB 2017b, The MathWorks”36. The implementation is available at https://github.com/tciremet2/TC-iReMet2.For implementation of TC-iReMet2 we used “MATLAB 2017b, The MathWorks”Supplementary Information 1.Supplementary Information 2.


The characterization of plant enzymes by expression in prokaryotic and eukaryotic (yeast and plants) heterologous hosts has widely been used in recent decades to elucidate metabolic pathways in plant secondary metabolism. Yeast and plant systems provide the cellular environment of a eukaryotic cell and the subcellular compartmentalization necessary to facilitate enzyme function. The expression of candidate genes in these cell systems and the identification of the resulting products guide the way for the identification of enzymes with new functions. However, in many cases, the detected compounds are not the direct enzyme products but are caused by unspecific subsequent reactions. Even if the mechanisms for these unspecific reactions are in many cases widely reported, there is a lack of overview of potential reactions that may occur to provide a guideline for researchers working on the characterization of new enzymes. Here, an across-the-board summary of rearrangement reactions of sesquiterpenes in metabolic pathway engineering is presented. The different kinds of unspecific reactions as well as their chemical and cellular background are explained and strategies how to spot and how to avoid these unspecific reactions are given. Also, a systematic approach of classification of unspecific reactions is introduced. It is hoped that this mini-review will stimulate a discussion on how to systematically classify unspecific reactions in metabolic engineering and to expand this approach to other classes of plant secondary metabolites. Escherichia coli  to [M(ST) + 324] (the mass of two glucose units minus the mass of two water units) .10) and a decrease of the rearrangement products costic acids and ilicic acid (11–14) by comparing extracts from buffered versus unbuffered yeast cultures on a GC-MS and a LC-MS system  to [M(ST) + 18]. This was observed for the rearrangement from germacrene A acid  to β-elemene (16). However, germacrene A (15) elutes later (at higher temperatures) from the GC-MS column than its heat-induced rearrangement product β-elemene (16) [Similar to acid-induced rearrangements, heat-induced rearrangement does not change the total mass and only slightly changes the MS-MS pattern for the conversion from germacrene A (ene (16) .All the above-mentioned unspecific reactions could theoretically occur in the natural plant except for the heat-induced Cope rearrangement. Why are these reactions rarely observed in nature, where plants can accumulate highly reactive enzyme products in high concentrations in a way that provides protection without poisoning the host plant cell? In the case of STL, several biosynthetic enzymes that have so far been characterized are naturally located in secretory cells of glandular trichomes ,11,13 orFor researchers engaging in the reconstruction of metabolic pathways, it is crucial to have a perception about the unspecific reactions the presumed enzyme product may undergo. Here, a systematic approach to categorize and explain the most frequent unspecific reactions for sesquiterpenes is presented. Expanding this approach to other classes of natural compounds such as mono-, di- and triterpenes as well as polyphenols and alkaloids could create a store of knowledge to better plan and interpret the reconstruction of the metabolic pathways of plant specialized metabolites.


Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis. Candida species are by far the most common cause of invasive fungemia in humans [Candida species, Candida albicans is the main etiological agent of invasive candidiasis [In the last few decades, a significant increase in nosocomial fungal infections has been observed, and n humans ,2. Amongdidiasis ,4, beingdidiasis ,6, its adidiasis ,8,9 makeCandida infections , and only some azoles and echinocandins are recommended as first-line agents [C. albicans, but even more so for other emerging non-albicans Candida species, such as C. glabrata, C. krusei and C. auris. For example, in recent studies, almost 40% of the Candida glabrata isolates shown to be resistant to at least one echinocandin were also resistant to fluconazole [albicans pathogenic Candida species, the scenario is even more frightening, as several of them display, either intrinsic or easily acquired resistance to several of the available antifungal agents. For example, in a recent case, Candida auris isolates were identified as resistant to the three classes of available antifungal drugs, further raising public concern on the future efficacy of current antifungal therapeutic options [Only three classes of antifungal drugs are licensed to treat e agents . Currentconazole ,12. In n options . The idePlasmodium falciparum, enabling the identification of potential drug targets that could be used as both, anti-malarial drugs and transmission-blocking agents [Genome-scale metabolic models have the potential to provide a holistic view of cell metabolism. Historically, these global mathematical descriptions of cell metabolism have mostly been linked to metabolic engineering of microbial cell factories, given their potential to simulate global metabolic behavior and provide hints to guide experimental optimization of such organisms for the production of added-value compounds . Howeverg agents .C. albicans, the iRV781. This model is provided in the well-established SBML format and can easily be read in most metabolic engineering platforms such as OptFlux [Here, we present the first validated in silico genome-scale metabolic reconstruction of  OptFlux  and COBR OptFlux . The modCandida albicans iRV781 genome-scale metabolic model was developed following the methodology represented in merlin 4.0.2 [Merlin is a platform that enables the semi-automatic reconstruction of metabolic models, providing a user-friendly interface that assists the user in the manual curation process [The in 4.0.2  for the in 4.0.2 , and Optin 4.0.2 , for the process .Candida albicans SC5314 was obtained from NCBI’s Assembly database, accession number ASM18296v3 (www.ncbi.nlm.nih.gov/assembly) [www.ncbi.nlm.nih.gov/taxonomy) [merlin to univocally identify the organism under study throughout the reconstruction process. In order to establish a proximity between species, the 16S rRNA gene of several known closely related species was used to construct a Phylogenetic tree, the sequences being retrieved from NCBI’s database and aligned using MEGA X 10.0.5  [merlin based on taxonomy and frequency of similar sequences trough remote Basic Local Alignment Search Tool (BLAST) [http://www.UniProt.org/) and HMMER [merlin, using genome annotation to determine which reactions will be included in the model, based on an algorithm described in detail elsewhere [The genome sequence of the reference strain ssembly)  and the axonomy) , which iPA, USA) . The evoPA, USA)     [In order to ensure that all reactions in the network are balanced, unbalanced reactions were identified, using the corresponding (BRENDA)  as refer(BRENDA) . Since t(BRENDA) , UniProt(BRENDA)  and KEGG(BRENDA) ) and litThis model includes four compartments: Extracellular, cytoplasm, mitochondrion and cytoplasmic membrane. The prediction of compartments for each enzyme and carrier was performed using the WoLF PSORT protein subcellular localization predictor .merlin’s TranSyT [2O, CO2, and NH3, which are often carried by facilitated diffusion, were added to the model with no gene association.Given the existence of compartments in the model, it is necessary to create transport reactions for the metabolites. Transport reactions were generated using genomic information together with the public database TCDB  by merli TranSyT . TranspoThe biomass formation was represented by an equation that includes proteins, DNA, RNA, lipids, carbohydrates, and cofactors, and detailed information for the composition of each one of these macromolecules. The content of each component was determined based on the literature or using experimental data. All the calculations were performed as described previously S. cerevisiae iMM904 metabolic model was applied, 1.5.This ratio represents the relationship between ATP synthesis and oxygen consumption, indicating the number of orthophosphate molecules used for ATP synthesis per atom of oxygen consumed during oxidative phosphorylation [merlin, and were updated to replicate the same ratio as in the iMM904 model:For the phosphorus to oxygen ratio the same theoretical ratio used in the rylation . Three gReaction R00081_C4:Reaction T02161_C4:Reaction T00485_C4:The final balance reaction:S. cerevisiae, adjusted for the composition in macromolecules of the biomass equation [This model also includes ATP requirements for biomass formation and maintenance (non-growth). The growth ATP requirements, 23.346 mmoles ATP/g Dry Cell Weight (DCW), were introduced directly into the biomass equation; this value was calculated based on ATP requirements for biosynthesis of cell polymers for equation .Candida tropicalis [Non-growth associated ATP maintenance, the amount of ATP required by the cell even when it is not growing, was represented in the model by an equation that forces ATP consumption via a specific flux. The boundaries of this flux were inferred from opicalis . See FilThroughout the curation process, reactions were edited, manually added to, or removed from the model to correct some gaps in the network, using KEGG pathways, MetaCyc Database, and literature data as standards.Candida albicans reference strain SC5314 was batch-cultured at 37 °C, with orbital agitation (250 rpm) in Yeast Nitrogen Base (YNB) medium without amino acids: 5 g/L glucose (Merck), 6.8 g/L YNB (Difco). Solid media contained, besides the above-indicated ingredients, 20 g/L agar (Iberagar).C. albicans. For the few carbon or nitrogen sources for which the model predictions were not consistent with literature data, wet-lab experiments were conducted. Specifically, the utilization of cellobiose, D-Ribose, and mannitol as carbon source, by the C. albicans reference strain SC5314, was evaluated in solid YNB medium containing either 5 g/L glucose as control, or 5 g/L of either one of the mentioned carbon sources. C. albicans cell suspensions used to inoculate the agar plates, were mid-exponential cells grown in YNB medium with 5 g/L glucose, until culture OD600nm = 0.5 ± 0.05 was reached and then diluted in sterile water to obtain suspensions with OD600nm = 0.05 ± 0.005. These cell suspensions and subsequent dilutions  were applied as 4 µL spots onto the surface of solid YNB media, with the indicated carbon sources. Growth was assessed after incubation at 37 °C for 24 h.The capability of utilizing different carbon and nitrogen sources for cell growth was assessed by comparing in silico predictions to literature data for All the phenotype simulations were performed with Flux Balance Analysis (FBA) in OptFlux 3.0  using thThe final version of the iRV781 model includes 781 genes associated with 1221 reactions, among which, 174 are transport reactions, and 196 are external drain reactions , involving 927 metabolites and four different compartments. Analyzing the distribution of proteins by compartments, 205 are plasma membrane proteins, 521 cytoplasmatic proteins and 139 mitochondrial proteins.C. glabrata [S. cerevisiae [C. glabrata, S. cerevisiae or both.In order to elucidate the characteristics of our model we selected well-characterized genome-scale metabolic models of glabrata  and S. crevisiae  as a comS. cerevisiae or C. glabrata). Furthermore, about 65% of the proteins are shared by the three models while about 20% are unique in iRV781 (Although our model has common standard identifiers for reactions (KEGG ID), it is not possible to assess how the reactions differ among the three models, since the remaining two models do not possess the same identifiers. However, considering only the proteins associated with an EC number, it is possible to make a comparison across the existing models. More than 80% of the proteins with an associated EC number in our model are also present at least in one of the other 2 models , but absent in C. glabrata.The enzyme 1.13.99.1, inositol oxygenase, responsible for the conversion of myo-inositol into D-glucuronate. This enzyme seems to be involved in resistance to toxic ergosterol analogs , is alsoC. albicans to use L-sorbose as carbon source [S. cerevisiae.The enzyme 1.1.1.289, sorbose reductase, responsible for the interconversion of L-sorbose into D-sorbitol. In fact, the presence this enzyme allows n source , unlike N-Acylsphingosine. This protein is involved in sphingolipid metabolism, with possible impact in azole resistance in C. albicans [Candida species, being present in C. parapsilosis, C. dubliniensis, C. auris, but not in C. glabrata.The enzyme 1.14.19.17, sphingolipid 4-desaturase, responsible for the conversion of dihydroceramide into albicans . The preThe enzyme 1.1.99.2, L-2-hydroxyglutarate dehydrogenase, is a metabolite repair enzyme responsible for the conversion of (S)-2-hydroxyglutarate into 2-oxoglutarate. In other organisms such as plants  or humanN-acetylglucosamine kinase, responsible for the conversion of N-acetyl-D-glucosamine into N-acetyl-D-glucosamine 6-phosphate. Many yeast species, including S. cerevisiae have lost their ability to utilize N-acetyl-D-glucosamine as carbon source, however, genetically altered yeasts are able to use it, based on expression of C. albicans genes [C. albicans to utilize this carbon source, a feature that is particularly important for its survival inside the phagosomes [The enzyme 2.7.1.59, ns genes . In factagosomes .N-acetylglucosamine-6-phosphate deacetylase, responsible for the conversion of N-acetyl-D-glucosamine 6-phosphate into D-glucosamine 6-phosphate. Like 2.7.1.59, this enzyme is also involved in N-acetyl-D-glucosamine metabolism.The enzyme 3.5.1.25, C. albicans.The enzyme 1.4.3.3, D-amino-acid oxidase, responsible for the conversion of a D-amino acid into a 2-oxo carboxylate and ammonia, is the first enzyme involved in the catabolism of D-amino acids and may allow the utilization D-amino acids as a source of carbon or nitrogen in some yeasts . It may In most cases, the observable differences in EC numbers were related to outdated EC numbers or were compensated with other enzymes that are responsible for the same reactions in the model. However, some cases stand out as potential unique features of S. cerevisiae. On the other hand, 336 reactions were removed from the initial model, have been removed for being unconnected reactions, general reactions, reactions using metabolites that are not included in the model, or reactions for which it was manually verified that the model does not have the enzyme coding gene. Additionally, the compartment of 79 reactions was changed, and 94 reactions were altered to become balanced. The complete list of alterations can be found in During the process of manual curation described in the methods section, a total of 66 reactions were manually added to the initial model obtained from the results of re-annotation to fill gaps. Additionally, evidence from the literature was always considered, or of the well-studied The biomass equation  includesS. cerevisiae [Carbohydrate , Lipid , Sterol revisiae .C. albicans strains, other than the reference SC5314 strain, was also considered in the analysis to increase the number of carbon and nitrogen sources tested.Based on the literature, phenotypic growth data were collected from different sources. Data related to C. albicans CBS562 strain seems not to be able to use cellobiose or D-ribose as sole carbon sources, contrary to the model’s prediction. Therefore, the utilization of cellobiose and D-ribose by C. albicans SC5314 was evaluated experimentally to assess whether the prediction failure could result from a different metabolic capacity exhibited by the reference strain. The results confirmed the model’s prediction regarding the utilization of cellobiose and D-Ribose  Fungal Biodiversity Centre webpage , the C. D-Ribose , suggest4 strain ,62 .Experimental data obtained elsewhere  from synhe model . Additiove yeast , under aC. albicans is unable to grow in anaerobic conditions in minimal media. However, C. albicans colonization is known to spread into anaerobic niches of the gastrointestinal tract or in the inner sections of biofilms where the oxygen availability is scarce or null. Dumitru et al., 2004 reported a defined anaerobic growth medium for studying Candida albicans. In this medium (GPP) oleic acid and nicotinic acid were added as required growth factors for anaerobic growth [S. cerevisiae under anaerobiosis also requires growth factors, such as ergosterol and Tween 80, a source of oleic acid, if growing in a defined medium (SMM), it should be noticed that SMM medium also contains nicotinic acid in its composition despite not being a required growth factor in anaerobic conditions [Glucose = 6.58 mmol g−1 dry weight h−1, to compare the growth parameters with the reported values for S. cerevisiae. Indeed, for the same anaerobic conditions, the specific growth rate of the model and the ethanol production are similar to data reported for S. cerevisiae [c growth . Interesnditions . Cultivarevisiae , though C. albicans essential genes [A set of al genes , were usal genes  , a very well-known class of drugs, used to treat infectious diseases [C. albicans has not been sufficiently investigated. However, it seems that sulfa-fluconazole combination results in increased antifungal activity against C. albicans, leading to the reversal of azole resistance in previously resistant strains [Another promising example of a predicted diseases . The eff strains .Since a reaction can be catalyzed by a protein encoded by more than one gene, and genes may encode more than one protein, we decided to analyze the model’s critical reactions. This analysis allowed to increase the number of confirmed drug targets predicted as essential enzymes.As an example, the FKS genes stand out. They are not considered essential as the enzyme beta-1,3-glucan synthase can be encoded by more than one FKS/GSC/GSL genes . HoweverC. albicans’ model (Candida species. In fact, if we search the 12 genes present in albicans Candida species, they all have orthologous genes in C. parapsilosis and C. dubliniensis, 11 of them have orthologs in C. auris and 8 of them in C. glabrata.Other drugs, such as ethionamide, sulfacetamide, azelaic acid, cerulenin or trimethoprim, were identified as targeting proteins from various organisms. These proteins are homologous to the proteins encoded by genes identified as essential in RPMI, in the s’ model . AltogetDespite the methodology for the reconstruction of genome-scale metabolic models being standardized, eukaryotic models remain a challenge, due to their large genomes and complexity . These mCandida albicans in systemic infections. However, it is important to highlight that in theses reconstructions, it is not considered that cells may need time to adapt to genetic perturbations or environmental variability [Genome-scale metabolic reconstructions are effective in drug target prediction and are expected to continue to expand in the future . Gene esiability . Additioiability . Despiteiability .C. albicans is presented in this study. The model was manually curated and validated thoroughly, constituting a powerful platform for the study of C. albicans metabolic potential and weaknesses. The iRV781 model includes 781 genes associated with 1221 reactions, the number of reactions in the main pathways being similar to those in C. glabrata and S. cerevisiae models. However, about 20% of the proteins associated with EC numbers in iRV781 are unique in relation to these models. The model proved accurate when predicting the utilization of different carbon and nitrogen sources, and in anaerobic growth in defined anaerobic media. In silico growth parameters are also in agreement with the experimental data. We were able to identifyas essential genes in RPMI medium some which are already known targets antifungal agents and other antimicrobial agents used in clinical practice. This observation suggests that the C. albicans global stoichiometric model, presented herein, may be a promising platform for the identification of further targets for new antifungal drugs.The first validated global metabolic model for the human pathogen


Megasphaera elsdenii has been considered as an interesting hexanoic acid producer because of its capability to utilize a variety of carbons sources. However, the cellular metabolism and physiology of M. elsdenii still remain uncharacterized. Therefore, in order to better understand hexanoic acid synthetic metabolism in M. elsdenii, we newly reconstructed its genome-scale metabolic model, iME375, which accounts for 375 genes, 521 reactions, and 443 metabolites. A constraint-based analysis was then employed to evaluate cell growth under various conditions. Subsequently, a flux ratio analysis was conducted to understand the mechanism of bifurcated hexanoic acid synthetic pathways, including the typical fatty acid synthetic pathway via acetyl-CoA and the TCA cycle in a counterclockwise direction through succinate. The resultant metabolic states showed that the highest hexanoic acid production could be achieved when the balanced fractional contribution via acetyl-CoA and succinate in reductive TCA cycle was formed in various cell growth rates. The highest hexanoic acid production was maintained in the most perturbed flux ratio, as phosphoenolpyruvate carboxykinase (pck) enables the bifurcated pathway to form consistent fluxes. Finally, organic acid consuming simulations suggested that succinate can increase both biomass formation and hexanoic acid production.Hexanoic acid and its derivatives have been recently recognized as value-added materials and can be synthesized by several microbes. Of them,  Clostridium kluyveri synthesizes hexanoic acid by fermenting ethanol with acetate or succinate [Rhodospirillum rubrum and Clostridium scatologenes are other hexanoic acid producers utilizing pyruvic acid and methanol as carbon sources, respectively [Megasphaera elsdenii fueled by glucose, maltose, and sucrose. Among such potential producers, M. elsdenii has noticeable advantages, as it can consume diverse carbohydrates such as starch, mannitol, glucose, maltose, and sucrose, which are economic carbon sources [Hexanoic acid and its derivatives are generally used for the production of artificial flavors and food additives, as well as plastics, rubber, transportation fuel, and pharmaceuticals ,2,3,4. Auccinate . Rhodospectively ,7. Furth sources , and als sources  and sucr sources , respectM. elsdenii, which is anaerobic Gram-negative bacterium found in rumen of cattle, sheep, and the human gut. In this regard, constraint-based metabolic modeling and in silico analysis have been appreciated as one of the attractive techniques to characterize it; a multitude of genome-scale metabolic models (GSMMs) have been built for elucidating the cellular physiology of various organisms. However, only a handful of GSMMs for volatile fatty acid (VFA) derivative producers are available to date, including Clostridium acetobutylicum, producing butyric acid and butanol [Clostridium beijerinckii for butanol production [Clostridium spp. Thus, we believe anaerobic bacteria producing higher value C6 compounds (hexanoic acid and hexanol) can be further characterized by resorting to this in silico modeling approach. To this end, we reconstructed a manually curated GSMM of M. elsdenii based on its genome annotation, which is poorly studied, although it has high biotechnological potentials [In order to enhance the titre further toward an industrial scale, it is highly required to systematically understand the cellular behavior under various environmental and genetic conditions, as well as the relevant metabolic states to the hexanoic acid synthetic pathway in  butanol , and Clooduction . Both motentials . SubsequM. elsdenii was reconstructed based on a genome sequence of M. elsdenii DSM20460 [The genome-scale metabolic network model of DSM20460  by usingDSM20460 . An initDSM20460  and BiocDSM20460 . Then, tDSM20460 ,13,14. Tin silico flux analysis, is composed of several building blocks such as amino acids, nucleic acids, and fatty acids. The building block synthetic reactions represent polymerizations of each metabolite precursor. The amino acids and phospholipid compositions were obtained from previous studies [E. coli [The formulation of the biomass, which is an important prerequisite for  studies ,16, whil studies . The ove[E. coli . DetaileM. elsdenii by employing constraint-based flux analysis under various culture conditions, as described elsewhere [We simulated the cellular metabolism of lsewhere ,19. The ijS is the stoichiometric coefficient of metabolite i involved in reaction j; jv refers to the specific rate of metabolic reaction j; and the reaction capacity is constrained using parameters jminv and jmaxv, representing the lower and upper bounds of reaction j, respectively. We describe jminv = −inf and jmaxv = inf as reversible reactions, or jminv = 0 and jmaxv = inf as irreversible reactions. Z denotes the cellular objective of all of the metabolic reactions, where the relative fluxes are determined by coefficient jc. The COBRA toolbox was used to solve the constraint-based flux analysis [Subject to the following:analysis .in silico resulting cell growth of the mutant is zero. The gene essentiality test was carried out using the COBRA toolbox [The gene essentiality analysis was conducted by evaluating the maximal biomass formation rate while constraining the relevant reactions to be zero under a defined glucose uptake rate. The gene is classified to be essential for cell growth when the  toolbox .In order to examine the fractional contribution for hexanoic acid production in a furcated pathway, we performed a flux ratio analysis using the COBRA toolbox. First, the model was simulated to investigate the flux distribution of the bifurcated pathway with hexanoic acid production in different cell growing states, by fixing the specific glucose uptake rate and growth rate while setting the hexanoic acid synthetic reaction as the objective function. Subsequently, in each specific growth rate, nine different flux ratios  were tested to check for changes in the flux distribution. M. elsdenii (iME375) was reconstructed, followed by model refinement. A number of network gaps were filled by adding suitable metabolic reactions based on the information obtained from metabolic pathway databases  and literature sources. For example, the draft model contained a few gaps in the synthetic pathway of propionic acid, which is the major fermentation product from lactate in M. elsdenii. Thus, we added the reactions corresponding to lactate racemase and lactoyl-CoA dehydratase for the conversion of d-lactate into l-lactate, and the dehydration of lactoyl-CoA to acryloyl-CoA, respectively, according to previous studies [M. elsdenii is able to grow on all of these carbon sources, we added new transport reactions based on the information available from the KEGG and Biocyc databases. The genome-scale metabolic network of  studies ,22. The M. elsdenii, including the glycolysis, tricarboxylic acid (TCA) cycle, pentose phosphate pathway (PPP), amino acids, and fatty acids synthetic pathways. Notably, the glycolysis and PPP are complete, but the TCA cycle is incomplete, as the gene of 2-oxoglutarate dehydrogenase was not found. The synthetic pathways of 20 amino acids and VFAs are also depicted in M. elsdenii, iME375, accounts for 375 genes , 521 metabolic reactions, and 443 metabolites. In iME375, all of the 521 reactions were classified into six major subsystems, namely: amino acids, carbohydrates, cofactors and vitamins, energy, lipids, and nucleotides metabolisms. Among them, amino acids metabolism constitutes the largest part of metabolic reactions, followed by cofactors and vitamins metabolisms , NADH:ferredoxin oxidoreductase (rnfA-E) and 4-hydroxybutyryl-CoA dehydratase (abfD) as compared to M. elsdenii. On the other hand, C. acetobutylicum has butyrate–acetoacetate CoA transferase (CA_P0163 and 0164) instead of acetyl-CoA hydrolase/transferase. As such, the coenzyme A (CoA) of butyryl-CoA is transferred to acetoacetate and the carbon chain elongation is terminated. In M. elsdenii, the butyryl-CoA (C4) is elongated to 3-oxohexanoyl-CoA (C6) by transferring the acetyl (C2) group from acetyl-CoA via acetyl-CoA C-acetyltransferase, or is converted to butyric acid by utilizing acetate (C2) as the CoA acceptor via acetyl-CoA hydrolase/transferase. However, C. acetobutylicum produces butyric acid (C4) and butanol (C4) through butyrate-acetoacetate CoA transferase and butanol dehydrogenase during acidogenic and solventogenic phases, respectively. Unlike M. elsdenii, acetoacetate (C4) is involved in CoA transferase in C. acetobutylicum. Thus, we suggest that the existence of the three enzymes  and the substrate specificity of other enzymes, such as thlA, hbd, crt, and cat, which contribute to acyl-CoA elongation, may affect the carbon length of the final product in M. elsdenii. The VFA synthetic pathways of n detail . The homM. elsdenii is closer to E. coli than C. acetobutylicum  in M. elsdenii, according to a previous study [The exchange fluxes of essential minerals and nutrients such as NOnditions . The grous study . To minimize the influence of other nutrients in the culture medium, such as amino acids or trace amounts of carbon sources, which can help cell growth during the early exponential phase, we used experimental data during the late exponential phase in three different experiments for simulation. Remarkably, all of the simulated biomass formation rates were highly consistent with the observed growth rates, within the acceptable error range of 10% .M. elsdenii under a glucose minimal medium condition by deleting each gene at a time (see Methods), allowing us to identify lethal genes for further genetic engineering in order to enhance productivity. The genes were classified into the following three types: completely essential genes (genes that are required for cellular growth), partially essential genes , and non-essential genes (genes that are not required for cellular growth). There were 221 and 24 reactions, corresponding to 182 and 21 genes, that were predicted to be essential and partially essential, respectively. Most of the amino acid, riboflavin, and coenzyme A synthetic genes are completely essential. Interestingly, VFA synthetic genes are non-essential or partially essential, as most VFA forming reactions are catalyzed by isozymes. Hexanoic acid synthetic reactions via succinate, acetate formation reactions, and butyryl-CoA:acetate CoA transferase are partially essential. In anaerobic microorganisms, acetate is known as one of the key metabolites that can accept electrons from other redox cofactors involved in butyric and hexanoic acid synthesis [M. elsdenii also requires acetate and protons as electron acceptors to generate energy and balance the intracellular redox potential by converting them to acetyl-CoA and H2, respectively [We conducted a gene essentiality analysis of ynthesis . Like otectively .M. elsdenii. We found two possible routes via acetyl-CoA (Route A) and oxaloacetate (Route B), in order to convert from pyruvate to crotonyl-CoA, which is further catalyzed to synthesize hexanoic acid. Route A is initiated with the oxidative decarboxylation of pyruvate to acetyl-CoA and CO2 by utilizing pyruvate–ferredoxin oxidoreductase (pfo), which is commonly found in anaerobic organisms. A part of acetyl-CoA is converted to acetate by generating ATP; the acetoacetyl-CoA (C4) is produced by condensing 2 mol of the remaining acetyl-CoA (C2), and further is catalyzed toward crotonyl-CoA. In Route B, M. elsdenii synthesizes oxaloacetate (C4) from pyruvate (C3) and HCO3 (C1) via pyruvate carboxylate (pyc). Succinate is then produced from oxaloacetate by running the TCA cycle in a reductive (counterclockwise) direction. Note that the reductive TCA cycle, from oxaloacetate to succinate, is also present in C. acetobutylicum [bcd/etfAB), and in turn elongated to hexanoyl-CoA (C6) by combining with acetyl-CoA.utylicum . Crotony2 loss—one 6C product (hexanoic acid) requires three molecules of pyruvate (3C * 3 = 9C), while wasting three carbon atoms as CO2. However, a more comprehensive comparison of Routes A and B should be made in terms of other energetically expensive consumables such as ATP, acetyl-CoA, and reducing equivalents (NADH).Routes A and B are decomposing and composing pathways, respectively. In Route A, the C3 metabolite is divided into C1 and C2 metabolites. Two molecules of the C2 metabolite are then integrated to build a C4 metabolite. Thus, two molecules of the C1 metabolite are withdrawn to synthesize one molecule of the C4 metabolite. However, in Route B, the C3 metabolite is combined with the C1 metabolite to synthesize a C4 metabolite. Route A is less efficient with respect to the C-balance because of CO2 via pfo, while reducing ferredoxin. A portion of CO2 is fixed with H2O in order to produce HCO3 and a proton via carbonate dehydratase, and the residual CO2 secretes it out of cell. While producing 1 crotonyl-CoA from 2 acetyl-CoA, 1 NADH is oxidized (reaction 1).Pyruvate is divided into acetyl-CoA and COOne crotonyl-CoA is synthesized from pyruvate through succinate in a reductive direction, while oxidizing 5 NADH and reducing 1 ferredoxin (Reaction 2).bcd/etfAB, while 1 crotonyl-CoA is converted to butyryl-CoA. The enzymes that catalyze for synthesizing hexanoyl-CoA from butyryl-CoA are the same as those that participated in butyryl-CoA synthesis from acetyl-CoA. Then, 2 reduced ferredoxin and 5 NAD+ are generated via the hexanoyl-CoA synthetic pathway from crotonyl-CoA according to Reaction 3  by reduced ferredoxin and NAD+ to regenerate the necessary NADH. The rnf complex couples proton translocation with the oxidation of ferredoxin and reduction of NAD+. The proton gradient, which is generated by the rnf complex, is a driving force for synthesizing ATP via ATPase (atpA-I). The resulting fluxes of Reactions 1, 2, and 3 allowed for the cell to make a balance of reduced ferredoxin and oxidized NAD in order to produce the required amounts of ATP and NADH. The excess electrons, which were not oxidized through the rnf complex, are delivered to H+ via hydrogenase (hyd) for H2 formation. The reduced molecular hydrogen (H2) is then secreted.Flux is driven through the NADH:ferredoxin oxidoreductase  acceptor, while butyric and hexanoic acid are converted from butyryl-CoA and hexanoyl-CoA, respectively. Therefore, it is also important to synthesize an adequate amount of acetate to produce the maximal yield of hexanoic acid in M. elsdenii since it is composed of many pathways such as 2 types of crotonyl-CoA synthesis, fatty acid elongation, CO2 fixation, electron transfer and ATP synthesis. In addition, the FBA result of iME375 by fixing the glucose uptake rate of 5 mmol g−1 DCW h−1 and maximizing the hexanoic acid synthetic reaction showed that 3.51 mmol g−1 DCW h−1 of hexanoic acid was produced by using both of the above-mentioned pathways. The flux values of Routes A and B were 2.97 mmol g−1 DCW h−1 and 0.54 mmol g−1 DCW h−1, respectively. In order to further investigate the effect of the specific cell growth rate and flux ratio of Route A to Route B on hexanoic acid productivity, we conducted the flux ratio analysis in different cell growing rates  and pyruvate:ferredoxin oxidoreductase (pfo) with nine different ratios . The highest hexanoic acid production rate was maintained through most of the flux ratios (pck) helps to form consistent flux through Route B. Unlike the results of other specific growth rates, at 0.6 h−1, a slight increase (6%) of hexanoic acid production was observed when setting the flux ratio as 5:1. Based on the result of the flux ratio analysis, we could guess that the hexanoic acid productivity may not be increased simply by the knockdown of pfo or the overexpression of pyc. Thus, in order to find the potential metabolic engineering targets for improving the productivity of the target metabolite, a comprehensive understanding of the microorganism and the in silico strain design methods would be needed.Flux ratio analysis was further performed in each cell growth rate by changing the flux ratio of pyruvate carboxylase , albeit the carbon yield of this flux is lower than in Route B  for balancing the redox potentials. On the other hand, hexanoic acid is produced only through Route B at a maximum specific growth rate condition. The additional flux ratio analysis suggested that a simple gene knockdown or overexpression may not be suitable for improving the target metabolite in this system. In future, we expect to further improve our understanding of M. elsdenii when the metabolic model is integrated with high throughput -omics data.In this study, we reconstructed a genome-scale metabolic model of hexanoic acid synthetic bacteria


Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism  Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from  Metabolic plasticity is critical for bacterial survival in changing environments, and fundamental to stable microbial communities ,2. In thGENREs have also become strong platforms for providing context to multiple forms of high-throughput omics data, allowing for the study of whole-network changes in metabolism due to upstream/downstream shifts in enzyme activity . To thisNumerous approaches to integrating transcriptomic data into GENREs primarily focus on identifying reactions associated with defined cutoffs of gene transcription or change, then maximizing flux through those reactions with many transcripts and minimizing flux through those reactions associated with genes with fewer transcripts –21. WhilSaccharomyces metabolism, pFBA alone with constraints on metabolic inputs was found to be able to largely outperform several other methods in predicting metabolic network behavior under various growth conditions despite not utilizing any transcriptomic data for these predictions [Escherichia coli grown in defined in vitro conditions have shown that levels of transcript were only predictive for roughly half of the concentrations of the corresponding enzymes in the cell [in situ biology.Recently, parsimonious Flux Balance Analysis (pFBA) was developed, which seeks to minimize the flux associated with each reaction in the model while maintaining optimum flux through the objective function . In essedictions . Recentldictions . These sthe cell ,34. In rthe cell . As arbithe cell , and absthe cell , we chosE. coli str. K-12 substr. MG1655. Transcriptomic data from multiple distinct in vitro growth conditions were compared against each other as well as the base implementation of pFBA. RIPTiDe was able to accurately discern context-specific phenotypes including growth rates that closely match experimentally measured values under the same conditions, as well as gene essentiality predictions in metabolic pathways relevant to the corresponding media. The platform was subsequently tested utilizing in vivo metatranscriptomic sequence data from clindamycin-treated mouse cecal content where E. coli is the dominant member of the bacterial community [in vivo RIPTiDe-contextualized model simulated significantly slower growth than in aerobic rich media and more central metabolites were produced intracellularly when in competition with other bacterial species. These combined analyses validated our methodology by correctly uncovering context-specific biological trends, and previously unappreciated host-associated behaviors of E. coli. Furthermore, this technique could present a powerful tool for the study of the microbiota through the simultaneous unsupervised contextualization of omics data from multiple organisms within the same community.We call this combined method RIPTiDe, or Reaction Inclusion by Parsimony and Transcript Distribution. RIPTiDe was then tested on the most well curated GENRE of the highly studied model organism ommunity . When coRIPTiDe relies on sequential optimization steps, guided by transcript abundances in order to identify the most efficient usage of metabolism that also reflects an organism’s transcriptional state . The reqAs with pFBA, reversible reactions are split into pairs of irreversible reactions carrying non-negative flux. Coefficients are subsequently transferred to the corresponding metabolic reactions for each gene. Reactions are then assembled into a single new equation which is set as the cellular objective where the magnitude of the linear coefficient drives the amount of flux carried by each reaction. Reactions without genes are assigned the median score to not be incorrectly penalized, nor overly biased to their inclusion. Larger coefficients correspond to the reaction receiving a larger weight per unit of flux during optimal growth, which would therefore be favored less when minimizing the overall sum of weights times flux for a given objective function. In this way, we were able to minimize overall flux subject to the transcript-guided linear coefficients added by RIPTiDe. Within the transcript abundance distribution, progressively smaller linear coefficients are assigned to genes with increased transcription . With anThe scope of feasible solution states in a GENRE typically out-scales the number of realistic metabolic states of the cell by a large magnitude . PlacingNear optimal flux through the objective function is set as a constraint ≧80%, .Linear coefficients are assigned as constraints to each reaction based on transcriptomic abundance of the associated gene-enzyme assignments. In cases where multiple gene products contribute to the same reaction, the lowest coefficient/largest weight among them is selected.Total sum of weights times fluxes in the model is minimized subject to previously outlined constraints.Standard flux balance analysis then identifies inactivated subnetworks (zero flux) which are subsequently removed from the model. Orphaned genes and metabolites are then pruned.Inverse linear coefficients within the original provided range are calculated and assigned for each reaction. Flux sampling is performed on the constrained and reduced model to find most likely pathway utilization patterns while maintaining 80% of the objective function.The resultant pruned model has a dramatically more focused, biologically feasible, metabolic solution space that has been adequately sampled for feasible metabolic states . Unlike To test RIPTiDe, we created a small, flux-consistent metabolic network reconstruction with two compartments and generation of ATP (in arbitrary units) as the cellular objective function. The model has two separate means of accomplishing this goal; either through glycolysis where glucose is the primary substrate, or Stickland fermentation which requires pairs of amino acids in coupled redox reactions to create energy . In natuAs a means to determine the context specificity of RIPTiDe output models, we then generated two distinct simulated transcript abundance distributions that each reflected elevated transcription of a single pathway . In Fig E. coli strain K-12 substr. MG1655 [in vitro media conditions including aerobic LB rich media, aerobic M9+glucose defined minimal media, and anaerobic M9+glucose defined minimal media. Each dataset was collected at approximately the same mid-exponential phase of growth [E. coli is a facultative anaerobe with known differences in metabolism between these lifestyles that we hoped to capture with RIPTiDe [To perform a thorough evaluation of our approach, we utilized the genome-scale metabolic network reconstruction iJO1366 , for the model organism . MG1655 . This GE. MG1655 . Leveragf growth ,48, and  RIPTiDe .E. coli K-12 growing in aerobic LB rich media. We elected to assess an array of distinct GENRE+transcriptome integration algorithms; including GIMME, iMAT, CORDA, MBA, and RegrEx [E. coli K-12 growing in LB rich media [in silico. Only after altering extracellular metabolite availability to reflect LB media components are all genes found to be essential across all platforms  from each other group as it has access to all possible growth substrates which combined allow for faster growth and distinct patterns of metabolism. A striking relationship emerged when RIPTiDe flux distributions were compared with those from media-constrained models, revealing that these groups were highly correlated. Importantly, no difference  was found when RIPTiDe was applied to the model with M9+glucose media constraints contrasted against results from RIPTiDe-generated flux samples without media constraints  as was the case with GIMME and CORDA-produced models. These results collectively indicated that RIPTiDe is also able to approximate the media condition from which the transcriptome was collected. This feature will likely be especially useful when growth conditions are much more difficult to quantify, as in environmental samples or in vivo.To evaluate how closely RIPTiDe-based predictions reflect predictions when media conditions are known, we performed NMDS ordination analysis of Bray-Curtis dissimilarities between common exchange reaction flux distribution samples from iJO1366 with and without constraints on available substrates compared with RIPTiDe-generated flux samples with no defined media . For thestraints . A similnditions . These dnditions . Growth r growth . Additioehaviors –54. ConvE. coli grown in M9 minimal media (aerobic & anaerobic), which supported that RIPTiDe was able to identify general biological trends that are conserved between similar media conditions. Interestingly, the next largest group was the intersection between Maximum Parsimony and LB rich media, which may reflect that growth in an environment with surplus growth nutrients is partially analogous to the artificial state of minimal cellular investment. These distinctions in network topology were further crystallized by conditional gene essentiality between the RIPTiDe-contextualized models (necessary for at least 1% of optimum growth). It is important to note that genes found to be essential during these analyses do not necessarily correspond with survival of E. coli, only that these genes may be critical to the optimal metabolic strategy under the associated conditions [in vitro transcriptomes , and also constrained to achieve at least 80% of the optimal objective function value. We first assessed the impact that each transcriptome had on the components of each model created by RIPTiDe. Different topologies of reaction inclusion were found among each of the contextualized models; however none were completely unique and a core set of metabolic reactions were necessary for optimal growth across all conditions  resulted in a bacterial community composed of >90% E. coli [in vivo transcript abundance distribution reflected the same negative binomial type as those derived from in vitro transcriptomes mapped to the same E. coli K-12 MG1655 genome , but more specifically between those bacteria grown in M9 minimal media and those found using LB rich media or in vivo transcriptomes . These results implied distinct metabolic strategies necessary for growth during resource scarcity or surplus, and these patterns were captured by RIPTiDe. Although similar, no group of flux distributions entirely overlapped another and indicated that although numerous reactions and metabolites are shared between all context-specific models, they are utilized significantly differently to manage growth across media conditions and supported differential metabolism is likely necessary to cope with distinct environments.Finally, we sought to test the utility of RIPTiDe for capturing metabolic behavior of bacteria from within host-associated communities of microorganisms. Computational analysis of a discrete bacterial species within a complex environment as a member of a larger microbial community has been historically difficult . To addr E. coli . To begi5 genome . This re5 genome  & S2 Fig5 genome . This anin vivo or LB rich media transcriptomes as they were more similar during unsupervised learning analysis. Aside from one environment being highly aerobic and the other largely anaerobic, previous research on E. coli physiology during growth in liquid LB concluded that the bacterium utilizes amino acids that are simple to metabolize for growth early on, then switches to other substrates in a diauxic-like growth pattern. LB broth has also been shown to have only modest levels of usable carbohydrate growth substrates, increasing the importance of amino acid catabolism for E. coli [in vivo amino acids and simple sugars are contested resources in the cecum as the majority are already absorbed by the host or used by other microbes upstream in the GI tract, making these growth conditions extremely different [in vivo, and 5 of these were peptides or peptide conjugates  are at elevated concentrations [Next, we contrasted the RIPTiDe models generated using  E. coli . Additioifferent . As an anjugates . Carbohyal phase . Indeed, microbe . AdditioLB media . ContinuLB media  on the slian gut . Putresctrations . AltogetIn order to survive in a given environment, an organism must express the appropriate forms of metabolism to cope with changing conditions. Production of transcript and protein is metabolically expensive, and over-production likely puts the cell at risk if further perturbation is placed on the system . Althougin vitro and in vivo metabolic phenotypes with reasonably high accuracy. In addition to these predictions, our method does not require prior knowledge of active metabolic subnetworks which can preclude work with understudied or uncultured species that cannot or have not been grown or characterized in isolation. Furthermore, the ability to accurately capture the context-specific metabolism of bacteria when media conditions are not easily defined will be an important component for the discovery of metabolic behaviors of microbes in complex environments or novel host-microbe metabolic interactions. Some possible limitations of RIPTiDe do exist however. First, as RIPTiDe relies on the distribution associated with shotgun transcriptomic sequencing, it is less suited for other types of omic data in its current form [in vitro and in vivo conditions, producing models that closely mirror known biological phenotypes. Additionally, RIPTiDe run time scales linearly with GENRE size and compartmentalization  techniques, avoiding computational demand brought on by mixed integer linear programming (MILP) approaches and making it compatible with all popular solvers (GLPK was used in this study). Optimal flux through the objective function was set as a constraint with a 20% range of allowed variation . Following sensitivity testing, this level was found to result in models which best achieved high growth rates while reaching acceptable levels of correlation with input transcriptomes  assignment of gene g, the reads-per-million normalized transcript abundance of gene Tg within the sorted  transcript abundance distribution is divided by the largest abundance Tmax this distribution. These gw values are then assigned to their corresponding reactions to generate a distribution of linear coefficients rwsampling and the maximum weight among reactions in each gene reaction rule (GPR) is assigned to all associated reactions. The sum of the minimum and maximum weights within gw is calculated and then the inverse weight is determined based on the weight associated with reaction r in rwsampling, creating a second distribution of reaction weights rwpruning.For reaction weight virrev. Linear coefficients wr for each vector can now be integrated and an objective function is then defined to minimize that total of flux across the entire model. These steps are all performed subject to a lower bound on the biomass flux  set to a fraction  of the previously identified maximum biomass flux (max vobjective) and mass-balance constraints. Optimization (standard FBA) is performed and reactions found in the solution to have <1x10-6 flux are then removed from the model (the threshold is adjustable by the user). The model is then checked for orphaned genes and metabolites, which are subsequently removed as well.As in parsimonious-FBA, each reversible reaction is separated into individual forward and reverse reactions rwsampling linear coefficient assignments. The overall sum of flux is then maximized. The assembly of the large function of weighted reaction fluxes is performed with symengine (https://github.com/symengine/symengine):In order to adequately explore intracellular flux through the remaining reactions, previous coefficients are reassigned to remaining reactions using the rwsampling) are then assigned to the corresponding irreversible reactions virrev. Flux sampling was then performed with the Gapsplit sampler using standard parameters and 500 samples (https://github.com/jensenlab/gapsplit) [p-value between median absolute flux and their associated linear coefficient during flux sampling for all reactions (Coefficients (apsplit) . Flux vaapsplit) . An overeactions .https://github.com/mjenior/Jenior_RIPTiDe_PLOSCompBio_2019github.com/csbl/Jenior_RIPTiDe_2020.The example model used for initial RIPTiDe testing consists of 16 reactions  and 14 metabolites across 2 compartments (extracellular and intracellular). The intracellular demand reaction for ATP generation was utilized as the objective. Simulated transcriptomes are shown in Escherichia coli K-12 MG1655 genes using Bowtie2 [broadinstitute.github.io/picard/). The remaining mappings were converted to idxstats format using SAMtools [In vivo untargeted metabolomic data were generated for a previous study [Sequenceread processing was performed as described previously in ,8. In su Bowtie2 . MarkDupSAMtools , and theus study .https://github.com/SchlossLab/Jenior_Metatranscriptomics_mSphere_2018/tree/lab_computer/data/metabolome. https://paperpile.com/c/vZ3H0e/ECBShttps://opencobra.github.io/cobratoolbox/stable/) using the specified thresholds. For both MBA and RegrEx, a flux consistent model was used based on the requirements for the integration algorithms. CORDA (v0.4.2) and RegRx were downloaded and installed on 12-1-2019.GIMME , iMAT 1, MBA 2020, CORDAAll statistical analysis was performed using R (v3.2.0). Significant differences between flux distributions and metabolite concentrations were determined by Wilcoxon signed-rank test with Benjamini-Hochberg correction. Principle Component Analysis and Non-Metric Multidimensional Scaling of sampled flux distributions was performed using the vegan package . Machinehttps://github.com/mjenior/riptide and also as a full package (v2.8.0) from the Python Package Index (https://pypi.org/project/riptide/). Complete analysis associated with this study is available in a Jupyter notebook hosted on a GitHub repository along with all other data and code associated with this study (https://github.com/mjenior/Jenior_RIPTiDe_PLOSCompBio_2019https://github.com/csbl/Jenior_RIPTiDe_2020).As previously stated, RIPTiDe is freely available for download at S1 FigComputed doubling time from biomass reaction objective value following FBA analysis of each contextualized model . Result from the unconstrained model is listed along the top axis. Biomass objective flux was constrained to ≧80% of the optimal flux value prior to identifying the state of Maximum Parsimony as it is during RIPTiDe contextualization.(TIF)Click here for additional data file.S2 Fig(A) Valine, (B) Deoxyuridine, (C) and Methionine. Significant differences were determined by Wilcoxon signed-rank test.Concentrations quantified with liquid chromatography mass-spectrometry previously . Compari(TIF)Click here for additional data file.S3 Fig(A) Spearman correlation results between RIPTiDe linear coefficients from flux sampling and median absolute values from sampled flux ranges from in vivo transcriptome. Assigned linear coefficients are significantly correlated with their associated absolute reaction activities. Correlation results are referred to as concordance in the output of RIPTiDe. (B) Run time in seconds with increasing metabolic network reconstruction size. Using RIPTiDe, each GENRE  was integrated with a simulated transcriptome of 1.0 transcript for all genes. Multicellular eukaryotic models include mouse (iMM1415), golden hamster (iCHOv1), and human (Recon3D). The relationship between model size and RIPTiDe run time fits a linear model . These analyses were completed using an Intel Core i7-7Y75 CPU @ 1.30GHz × 4 processor and 15.4 GB of memory.(TIF)Click here for additional data file.S4 Figin vivo and LB rich media RIPTiDe-contextualized transcriptomes of E. coli.Mean Decrease Accuracy for an optimal subset of 20 reactions determined by AUC Random Forest that flux levels differentiate the distributions from shared reactions between (TIF)Click here for additional data file.S5 Fig(A) M9+glucose anaerobic and (B) LB aerobic. Significant differences between constrained objective flux only and all other groups determined by PERMANOVA. The dissimilarity of RIPTiDe-generated flux samples in these analyses highlight context-specific metabolic patterns that were unobservable through other methods.NMDS ordination of Bray-Curtis dissimilarities between flux samples for each version of iJO1366 in (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.