Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous—i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).Our workflow utilizes PROXIMAL—a tool that uses reactant–product transformation patterns from the KEGG database—to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in The online version of this article (10.1186/s12934-019-1156-3) contains supplementary material, which is available to authorized users. The engineering of metabolic networks has enabled the production of high-volume commodity chemicals such as biopolymers and fuels, therapeutics, and specialty products –5. ProduWe focus in this paper on another major source of uncatalogued cellular activity–promiscuous enzymatic activity, which has recently been referred to as ‘underground metabolism’ –25. WhilEscherichia coli MG1655 [E. coli. The putative products are then compared to measured metabolites as reported in Escherichia coli Metabolome Database, ECMDB [E. coli model iML1515. Four of these reactions have not been catalogued prior for E. coli or other organisms, suggesting novel undocumented promiscuous transformations, while five other reactions are catalogued for species other than E. coli. Further, there were ten reactions that were cataloged in other E. coli databases , and to the workflow to create them as EMMA (EMM annotation). Each metabolic model is assumed to have a set of reactions and their compounds and KEGG reaction IDs. Each reaction, and thus transformation, is assumed to be reversible unless indicated otherwise. EMMA utilizes PROXIMAL , a methoi MG1655 . EMMA pre, ECMDB , 43. We . EcoCyc , and KEGE. coli per ECMDB. After manual curation . If the biotransformation is present in KEGG or EcoCyc, then the predicted metabolite is classified into Category 2 (C2), reflecting a curation issue where some reactions were not included in the iML1515 model. If the predicted metabolite is not in iML1515 and not associated with E. coli in KEGG nor listed in EcoCyc, then the decision tree determines if the same chemical transformation (same substrate and same product) is documented to occur in other organisms. Predicted biotransformations documented in KEGG for organisms other than E. coli are classified in Category 3 (C3). While biotransformations not found in KEGG are classified as Category 4 (C4).If a predicted metabolite is not one of the known metabolites in iML1515, the decision tree determines whether the predicted metabolite and reaction are associated with E. coli due to EC 2.4.2.10 (orotate phosphoribosyltransferase)     . Both EC 4.1.3.17 and EC 4.1.3.34 are lyases enzymes that form carbon–carbon bonds. 4-Carboxy-4-hydroxy-2-oxoadipate is known to be formed/consumed by EC 4.2.1.80 (2-keto-4-pentenoate hydratase) in E. coli (KEGG R04781). Another predicted reaction is the (de)aminating redox transformation between l-histidine and imidazol-5-yl-pyruvate, catalyzed by EC 1.4.1.4 (glutamate dehydrogenase)  [trans-polyprenyl diphosphate to 4-hydroxy-3-polyprenylbenzoate is known to occur in E. coli, the specific transformation between geranyl diphosphate to geranyl hydroxybenzoate is known to occur in plants as part of shikonin biosynthesis, by EC 2.5.1.93 (4-hydroxybenzoate geranyltransferase) [d-alanine and 2-aminoacrylic acid . While 2-aminoacrylic acid is not known to be produced in E. coli in any other way, the transformation between d-alanine and 2-aminoacrylic acid occurs in other organisms such as Staphylococcus aureus [C3 consists of five predicted reactions that are not documented in i KEGG R081. Anothsferase) . The fous aureus . Lastly,s aureus .Fig. 4Thl-serine by EC 1.1.1.23 (histidinol dehydrogenase). There is one reaction (KEGG R02970) catalyzed by EC 2.6.1.47  that produces aminomalonate; but it is not a redox reaction and is associated with rat and silkworm, not E. coli [N-acetylputrescine and N-acetylornithine , 2.3.1.57 (spermidine acetyltransferase), and 3.5.1.62 (acetylputrescine deacetylase)—in many organisms that include both eukaryotes and bacteria [N-carbamoyl-l-aspartate also catalyzed by EC 4.1.1.36 (PPC decarboxylase). 3-Ureidopropionate is present in eukaryotes and bacteria (but not E. coli) and is involved in reactions catalyzed by ECs 3.5.1.6 (β-ureidopropionase) and 3.5.2.2 (dihydropyrimidinase). The last reaction is the transformation between d-gluconic acid and d-galactarate by EC 1.1.1.23. d-Galactarate is involved in reactions catalyzed by 4.2.1.158 that is present in Oceanobacillus iheyensis [C4 consists of four predicted reactions that are not currently catalogued in KEGG for any organism Fig. . The fir E. coli . The secine Fig. b predictbacteria . The thiheyensis .Fig. 5ThCurrent practices for reconstructing genome-scale metabolic models, which are derived using sequencing and functional annotation, can be improved by utilizing metabolomics data. However, directly utilizing metabolomics measurements to augment existing models is challenging. Not every metabolite is measurable due to limited resolution and fidelity of mass spectrometry instruments. Further, assigning chemical identities to measured metabolites remains a challenge. Even if new metabolites are identified, their formation cannot be easily assigned to enzymes without significant experimental effort involving either genetic or biochemical screens. Additionally, metabolomics data alone cannot differentiate reactions catalyzed by different enzymes yet between the same substrates–product pairs without additional experimental efforts. Computational tools and workflows, as presented in this paper, can significantly guide such studies and aid in metabolic model construction and augmentation based on metabolomics data.The workflow we developed here is designed to identify metabolites that can form due to promiscuous enzymatic activity within a specific model organism. Further, the workflow provides balanced reactions to document such enzymatic activities. We utilized PROXIMAL , which fFuture experimental and computational efforts can further advance this work. Experimentally, the list of putative products generated by PROXIMAL but not documented in any metabolomics databases can be used as a resource to identify as yet unidentified metabolites. Experimental validation of reactions in the C1, C3 and C4 categories would provide further evidence of the suggested reactions, and would provide a means for expanding existing databases such as KEGG and EcoCyc. Computationally, PROXIMAL can be upgraded to consider enzymes that act on more than one Reaction Center (R) within a metabolite (e.g. transketolase). This would produce multiple operators per reaction and generate a more comprehensive list of putative reactions and products. When applying PROXIMAL, we did not consider whether products of promiscuous reactions can themselves act as new substrates for promiscuous reactions. This is due to the large number of putative products. We are currently developing machine learning techniques to improve the prediction accuracy of PROXIMAL.E. coli MG1655. PROXIMAL generated 1875 biochemical operators based on reactions in iML1515 and predicted 1423 derivatives of 106 high-concentration metabolites. To validate these products, EMMA compared the set of putative derivatives with the set of metabolites documented in ECMDB as part of E. coli metabolism. For the overlapping set, we generated corresponding atom-balanced reactions by adding suitable cofactors and/or co-substrates to the substrate-derivative pair suggested by PROXIMAL. The balanced reactions were compared with data recorded in EcoCyc and KEGG. Our workflow generated a list of 23 new reactions that should be utilized to extend the iML1515 model, including parallel reactions between existing metabolites, novel routes to existing metabolites, and new paths to new metabolites. Importantly, this study is foundational in providing a systemic way of coupling computational predictions with metabolomics data to explore the complete metabolic repertoire of organisms. The described workflow can be applied to any organism utilizing its metabolic model to predict sample-specific promiscuous enzymatic byproducts. Applying this workflow to other biological samples and their metabolomics data promise to enhance our understanding of natural, synthetic, and xenobiotic metabolism.This study investigates creating extended metabolic models (EMMs) through the augmentation of existing metabolic models with reactions due to promiscuous enzymatic activity. Our workflow, EMMA, first utilizes PROXIMAL to predict putative metabolic products, and then compares these products against metabolomics data. EMMA was applied to iML1515, the genome-scale model of E. coli iML1515 model based on the availability of the metabolic measurements in ECMDB, and the availability of cataloged reactions and metabolites for E. coli in other databases (EcoCyc and KEGG)  Fig. . The iMLEMMA used PROXIMAL to predict putative products that can be added to the model. PROXIMAL utilizes RDM patterns  specificE. coli strain K-12 and related information such as reactions, enzymes, pathways, and other properties. This information was either collected from resources and databases such as EcoCyc, KEGG, EchoBase [Metabolites predicted by PROXIMAL were compared with measured metabolic data in ECMDB. ECMDB contains 3760 metabolites detected in EchoBase , UniProtEchoBase , 57, YMDEchoBase , and CCDEchoBase , or fromFor each putative product, a mol file was generated and then converted to a SMILES string using Pybel , a pythoverifiable predicted promiscuous transformation of an E. coli metabolite. Each predicted transformation was manually examined and compared against the RDM pattern causing the transformation. Transformations were discarded if the they seemed infeasible, if the substrate was a cofactor, or if the RPAIR entry associated with the PROXIMAL operator required the presence of more than one Reaction Center (R). For each valid verifiable predicted transformation by PROXIMAL, we developed a new reaction by examining the reaction(s) template associated with the enzymatic transformation and adding suitable cofactors to the reactant and product of the biotransformation identified. The set of developed balanced reactions, where the added cofactors to a reaction caused the number of atoms of reactants and products to match on both sides of the reaction, are then compared to reactions recorded in EcoCyc, KEGG, or the literature.If a metabolite predicted by PROXIMAL was in ECMDB, then steps 1 and 2 resulted in the identification of a The outcomes were divided into four categories. C1 reactions consisted of metabolites predicted by PROXIMAL that are already in iML1515 but catalyzed by different enzymes than the ones already listed in the model. These reactions reflect promiscuous activity that enabled the same biotransformation catalyzed by a different gene in the model. C2 reactions already existed in EcoCyc and/or KEGG but not in iML1515. This reflected a curation problem where some reactions were not included in the iML1515 model. C3 reactions were not in EcoCyc but documented in KEGG for other organisms. C4 reactions did not exist in either EcoCyc nor in KEGG. These reactions were thus novel reactions that have not been reported in the literature.Additional file 1. Listing of derivatives that were predicted by PROXIMAL and had a chemical ID in PubChem.Additional file 2. Results of Flux Balance Analysis and Flux Variability Analysis for the added EMMA reactions.


Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies. SOD1), in TAR DNA binding protein (TARDBP) encoding TDP-43, and in fused in sarcoma (FUS), as well as hexanucletoide repeat expansions in chromosome 9 open reading frame 72 (C9ORF72) are the most common genetic causes of ALS -2-fluoro-2-deoxy-D-glucose positron emission tomography (PET) reported that cortical glucose uptake is reduced in ALS patients while local glucose uptake seems to be increased in both the cervical and dorsal spinal cord -glucose as tracer. When glycolysis is active, 3H2O will be produced when 2-phosphoglycerate is converted to phosphoenolpyruvate by the enolase enzyme. As a result, the flux rate of glycolysis can be assessed by measuring the produced 3H2O over time using a scintillation counter [Intracellular flux analysis is a valuable tool to directly quantify cellular metabolism. A longstanding and robust approach to measure intracellular metabolic fluxes is achieved by performing radioactive tracing, as measuring radioactivity gives direct information about the metabolic flux through the pathway of interest. In this assay, metabolic energy substrates are labeled using radioactive isotopes. Common radioactive tracers in the assessment of metabolism are iew, see ). For ex counter .FUS and showed that various mutations did not affect the energy metabolism of patient-derived motor neurons [In the context of ALS, this method could be used to compare healthy with diseased motor neurons in culture. We recently used radioactive flux tracing to show energy substrate-specific metabolic rewiring during motor neuron differentiation . Moreove neurons . However neurons ,83,84. WThe measurement of abundance for various metabolites is an unbiased way to explore the metabolic condition linked to health or disease. Metabolites are more challenging to measure when compared to peptides, as they are smaller (0.5–2 kDa) and often lack common building blocks . Metabolm/z). Mass analyzers resolve the created ions either in a time-of-flight tube or in an electromagnetic field and can be used in a single (MS) or tandem (MS/MS) configuration. For targeted metabolomics, the MS methods of choice are single quadrupole, quadrupole ion trap, triple quadrupole and orbitrap [m/z and allow detection of a specific biomolecule [m/z [m/z providing high acquisition speed together with high mass resolution and accuracy [Separation of polar/non-polar metabolites and charged metabolites by, respectively, LC- and GC-MS is followed by ionization ,89, typiorbitrap . Single molecule . The quaule [m/z . The triule [m/z . Here, saccuracy .In addition to MS, NMR analyses have also been used to perform metabolite analysis . Using tSOD1 in NSC34 motor neuron-like cells resulted in increased glycolysis [SOD1 [Although promising, few studies have used metabolomics to explore energy metabolism in the context of ALS. Nevertheless, metabolomics showed that introducing mutant ycolysis . Metabolis [SOD1 . Furtheris [SOD1 ,103,104.2H, 13C and 15N [Conventional metabolomic techniques, as described above, measure the abundances of metabolites in a given sample but can also be combined with stable isotope tracing to map the detected biomolecules to metabolic networks and pathways. Unlike radioactive isotopes, stable isotopes do not emit energy in the form of ionizing radiation and are detected based on their mass-difference. The most commonly used isotopes in metabolomics are  and 15N ,106,107. and 15N . Further and 15N .13C6-glucose and 13C3-lactate fate mapping to show that during motor neuron differentiation, iPSCs undergo a prooxidative metabolic switch fueled by lactate oxidation and rewire metabolic routes to import pyruvate into the TCA cycle in an energy substrate-specific way. In addition, we showed that major catabolic pathways were not affected in FUS-ALS patient derived motor neurons [For example, we used  neurons . Also in neurons . Applica neurons ,112,113.Several studies suggested that energy metabolism is disturbed in ALS but to date, the contribution of changes in energy metabolism to ALS pathology remains unclear. Metabolomics thus offers a way to analyze the metabolome and represents a promising tool for ALS research as it could lead to a better understanding of the suggested failure in energy metabolism. However, the conventional metabolomic tools described above have limitations to consider. For example, MS-based metabolomics, as discussed above, requires a significant amount of starting material and fails to discriminate potential differences between individual cells or cellular compartments.In this section, we present a snapshot of emerging tools in this rapidly progressing omics field that could be of particular interest to ALS research . To our The rapid growth of the metabolomics field relies on more advanced, high-throughput analytical platforms together with new tools for data processing . RecentlSome issues arise using untargeted metabolomics regarding the compound identification and the contextualization of metabolites into biological pathways . An analThe majority of metabolomic tracing studies are performed on in vitro models, in which one can easily apply stable isotopes and efficiently extract metabolites. ALS can be modeled by primary cultures of rat or mouse motor neurons, but also by differentiated cultures obtained from patient-derived iPSCs ,126,127.G93A mouse model is best characterized, other SOD1, as well as TDP-43, FUS, UBQLN2, VAPB and VCP mouse models have been generated , shotgun MS and DIA-based Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) protein profiling assay, can be successfully used [2H2O) [A suitable model for in vivo metabolomics in ALS is the rodent model, given the valuable information gained from the use of previously established mouse models. While the mutant SOD1iew, see ). Stableiew, see . After iiew, see ). Indeediew, see ,144,145,iew, see ,148,149.lly used ,154,155.lly used . Aside fd [2H2O) . Moreoved [2H2O) . Measured [2H2O) . This ded [2H2O) ,160. MS The ability to perform stable isotope tracing in vivo offers great opportunities for ALS research, as it is a complex disease in which motor neurons are known to be affected by surrounding cells ,130,161.Another tool that can incorporate the microenvironment of motor neurons in the analysis of the metabolism is co-culture, either in 2D or 3D. In these conditions, multicell or tissue-scale metabolomics could produce misleading data about the conditions of a cell since it yields averaged data. However single cell metabolomics could enable us to look at the metabolic state of the different cell types and subtypes separately. Moreover, as it has been suggested that motor neurons could suffer from non-cell autonomous toxicity, single cell metabolomics could provide a way to investigate the metabolic state of all cells present in the brain and spinal cord in parallel, instead of focusing on the motor neurons.One of the most critical steps in single cell metabolomics is the cell sampling. The methods used for traditional metabolomics are not applicable to single cell applications because a higher amount of input material is required and single cell methods are often too time consuming, affecting the metabolic state of the cells. Two solutions have been proposed to deal with this issue. The first one is the use of nanodevices to separate the cells. An example of a nanodevice is the micropipettes, which can be coupled to automated systems targeting different cell types based on visual cues. After the selection, the target cell can be picked and manually guided by a robotic system . NeverthMost of these single cell studies use shotgun-like approaches, which means that MS is not preceded by a separation step. This approach makes it more difficult to distinguish isomeric metabolites. In addition, random and systematic errors could arise due to matrix effects like ion suppression or enhancement due to the simultaneous ionization of multiple analytes. Single cell metabolomics can nevertheless lead to new information about the metabolic conditions of motor neurons and surrounding cell, potentially lost in the bulk measurement of traditional metabolomics.The abovementioned techniques measure the average metabolic activity of the cell. However, recent knowledge has been gained about subcellular compartments suggesting each could harbor specific metabolic activities . SpatialIn the context of energy metabolism, one of the most obvious subcellular compartments are mitochondria. Mitochondria harbor both the TCA cycle and fatty acid oxidation, and both their morphology and function are disturbed in ALS patients and models ,35,36,37The results on the mitochondrial metabolome could and perhaps should be combined with data on mitochondrial oxygen consumption to obtain feedback regarding the metabolic state of the mitochondria. Furthermore, the distribution of mitochondria across the cell is also an essential consideration in energy homeostasis. The ability of the mitochondria to traffic across the cell is based on local energy demand. The axonal transport of mitochondria, driven by on-board glycolysis, toward the energy demanding synapses is crucial for the normal function of neurons ,185. ThiIn this review, we focused on tools which we believe are of particular interest in the context of ALS research. Given that disease etiology is incompletely understood for most ALS cases, omics technologies offer an advantage as these are not hypothesis driven by nature. While genomics and transcriptomics provide information about the phenotype, metabolomics is unique as it is able to link the genotype to the phenotype . The comIndeed, great strength lies in the combination of these various existing and emerging metabolic tools to study metabolic defects. The role of energy metabolism in ALS is underappreciated and while it becomes more apparent that the mitochondrial, carbohydrate and lipid metabolism are dysregulated in ALS, the mechanisms underlying this dysregulation remain mainly unknown. Therefore, there is a need for studies applying a combination of state-of-the-art metabolic tools to unravel the possible causal link of metabolic defects to ALS pathology.In addition, the combination of these novel metabolomic tools with new modelling strategies could increase the value of metabolic findings in ALS. Although mouse models of ALS are a valuable tool, a limitation of these current rodent models is that they are often based on the overexpression of disease-related transgenes. Indeed, overexpression of a transgene itself is proven to induce mitochondrial vacuolization and hence metabolic dysfunction ,190. NewGiven its unbiased nature, metabolomics could also be a valuable tool in the search for reliable biomarkers in ALS. A range of metabolic biomarkers are suggested, though no single metabolic biomarker is deemed to be reliable on its own . While o


Mycoplasma hyopneumoniae is cultured on large‐scale to produce antigen for inactivated whole‐cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint‐based genome‐scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time‐series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non‐growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model‐driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3–4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339–2347. © 2017 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc. M. hyopneumoniae causes enzootic pneumoniae in pigs. Multiple vaccines are available that provide protection against M. hyopneumoniae infection; all contain the inactivated whole‐cell bacterium as active component. Manufacturing of these vaccines is done in large‐scale fermenter systems in which a sufficiently high biomass concentration should be reached to meet production requirements. However, reaching sufficiently high biomass concentrations in M. hyopneumoniae fermentations is challenging due to the fastidious growth requirements of this organism, which remain largely unknown.Only for a small number of mycoplasma species a chemically defined medium enabling growth is available Rodwell, . As a reHaemophilus influenzae  provides a list of biochemical reactions, describes the network topology of metabolic pathways and provides a modeling framework to predict and understand biological processes  column. Injection volume was 5 μL; eluents used were 100 mM NaOH and 5 mM NaOH. Lactate, acetate, formate and pyruvate were determined using the same HPLC system but fitted with a Dionex IonPac AS‐11‐HC column (250 × 4.0 mm) with a 5 μL injection volume and three eluents: 100 mM NaOH, 5 mM NaOH, and purified water. Each fermentation sample was measured once per method.The genome was annotated using the SAPP annotation pipeline,  and to the gene content of the M. pneumoniae metabolic model  were used to model glucose, lactate, glycerol, oxygen, and pyruvate transport using first‐order Michaelis–Menten kinetics  based on the protein domain repertoire , consisting of 5 scaffolds, of lengths 341707, 244821, 176273, 32027, and 4049 bp, with an average GC content of 28.6% (NCBI accession number MWWN00000000). We identified 681 protein coding sequences, 466 proteins with at least one protein domain, 855 unique protein domains, and 77 EC numbers using InterProScan  reactions unlinked to genome encoded functions. This was a direct result of the approach used during manual curation, where we assigned functionality based on the presence of protein domains associated to a metabolic reaction and, in general, only added reactions for which associated protein domains where found in the genome, which resulted in a reduction in the number of orphan reactions. For example, we did not find protein domains related to fumarate production, succinate production, folate metabolism, phosphatidylcholine synthesis, and synthesis of glycolipids and therefore did not incorporate these reactions in our model although they were present in the reference model as orphan reactions. Interestingly, in the reference model of M. hyopneumoniae strain 232 there was no annotated gene for alcohol dehydrogenase (EC 1.1.1.1), while other M. hyopneumoniae strains did have this functionality annotated. In strain 11 we did not find a gene for alcohol dehydrogenase either. Comparison of our M. hyopneumoniae metabolic model to the M. pneumoniae metabolic model showed notable differences such as the presence (in our model) of a myo‐inositol degradation pathway, the presence of a N‐acetyl‐D‐glucosamine degradation pathway and a likely absence of folate metabolism as only a single gene related to folate metabolism could be found in the strain 11 genome: EC 2.1.2.1: glycine hydroxymethyltransferase. Also absent in our model when compared to M. pneumoniae were alcohol dehydrogenase, arginine fermentation, glycolipid metabolism, and thymidylate synthase.The number of reactions in our model was lower than the number found in the model of −1 . Part of the growth rate variation between batches is most likely caused by variations in the medium composition due to the use of components from animal origin. For initial model simulations the only carbon sources for which consumption was allowed were glucose, glycerol and glycerol‐3‐phosphate. We had no analytical method to measure glycerol‐3‐phosphate in the medium and therefore copied the consumption rate for this compound from the M. pneumoniae model. Additionally, we allowed in silico consumption of other components such as amino acids, nucleotides and lipids as listed in Table S3. We used the CBM to estimate (and subsequently fix in the model) an energy expenditure of 18.4 mmol · gDW−1 · h−1 as non‐growth associated maintenance rate (NGAM). The NGAM was estimated by adding a non‐specific energy consumption reaction to the model. The lower limit of this reaction was then gradually increased until agreement was found between the measured and the model predicted growth rate.We measured the initial substrate concentrations in several medium lots to determine starting concentrations of metabolites (Table S2). The average growth rate reached in FRIIS medium was 0.0177 h−3 gDW · l−1. Measured biomass and glucose concentrations were in agreement with predicted values  exponential growth showed that in the simulations with pyruvate addition, a higher fraction of energy was allocated to growth  upon consumption of 0.25 mmol myo‐inositol · gDW−1 · h−1 (Table S8). If a knock‐in with an ABC transporter for myo‐inositol is done, the ATP demands of the transporter would enable a slightly lower growth rate (0.0308 h−1). An even more challenging knock‐in would be the expression of the complete pathway for arginine fermentation, which is absent in M. hyopneumoniae. In M. pneumoniae this pathway consists of five genes (MPN304‐307 and MPN560) coupled to three reactions. Simulation of a knock‐in with the five M. pneumoniae genes and an additional transporter for one of the by‐products L‐ornithine leads to an increased in silico mutant growth rate of 0.0235 h−1 (Table S8), as long as arginine consumption is allowed with the same flux as determined in M. pneumoniae.The model was used to study the impact of gene knock‐in mutants on growth assuming growth on FRIIS medium with only glucose added. Our model includes a complete myo‐inositol consumption pathway which was absent in other mycoplasma species. We did not observe consumption of this component in aerobic fermentor studies and in our model the myo‐inositol transporter is an orphan reaction as there was no annotated transporter. A M. hyopneumoniae strain 11 to study and improve biomass yield in fermenter systems. Our aim was to understand cellular energy balances during growth and find methods to increase biomass yield. We found that in the standard cultivation medium used for M. hyopneumoniae, 84% of total ATP production by the cell is used for non‐growth associated maintenance and only 14.9% is used for growth. This confirms the results found in M. pneumoniae. A possible explanation for the high percentage of energy used for NGAM could be the high surface‐to‐volume ratio of mycoplasma species which means that maintaining cellular homeostasis is energetically expensive . This shows that strictly for understanding the cellular energy distribution, the contribution of reactions in co‐factor and vitamin production can be neglected. Moreover, it remains unclear which of these components are consumed directly from the complex medium and which are produced by the bacterium. Our CBM also lacked almost all reactions related to folate metabolism although this component is generally assumed to be essential for growth because formylation of initiator methionyl‐tRNA is needed to start translation in bacteria . Also our predictions for gene essentiality were based on growth in complex medium while conditions in the host will be different and gene essentiality might be higher because nutrients are scarce. Though not all genes were found to be essential, our transcriptomics dataset indicated that all genes in the model were expressed and although the transcriptome dataset collected is small, we identified a correlation between gene expression and flux for the glycerol uptake facilitor (glpF) and for the glucose PTS transporter (MHP629) (Table S10).M. hyopneumoniae, the potential of the described knock‐in mutants has to be carefully evaluated. Our model predicted that both the introduction of a myo‐inositol transporter (single gene knock‐in) as well as an arginine fermentation pathway (5 gene knock‐in) potentially increases the growth rate of mutant strains.Given the challenges associated with introducing genetic modification in M. hyopneumoniae growth is studied in controlled fermenter systems. In these systems pH and oxygen concentration were controlled resulting in a stabilized growth profile and allowing accurate measurement of growth rate, substrate uptake rates and by‐product formation which is essential data for model validation and parameterization. Our stringent approach for model creation, applying annotation based on functional protein domains, resulted in a more condensed model of M. hyopneumoniae when compared to the reference model. Following an iterative cycle of model driven experimentation, we were able to double biomass production and reduce total process time by pyruvate addition to aerobic fermentations, which improved the economic potential of the production process. The model we present provides a solid basis to understand the metabolic capability of M. hyopneumoniae and further optimize the production process regarding biomass yield and process robustness.To our knowledge, this is the first study where CBMConstraint‐based modelSBMLSystems Biology Markup LanguageCOBRAConstraint Based Reconstruction and AnalysisDWDry weightBASHBourne‐again shell; Metabolite abbreviations are explained in Table S11Authors acknowledge Mark Davids for help with model transformation from Pathwaytools to SBML and further adaptation of the model using the COBRA toolbox in Matlab. Also, we acknowledge the P‐ATS department at MSD‐AH for analytical support. This work was financially supported by MSD Animal Health, Bioprocess Technology and Support, Boxmeer, The Netherlands. VMS, PS, and MS‐D also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 634942.TK, SJS, PS, and VMS contributed to study design. TK, MS‐D, and PS interpreted the results. TK, PS, JB, and MS‐D drafted the manuscript. All authors revised the manuscript and approved the final version. All authors take responsibility for accuracy and integrity of the work.Additional supporting information may be found in the online version of this article at the publisher's web‐site.Figure S1. Dissolved oxygen concentration in aerobic fermentations. Dashed lines show measured values and the solid lines shows values assumed during dynamic modeling. (A) Measured dissolved oxygen concentration in aerobic batches grown on glucose. Solid line was assumed for the model based on a second order polynomial fit (−0.0317*t ^ 2–3.1847*t + 81.837). (B) Measured dissolved oxygen concentration in aerobic batches grown on glucose and pyruvate. Solid line was assumed for the model based on a second order polynomial fit (−0.0638*t ^ 2−3.0848*t + 101.83).Figure S2. SCAN_UPC analysis for genes in TK284‐MHyo11. Evaluation of gene expression was done based on read counts per gene present in the genome‐scale model using the package SCAN_UPC . UPC expression scores are high or 1 for all genes in the model indicating that these genes were expressed. Calculations were done with reads counts obtained from the fermentation with basic FRIIS medium with only glucose added.Click here for additional data file.Table S1. Initial model constraints for simulations in medium with glucose and early growth in medium with glucose and pyruvate. Highlighted in yellow are the model constraints that were changed in the simulations with pyruvate present.Click here for additional data file.Table S2. Metabolite starting concentrations FRIIS medium.Click here for additional data file.Table S3. Components consumed directly from the growth medium.Click here for additional data file.Table S4. Calculation of energy distributions from model predicted reaction fluxes.Click here for additional data file.Table S5. Flux variability analysis at 99.99% of maximal initial growth rate.Click here for additional data file.Table S6. Flux variability analysis at 99.9% of maximal initial growth rate.Click here for additional data file.Table S7. Gene essentiality analysis using the metabolic model. Highlighted genes were found to be essential with a biomass equation containing NADPH, FAD, and CoAClick here for additional data file.Table S8. Flux distributions for knock‐in strains.Click here for additional data file.Table S9. Predicted fluxes for the exponential growth phase.Click here for additional data file.Table S10. Expression level of model genes determined using RNA sequencing in the glucose batch and pyruvate batch. Highlighted genes were mentioned in the manuscript.Click here for additional data file.Table S11. Abbreviations used for metabolite names in the metabolic map.Click here for additional data file.


Drosophila melanogaster. We chose this system, as survival of the animals depends on the successful allocation of their available resources to the conflicting processes of growth and storage metabolite deposition. First, we generated “FlySilico”, a curated metabolic network of Drosophila, and performed time-resolved growth and metabolite measurements with larvae raised on a holidic diet. Subsequently, we performed flux balance analysis simulations and tested the predictive power of our model by simulating the impact of diet alterations on growth and metabolism. Our predictions correctly identified the essential amino acids as growth limiting factor, and metabolic flux differences in agreement with our experimental data. Thus, we present a framework to study important questions of resource allocation in a multicellular organism including process priorization and optimality principles.Organisms depend on a highly connected and regulated network of biochemical reactions fueling life sustaining and growth promoting functions. While details of this metabolic network are well established, knowledge of the superordinate regulatory design principles is limited. Here, we investigated by iterative wet lab and modeling experiments the resource allocation process during the larval development of  This is especially true for multicellular heterotrophic organisms, which often have an almost unlimited amount of destinations for channeling their available resources. On top of a plethora of energy consuming processes available to the whole organism , higher order multicellular organisms are composed of a multitude of organs. This results in an even higher complexity, given that many organs have distinct and different metabolic preferences, which is important during health and disease states5. The mammalian brain, for example, depends on sugars for energy production, whereas most other body cells can additionally utilize other energy liberating pathways such as fatty acid beta-oxidation. To this end, a bottom-up understanding of the resource allocation regulation therefore appears close-to impossible based on this complexity and lack of detailed information. Thus, an abstraction in the form of a top-down modeling paradigm has the potential to reveal design principles, which serve as a starting point to investigate resource allocation principles. Yet, the various degrees of freedom available to complex organisms complicate the modeling procedures, as model solving usually targets the optimization (maximizing or minimizing) of a distinct objective function such as e.g. biomass production or growth6. Given that multicellular organisms can have multiple and often conflicting objective functions , or objective functions without the aim of maximization or minimization , the identification of a single and clear-cut objective function is difficult.Balancing limited resources to concurrent processes is an essential task in all areas of life. Every organism, for example, needs to allocate its available resources – mostly in the form of diet-derived nutrients – to concurrent processes such as live sustaining functions, reproduction, or storage metabolite synthesis. While the metabolic pathways involved in these processes, as well as some regulatory signaling pathways, are well established, the overarching design principles governing resource allocation and prioritization are elusiveDrosophila melanogaster7. Given the variations many organisms face during development – based on e.g. fluctuation in temperature or food supply and quality – developmental programs are at the same time also flexible  Drosophila larval development and resource allocation shows an inherent plasticity. Poor nutritional conditions, for example, result in a prolonged developmental timing based on a lowered rate of development  For the modeling procedures, we benefitted from a previously published chemically defined, fully synthetic minimal Drosophila food 26, which allowed us to clearly define the input of our model. On top of the general advantages of working with small animals, such as the large number of progeny and accessibility of the developmental stages, these characteristics clearly facilitated our investigations.Here, we use the larval development of nts Fig. . This sysis Fig. 13. In orent Fig. 15. This Drosophila larval development, we generated the – to the best of our knowledge – to date largest curated metabolic network for fruit flies and subjected it to flux balance analyses. To validate and optimize our metabolic network, we used targeted metabolite quantifications and GC-coupled mass spectrometry metabolomics measurements of larvae grown on the HD. We built the model capturing previously known requirements of Drosophila metabolism, such as sterol auxotrophy27, and tailored it to incorporate all prominent ingredients of the HD. Our model predictions allowed us to correctly identify the amount of essential amino acids as growth limiting factor. Further, the model predictions resulted in flux differences, which are in line with the measured metabolite alterations associated with growth of the larvae in food with elevated amounts of sucrose or essential amino acids. These proof-of-principle experiments provide a starting point to investigate the optimality principles of multicellular resource allocation.To target resource allocation in Drosophila larval growth and resource allocation, we first constructed a flux balance capable metabolic network covering the biochemical pathways necessary to metabolize the major constituents of the minimal, synthetic medium 26, which we used to grow the fruit flies during the wet lab procedures. On top of the central carbon metabolism, we therefore included e.g. amino acid, lipid, and carbohydrate metabolism .In order to model ism Fig. . In totaDrosophila model. While of course still limited in size, our model has a low amount of biologically unfeasible reactions. The importance of this became evident when we performed simulations with the whole genome, in silico generated Drosophila metabolic network. Here, simulations resulted in a positive Biomass production  as well as on minimizing the number of exchange reactions (see methods section). Figure ion Fig.  even witDrosophila animals on the HD. The complete larval development (until prepupae emerge) appears quasi linear and takes on the HD about 170 hours. In order to follow the development and metabolite profile over time, we collected larvae at three equally spaced time points during development . To determine growth progression over time, we measured the wet and dry weight  costs, as this measure is difficult if not impossible to obtain for a multicellular organism. The other coefficients of the biomass function are based on our own measurements . Calculations based on both parameters resulted in an amount of 0.064 g/h food consumed per hour. We used this approximated food intake amount to calculate for each food ingredient the maximum amount consumed per hour. Of course, the calculated amount of consumed food is likely a prominent overestimation. For example, the larvae will most likely not fill their mouth completely with every bite. Further, not all food ingredients passage the gut barrier with 100% efficiency and at this point, we do not know the resorption rate for the different nutrients. Thus, we sought to identify a correction factor to limit the nutrient influxes to a reasonable level. For this purpose, we solved the FBA-model with a wide range of diminishing correction factors -transferase, MGH = maltose glucohydrolase SGH = sucrose glucohydrolase, UDPGTRA = UDP-glucose:glycogen 4-alpha-D-glucosyltransferase) increased prominently if the sucrose input was increased Fig. . The simDrosophila activates a metabolic program for an overflow metabolism perhaps associated with a larger burning or excretion of sugars. The 2x EAA diet, however, resulted in a significantly higher glucose content of the animals . Our initial rationale that a simple model might be already useful appears valid, given that it is already able to recapitulate basic biology  with metabolic networks of heterotrophic multicellular organisms usually appears complicated due to the difficulty of identifying a clear-cut objective function. Further, the various cell types of complex organisms, with their signature gene expression profiles and distinct metabolic and functional tasks, complicate modeling approaches even more. To tackle these difficulties, methods and strategies were designedogy Fig.  and succogy Fig. –8. In thDrosophila S2R+ cells43  might appear as a weak spot of our approach. So far, we determined the NGAM values in an iterative process based on the oxygen consumption rate of We parameterized our model using absolute enzyme-based biochemical quantifications as well as via GC-MS based metabolomics measurements Figs . Overall34. Nevertheless, in the future, methods that are more sophisticated should be used to eliminate this shortcoming using e.g. radiolabeled tracer experiments, which will also allow the direct estimation of metabolite flux rates. These in turn will allow a much better comparison to the predicted flux rates as our endpoint measurements. Another point to consider is that the HD represents a minimal medium, which results in a slowed down growth. Here, we started to investigate which nutrients might act as growth limiting factors and our strategy appears to be successful in predicting nutrients allowing a faster growth. For the investigation of resource allocation and optimality principles, the slowed-down growth appears less problematic, given that optimality of resource allocation is situation dependent, and thus an inadequate diet can also be utilized in an optimal manner.The use of the chemically defined HD significantly facilitated the connection of the biological data to the modeling both under basal Figs –8 as wel46. Our early results with the predictions of the impact of altering sucrose or EAA levels in the food are particularly promising. They suggest that simulations with FlySilico should facilitate the identification of suitable parameter ranges for experiments targeting e.g. the nutritional requirements of larvae and flies or the impact of diet alterations on the metabolic and/or growth phenotype. The future FlySilico-based investigations concerning the impact of varying amounts of essential and branched chain amino acids on growth processes, life history traits such as fecundity, ageing related diseases and cancer will be exciting as these aspects gained a lot of attention recently50.Investigations concerning the minimal nutritional requirements of an organism, as well as concerning the impact of nutritional alterations on the physiology of an organism, is an active field of researchDrosophila larval growth is marked by an impressive increase in size and mass. This expansion is necessary for a successful completion of metamorphosis, which involves a drastic remodeling of body structures and a food intake cessation. Therefore, the larval development is subject to hard biological constraints. Still, the organism can react to fluctuations in the quality or quantity of food by adjusting the rate of development and resource allocation, e.g. by channeling less nutrients into storage forms15. Our experiments, where we raised animals on either standard HD or HD supplemented with additional sucrose or EAA  and incorporation of additional fly genotypes and perhaps single gene mutations will help to further elucidate this intriguing possibility.An important aspect is that solving the flux balance model is by definition following optimality principles. Given that our relatively simple model already was able to result in correct predictions suggests that For all our experiments, we used the wild type Oregon-R fly strain reared under standard culture conditions .Holidic diet; HD) was introduced in26. Animals developing on HD are viable, fertile, and have no aberrant phenotypes, although the development is slowed-down and the HD is thus classified a minimal medium. Food was prepared according to the instructions of the Piper et al. publication with use of the Yeast-like amino acid composition (“Yaa”). For our perturbation experiments we either added the double amount of sucrose (2x sucrose) or the double amount of essential amino acids (2x EAA).The chemically defined (holidic) medium , 7.1 g polenta , 0.95 g soy flour , 1.68 g yeast , 4 g treacle , 4.5 g malt extract , 0.45 mL propionic acid  and 1.5 mL nipagin   before we transferred them to the chemically defined medium to allow oviposition. After six hours we discarded the parental flies to have a defined time period for the egg-laying. As the maximum time point for collecting larvae we used 168 h after egg laying as afterwards the larvae start to pupariate on the HD. Based on this terminal point, we added two equally spaced time points earlier in development  as growth is quasi linear.The possibility that larval growth and metabolism is showing a sexual dimorphism appeared intriguing. Pilot experiments using animals reared on the holidic or a standard diet, however, showed that at the latest timepoint used by us female and male larvae do not significantly differ in terms of the size, the triglyceride, glucose or glycogen levels (data not shown). Thus, we did not consider the sex of the animals during our experimental timeframe a prominent factor and therefore collected unsexed larvae 96 h, 132 h and 168 h after egg laying and washed them in PBS with 0.1% Tween-20 (PBT) for the weight measurements and metabolic assays or in HPLC-graded water for the GC-MS analytics. We used quadruplicates for every condition and collected 25 larvae (96 h) or eight larvae  for the GC-MS and the metabolic assays. For the dry and wet weight measurements, we collected 100 animals from the 96 h time point and 40 animals from the 132 h and 168 h time points, respectively.For the determination of the wet weight, we transferred the washed larvae into pre-weighed 1.5 mL tubes and weighed them on an analytical scale. The animals were then snap-frozen in liquid nitrogen and dried in an oven at 60 °C with the tube lids open. After 24 h we measured again the weight ( = dry weight) and calculated the water content by subtraction of the dry weight from the wet weight.For the larval size measurements, we collected at the indicated time points the animals in ice cold PBS to minimize their movements and to ensure their elongation. Subsequently, we recorded images with a Zeiss SteREO Discovery.V8 dissection microscope, which were analyzed with the Zeiss Zen Software (Zen 2.3 lite – blue edition). For each larva, we measured the area, the length and its width. In total, we performed three biologically independent experiments and measured 20 to 30 animals per repetition.31. We collected the larvae from three different time points and snap-froze the animals in liquid nitrogen before storage at −80 °C. For the homogenization, we used 1 mL 0.05% Tween 20 in water in 2 mL screw-cap tubes and a Fast Prep FP120 machine (Bio101 Savant). After homogenization and heat-inactivation for 5 minutes at 70 °C, the supernatant was transferred to 1.5 mL tubes as a reservoir for the metabolic assays, which were performed in 96-well plates. We normalized each measurement to the amount of animals per sample.All targeted biochemical measurements were essentially carried out as described inThe free protein content was measured using the Pierce BCA assay kit (Life Technologies) according to the manufacturer’s instructions. We used bovine serum albumin (BSA) as a standard to determine the protein content of the samples. The 0.05% Tween-20 used by us in the homogenization procedure most likely was not sufficient to solubilize also integral membrane proteins. Thus, the protein amounts per animal might represent an underestimation of the real total protein content.For the determination of the triglyceride levels in the samples, we used the Triglycerides Reagent (Thermo Scientific). We transferred 50 µL of the samples and a serial dilution (1:2 in 0.05% Tween 20 in water) of the glycerol standard (Sigma Aldrich) to a 96-well plate and added 200 µL of the Triglycerides reagent. The samples and the standard were incubated 45 minutes at 37 °C and the absorbance was read at 510 nm.The glycerol content of the samples was determined using the Glycerol Assay Kit (Sigma-Aldrich). We followed the manufacturer’s instruction for fluorometric measurements.51. For both measurements, we transferred 30 µL of the undiluted samples and the standards to a 96-well plate. We added 100 µL GO reagent to measure free glucose and 100 µL GO reagent with amyloglucosidase (1 µL per 1 mL GO reagent) to measure the total glucose content (free glucose plus glucose liberated from the glycogen). After 60 minutes incubation by 37 °C we stopped the reactions by adding 100 µL 12 N H2SO4 and measured the absorbance at 540 nm. We calculated the glycogen content by subtraction of the free glucose from the total glucose content.For the determination of glucose and glycogen, we used the GO Assay Reagent (Sigma-Aldrich) and a modified form of a protocol described inFor the quantification of lactate, we used the Lactate Assay Kit (Biovision). We transferred 50 µL of the pre-diluted samples (1:50) to a 96 well plate and followed then the manufacturer’s instruction for fluorometric measurements.52.Measurements were performed as described inMetabolites were extracted using 105 µL chloroform and 245 µL methanol. After incubation for 1 h at −20 °C we added 560 µL HPLC grade water twice. The samples were centrifuged for two minutes at high speed in a table top centrifuge at 4 °C and the aqueous phases were collected for the GC-MS measurements .53. The data were analyzed with the Mass Hunter Software (Agilent). For absolute quantifications, we used five different dilutions of the standard mix  system (7200 GC-QTOF from Agilent) was used as described inin silico reconstruction of Drosophila melanogaster growth and metabolism we focused on core metabolic pathways required to metabolize the HD ingredients, and used the cameo package for the Python programming language54. The Yeast iMM904 model from the BiGG data base32 and a previously published Drosophila model for hypoxia investigations30 served as starting points for our network reconstruction. First, we incorporated major metabolic pathways of the carbohydrate metabolism  cycle and pyruvate metabolism), the lipid metabolism (with a focus on the glycerolipid metabolism), the energy liberating metabolic reactions (e.g. oxidative phosphorylation) and anaplerotic reactions. Subsequently, we successively integrated metabolic reactions necessary to metabolize the HD ingredients, such as amino acid metabolic pathways , pathways of vitamin synthesis (e.g. folate and riboflavin) and various pathways needed for the transport and synthesis of cofactors. For the initial version of FlySilico we only focused on three different compartments  and adapted the transport reactions accordingly. We manually curated all reactions by cross-validation with multiple resources  and paid special attention on the biochemical pathways and the genetics of Drosophila melanogaster. We attached to each reaction a confidence score based on evidence of sequence, physiological, genetic or biochemical data (as previously suggested by e.g.62). Reactions required for the modeling, but without any evidence of correctness, received the lowest confidence scores. FlySilico version 1.0 covers 293 metabolites and 363 reactions. Supplemental Table For the i) and upper (βi) bounds to each flux vi impose additional constraints to the system. The null space of S includes any v that satisfies the solution under this steady state assumption with the given constraints. The model is solved by optimizing the system for a given objective function, i.e. the primary goal of an organism (such as biomass production for fast growing unicellular organisms as for example E. coli), using linear programming. A detailed explanation of constraint-based modeling, flux balance analysis and linear programming is provided e.g. in:63 or64. For our model solutions we compared solutions allowing loops as well as loopless66 variants  is a computational method to identify the maximum and minimum fluxes of reactions from a given network while it preserves a certain network state (e.g. maximum biomass production rate). FVA solves two optimization problems for each reaction vi after solving a given objective function.γ is a parameter which controls the optimality of the solution , c is the vector which represents the linear objective function.Flux variability analysis  and on top of free protein, glucose, triglyceride and glycogen amounts, we also determined the levels of lactate and free glycerol by targeted biochemical assays (see above). Our GC-MS measurements further covered metabolites from the central carbon metabolism as well as almost all free amino acids.Drosophila proteome. Thus, we first calculated the frequency of each of the twenty classical amino acids in the complete proteome of Drosophila melanogaster (http://www.uniprot.org/uniprot/?query=proteome:UP000000803). Figure i between 168 h and 96 h in i at 168 h in i at 96 h in i in fAAi which represents the frequency of amino acid i from Fig. i in gram, and Biomass functions usually cover each amino acid separately. Yet, our free protein measurements did not provide such fine-grained information. The GC-MS based metabolomics measurements resulted in the identification of free amino acid amounts; yet three amino acids  were missing in our measurements. In order to approximate the levels of the different amino acids, we followed a bioinformatics strategy with the reasoning that the amino acid fractions would relate to the respective amino acid frequency across the i in i at 168 h in i at 96 h in i in i in The GC-MS analysis quantified free metabolite amounts. In order to calculate a GC-MS coefficient, we calculated the difference of metabolite amounts between 168 h and 96 h by the following equation:i, i, and i. All coefficients of Eq. Drosophila melanogaster is cholesterol auxotroph68. To simulate the cholesterol auxotrophy of Drosophila, we included cholesterol in the biomass function based on measurements of cholesterol levels from larvae reared on HD  and used the different uptake rates to calculate the corresponding growth rate. We selected for χ the value where the calculated growth rate fitted the experimentally determined growth rate best (Fig. Our calculation is of course an overestimation given that each sclerite reaction most likely does not fill the mouth volume completely and that the uptake from the gut is not 100% efficient. To account for this limitation, we introduced a correction factor est Fig. . We detei in χ is the correction factor, and i in the holidic medium.As a result we obtained the experimental growth rate i in i in i in gram.For the network modelling, all dietary internalizations have to be flux rates or uptake rates, which we calculated as follows:Drosophila melanogaster is presented, and its use for the investigation of larval growth and metabolism is demonstrated.FlySilico, a flux balance analysis suitable metabolic network of Supplemental InformationDataset 1Dataset 2Dataset 3Dataset 4Dataset 5


Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the “metabolic objective” of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.Genome-scale metabolic models of  GEMs comprise linear equations describing conversions among metabolites, uptake or secretion processes, and transport processes over different compartments. These equations are referred to as flux balance constraints and are founded on an underlying metabolic network wherein all metabolites are interconnected by conversion and transport reactions. The flux balance constraints are captured in a stoichiometric matrix  enable prediction of metabolic states. A metabolic state is defined as a vector of all fluxes or conversion rates  problem. FBA finds the optimal (maximum or minimum) value of a selected function, the so-called objective function, while satisfying all constraints. Solution of the FBA problem leads to a vector of reaction fluxes that represents a calculated metabolic state of the organism. This calculated metabolic state is more likely to represent the actual metabolic state as the solution space is shrunk by additional constraints , a pathogenic bacterium able to withstand the harsh environment of the phagosome. Mtb is even capable of halting the maturation of the phagosome inside immune cells and providing a niche for the bacterium to thrive  the metabolic state of a cell, such as transcript profiles Hoppe, . For MtbTranscript abundance data can be used to constrain models in environments where knowledge regarding nutrient availability and objective(s) is limited. Methods such as iMAT  ensures unbiased assumptions on the types and quantities of metabolic precursors. During on-going infection mycobacterial cells might enter a non-growth state on which maximal growth rate is not the metabolic objective. However, still minimal macromolecular components need to be synthesized and energetic requirements need to be fulfilled to ensure survival. The condition-specific biomass reaction representing infection combines both aspects as it reflects the composition of mycobacterial cells during infection, and it also represents the metabolic requirements for its survival and interaction with the host which are incorporated in the RNAseq data as well. To simulate the metabolic state of the bacteria upon infection, flux through the condition-specific biomass reaction is maximized, while the total usage of enzymes is minimized. As Mtb faces several types of stress and adverse conditions imposed by the host's immune system during infection of the host . By changing these weight factors, a ratio between these two factors, fr, is established that enables the prediction of metabolic genes essential to Mtb within the macrophage as well as metabolites that are sequestered by Mtb from the phagosome. Comparison of these predictions with experimentally obtained data   and for gene expression data of M. bovis BCG cells infecting THP-1 cells (infection condition). Firstly, upper bounds on unidirectional (forward) reactions, and upper and lower bounds on bidirectional reactions were replaced by the normalized counts assigned to that reaction   . The weight factor for the biomass reaction, fb, was varied while keeping the weight factor for enzymatically catalyzed reactions, fe, i, constant at 0.001 for all reactions i, hence effectively varying fr, the ratio between fb and the sum of all fe, i-values (Equation 2). Subsequently, a flux variability analysis was performed, wherein the maximum objective function value, w  > −0.5 flux is channeled through all (condition-specific) biomass reactions  < 0.3 minimizing flux through the sum of all enzymatically catalyzed reactions is still relevant and the restrictions of the oxygen uptake threshold are not yet overcome. Beyond this point, the (condition-specific) biomass reaction weight factor, fb, is so large as compared to the sum of all fe, i-values, that an optimal objective function value, w, is obtained by solely minimizing enzyme usage, and ignoring maximizing flux through the (condition-specific) biomass reaction. An fr-value of 0.8 was chosen from Figures fr) = 0.5 and log(fr) = 0.3. The growth rates  were maximized indirectly by maximizing the aforementioned bi-objective optimization problem. Each biomass reaction will always obtain its maximal value using this approach. Thereafter, using the COBRA toolbox  based on transcript abundance data in two conditions. The term “biomass reaction” is perhaps not the most suitable term as these reactions not only cover synthesis of metabolites used for biomass production, but also synthesis of excreted enzymes and small molecules, repair of damaged lipid membranes and other metabolites involved in host-pathogen interaction. Even though these processes themselves are largely unknown, transcript abundance data indirectly reflects these processes and combined with a GEM can give a picture of required metabolic precursors for these processes.M. bovis BCG, a close relative of Mtb having a highly similar genome  and CSM (condition-specific medium), for both aforementioned conditions, respectively. A list of all metabolites known or expected to be present in the phagosome was assembled  and a regular biomass growth reaction (REB), both present in sMtb  biomass reaction, and was therefore an unsuitable strategy as well.Simulation of the metabolic state of Mtb in the phagosome is complicated by a lack of knowledge on the rate at which nutrients are acquired from the host. However, various studies have shown that the phagosomal environment is likely to be hypoxic , i.e., the flux through one of the five aforementioned (condition-specific) biomass reactions; fb represents the weight factor for the biomass reaction; n is the total number of reactions catalyzed by at least one enzyme; S represents the stoichiometric matrix; v represents a vector with all fluxes ; b represents a vector with zeros; l represents a vector with lower bounds for all fluxes and u represents a vector with upper bounds for all fluxes. The weight factor ratio, fr, between growth and total enzyme utilization is given by:Wherein fe) and the weight factor (fb) of the (condition-specific) biomass reaction was varied such that log(fr) varied around a value of 0. A log(fr) value of 0 entails that the numerator and denominator of Equation (2) are of equal size and reflects a balanced weight distribution between minimization of enzyme usage  and maximization of growth. By changing the weight factor ratio, the relative importance of enzyme usage minimization and biomass reaction maximization changes  growth related maintenance coefficient enforcing a small minimum flux of ATP to ADP conversion.Each reaction in the model catalyzed by one or multiple enzymes was given the same weight factor . As can be seen in Figure fr-value  = −0.5 and log(fr) = 0.3 (0.3 ≤ fr ≤ 2.0). Beyond log(fr) = 0.3 the restrictions of the oxygen uptake threshold are overcome, and vb and vi-values jump to infinite . An appropriate value for fr was selected from Figures fr-value >0.3 [log(fr) > −0.5], when hardly any nitric oxide is predicted to be taken up  = −0.1, dashed vertical lines], uptake of glutamate and glycerol-3-phosphate is predicted for all biomass reactions except for NRC, the biomass reaction describing non-replicating cells. For this biomass reaction uptake of glutamate is not predicted. In addition, at this point (fr = 0.8) uptake of asparagine is predicted for the condition specific biomass reaction of infection (CSI) and predicted to be likely (the average of minimum and maximum uptake rates is above zero) for the other four objectives. The uptake of alanine at this point is predicted to be likely for all five objective functions.Figures fr-value, regardless of the chosen biomass reaction.As can be seen in Figures fr-values, while asparagine becomes more important at higher fr-values. The routes of glutamate toward most metabolic precursors are shorter than those of asparagine, which is predicted to be taken up at a higher fr-value. In this way the change of the uptake rates with the fr-value reflects the metabolic versatility of each component.Beste and colleagues determined that the amino acids asparagine, alanine and glutamate are likely taken up during infection. Acetate- or acetyl-CoA-derived from β-oxidation of host lipids and CO2 is utilized intracellularly and glycerol-3-phosphate could be a potential carbon source as well  mutants and comparing the maximal predicted growth rate of the wild type strain with the KO mutant. A reduction in the predicted specific growth rate of 95% or more is generally accepted as a threshold value for gene essentiality   = −0.1] is likely to represent the metabolic state in the host. This suggests that, under these conditions, growth represents a major sink to cellular resources. Here we have selected an equal fe, i for all enzymatic reactions i, however this could be modified to account for differences in enzymes, such as size (molecular weight), activity or degradation rates.We have used a bi-objective optimization approach to simultaneously take into account growth requirements and parsimonious enzyme utilization. The tradeoff between both objectives is apparent in Figure Finally, it should be borne in mind that the transcriptomics data do not represent later infectious states, but a single time point 24 h post infection, before the onset of growth arrest. As can be seen from Figures −1 h−1) of the rate used in previous predictions on Mtb metabolism  biomass reactions are not overwhelmingly accurate. In general, the specificity  is quite good, but the sensitivity  is very poor. This is rather remarkable, as such a long list of biomass precursors . This value is similar to the values obtained using the other four biomass reactions. Poor correlation between transcriptomics data and proteomics measurements has been shown in a wide number of publications (Maier et al., in vitro growth or that the list of precursors is not comprehensive. The latter explanation would be plausible, as the predictions on nutrient uptake are quite accurate, suggesting that nutrient uptake is driven by energy efficiency constraints.It is important to notice that during the onset of infection not only the bacterium undergoes metabolic changes, but also the host environment it thrives in most likely undergoes changes as the host responds to infection. This interplay between the host and the pathogen has not been taken into account as here only the bacterium is modeled. Another reason for the inaccurate gene essentiality predictions could be that many enzymes play additional roles in the synthesis of precursors that are not required during RR drafted the manuscript, conceived the study and performed the data analysis. MS-D participated in drafting the manuscript and helped with the data analysis. PS and VM participated in drafting the manuscript. All authors contributed to the study design and critically read, revised and approved 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.