Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrated computational and experimental workflow.Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the microorganisms tend to interact with each other and perform complex actions. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia. Here, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named “Metabolic Support Index (MSI)”, which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-cultures and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of  They exhibit division of labour and can be used to explore the joint metabolic capabilities of the constituent organisms. Microbial co-cultures have been employed to carry out complex functions ranging from xenobiotic degradation Escherichia coli cells were manipulated by engineering pathways to exchange metabolites for improving the titres of n-butanol. In a few other studies E. coli cells were engineered with specialised metabolic pathways to communicate with one another through metabolic exchanges. This engineered E. coli co-culture was used for the production of industrially essential chemicals such as cis,cis-muconic acid, 3-aminobenzoic acid and resveratrol.Besides, there have also been advances in engineering microbial co-cultures to produce fine chemicals. Such synthetic microbial communities consist of organisms that have been engineered to communicate with each other via metabolic exchanges. For instance, in one such study In addition to industrial applications, co-cultures are also useful to understand interactions between organisms. In a community, microbes orchestrate several complex functions by communicating with one another, commonly via metabolic interactions Understanding the metabolism of individual organisms, as well as investigating the metabolic interactions that happen in a community, is central to designing a microbial consortium for a given application. There are likely multiple metabolites exchanged between the consortium members. Experimentally identifying and understanding the role of these metabolic exchanges and interactions entails the profiling of both intracellular and extracellular metabolites. Such untargeted metabolomic profiling becomes very difficult Saccharomyces cerevisiae and Pichia stipitis, drawing on computational analyses, physiological studies and 13C-labelling experiments. First, using our previously developed graph-theoretic algorithm, MetQuest Metabolic Support Index (MSI) to determine which organism derives a relatively higher benefit from the interaction. We show that this metric can successfully quantify the interactions between the organisms using a few examples of previously demonstrated microbial co-cultures. Using one of the widely used co-cultures consisting of S. cerevisiae and P. stipitis, we test the various predictions from our computational analyses. Several studies previously performed have used these organisms not only to produce ethanol from multiple carbon sources P. stipitis benefits from the interaction and takes up the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions. Our approach represents an important step in integrating modelling with experiments to understand and characterise microbial interactions in communities. Ultimately, the design of such microbial communities for specific applications is envisaged.In this study, we design a comprehensive workflow integrating computational and experimental approaches to predict and validate the microbial interactions in a co-culture. We adopt a three-pronged approach to study a co-culture of two industrially important yeast species 22.1in silico methods to calculate the Metabolic Support Index (MSI) and identify the metabolic exchanges between the organisms.In this section, we describe the 2.1.1A in the community stuck in the metabolic network of A, but relieved in the presence of organism B in the community . We compute MSI for a given organism nity see  as(1)MSIvisited or stuck, depending on the presence of precursor metabolites. Stuck reactions are those reactions whose precursor metabolites cannot be synthesised by the metabolic network using the given input conditions, while visited reactions are those reactions whose input metabolites are all present and hence the reaction can proceed.We used our previously developed algorithm, MetQuest https://github.com/RamanLab/metquest). Depending on the availability of the GSMMs of the organisms, we obtained them either from the respective publications S. cerevisiae and P. stipitis, the seed metabolite set consisted of components from YNB minimal medium, D-glucose as the carbon source along with the set of co-factors and co-enzymes. All the data files are available in To calculate MSI for a microorganism, we first constructed the directed bipartite graph of the individual organism, followed by the joint bipartite graphs of the combination of organisms from their respective genome-scale metabolic models (GSMMs) using the construct_graph module from the previously developed metquest Python package (stuck reactions from two different scenarios: (a) when the organisms are analysed independently as “single” graphs  and P. stipitis (NCIM 3497), procured from Microbial Type Culture Collection, Chandigarh, India, and National Collection of Industrial Microorganisms, Pune, India, respectively.In this study, we used 2.2.2The yeast strains were independently maintained on YPD agar consisting of 3 g/L Yeast extract, 10 g/L Peptone, 20 g/L Glucose and 1% agar . From the agar plate, one colony was picked and inoculated in YPD medium and was grown at 30 Glycerol stocks of the respective yeast strains were revived by streaking them onto a YPD Agar plate and incubating at 30 2.2.3S. cerevisiae and P. stipitis were carried out. The culture from the well-grown primary inoculum was inoculated in 500 mL Erlenmeyer flasks consisting of 25 mL of YNB medium with 10 g/L Glucose. For all the experiments, we maintained the starting Optical Density (OD) as 0.17. The experiment was carried out at 300 rpm, 30 To determine the substrate utilisation and product secretion profiles, growth kinetic experiments of both the yeast strains, 2.2.4S. cerevisiae and P. stipitis were independently grown in minimal YNB medium with 10 g/L Glucose. The growth was continuously monitored online using CGQ , and the samples were withdrawn at the start of the experiment, early and late exponential phase. CGQ measures the backscattered light emitted by the growing microbial cells. Data analysis, processing and visualisation, were carried out using CGQuant software version 7.3 .We designed a two-step shake flask experiment to determine if there were any interactions between the organisms. Briefly, in the first stage, S. cerevisiae and P. stipitis as SupSce and SupPst respectively. We inoculated these supernatants with either of these organisms, i.e., to the SupSce we added P. stipitis and to the SupPst we added S. cerevisiae, such that the initial OD was Samples were analysed in HPLC to check if the carbon source was completely depleted. At this stage, the cells were separated from the broth by centrifuging at 8000 rpm, 4 13C) glucose as the carbon source in the first step to cultivate the microorganisms individually. Samples withdrawn were analysed through HPLC for the residual carbon and the extracellular metabolites. The biomass at the end of 24 h was analysed using Gas chromatography-mass spectrometry (GC–MS) to identify the labelling patterns of the amino acids alanine, valine, serine and aspartate.To check for the metabolic interactions, we repeated the same two-stage experiments as above, with a single modification. Instead of naturally labelled glucose, we used 100% uniformly labelled (U-2.32.3.12 = 0.98) for the metabolites ethanol  (0.25–2 % (v/v)) and glucose (2–15 g/L) were prepared by injecting known quantities and measuring the area under the respective peaks.The yeast supernatant was quantified using High Performance Liquid Chromatography (HPLC)  with an organic acid stationary phase  and a ThermoScientific TriPlus RSH Autosampler. The separation of the amino acids was achieved using Trace GOLD TG-SilMS fused silica column  to confirm the presence of corresponding amino acid. Also, we determined the average carbon labelling and the relative abundance of every fragment after performing corrections for proton gain and original biomass using the iMS2Flux software From the chromatogram and the mass spectra, the peaks were identified by comparing the retention times of different amino acids with that of standards. The mass spectrum of each derivatised amino acid was also compared against the NIST Library using NIST MS Search software , as described in Methods §We calculated MSI for several pairs of organisms, which have been experimentally demonstrated to grow together as a community and where mutual interactions between the organisms have been reported . By compKetogulonicigenium vulgare is 0.1, while that of Bacillus megaterium is 0. This indicates that no additional pathways have been activated in B. megaterium, in the presence of K. vulgare. On the other hand, in K. vulgare, 41 additional reactions were visited in the co-culture  derives a higher benefit in the co-culture than organism B (Column 2). These observations are in exact agreement with those made by the experimental studies (Column 5) evaluating the relative biomasses of the two organisms and their ability to co-exist. For instance, we observe that the MSI of -culture , which wYarrowia lipolytica and Cellulomonas fimi, we observe that MSI of Y. lipolytica (0.119) is almost 10 times that of C. fimi (0.014). This is because of the additional 123 reactions in Y. lipolytica that have now been visited, in the presence of C. fimi. The conversion of these stuck reactions to visited reactions in the co-culture also points to the enrichment in the metabolic capabilities of Y. lipolytica, as also observed from the increase in the scope size of joint metabolic networks . In addition, we observe 21 reactions in P. stipitis that have been enabled in the presence of S. cerevisiae. These results indicate that P. stipitis benefits from the interaction with S. cerevisiae, which we proceeded to verify using growth kinetic experiments.Next, we calculated the MSI for the co-culture of our interest, consisting of 3.2S. cerevisiae and P. stipitis, we exhaustively enumerated all the pathways on a community metabolic network, using our previously developed algorithm MetQuest (see Methods). MetQuest identifies all possible pathways from a given set of seed metabolites to all the reachable metabolites, whose size is less than or equal to a given cut-off. The pathway so obtained is complete, i.e., it contains all the reactions necessary to produce every metabolite constituting the pathway.To investigate the metabolic exchanges between S. cerevisiae that lead to various metabolites in P. stipitis. In total, we observed multiple pathways in P. stipitis that were involved in the production of 668 different metabolites  when grown in the supernatant of S. cerevisiae, in comparison to that seen in the control (310). In addition to the increased cell density, P. stipitis clearly exhibited a diauxic growth pattern, indicating the presence of an alternate metabolite in the supernatant that was used as the carbon source. We did not observe any increase in the cell density of S. cerevisiae when grown in the supernatant of P. stipitis. This observation also corroborates the higher MSI for P. stipitis in the community, indicating that P. stipitis derives a higher benefit when grown together with S. cerevisiae under these conditions.Next, to identify the organism that benefits from the interactions, we designed and carried out the two-stage experiment. In stage 1, the first organism was grown in a minimal medium consisting of glucose. In stage 2, the supernatant from the first organism was used as the medium to grow the second organism, and the growth was compared with that of the control where sterile distilled water was used instead of the supernatant refer §, we obse3.4S. cerevisiae and P. stipitis using the same two-stage experimental setup. We performed HPLC analyses on the samples withdrawn at 0 h and 24 h time points of the second stage, where P. stipitis was grown in the supernatant of S. cerevisiae. Interestingly, we observed that the concentration of ethanol had reduced from 2.4 g/L (in 0 h) to 0.0 g/L (24 h).In the next step, we experimentally determined the metabolic exchanges happening between P. stipitis was consuming the ethanol produced by S. cerevisiae. To confirm this, we studied the 13C labelling patterns of amino acids. We used the metabolic network of S. cerevisiaeP. stipitis + fragment of the respective amino acids. Interestingly, in all these amino acids, we observed isotopomers with a higher fraction of 13C incorporated  in these amino acids is significantly higher than that found in the control (0.005), indicating the uptake of a labelled metabolite from the supernatant  that quantifies the metabolic support each organism receives from the other in a community, in terms of the number of reactions “enabled”. We computed this metric on several co-cultures presented in literature and demonstrated that this metric can correctly identify the beneficiaries from the microbial interactions.In this study, we systematically identify the metabolic interactions between the microorganisms in a co-culture by integrating modelling with experiments. To this end, we applied our previously developed graph-theoretic algorithm that operates on the metabolic networks of the microorganisms. We defined a new metric termed There have been a few studies carried out in the past that incorporate the nutrient conditions and quantify the co-operation between the organisms in a community. These studies identify the synthesising capabilities of “joint metabolic networks” P. stipitis and S. cerevisiae and identified that P. stipitis, with a higher MSI, benefits from this interaction. Moreover, we also identified the pathways spanning the two organisms and identified the set of metabolites that can be exchanged between the two organisms. Interestingly, we also note that our computational analyses indicate some benefit for S. cerevisiae (MSI  = 0.025), while experimentally we observe no such benefit. This is because the calculation of MSI involves only the stuck reactions in one organism that have been relieved in the presence of other organisms. Due to the redundancy in metabolic pathways S. cerevisiae using its alternate pathways since they are a part of essential components of the cells. Hence this benefit is not observed experimentally.Further, to integrate the predictions with the experiments, we calculated MSI for a well-known yeast co-culture system P. stipitis benefits from the interaction, as shown by a higher value of MSI compared to that of S. cerevisiae and (b) P. stipitis consumes the ethanol produced by S. cerevisiae as also observed in previous studies. Also, from the labelling studies, it is also interesting to note the complete absence of m1 isotopomer in all the amino acids analysed. Moreover, since the labelling patterns in alanine and valine are different, it can be inferred that both cytosolic and mitochondrial routes of their synthesis were operational We also performed two-stage experiments using this co-culture and found that: (a) proof-of-concept for identifying the microbial interactions in a community. Indeed, the dynamics of the organisms in the co-culture may be different from that observed when the organisms are independently cultivated on the cell-free supernatants. Nevertheless, this study provides first glimpse into interactions in microbial communities. Also, deciphering the co-culture dynamics would entail the use of advanced molecular biological techniques. Moreover, isotope labelling patterns of the individual organisms from the co-culture experiments are difficult to obtain. Although recent studies seek to identify labelling patterns in co-culture, these methods either require extraction of a large number of peptides Our two-stage experiment is a simple methodology to establish a Also, our computational method to determine the relationship between the organisms in a co-culture, captures only the positive interactions between the organisms, as with other graph-based method based on network expansion In sum, this study provides a systematic methodology to understand the interactions in a microbial community by integrating computational and experimental paradigms. A model-integrated approach, combining data from modelling and experiments, allows to identify the complex metabolic interactions possible between the microbes in a co-culture. Our study also underlines the utility of computational analyses to generate testable hypotheses regarding the interactions between various microbes. This generic workflow can be extrapolated to study the metabolic interactions in many microbial communities. For instance, in a community of three organisms, a simple weighting scheme could be followed using the set of stuck reactions. This unique set, relieved by two individual organisms can be suitably weighed by comparing with the common set of reactions relieved by both the organisms. Multiple pair-wise interactions within the larger communities can be compared to identify how different organisms interact with one another. Overall, our integrated workflow can pave the way for rationally designing and engineering microbial consortia tailored towards specific industrial applications.None.Aarthi Ravikrishnan: Conceptualization, Data curation, Formal analysis, Validation, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review & editing. Lars M. Blank: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing. Smita Srivastava: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing. Karthik Raman: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing.


Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity. Lachancea kluyveri, previously known as Saccharomyces kluyveri, is a weak Crabtree positive yeast that has been presented in numerous studies due to its quintessential metabolic properties3. Its unique pyrimidine catabolism pathway has been well characterized and its importance has been discussed4. It has the ability to utilize purines and pyrimidines as sole nitrogen sources which makes it a great interest to the pharmaceutical industry5. The ability to assimilate and ferment melibiose by L. kluyveri is also a differentiating feature from other characterized yeasts3. The organism diverged from Saccharomyces cerevisiae after the Whole Genome Duplication (WGD) event4. Previously, using searchDOGS algorithm, the position of the whole genome duplication event has been described for 11 yeast species and they marked the location of the whole genome duplication (WGD) event which separated the Saccharomyces genus from Lachancea6. The effect of the WGD event is reflected in the number of ortholog duplicates of L. kluyveri genes in S. cerevisiae. The single knockout simulation would also explain the effect of such duplicate genes on the viability of the model and metabolic flux. Genome-scale metabolic models (GEMs) are one of the important systems biology tools as they integrate genomic information, transcriptomics, metabolomics and associated experimental data for a given organism into a whole-cell metabolic network9. Moreover, complementary to general omics analysis, GEMs provide a scope to integrate ‘omics’ data into metabolic networks and help to predict condition-specific metabolic capabilities. To date, GEMs have been successfully applied to design microbial strains for bioproduction and evaluate their capabilities in different conditions10. Specifically, the model yeast S. cerevisiae was the first choice among the eukaryotic organisms to be fully sequenced12, and it is one of the conventional workhorses in cell factory engineering for bio-production of several compounds with various applications in chemical14, food16 and pharmaceutical industries17. These models are widely being used for various biotechnological predictions and experimental designs. However, non-conventional yeast species include important human pathogens or have also been reported to be suitable platforms for several biotechnological applications and various models have been therefore reconstructed for many yeasts.L. kluyveri allows minimal fermentation in aerobic conditions18. Crabtree effect describes the preferential respiro-fermentative metabolism of yeasts i.e. production of ethanol in aerobic condition over respiratory metabolism. Since L. kluyveri is a weak Crabtree-positive, this enables it to produce more biomass compared to S. cerevisiae, which diverts substantial flux towards ethanol production even in aerobic conditions2. This makes it a lucrative host for protein production19. Ethyl acetate has also been measured to be a major overflow metabolite in growth studies2. L. kluyveri, therefore, could be leveraged to uncover the mechanisms behind these metabolic routes and also serves as a potential industrial organism.The weak Crabtree energy metabolism of 10. Genome-scale metabolic reconstructions (GEM) are expanding our understanding of cellular metabolism and its dynamics. It supplies us with a systems-level picture of the metabolism derived from its genotype. GEMs have been used to analyze disease phenotypes and improvise the production of chemicals from cell factories. Fungal GEMs have been widely used for metabolic engineering efforts to overproduce target metabolites like fuels and chemicals21. As an obvious target, S. cerevisiae’s GEM was constructed iteratively due to its immense industrial and academic importance. Beginning from iFF708i9, other metabolic models like iND75022, iMH805/77523 and iMM90424 have been reconstructed. iMH805 has gene regulatory information making it a powerful model to analyze the coordination of metabolic pathways with gene circuits23. This has begun the quest for exploring related yeast strains that can furnish novel phenotypes. In this regard, numerous yeast GEMs have been published for Kluyveromyces lactis25, Pichia pastoris26 and Yarrowia lipolytica27. GEMs can be used to visualize a systems-level effect of environmental conditions and induced perturbations.When GEMs are combined with constraint-based algorithms, they offer an excellent chance to analyze metabolism and genotype–phenotype relationshipsL. kluyveri, here we built the first GEM named iPN730, which comprises of 1235 reactions, 1179 metabolites, 730 genes, and 8 compartments. Lachancea kluyveri NRRL-12651 strain was selected for the genome annotation due to the extensive experimental analysis that has been conducted on this organism particularly with respect to ethyl acetate production and uracil (pyrimidine) degradation. Single knockout analysis using Flux Balance Analysis (FBA)8 led to a significant agreement with experimental knockout data28 for S. cerevisiae in various in silico media. Dynamic Flux Balance Analysis (DFBA)29 resulted in growth dynamics, product formation that agreed with experimental observation as reported in previous studies. The reported pathways for pyrimidine catabolism and ethyl acetate production are also incorporated into the model to enable further studies. Furthermore, integration of gene expression data into iPN730 for growth on uracil or ammonium salt as the sole nitrogen source helped us in finding pathways associated with uracil degradation. To our knowledge, it is the first attempt to develop a GEM for L. kluyveri through which we anticipate a better understanding of the metabolic routes in the organism and streamlining its industrial applications.To understand the metabolic capabilities of Lachancea kluyveri NRRL 12,651  using Yeast Genome Annotation Pipeline30 yielded 5,505 ORFs. The draft model from the annotated genome constructed using Build Fungal Model in KBase contained 1180 reactions, 1232 metabolites, and 730 genes. The draft model harbored the integration of data from 13 well-curated fungal genome-scale reconstructions. S. cerevisiae was used as the template model due to its high quality and reliability. The homology table generated from the BDBH algorithm for proteome comparison has been incorporated  followed by Candida tropicalis (13.6%) with L. kluyveri.The genome annotation of 7. Elemental and charge balances can be calculated and analyzed only if the model contains the formula and charge for the metabolites respectively. All the metabolites were assigned formula and charge with reference from CHEBI , PubChem (NIH) and BiGG Database (UCSD)31. For metabolites having ambiguous alkyl group (-R), the alkyl group was considered as a conserved moiety “R” in the metabolite formula. The default metabolite ID was replaced by standard nomenclature (MIRIAM) for genome-scale metabolic reconstruction as used elsewhere for well-established models iMM904, iAF1260, etc. The assigned ID captured both the information regarding the chemical name and the compartments of the metabolites. Similar assignment was made for reactions in the reconstruction. Further refinement of the model was necessary with regard to the gaps in the biosynthetic pathways and the ambiguous reversibility of the reactions.In a metabolic model, correct mass balance in the reactions is critical for the quality of predictive power32. Upper and lower bounds for internal reversible reactions were set to a high flux value i.e. 1000 mmol/gdw/h to allow unconstrained flux through the internal metabolic network. For irreversible reactions, a lower flux bound of zero was assigned to restrict any flux in the reverse direction. The reversibility information on such ambiguous reactions was obtained from MetaCyc, KEGG and previously published datasets33 on metabolic reactions. The irreversible reactions comprised of reactions that involve: (1) ATP as reactant, (2) Oxygen as the product, and (3) Reactions involving NADH and NADPH. . The mannan yield on glucose for S. cerevisiae (iMM904) was around 0.0214 g/g. We anticipate the differences between the values could be directly due to the differences in metabolic objectives of both the organisms. S. cerevisiae has definite tradeoffs between biomass and ethanol production. It prefers the production of ethanol even in aerobic conditions (Crabtree Effect). Therefore, in the case of S. cerevisiae there is a reduced diversion of glucose into biomass at physiological uptake rates, compared to L. kluyveri. Similarly, the fatty acid pathway comprises of elongation of long-chain fatty acid with the addition of an even number of carbon through malonyl-CoA35. Due to this, any reaction blockage owing to incorrect flux direction or missing metabolites could render no flux through the whole pathway. We therefore, manually checked through the whole pathway to ensure that no reaction is blocked. By gap filing the fatty acid pathway manually, we enabled the model to produce basic fatty acid products i.e. phospholipids, diacylglycerol, etc. To find the blocked reactions, the corresponding reactions in the fatty acid pathway were set as the objective function and were checked for any flux through them. Iteratively all the blocked reactions were identified in the pathway and corrected using MetaCyc and KEGG as a reference. Additionally, there were significant inconsistencies in the transport of gases across the compartments. The diffusion of gases from extracellular space to a specific compartment and vice versa was important. The transport of oxygen from the extracellular space to the mitochondria through the cytosol was corrected by adding the transport reactions at the cell membrane and mitochondrial membrane. Any erroneous reaction which consumes oxygen (fatty acids desaturase reactions) were removed or given the proper reaction directionality. This was to avoid the diversion of oxygen to these reactions in the cytosol which is physiologically irrelevant. The numerical confidence on the reactions in the model was assigned based on the gene-protein reaction (GPR) and observed flux through it  and FAA4 (Gene symbol: YMR246W) whereas L. kluyveri has just one that encodes for a fatty acyl-CoA synthetase. Similarly in the annotated genome, 527 instances showed that multiple S. cerevisiae genes were mapped to a single locus in L. kluyveri. The metabolites and the reactions are distributed in various metabolic modules in the network i.e. different functionalities of the metabolic network  solves a linear optimization problem with the objective as the specific growth of the cell i.e. flux through the biomass equation. This assigns fluxes to active reactions in the model given the constraints. The constraints of the optimization are defined by the bounds on the flux through the reactions and the uptake rate of substrates and other media components. This is further dictated by the thermodynamic Gibbs free energy (∆G) of the metabolic reactions.40, the accuracy of the prediction of the model was evaluated. Using the similar substrate and oxygen uptake rate as measured in the experiment, the specific growth rate and the product secretion rates were evaluated in silico by FBA. In the experiments reported previously, for a glucose uptake rate of 2.28 mmol/gdw/h and an oxygen uptake rate of 6.2 mmol/gdw/h, a specific growth rate of 0.2/h were observed18. This corresponded to a carbon dioxide production rate of 6.4 mmol/gdw/h and a respiratory quotient (RQ) (the ratio of the volume of carbon dioxide evolved to that of oxygen consumed) of 1.03. To this, the predicted specific growth rate and the respiratory quotient were compared to the experimentally measured values41 . DFBAass Fig. A. It agrass Fig. B. The prons Fig. C. Similaons Fig. D. In the3. The variation in growth rate while growing on different carbon sources can be attributed to the energetic cost and the benefit of metabolizing them. Ethyl acetate and ethanol production phenotype observed in in silico growth kinetics showed agreement with the reported overflow metabolite production. This model can be potentially leveraged to generate hypotheses in relation to the metabolism and physiology of the organism.The model was able to predict the growth rate and the respiratory quotient with good accuracy in comparison to the experiments. The ability to grow and ferment on melibiose differentiates the organism from the related yeasts22. Each of the reactions is assigned a flux solution space through FBA. This enables the metabolic model to predict the changes in flux phenotypes with respect to perturbation in genotype i.e. single knockout or combinatorial knockout analysis. To gain a high degree of confidence, it was imperative to validate such predictive power of the model in comparison to experimentally established datasets. The genotype information was mapped to the reaction information through Boolean logic. For encoding a protein complex, either all genes or only some of them are required. This is well presented by the Boolean logic using AND or OR to map the genes to the protein. The viability should remain unaffected by the deletion of genes that do not have obligatory functions. Two large scale gene knockout studies in S. cerevisiae were used to evaluate the predictive power of the model. Comparative gene knockout analysis of the model with reference to the published datasets45 for homologous genes in S. cerevisiae was performed. Statistical analysis was performed in terms of sensitivity, specificity, F1 score and Mathews Correlation Coefficient (MCC)  were performed to assess the viability of the model based on single-gene knockouts. Due to the similar performance of both the approaches i.e. FBA and MOMA, only FBA was considered for any further discussion.Genome-scale metabolic models map the genotype of an organism i.e. gene information to the metabolic flux phenotypes of the cell. Each gene encodes for protein/subunit of a protein that catalyzes a specific reaction47. In the genome-wide single-gene knockout analysis, the metabolic model supplemented with all growth precursors has significantly fewer essential genes i.e. true positives compared to growth on minimal media  genes had a homolog in S. cerevisiae. The model had a sensitivity of 0.58 for rich media and 0.32 for the minimal media. It had almost equal specificity scores of 0.84 in rich and minimal media. For evaluating the performance of the model in classifying essential and non-essential genes, F1 score and MCC were used. F1 score for predictions was 0.324 in rich media, 0.317 in minimal media with glucose. MCC for predictions was 0.28 in rich media, 0.162 in minimal media with glucose. The radar plot , galactose minimal (69) and ethanol minimal (73). The gene essentiality prediction statistics for S. cerevisiae (iMM904)36 for YPD and glucose minimal media has been added for comparison to L. kluyveri  catabolism (URC) pathway has been uniquely characterized in L. kluyveri48. Locus URC1-6 and URC8 have been experimentally characterized to be an essential component of the URC pathway for uracil degradation. This enables L. kluyveri to grow on uracil and the intermediates of the degradation pathway as the sole nitrogen source. This ability is supposedly lost in S. cerevisiae after the whole genome duplication event4. In the URC pathway, the gene products of URC1 and URC6 degrade uracil to 3-hydroxypropionate and ribosylurea through multiple steps that have not been characterized. The gene product of URC4 degrades ribosylurea to urea. The urea is further broken down by the action of gene products of URC3 and URC5 to ammonia and carbon dioxide. The ammonia/ammonium then replenishes the nitrogen pool for amino acid and nucleotide biosynthesis. This enables the organism to grow on uracil and urea as the sole nitrogen source. The whole pathway  and an enzyme alcohol acetyl transferase (AAT)  Fig. C. AAT caAT) Fig. D. The PPL. kluyveri has been discussed in relation to anti-cancer drug design as understanding pyrimidine homeostasis directly affects the DNA replication machinery4. Although the pyrimidine catabolism pathway has been sufficiently characterized, iPN730 will give an opportunity to understand the genotype to flux phenotype relations and its interaction with other pathways at a systems level.Pyrimidine degradation pathway in L. kluyveri growing on either ammonium or uracil as the sole nitrogen source was prepared using GIMME algorithm49. The gene expression data (GSE48135)48 was used here for building the context specific model of L. kluyveri by eliminating the inactive reactions. The expression was measured in wild type L. kluyveri NRRL 12,651 in minimal media with either ammonium or uracil as the sole nitrogen source. There would be several reactions activated or deactivated between both the conditions which would alter the metabolic flux through pathways. The ability of L. kluyveri to use uracil as the sole nitrogen source comes from its unique pyrimidine catabolism pathway4. We earlier integrated the pathway in L. kluyveri and validated the flux balance analysis prediction of growth rate  allows to explore the bounds of the flux space under constrained conditionsium Fig. A. The imium Fig. B i.e. (14. Purine metabolism pathway has multiple reactions affected when the cells are growing on uracil with respect to growth on ammonium. This is well explained by the high level of interaction between the pyrimidine pathway to which uracil belongs and the purine biosynthesis pathway. Both the pathways share the same precursor i.e. Inosine Monophosphate (IMP) for de novo biosynthesis of the purines and pyrimidines. It is expected that any flux difference in uracil metabolism is going to interfere with the flux values in the interacting pathways. Similarly, Histidine metabolism is closely connected with nucleotide biosynthesis pathway51. Both the pathways share the same route beginning from Phosphoribosyl Pyrophosphate (PRPP) to Phosphoribulosylformimino-AICAR phosphate which leads to the formation of IMP in the case of nucleotide biosynthesis pathway. The flux through the Histidine biosynthesis pathway is therefore altered when growing on uracil as the sole nitrogen source. GTP serves as the immediate precursor to riboflavin biosynthesis pathway52. Therefore, it is evident that an altered purine biosynthesis pathway to which GTP belongs, will impact the biosynthesis of riboflavin. Riboflavin is also associated with folate biosynthesis pathway which interacts with nucleotide biosynthesis pathway by feeding one carbon pool like methyl and formyl groups. It is interesting to note that phospholipid pathway is also affected to a considerable extent due to growth on uracil compared to ammonium. A change in flux through Acetyl CoA Carboxylase (ACC1) which is the first committed step of fatty acid biosynthesis is also affected. Nucleotide products not only serve as a precursor pool to the DNA and RNA biosynthesis, they are also involved in energy metabolism and phosphate transfer to other biosynthetic intermediates. Phospholipids are one of such intermediates which has a high demand for phosphate groups which are supplied by the nucleotides. Uracil degradation pathway is activated when the model is simulated on uracil as the sole nitrogen source  of ~ 5%. It is an important industrial solvent and an essential component of paints. Production of ethyl acetate from microbial cell factories is an emerging avenue. Very few yeasts including L. kluyveri and K. marxianus produce ethyl acetate. Metabolic engineering efforts have enabled Escherichia coli and S. cerevisiae to produce ethyl acetate with considerable yields but there is significant room for improvement. L. kluyveri naturally produces 0.12 g/g glucose of ethyl acetate in aerobic conditions and targeted metabolic engineering can further improve the yields. In this regard, iPN730 will potentially play an important role of target metabolic engineering leveraging constraint based analysis tools. The production simulations of ethyl acetate and ethanol on different carbon sources under varying uptake rates helped in understanding the high production rates in the case of maltose in semi-aerobic conditions due to high carbon influx. Moreover, phenotypic phase plane analysis helped in understanding the cost of ethyl acetate and ethanol production incurred to the growth rate. This will potentially be an important information for strain optimization for metabolic engineering. The phase 1 region represents the growth optima regime where ethyl acetate and ethanol production rates are coupled with biomass formation. Later in Phase 2, we see the detrimental effects of overproduction on the growth rates. Algorithms like OptKnock and OptForce would help us in designing strains that couple the bioproduction with growth rates even at high bioproduction rates.Currently, ethyl acetate market stand at $3.7 billionL. kluyveri amongst its all neighboring Saccharomyces yeasts4. S. cerevisiae seems to have lost this ability during the whole genome duplication event. 5-fluorouracil is used for treatment of colorectal, breast, as well as head and neck cancer. It still remains one of the highly prescribed drugs for anti-cancer chemotherapy. More than 80% of the dose is degraded in our body due to the pyrimidine catabolism pathway4. It is therefore imperative to study the pyrimidine catabolism pathway and its interactions with other pathways. The results derived from flux variability analysis of context specific genome-scale metabolic models highlighted the effects of uracil uptake on multiple pathways. Some of the pathways are closely associated with uracil metabolism whereas others do not have direct connections i.e. phospholipid and alcohol metabolism. The context specific versions of iPN730 could be used to probe pathways that have epistatic interaction with uracil degradation. This could potentially open a whole new window for therapeutics supplementing 5-flurouracil and other nucleotide analogue drugs.Pyrimidine degradation pathway is uniquely present in 54. Their yields can be estimated in silico per unit mole of glucose consumed for metabolic engineering purposes. We anticipate the use of this model for deriving predictions on the flux distribution in the metabolic network in varying growth conditions. This model will add up to the non-conventional yeast genome-scale metabolic reconstructions that will enable hypotheses generation for the exploration of diverse phenotypes of fungi. This will potentially lead to the discovery of new aspects of metabolism in lower eukaryotes and the design of engineering strategies to overproduce fine chemicals of industrial relevance from L. kluyveri.The pathways for phenyl acetate, 2-methylbutanol and isobutanol were also present in the reconstructed metabolic model which are important industrial chemicals55 workspace was used for annotation and generation of draft metabolic model iPN730. For the genome annotation using YGA pipeline, we have applied BDBH algorithm  with an E-value cutoff of 10–10 and S. cerevisiae (S288C) as the base template model. The contigs were uploaded in a multi-FASTA format into the server. The homology table generated from the BDBH algorithm for proteome comparison can be used to predict possible duplication, inversion, deletion, insertion or synteny loci between the organisms  for reading and manipulating the draft model in Systems Biology Markup Language (SBML) format. COBRA Toolbox v3.0 was used in MATLAB R2017b  for identifying the inconsistencies in the model. Gurobi version 8.0 (Academic license version) and GLPK solver were used for linear programing for FBA in both MATLAB and Python. The metabolite formula and charge were added using PubChem, CHEBI  and BiGG database. The reaction bounds by default were reversible. The reaction bounds were corrected using iMM904 as a reference and similar reactions were mapped. Remaining reactions were manually corrected with reference from MetaCyc, KEGG and previously published reaction reversibility database33. KEGG pathway database was used as a reference to find gaps in the biomass precursor synthesis pathways. Inferred reactions were added wherever necessary to enable biosynthesis of all precursors64. Due to insufficient information on the biomass composition of L. kluyveri, the biomass equation for S. cerevisiae (iMM904)24 was used for all purposes. A confidence score was assigned to each reaction considered in the model. The confidence score was assigned based on the existence of a GPR for the reaction and flux through it. A high confidence score of ‘4’ was assigned to the reactions which had a GPR assignment along with significant flux through the network calculated by flux balance analysis. The confidence score of ‘4’ was also assigned for exchange reactions necessary for growth on defined media. A score of ‘3’ was assigned for reactions having significant flux through them but no GPR assigned for them. A score of ‘2’ was assigned for reactions having GPR assignment but no flux through them for growth in defined medium. A low confidence score of ‘1’ was assigned for reactions having no GPR assignment or flux through them.KBase (US Department of Energy Knowledge Database)FBA was performed using Gurobi solver in Python 3.6 . In silico media was assigned to the model for FBA by assigning experimentally reported uptake rates to the exchange reactions. The glucose and oxygen uptake rates were assigned as per experimental data. The bounds for ammonium ion and trace metals were set to very high values i.e. 1000 mmol/gdw/h to remove any limitation. In silico growth rate and respiratory quotient were compared to experimental data based on the Pearson correlation coefficient. We used the ‘jointplot’ function in the seaborn visualization and analysis library in python 3.7 for Fig. 42 database. The data regarding the growth of the strain on the mentioned carbon sources can be accessed from the BioloMICS database of MycoBank using “Lachancea kluyveri” as the identifier. The in silico viability was qualitatively compared with the reported experimental growth on glucose, galactose, maltose, sucrose, trehalose, melibiose, arabinose, ethanol and citrate. The in silico growth rate was simulated for glucose, galactose, maltose, sucrose, melibiose, ethanol and glycerol in anaerobic and aerobic conditions. For aerobic condition, an oxygen uptake rate of 1000 mmol/gdw/h was considered and for anaerobic condition, an uptake rate of 0.25 mmol/gdw/h was considered.The information regarding the growth on various carbon sources was procured from Westerdijk Fungal Biodiversity Institute’s MycoBank18. The uptake rate of oxygen was varied from 1000 mmol/gdw/h i.e. aerobic condition (Glucose limited regime) to 0.25 mmol/gdw/h i.e. anaerobic condition (Oxygen limited regime). An uptake rate just below the optimal uptake rate in the aerobic condition of 3 mmol/gdw/h was considered as the semi-aerobic condition. The parameters considered for the dynamic flux balance . The glucose uptake rate was kept fixed at 2.28 mmol/gdw/h as measured in previous studies29. The in silico media composition was recalculated based on the uptake and growth rate after every sampling interval. The following equations define the growth kinetics, substrate uptake and product formation in an in silico batch fermentation:The DFBA algorithm applies FBA at regular intervals i.e. DFBA sampling intervals on the model and assumes that the substrate uptake rate remains constant throughout the fermentation−1. vEX_S and vEX_P are the uptake rate and the production rate for substrate and products respectively. The products considered for DFBA were ethanol and ethyl acetate. To explain the production of ethyl acetate, the fluxes associated with the precursor-compounds ethanol and acetate were analyzed in oxygen-limited conditions. The alterations in the production fluxes for ethanol i.e. R_ALCD2ir_c0 (Alcohol Dehydrogenase 1.1.1.1) and acetate i.e. rxn00507_c0  with the change in the uptake rate of the oxygen was evaluated through FBA. For example, model.reactions. R_ALCD2ir_c0.flux was used to access the flux through the production reaction of ethanol in COBRApy.L. kluyveri NRRL 12,651 strain was used in all the experiments. The yeast was grown in Yeast Extract-Peptone-Glucose (YPD) media  overnight at 30 °C shaking at 240 RPM. The cells were washed thrice in Yeast Nitrogen Base buffer (0.67%) to remove any remaining media with centrifugation at 3000 RPM for 3 min. The growth experiments were conducted in shake flask. The media containing different carbon sources comprised of Yeast Nitrogen Base with Ammonium sulphate and without amino acids ; 6.7 g/L, 20 g/L of respective carbon source. The carbon sources used in the experiments were Maltose (Merck), Sucrose (Merck), and Galactose (SRL). The cells were diluted to 0.2 OD in the growth media. The optical density was measured at 600 nm using Nanodrop ONE . The substrate and extracellular product concentrations were measured in High Performance Liquid Chromatography  equipped with an Aminex HPX-87H ion exchange column  where 5 mM H2SO4 was used as mobile phase and flowrate was 0.6 ml/minute. Briefly, 20 uL syringe filter purified sample was injected in the column. The specific growth rate L. kluyveri. Hence, for this purpose, the S. cerevisiae gene essentiality dataset was considered due to the evolutionary closeness between them and high confidence in the dataset45. The gene essentiality analysis was conducted for YPD, synthetic yeast media with glucose, synthetic yeast media with galactose and synthetic yeast media with ethanol. All the analysis was conducted in complete aerobic conditions where oxygen uptake rate was set in the non-limiting regime. Gene essentiality information for S. cerevisiae genes was transferred to homologous genes in L. kluyveri. In silico single-gene knockout was performed on iPN730 by constraining the flux to zero for the reactions mapped from the genes by the GPR association information. For this, cobra.flux_analysis.single_gene_deletion function was used in COBRApy32 in Python 3.6  and Gurobi solver was used for the optimization. The viability of the cell in terms of the growth rate was estimated by FBA for each in silico gene deletion in the model. The in silico essentiality of the genes were compared with experimental data mapped from the gene deletion studies on S. cerevisiae and the statistics of the predictions were calculated as follows:1 score, both, help us in understanding the quality of the model for binary classification i.e. into essential and non-essential genes. In this case, we are using these statistical tools for evaluating the quality of our model to classify essential and non-essential genes based on their effect on its fitness. F1 score ranges from 0 to 1. A value of 1 is attained when there is perfect prediction. MCC score ranges from − 1 to 1, where − 1 denotes absolute disagreement between ground truth and the prediction while 1 denotes perfect prediction. The single-gene deletion analysis was performed in different in silico media. Aerobic conditions in which original experiments for S. cerevisiae were performed were assumed for the analysis. For rich media i.e. YPD, the model was allowed to uptake all the trace elements, nitrogen sources, 20 amino acids, all the nucleotides and carbohydrates. Whereas in the minimal media, the model was allowed to uptake only glucose (carbon source), ammonium ions (nitrogen source) and trace elements.Genome-scale metabolic models link the metabolic phenotypes to the genotype of the organism through GPR association. This enables the prediction of the alterations in the metabolic phenotype of the organism in response to genetic perturbations. To gain confidence in the accuracy of the model, it is imperative to validate the predictions with reference to experimental data. Unfortunately, such a genome-scale single gene deletion dataset was not available for L. kluyveri (previously known as S. kluyveri in many studies) metabolic pathways have been reported with a particular focus on pyrimidine metabolism and overflow metabolism. URC pathway for pyrimidine degradation allowed the model iPN730 to grow on uracil, urea, and intermediates of the pathway as the sole nitrogen source. The pathway information on the URC pathway was obtained from the literature48. The metabolites and reactions for this pathway were incorporated into the model in COBRApy using reaction.add_metabolites and model.add_reactions respectively. URC6 gene locus encodes uracil phosphoribosyl transferase (EC: 2.4.2.9) which breaks down uracil to phosphoribosyl and 3-hydropropionate. URC3 and URC5 encode urea amidolyase (EC 3.5.1.54) which breaks down urea to ammonia and carbon dioxide. To check for the uptake of uracil and urea as a sole nitrogen source, the uptake for ammonium ion was restricted. The uptake rate for uracil and urea was set to 1000 mmol/gdw/h. The carbon source was kept as glucose while setting up an oxygen uptake rate very high for aerobic metabolism. FBA was performed to check for any in silico growth.In Phenotypic phase plane analysis was conducted using doubleProductionEnvelop function in COBRA Toolbox v3.0. The flux through transport reactions for ethyl acetate and ethanol i.e. EtAc_t and ‘etoh_t’ were used as a proxy for their production rates.48 from GEO Omnibus was used for constructing the context specific genome-scale metabolic model of L. kluyveri. We used GIMME (Gene Inactivity Moderated by Metabolism and Expression) algorithm49 for incorporating the gene expression data into the model. GIMME uses flux balance analysis along with gene expression data to prune reactions in the model according to their (1) gene expression and (2) contribution to metabolic objective. GIMME takes in the genome scale metabolic model, gene expression data and a user defined threshold for gene expression. GIMME tries to maximize the incorporation of reactions having expression of their constituent genes above the threshold while minimizing the reactions below it. We used a gene expression cutoff of 25th percentile as conducted in earlier studies66 with proven results.The gene expression data from previous study (GSE48135)L. kluyveri when growing on ammonium salt or uracil as the sole nitrogen source in minimal media was considered. We constrained the model to only uptake of either ammonium or uracil as nitrogen source prior to integration of gene expression. We used mapExpressionToReactions to map the gene expression values to reactions based on their GPR relations. GIMME function in COBRA Toolbox v3.0 was used for integration of gene expression into iPN730.The gene expression data (GSE48135) was normalized using custom scripts in R. The gene expression of Flux Variability Analysis (FVA) was used for calculating the flux ranges of reactions of both the model. We used a loop-less version of FVA in COBRA Toolbox v3.0 to restrict the formation of futile cycles. The differential flux through reactions in each of the model was calculated using Jaccard index and the FVA ranges for both the models. Jaccard Index calculates the extent of intersection between given ranges. A Jaccard index of zero implies no overlap between two ranges.Briefly, Jaccard index is calculated as follows:For two given ranges/sets A and B,Using the minimum and maximum flux ranges obtained from FVA from both the models, we calculated the Jaccard Index for all the reactions. We considered all those reactions having a Jaccard index of zero between both the models as having differential flux. The differential reactions were then clustered based on their metabolic functionalities obtained from KEGG database.Supplementary file 1Supplementary file 2


Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict essential components validated with in vitro serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach allowed us to optimize medium composition, enabled growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive production. Most large-scale animal farms make use of antibiotics to fight and prevent infections, which often lead to development of antibiotics-resistant bacteria6. Moreover, chronic infections caused by several Mycoplasmas are not immediately detectable. Therefore, the use of effective vaccines against various Mycoplasmas in farm animals might be a solution to prevent wide spreading of these chronic infections7. It has been suggested that, despite being primarily considered a human pathogen, Mycoplasma pneumoniae could potentially be used as a universal chassis to be deployed as single-vaccine or multi-vaccine in a range of animal hosts (www.mycosynvac.eu).Mycoplasma are a small genus of bacteria belonging to the Mollicute classM. pneumoniae causes atypical or walking pneumonia8. With its highly reduced genome (816 kb), it is among the smallest self-replicating living organisms and has an exclusively parasitic lifestyle. This feature greatly determined its evolution towards minimal functions  and inability to detoxify metabolic waste , tasks for which this bacterium fully relies on the host9. M. pneumoniae primarily ferments glucose10 and uses most of its energy (71 to 88%) for non-growth associated tasks11. One of the key features of this pathogen during infections is its ability to evade the immune system: the unique characteristics of its membrane (in particular the high cholesterol contents) allows the mimicking of the host membrane12. Moreover, the lack of a cell wall makes it resistant to antibiotics which target this structure13.M. pneumoniae requires the large-scale production of this bacterium in a serum-free defined medium. This is due to the high economic cost of rich media, the variable composition of animal-derived compounds14 and the possible presence of toxins, viruses or antigens that could reduce the sensitivity of immunological assays used on farms to screen for infection15. However, the production of M. pneumoniae in serum-free media is a major challenge. For decades, the ability to culture M. pneumoniae was reported to be possible only in serum-rich media16 due to a requirement for sterol components17. Only in 2009 Yus et al. reported a defined medium based on the metabolic map reconstruction of M. pneumoniae that allowed some growth of the bacterium but required daily changes of the medium18. This medium is not valid for large-scale growth needed during vaccine production. For these reasons, we focused our study on the model-driven design of a serum-free medium that could improve M. pneumoniae growth rate.The development of vaccines based on 26. We previously described the development of a genome-scale, constraint-based model (GEM) of M. pneumoniae metabolism, iJW14511, and its use to carry out predictions related to growth rate and medium design, relying upon a biomass composition that had been formulated from literature3, sequencing, proteomics and mass spectrometry data. However, no novelty in the defined medium composition was reported11. Several studies have shown that lipids are among the most important component limiting growth, mainly cholesterol, regulating membrane fluidity , and fatty acids30. Determining the lipids composing the membrane of M. pneumoniae is challenging, as it is strongly dependent on the composition of the growth medium, growth phase and culture conditions31. Some key features remain invariant, such as the high cholesterol proportion, directly incorporated from the medium32. The model iJW145 did not account in detail for lipids in its biomass composition11. Therefore, we integrated native lipid pathways of M. pneumoniae, and reactions involved in the membrane formation to develop an updated GEM of M. pneumoniae metabolism, called iEG158_mpn.We approached the study through metabolic modeling, a method widely used for genome-scale biochemical networks analysisM. pneumoniae and to study how certain culture conditions may affect the growth of this bacterium, focusing on its intracellular metabolism. These predictions were corroborated with defined-growth media that support robust growth (up to 60% of the biomass obtained with rich medium) and shows the synergism of components. Our study, in combination with the existing literature, suggests membrane formation and adaptability are key aspects to be investigated in relation to M. pneumoniae growth, which appears to be limited by several factors.We aimed to describe the use of the energy available, considering not only cytosolic processes but also transmembrane transport. We used the model iEG158_mpn to predict the essentiality and synergism of components in serum-free media for M. pneumoniae metabolism has two compartments, one extra-cellular and one cytosolic, and consists of 490 reactions, 442 metabolites and 158 genes. Of the total reactions, 314 are gene-associated, 329 are cytosolic conversion reactions, 65 represent transport between the extracellular compartment and the cytosol and vice versa, and 96 are of metabolite exchange, meaning they allow uptake/production of compounds. Only 61 of the cytosolic reactions have a zero-flux range, meaning they are not active under the simulated conditions. Excluding the zero-flux reactions, 293 reactions have same minimum and maximum fluxes according to FVA, meaning in total 354 reactions with fixed flux. The high proportion of active reactions and the lack of flexibility in the usage of alternative reactions reflect the evolution of the organism in minimal genome. In the biomass equation, formulated according to Wodke et al.11, cofactors and vitamins, known to be essential for growth but with no available measurements, were symbolically included as traces in the biomass equation. Similarly, DNA repair and RNA turnover were considered in the biomass composition by slightly increasing their proportions. Protein turnover is modeled by the addition of degradation reactions33, which is reflected in iEG158_mpn as the decomposition of a general protein compound into amino acids with the associated ATP cost.The model iEG158_mpn of M. pneumoniae contributes to the increase in maintenance costs, as the high surface-to-volume ratio induces higher proton leakage through the membrane. The ATP maintenance reaction was determined in iJW145 by multiplying the number of ATPase complexes on the cell surface by the number of cells in a gram of biomass, so considering only the cytosolic side of the energy requirement. However, the reaction fluxes representation allowed the visualization of a flux of protons out of the cell, required for cytosol de-acidification. Moreover, it is stated in Wodke et al.11 that the proportion of ATP used for cellular tasks not directly related to growth  is about 76% at 24 h after inoculation. We updated the non-growth-associated maintenance reaction (model reaction ID: “ATPM”) to specifically include proton leakage component  (model reaction ID: “protonLeak”), as represented in Fig. The very small size of M. pneumoniae genome. The respective transport reactions (IDs “LACLt” and “ACtr”) were therefore updated in iEG158_mpn as irreversible, removing the proton symport and the maintenance costs adjusted to the maximum value allowing growth, computed as 10.46 mmol.gDW−1.h−1 of ATP (gDW = grams dry weight) used for non-growth-associated tasks at the quasi-steady state (24 h time point).The maintenance reaction in iEG158_mpn was further updated according to the results of the sequence alignment for monocarboxylic acids transporters discovery: no lactate or acetate proton symporter were identified to be present in DW−1.h−1 of ATP, the energy fraction spent for proton leakage, EP, equals to 0.67 and the fraction spent on other maintenance requirements, EL, equals 0.33. Therefore, the lower bounds PLB and LLB of the correspondent reactions are computed as:Assuming the total maintenance cost, Mt, equals to 10.46 mmol.gPLB is multiplied by 4 as the ATPase reaction removes four protons at a time.M. pneumoniae with iEG158_mpn supplementing in silico the medium predicted with model iJW145: the resulting growth rate is 0.053 divisions per hour at quasi-steady state of MPN (24 h), correspondent to a doubling time of 13.4 h, in line with the experimental evidences.We simulated the growth rate of M. pneumoniae, we assumed that accounting for membrane formation in the model would give important insights into its growth requirements. Focusing on membrane lipid components, we included in iEG158_mpn the pathways for lipids assembly34 and reconstructed the membrane lipid composition through an extensive literature search. Articles listed in this section were used to determine lipids percentages in the membrane and their acyl chains composition. Only lipids whose percentage in the membrane has been reported to be higher than 1% of the total lipid biomass were considered. Presence and proportions of the lipids are strongly dependent on the compound’s availability in the medium, therefore quantities must be interpreted as ranges.Because membrane adaptability is one of the key features of M. pneumoniae is the high proportion of cholesterol, from 35 to 50% of the total lipid fraction35. In vitro, cholesterol is directly incorporated from the medium without being processed by the bacterium. For this reason, in iEG158_mpn, cholesterol has been included into the biomass composition without being metabolically processed. Available literature indicates it constitutes about one third of the final membrane and about half of the lipid components of the membrane.One of the key features of the membrane of M. pneumoniae utilize Hayflick media, composed of 16% of horse serum, rich in sphingolipids and phosphatidylcholine36. Sphingolipids-phosphatidylcholine ratio in M. pneumoniae is 2.437, constituting on average 12 and 5% of the membrane lipids, respectively38. However, sphingolipids have a good affinity for cholesterol, while phosphatidylcholine repels it, meaning their proportions in the membrane are strongly correlated to the amount of cholesterol directly incorporated from the medium: a higher amount of cholesterol leads to the incorporation of a higher fraction of sphingolipids and a lower fraction of phosphatidylcholine39. The interrelation between these two lipids suggests their essentiality in the membrane is strongly related to their fatty acid chains, being the only lipids carrying the unsaturated fatty acid chain C18:2, whose presence might be determinant in M. pneumoniae membrane. Moreover, sphingomyelin incorporated into the membrane has a lower proportion of fatty acid chains with 20 carbons or more in respect to the sphingolipids in the serum40, suggesting M. pneumoniae preferentially incorporates in the membrane sphingomyelin with less than 20 carbons-fatty acid chains. M. pneumoniae is a fatty acid and sterol auxotroph, but it can assemble phosphatidic acid, diacylglycerol, phosphatidylglycerol and glycolipids from provided fatty acids in the medium34. Glycolipids constitute a range of 5–15% of the total membrane lipids41, while the remaining percentage should be constituted by phosphatidylglycerol; phosphatidic acid and diacylglycerol, being lipid synthesis intermediaries, are not found in relevant proportions in the membrane.Most of the studies on M. pneumoniae, where for optimal fluidity it is meant the combination of thickness and viscosity that allows M. pneumoniae to perform its membrane-related activity. While the optimal thickness is considered to be given by a range of lipid proportions, i.e., measured when the bacterium is grown on rich-medium with full metabolites and lipids availability, the optimal viscosity is given by the fatty acids distribution that M. pneumoniae aims at incorporating in the membrane when lipids availability is limited. Therefore, the acyl chains of the lipids constitute a critical aspect of our study.The lipids, with their specific molecular weight, were included in the biomass equation to reflect the predicted membrane composition. We assumed a lipid composition is optimal when it confers optimal fluidity, and so permeability, to the membrane of 11 and Worliczek et al.40; a comparison of the total fatty acid composition between iEG158_mpn and the ones available in literature is provided in Supplementary Fig. M. pneumoniae tends to selectively incorporate palmitic acid over stearic acid in the membrane30, with a preference for saturated fatty acids, suggesting oleic and linoleic acids are incorporated in a lower proportion with respect to the amount found in the media. This indicates that the proportion of different acyl chains for de-novo assembled phosphatidic acids is conserved during assembly of downstream lipids phosphatidylglycerol, diacylglycerol and glycolipids. We assumed phosphatidylcholine could be degraded in silico into glycerol-3-phosphocholine and fatty acids, to represent the re-arrangements of the phospholipid’s acyl chains, which could be triggered by membrane lipases in vivo42. Sphingomyelin, on the other hand, is in our model directly incorporated into the membrane without modifications.With the aim of linking the membrane lipid profile to the availability of fatty acids in the medium, we reconstructed the acyl chains of the various lipids composing the membrane by considering the sum of fatty acid chain fractions previously determined by Wodke et al.M. pneumoniae grown on serum rich medium is shown in Fig. M. pneumoniae, we added to the biomass equation the above representative medium composition, excluding those whose supplementation is linked to the addition of serum or would deviate from the established fatty acids proportions . We excluded phosphatidic acid from the biomass equation in iEG158_mpn and consider it solely as precursor of phosphatidylglycerol and glycolipids. Figure M. pneumoniae.A representative average membrane lipid composition of The possible ranges of lipids and fatty acids proportions assuming growth in serum-free media are summarized in Supplementary Table M. pneumoniae and predict essential fatty acids and lipids that, if added to the medium, could allow growth. The lipids in the biomass reaction of iEG158_mpn have been distributed as reported in Table We integrated into iEG158_mpn reactions involved in fatty acids, lipids and membrane formation pathways to reconstruct the membrane of 18, with addition of sphingomyelin and phosphatidylcholine. Our analysis pin-points the synergy between the three key lipid components  that were integrated in iEG158_mpn, in terms of percentages of total lipid mass in the membrane, as follows: the most enriched components must be cholesterol, constituting a range of 35–50% of the total lipids; sphingomyelin and phosphatidylcholine proportions are adjusted accordingly, as sphingomyelin has high affinity for cholesterol, while phosphatidylcholine has a low one39. Therefore, they constitute, respectively, 9–15% and 6–10% of the total lipids. Figure M. pneumoniae as simulated by iEG158_mpn. Sphingomyelin is preferably incorporated carrying an 18-carbon acyl chain and, therefore, its essentiality might be linked to it being the only one carrying the acyl chain C18:2.iEG158_mpn was used to identify media components for increased growth, predicting all the components already highlighted in Yus et al.M. pneumoniae and its downstream products phosphatidylglycerol and glycolipids. Stearic acid (C18:0) presence is important depending on phosphatidylcholine proportion: once phosphatidylcholine is incorporated, its unsaturated fatty acid chains are replaced with saturated ones, resulting in the membrane di-saturated phosphatidylcholine43. Moreover, supplementation of phosphatidylcholine might be important not only as membrane component but also as source of those fatty acids whose presence has been detected in lower percentage with respect to palmitic and oleic acids: linoleic acid (C18:2) and palmitoleic acid (C16:1).The essential fatty acids to be incorporated in a serum-free medium are palmitic (C16:0) and oleic (C18:1) acids, as they are the preferred components of phosphatidic acid in M. florum44. Cholesterol and fatty acids were added as previously reported in Yus et al.18 to be essential for M. pneumoniae growth. The new developed medium has been called MCMyco and its composition consists of Mycoplasma broth base supplemented with 17% yeast extract 15% Solution (Gibco), 0.5% glucose, 500 U/ml of penicillin G and 0.002% phenol red. Delipidated Bovine Serum Albumin was added at a final concentration of 0.5% premixed with the lipid components predicted to be essential by model simulations: fatty acids, cholesterol, sphingomyelin and phosphatidylcholine. We refer to this medium as “semi-defined” due to the presence of the undefined component yeast extract in the MC base. However, yeast extract is reported to contain amino acids, carbohydrates, vitamins and minerals.Following the model predictions, we have added sphingomyelin and phosphatidylcholine to the so-called MC medium, previously used for growing M. pneumoniae strain M129, resulting in a doubling time of 17.6 h after three culture passages, compared to the doubling time of about 8 h obtained in rich media18. The importance of sphingomyelin and phosphatidylcholine is hereby confirmed by the absence of M. pneumoniae growth when the two phospholipids are removed from MCMyco  do not change. All the lipid synthesis reactions show a consistent flux reduction, as well as the reactions related to the carbon degradation pathways and the NAD kinase. Imports of some amino acids and nucleobases  show a flux decrease when oxygen is reduced. Contrarily to the general tendency, deoxyuridine kinase and phosphorylase show a higher flux respect to the baseline situation. CO2, H2O2, acetic acid, and lactic acid production increase under hypoxia.Not considering the zero-flux reactions, only 18 show the same range of fluxes comparing the two situations (baseline and reduced oxygen) in iEG158_mpn, assuming proton leakage and maintenance energy  antigens48 and other unpredictable contaminants.Owing to its fastidious requirements, M. pneumoniae to grow on serum-free media. This prediction was validated by (i) adding these two phospholipids, together with cholesterol and fatty acids, to the previously developed MC medium47. This led to the development of a medium called MCMyco, supporting robust M. pneumoniae growth; and (ii) removing sphingomyelin and phosphatidylcholine from the defined serum-free medium vB13 , showing the absence of either both phospholipids or one of the two causes lack of growth. Therefore, our study shows, theoretically and experimentally, that supplementation of sphingomyelin and phosphatidylcholine to serum-free media, together with cholesterol, enables growth of M. pneumoniae. Moreover, in agreement with the model, we have shown in vB13 that the effect of the two phospholipids is synergistic and that the different incorporation of these lipids depends on their availability in the medium. Indeed, the lipid composition of the membrane might be strongly variable according to the environmental conditions, which affect its permeability49 and curvature50; for instance, a sphingomyelin-cholesterol packing results in a very tight membrane51. Therefore, the key medium lipids herein found confirm that one of the main limitations of M. pneumoniae growth resides in its membrane composition, viscosity and permeability. This is, in turn, determined by the proportion of acyl chains in the membrane lipids. While the requirement of cholesterol for growth have been well known for decades52, phosphatidylcholine supplementation in a medium for M. pneumoniae was first reported in 200918. Sphingomyelin, in contrast and to the best our knowledge has not been specifically reported as to be an essential component in media deployed to grow M. pneumoniae, although its positive effect on growth was previously suggested in other Mollicutes53 and observed in Spiroplasma54.Hereby, by using in silico modeling approaches based on literature data, we propose that sphingomyelin and phosphatidylcholine are essential growth factors allowing M. pneumoniae growth, the way they are supplemented might be critical: M. pneumoniae cultures where lipids are supplemented as cholesterol-phosphatidylcholine vesicles show a similar growth rate of the ones in serum-rich media55.Having established that lipids supplementation is a key aspect for M. pneumoniae growth in vitro: batch cultures may result in limitation in oxygen supply as a result of gradients that arise due to biofilm formation. Our analysis shows oxygen level is critical, affecting the whole metabolism, with only a few reaction fluxes that remain intact under hypoxic conditions in respect to baseline conditions. Low oxygen not only pushes the lactate dehydrogenase towards the production of lactate, but also critically affects the whole glycolytic pathway, with a reduction of sugar uptake and ATP production. More NADH is consumed and less is regenerated by the GADPH enzyme, which represents one of the main bottlenecks of the metabolic network. The production and accumulation of compounds such as lactate and acetate augments, enhancing toxicity and acidifying the medium when exported: it is observed that the doubling time of M. pneumoniae in batch culture increases over time due to the decrease in media pH11 and the accumulation of toxic waste compounds, namely acetic and lactic acid. Cells growing in an acidified medium have a different membrane composition31. It is, however, still unclear whether the adaptability of M. pneumoniae membrane resides in its genome-coding function, if it is a chemical-physical response of the lipid bilayer to pH changes or if it is a combination of these two aspects.Medium composition is not the only growth-limiting factor of 56 and to alter the internal biochemical state. Our model iEG158_mpn improves iJW145 by not only integrating the membrane, the specific lipids with their pathways and carried acyl chains, but also rearranging the ATP distribution in the maintenance energy, taking into account the effort made by M. pneumoniae while de-acidifying the cytosol: a cytosolic pH decrease is equivalent to an increase in the H3O+ intracellular concentration, which might alter reactions involving H+ as reactant57. This might be a cause of high energy maintenance costs and reduced growth rate. For this reason, a study on the effects of the intracellular pH change on the metabolism of M. pneumoniae would be of main interest.The cytosol of Mycoplasmas does not maintain a constant pH, with the risk of being unable to maintain the proton gradient required for trans-membrane transportM. pneumoniae under different environmental conditions would not only provide insights into the way it reacts to pH changes, but also disclose the strategies bacteria with a reduced genome use to exploit the host’s resources to accomplish essential tasks, whose capabilities it has lost during the course of evolution: our analysis highlights a lack of metabolic versatility, reflected by an in silico deficiency of flux variability, possibly due to an extremely reduced metabolic network size.A study of Altogether, our model provides the framework for an integrated approach, incorporating available literature data to systematically discover dependencies that are not of immediate recognition, such as relevant synergies and interconversions, metabolic bottlenecks, cofactor regeneration and redox balancing. Pinpointing knowledge gaps and essential pathways, it demonstrates the usefulness of modeling resources for exploring the metabolic capacity of mycoplasmas, and the use of these insights to rationally influence, modulate and design conditions for optimized performance.11 was extensively updated, manually curated and expanded to generate the genome-scale metabolic model iEG158_mpn. Reactions and metabolites have been annotated adding BiGG Models identifiers58 when possible. Reaction reversibility has been assessed through eQuilibrator59, a thermodynamic calculator of the Gibbs energy released by a specific reaction at a set pH and ionic strength . Reactions where changes in Gibbs energy comprised between −30 and 30 kJ/mol were considered reversible, otherwise irreversible. All model reactions have been manually verified using information from the MyMpn database60. Information gathered from literature was used to modify the biomass synthesis reaction adding detailed information on lipid composition. The biomass equation of iEG158_mpn has been standardized to obtain a molecular weight of 1 g.mmol−1. Reactions for fatty acid integration into membrane lipid chains have been added. The genome-scale model iEG158_mpn is provided in Dataset 3 in SBML format and its syntax and consistency has been checked with SBML Validator61 and MEMOTE, which reported stoichiometry consistency 99.1%, charge balance 87.8% and metabolite connectivity 97.7%62. A list of all reactions and metabolites IDs is given in Dataset 4. The model has been deposited in BioModels65 and assigned identifier MODEL1912060001. A python script to simulate growth with this model, imposing constraints and supplementing in silico the medium, is provided as Dataset 5.The metabolic model iJW14566, which graphically depicts a solution for maximum attainable growth rate, as computed by Flux Balance Analysis (FBA), with biomass yield, as a proxy for growth, as the objective function67. FBA solves a linear programming problem, which is characterized by having more variables than equations, meaning more than one optimal solution can exist for the same objective function maximization. FBA computes a possible flux distribution leading to the optimal objective solution. The same optimal growth rate might be reached by several different reaction flux combinations. Flux Variability Analysis (FVA) explores the range of each metabolic flux at the optimal solution68. Both FBA and FVA were run with biomass synthesis reaction as objective function to maximize, respectively, through the commands optimize and flux_variability_analysis of the Python tool cobrapy69, version 0.5.11 (Python version 3.4.4). Exchange reactions in a genome-scale metabolic model allow metabolites to enter and egress the in silico network. Simulations with iEG158_mpn were performed by conserving the same flux constraints as have been experimentally determined: these assumed unlimited availability of the compounds which are not directly metabolized for energy or carbon and/or cannot be measured experimentally . Additional fatty acids that could be metabolized or integrated as membrane lipid chains  have also been assumed to have unlimited availability as no related uptake rates were experimentally measured. Specifically, exchange reactions corresponding to metabolites whose uptake or secretion rate was not measured were set with bounds of ±1000 when production and secretion was possible, with a lower bound of −1000 and an upper bound of 0 when only uptake from medium was possible and with a lower bound of 0 and an upper bound of 1000 when describing secretion.Pathways and whole-metabolism maps for fluxes visualization were constructed with the tool EscherM. pneumoniae growth media, but for which transporters are present in M. pneumoniae genome, by adding the corresponding exchange reactions. To equilibrate the energetics in the model, we assigned a cost of 1 proton for the uptake of uncharged compounds, 1 ATP for the uptake of compounds with a charge of 1 or 2 and 2 ATPs for the one of compounds with charge greater than 2. To design a minimal serum-free chemically defined medium that optimizes M. pneumoniae growth, we iterated through all the exchange reactions present to find the combination of bounds that lead to the highest biomass production: we have initially set all the exchange reactions’ lower bounds to −1000, allowing the metabolites of interest into the system. All the lower bounds of the exchange reactions were set to 0 one at a time, to be then repristinated to −1000 for the next iteration (next exchange reaction to close). When biomass yield was not changing in consequence, we assumed the component was not required for maximizing growth rate. Each time a component was removed from the list, a new set of iterations was started on all the other components. The minimal medium was obtained once all the remaining components, when removed , were changing the biomass yield obtained by FBA. The relation between the growth rate and the doubling time has been computed using the exponential function eµT = 2, where µ is the growth rate in divisions per unit of time and T is the doubling time.Growth medium optimization consists of changing the bounds of exchange reactions to enable or limit uptake or secretion in order to find the combination of supplemented compounds that maximizes the flux through the biomass reaction if this corresponds to maximizing growth rate. We tested the possible uptake of additional native compounds not previously considered in To simulate hypoxia, we first ascertained, through Flux Balance Analysis, the minimal oxygen uptake rate required for optimal growth by iEG158_mpn. By allowing in silico an infinite supplementation of oxygen (“EX_O2_e” reaction lower bound = −1000), we ran FBA towards flux maximization of the biomass reaction. Constraining the growth rate to the maximal one, following FBA with objective function “EX_O2_e” (minimization of oxygen uptake), we computed the minimal oxygen required. Once this value is established as the minimal oxygen uptake required for optimal growth, we reduced the oxygen supplementation  until reaching a biomass flux of 0, meaning no growth in silico. Following this approach, we extracted the minimum required oxygen to be supplemented in silico to allow growth. We then performed simulations of iEG158_mpn with an intermediate oxygen supplementation resulting from the rounded calculation of the mean of the minimal oxygen uptake required for optimal growth and the minimal oxygen required for allowing growth.Lipid composition of the medium was predicted according to the scheme in Fig. 70, we verified the presence of lactate and acetic acid transporters and general monocarboxylic acid transporters in the M. pneumoniae genome (Dataset 6). PSI-BLAST algorithm71 was run with default parameters until convergence was reached to build a sequence profile of the transporters (Position Specific Scoring Matrix provided in Dataset 6), then used to query within M. pneumoniae genome for presence of these transporters. In addition, we searched for monocarboxylic transporters in M. pneumoniae by running a BLASTp search with default parameters on all its transmembrane proteins, whose list was available on the MyMpn portal (August 2018)60, filtering for proteins containing transmembrane helices.By querying manually curated sequences in the UNIPROT databaseM. pneumoniae strain M129 was grown in 75cm2 Tissue Culture Treated Flasks  in MCMyco medium, a serum-free medium based on the MC medium previously used for growing Mesoplasma florum44. Palmitic acid, oleic acid and cholesterol were added at a final concentration of 16.5 µg ml−1, 20 µg ml−1, and 20 µg ml−1, respectively. Supplementary Table −1 before performing any analysis. Each passage was performed by diluting the cells 50 times in fresh serum-free medium after two weeks of culture. Medium was replaced by fresh serum-free medium every 7 days.−1 of DNA and distributed in aliquots of 0.2 ml in 96-well Tissue Culture Treated flat plates (Nunc). Every 12 h, the medium from 6 wells were recovered in 1.5 ml tubes and centrifuged 15 min at 12,000×g. 0.2 ml of nuclease-free water were added to the empty wells to lyse adherent cells by osmotic shock and then recovered and added to the previous pellet to lyse non-adherent cells. Finally, they were incubated 10 min at 98 °C and stored at −20 °C. After 120 h, the DNA of each sample was measured by qPCR as described above and the growth curve was then plotted and analyzed using the R software (version 3.4.4).Passage 3 cells DNA were measured by qPCR using oligonucleotides MPN628/5 (GCCATTTTGGATGGTTATGG) and MPN628/3 (GGTGACCCACTTCCGAGTTA) and the Luna® Universal qPCR Master Mix (New England Biolabs). The DNA quantification was performed using the LightCycler® 480 software (Roche). Cells were diluted in 15 ml to an equivalent of 0.6 pg mlM. pneumoniae strain M129 was grown in the defined serum component-free medium vB13, developed by Burgos R., Garcia-Ramallo E., Shaw D., Lluch-Senar M. and Serrano L., based on the medium defined by Yus et al.18 by experimental screening of different components. vB13 composition is provided in Supplementary Table M. pneumoniae in vB13 was compared to rich medium (Hayflick) by growth curve analysis as follows. M. pneumoniae strain M129 was inoculated with frozen stocks (1:200 dilution) in tissue culture flasks of 25 cm2 containing 5 ml of medium. Cultures were incubated at 37 °C under 5% CO2, and protein and DNA biomass were measured at different time points  as follows. For each time point, cells were scraped off from the flasks in the culture medium and 1 ml of cell suspension harvested by centrifugation . This was performed per duplicate to obtain samples for both, protein and DNA measurements. For protein biomass quantification, the cell pellet was washed twice with PBSx1 and lysed with 100 μl of lysis buffer  prior to duplicate protein measurements using the Pierce TM BCA Protein Assay Kit. For DNA biomass quantification, the cell pellet was directly analysed, and the DNA extracted using the MasterPure DNA purification Kit (Epicentre) following the recommendations of the Kit manufacturer. Finally, extracted DNA for each time point was measured using a fluorometric method .M. pneumoniae was grown in a 96-well plate format. Growth curve analysis comparing the performance of rich medium (Hayflick), vB13 and vB13 derivative versions containing only phosphatidylcholine (PC), only sphingomyelin (SPM) or vB13 without PC and SPM were recorded in a Tecan Spark plate reader by determining the growth index value, which is the ratio of absorbance at 430 nm and 560 nm of the culture medium18. Each media was tested in duplicate wells containing 200 μl of medium inoculated with frozen stocks at 1:100 dilutions. Total cell biomass obtained at the end of the growth curve (96 h) was also determined by protein quantification as follows. Briefly, wells of the 96-well plate were washed twice with PBS ×1 and attached cells lysed with 100 μl of lysis buffer . Protein concentrations were then determined using the Pierce BCA protein assay kit by measuring absorbance at 562 nm in a Tecan plate reader18.To assess the contribution of phospholipids in the animal component-free medium, Further information on research design is available in the Supplementary InformationReporting SummaryDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6