Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.Metabolism underpins the pathogenic strategy of the causative agent of TB,  Mycobacterium tuberculosis (Mtb), is a global killer causing millions of deaths every year and is therefore a major burden to human health. Treatment of tuberculosis requires a cocktail of antibiotics for a minimum of 6 months. Treatment failure is common and is a major driver in the upward trend of antibiotic resistance, recognized by the World Health Organization as one of top ten threats to global health. A key to the success of Mtb as a human pathogen is ascribed to its extraordinary metabolic flexibility. Understanding the metabolism of Mtb is therefore an important goal of TB researchers as metabolic pathways present attractive drug targets. A powerful approach to study metabolism is through the use of genome-scale metabolic networks which enable metabolism to be studied at the whole system level rather than one enzyme at a time. Here, we comprehensively compare available genome scale metabolic networks. Our results identify the best performing networks for a variety of modelling approaches. This work allowed us to refine these models for the TB community to use in future studies to probe the metabolism of this formidable human pathogen.The tuberculosis bacillus,  Mycobacterium tuberculosis (Mtb) is the causative bacterial agent of the global tuberculosis (TB) epidemic, which is now the biggest infectious disease killer worldwide, causing 1.6 million deaths in 2017 alone  + XTP[c] -> H[c] + XDP[c] + Pi[c] and 2) maximization of each EDR flux vd while no substrate uptake is allowed into the model as follows:Energy generating cycles (EGCs) exist in metabolic networks and can charge energy metabolites like ATP, GTP, CDP, and UTP without any input of nutrients; therefore, their elimination is essential for correcting energy metabolism ,94. FritS is the stoichiometric matrix, v the vector of fluxes, d the index of EDRs, E is the set of indices of all exchange reactions of the model. If the optimal value of vd for this optimization is vd>0, there exist in the genome-scale model at least one EGC that is able to generate energy metabolites like ATP, GTP, CTP, or UTP.Here, Two types of modifications were performed on the curated Mtb genome-scale metabolic network in order to eliminate TICs .+- and NADP+-dependent) with the same catalytic activity. In this instance, we forced the use of NADPH/NADP+ in anabolic reactions and NADH/NAD+ for catabolic reactions, as recommended by Pereira and colleague Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this paper, the authors evaluate 8 published genome-scale models (GEMs) of M. tuberculosis H3Rv and refine the best performing GEMs for future use. Specifically, the authors compare the gene content, currency metabolite connectivity, biomass reactions, charge balance, mass balance, blocked reactions, thermodynamics, gene essentiality predictions, and utilization of carbon sources across the different models. The authors use standard metrics to assess the various model performances and also place the model predictions within the context of TB literature to help discern biological relevance. The authors identify iEK1011 and sMtb2018 as the best overall GEM and improve their quality by adding various metabolic reactions to the models with detailed reasons. Given the power of GEMs to enable deep analysis of TB metabolism, as well as the large availability and variability in TB GEMs, we believe that this study adds significant value to the TB research community and thus should be published in PLOS Computational Biology. However, there are more GEM evaluations and details described below in the major comments that we would like to see the authors address before publication. In particular, we would like to see the authors detail differences in pathway utilization strategies between the improved models, if there are any.To the Authors:Major commentshttps://www.biorxiv.org/content/10.1101/350991v1). Rather than dictating whether the models are worthy of being published, the MEMOTE report should help understand what the limitations are and where they should focus their efforts in potentially improving the model. Importantly, the MEMOTE report may help the authors improve improve the refined models and comparisons before publication.1. It is highly recommended by the COBRA community that the authors provide a MEMOTE report of both the compared and improved reconstructions . Currently, only a comparison of the biomass molecular weight is provided at the end of the “Checking mass and charge balances of biochemical reactions” section.3. The authors should provide a description/comparison of the growth-associated maintenance (GAM) or non-growth associated maintenance (NGAM) reactions across the different TB models. One or two sentences is sufficient.4. Both flux variability analysis (FVA) and flux sampling are key FBA tools used to evaluate the solution space of GEMs. We recommend that the authors perform FVA and flux sampling of the improved GEMs (iEK1011_2.0 and sMtb2.0) to both evaluate the flux bottlenecks and distributions between models.5. Continuing from comment 5, the authors should provide detailed comparisons of the flux profile between the improved GEMs (iEK1011_2.0 and sMtb2.0) in standard TB media using either parsimonious FBA (pFBA), FVA, or flux sampling. For example, do the models prefer different nutrients? Do they have similar flux strategies in TCA and oxPPP? We would like to see the authors detail differences in pathway utilization strategies between the improved models, if there are any.Minor comments1. Line 100: The description of red in Fig 2 is written the same as blue.Reviewer #2: In their manuscript titled “A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks” Lope-agudelo et al. have systematically examined 8 of the most recent Mtb GEM models and have identified a significant number of problems with them. As I note below, I have questions about some of their choices but overall, I congratulate and thank the authors for a detailed examination of models for an organism of significant importance.Despite this, I don’t find the work novel and suited for Plos Computational biology. I expect Plos Comp bio papers to either introduce a novel computational biology method or report a significant biological find. Neither of these apply to this paper. The manuscript is better suited for more general journals like Plos One or Scientific reports.Perhaps if the authors had generated a single unified improved model of Tb metabolism, then it would have been appropriate for Plos Comp Bio. As it stands, by their own admission, one of their models is better suited for interacting with Recon model while the other is better suited for simulating growth under a variety of different conditions.Major concerns• The paragraph starting on line 38 is not sufficient to describe the examined models and how they differ. The order of publication and combination of models is included in figure 1 and further detail is provided in S1 but there is no mention of it in the main text.• The model with the greatest number of reactions and genes is iAB-AMF-1410-Mt-661 updated published on 2017. Why was this model ignored? There is no mention of this model. How can the purported updated models be considered most complete if they still have fewer reactions and genes than the noted model?• Examining figure 2 it is obvious that some models are just the old models plus some new reactions e.g. sMtb and sMtb2018. Why include the former when instead iAB-AMF-1410-Mt-661 could have been examined?• Line 63, what do you mean by multi-scale simulation platform? What scales are you talking about?• Line 92, I’m confused about the statement “has poor annotation of genes”? Does that mean the genes identified were incorrectly assigned a function or that the coverage of the pathways was lacking?• How does iCG760 operate as a system-level model without water as one of its metabolites? To me the sorry biochemical states of three of the published models is the most shocking find of the paper.• When highlighting the results of analyses for blocked reactions and dead-end metabolites it is important to note if the cause is due to gaps in genome or incomplete GPRs. The former could be viewed as the modeler trying not to add a new phenotype without experimental validation whereas the latter is a poorly curated model.• Why not combine sMtb2.0 and iEK1011_2.0 into one unified model that would work well with Recon for host pathogen interactions as well as correctly predicts growth on a variety of different compounds? That would be the perfect outcome from your extensive efforts.Minor concerns• Do not use acronyms in the abstract prior to defining them, i.e. TB• Some figures are labeled Fig. others Figure. Please choose one format.• The sentence starting on line 152 is incomplete.Reviewer #3: The article entitled, 'A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks', Lopez-Agudelo et al present a comparative analysis of Mtb genome scale metabolic reconstructions.This is a welcome analysis in the literature, given the growing number of models and derivations of models and the need for reconciliation and assessment of the current state. While the most recent update iEK1011 presented an update, the authors identify aspects of the model that area missing.+ Figure 1: Provides a nice summary of an otherwise confusing development of multiple models using genome-scale models of Mtb.+ p7: How did the authors arrive at the cutoff of 10% for the deviation of 1 g/mmol? Why is iEK1011 so much higher than some of the other models?+ Fig 5: It is understandable to revise reaction directionality based on thermodynamic feasibility assessment, however to remove a particular reaction(s) (DFRA1 and DFRA3) should require a little more justification, including any experimental support/arguments and/or explanations of why the arguments made for a different genus (yeast) are also relevant for Mtb.+ p13: the identification of a need for a menaquinone-dependent succinate dehydrogenase reaction is an interesting modification that appeared to improve the predictive capability of the model. However the authors state that they had to add this reaction to iEK1011, whereas in reality all that was required was to change this from an irreversible to a reversible reaction. It would be helpful to note whether there is experimental evidence to suggest that concentrations are present to enable thermodynamic reversibility of the reaction. Additionally, will the reversible form of the reaction result in any infeasible reaction cycles?+ p16: similar to the question raised on p13, which one of these involved addition of new reactions vs making reactions reversible+ p18: 'thermodynamically unstable' -- perhaps the authors meant thermodynamically infeasible, since there does not appear to be any discussion of stability of particular flux states+ p17: what specifically is meant by "unblocking B12 synthesis"? The authors themselves cite specific articles that highlight the incomplete knowledge about B12 handling in Mtb.+ p17: it is not clear what addition is made by addition of biotin?+ p17: again, it is not clear what specifically the authors mean by "including ... pyridoxal-5-phosphate", although from the referenced article, in this case they presumably are referring to the addition of pyridoxal 5' synthase. For biotin, it is not clear what revisions they made or the rational/justification for it.+ Table 5: it is not clear where the cited values for O2, NADH/NAD, and NADPH/NADP are obtained from the referenced citations, since the paper and textbook (non-specific reference) do not appear to cite measurements for Mtb. Additionally it is not clear what is meant by "Other metabolites".+ p26: What was the scientific rationale/calculation to identify the 5% cutoff for+ minor comments:there are a number of small grammatical errors, e.g.p13, l255: "is as a result"p16, l312: "exempler", did you mean exemplar?p 29: supplemental file legends: S10, S11, S12, S13, "for classify essential"etc.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Anders WallqvistAssociate EditorPLOS Computational BiologyWilliam NobleDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: We are happy with the authors revisions and have no further comments.Reviewer #2: The authors have answered all my concerns.Reviewer #3: The revised version provides clarifications for many of the points that were raised. Further the incorporation of MEMOTE report, as suggested by a different reviewer, is a nice addition.A few follow up points are worth addressing,+ Regarding the question of Vitamin B12 synthesis, regardless of whether another paper has included the pathway based on ‘physiological’ argument is generally not considered sufficient criteria for incorporating content into genome-scale metabolic networks . Generally even if ‘physiological’ evidence arguments are made, this typically involves inclusion of a single enzyme or transporter, thus the inclusion of multiple enzymatic steps introduces the potential for significant error into the model.The manuscript by Minas et al poses a possible pathway, but lacks any strong biochemical evidence for the actual enzymes and pathway. The authors should clearly identify which enzymes/reactions were added. Microbial vitamin B12 synthesis is not a trivial or singular pathway, as recently summarized by Fang et al, Microbial production of vitamin B12: a review and future perspectives, DOI 10.1186/s12934-017-0631-y. It should clearly be stated which enzymes/reactions were added and ideally some type of justification , this issue is adequately addressed.+ There are still a few grammatical/spelling errors, e.g. Mathews Correlation Coefficient should read Matthews Correlation Coefficient, the authors likely mean ‘bona fide’ and not ‘bone-fide’ on page 10, etc. Recommend one more read through by authors to minimize misspellings.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see  8 May 2020Dear Dr. Beste,We are pleased to inform you that your manuscript 'A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Anders WallqvistAssociate EditorPLOS Computational BiologyWilliam NobleDeputy EditorPLOS Computational Biology*********************************************************** 5 Jun 2020PCOMPBIOL-D-19-01934R2 A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic NetworksDear Dr Beste,I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work! With kind regards,Sarah Hammondploscompbiol@plos.org | Phone +44 (0) 1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes  in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin–diadinoxanthin pathway over violaxanthin–neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions , validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds. They plompounds ,4,5. Diaompounds , a polygompounds  and syntompounds ,8 and fuompounds ,9 makes Thalassiosira pseudonana is a model diatom. It was the first diatom chosen for genome sequencing due to its utility in physiological and silica biomineralization [T. pseudonana for enhanced production/accumulation of lipids, TAGs, and omega-3 fatty acids [lization  studies.lization . The abity acids ,13,14.Chlamydomonas [Chlorella [Nannochloropsis [Phaeodactylum tricornutum [P. tricornutum, a GEM for T. pseudonana is still not available. P. tricornutum and T. pseudonana have been segregated under taxonomically distinct subgroups (order and genus) [P. tricornutum and T. pseudonana, respectively [+ was found to be an essential nutritional component for the growth of T. pseudonana [P. tricornutum [PEPCK (phosphoenolpyruvate carboxykinase) show differential regulation in T. pseudonana and P. tricornutum [P. tricornutum cannot be used to investigate the metabolism and biotechnological potential of a significantly different organism, T. pseudonana, as suggested by a recent work where eight different strains of the same organism (E. coli) were found to be physiologically different [Genome-scale metabolic models (GEMs) make it possible to simulate metabolic behavior of the organism and predict internal metabolism under different conditions ,16,17. Tydomonas ,25, Chlohlorella , and Nanloropsis ,27) and cornutum ,29) haved genus) ,31. Theyectively , based oeudonana  while itcornutum ,35. Morecornutum . Thus, aifferent . TherefoiThaps987) of T. pseudonana, to overcome this gap and perform its systems level investigation. The model iThaps987 includes 987 genes, 2477 reactions, and 2456 metabolites. The FBA analysis revealed the CO2 fixation by C3/Calvin–Benson–Bassham (CBB) cycle. The different pathway routes for biosynthesis of fucoxanthin were explored for their activity and the results indicate a preference for the violaxanthin–diadinoxanthin pathway over the violaxanthin–neoxanthin pathway. The features of this model were compared to the previously published P. tricornutum model (iLB1025) [T. pseudonana to photosynthetically produce some industrially useful compounds. In this work, we present the first high-quality manually-curated GEM (iLB1025) . The worThalassiosira pseudonana, iThaps987, was reconstructed and analysed using established protocols and an approach described for a number other organisms such as Geobacillus thermoglucosidasius [Salmonella typhimurium [Arabidopsis thaliana [Thalassiosira pseudonana biomass was measured to construct the biomass reaction which provides molar fractions of metabolites required to make a gram of biomass. Information from various biological database were used as per requirement. For example, reaction directionalities were taken from BioCyc [The genome-scale metabolic model of sidasius , Salmonehimurium , and Arathaliana . The modthaliana . The modthaliana  databasem BioCyc  and Metam BioCyc  databasem BioCyc  databasem BioCyc ,20,41.Thalassiosira pseudonana  was downloaded from BioCyc and extracted to obtain a flat-files database. The PGDB of Thalassiosira pseudonana has information related to reactions, their genes and sequences. ScrumPy tool was used to fetch these flat-file databases using the inbuilt module ‘PyoCyc’. Finally, a draft model was created by obtaining all the reactions of the database.The Pathway Genome Database (PGDB) for The initial draft model included several non-specific metabolites, e.g., ‘Aldoses’, ‘Fatty aldehydes’, and reactions—e.g., ‘DNA-LIGASE’—that would create uncertainties in the model. The draft model was refined iteratively using literature, biological database(s), and previously published models until a final, robust model is obtained. In the first round of refinement, the non-specific metabolites and reactions were removed from the model. In the subsequent step, each reaction was checked and corrected, wherever required, for mass balance in terms of carbon, nitrogen, phosphorus, and sulfur. Further refinements were carried out as explained below.Thalassiosira pseudonana genome sequence.The draft model obtained after the initial refining was unable to synthesise some essential biomass precursors, indicating an absence of at least one key reaction that participates in the synthesis of the corresponding precursor. The absence of such key reactions results in gaps in the network. These gaps in the draft model were manually identified by performing optimisations for production of all the biomass precursors one by one. Those precursors which were unable to be produced by the draft model were termed as gapped metabolites. The presence of gapped metabolites in a model indicates existence of some missing links (reactions/pathways), also known as gapped reactions/pathways, in the network. These gaps were filled manually by searching for biosynthetic pathways and reactions that will ensure biosynthesis of these biomass precursors, thereby filling those gaps in the network. The gene sequences of these added reactions were subjected to BLAST searches against the Thalassiosira pseudonana is a eukaryotic microbe and has different compartments for different functions. All reactions of the model were localized into three different compartments: cytosol, chloroplast, and mitochondrion. The sub-cellular localisation of reactions was obtained using ngLoc web-server [b-server . A reactPhaeodactylum tricornutum [Synechococcus sp. BDU130192 had utilized ATP maintenance values from previously published models of Synechococcus sp. PCC 7002 [Thermus thermophilus HB27 [Solanum lycopersicum L.) [E.coli and Arabidopsis thaliana models, respectively. We have also evaluated the effect of variation of NGAM on intracellular flux distributions.A characteristic feature of all organisms is to grow and maintain their structural integrity. They require energy to perform these two basic functions of life. This energy is required in the form of the cellular currency ‘ATP’. The energy (ATP) required for growth is termed as growth-associated ATP maintenance (GAM). The GAM accounts for energy required to synthesize precursors, polymerize purine and pyrimidine bases and amino acids to form DNA and proteins, respectively, and polymerize cell wall precursors, etc. The other type of energy (ATP) which accounts to maintain cellular structure and integrity is termed Non growth-associated ATP maintenance (NGAM). As the values of GAM and NGAM are not available for this strain, the values from the GEM of cornutum  were usePCC 7002 , while tlus HB27 , and tomicum L.)  have utiThe biomass equation was formulated by experimental measurement of important biomass components like proteins, carbohydrates, lipids, DNA, and RNA while the minor components—e.g., pigments—were taken from previously published models . FurtheriThaps987, representing the metabolic reactions of T. pseudonana was reconstructed as a ScrumPy-readable text file , a constraint-based modeling (CBM) approach based on linear programming technique using ScrumPy. FBA determines the internal flux distribution which satisfies the steady state assumption besides some other constraints. According to the steady state assumption, the concentrations of intracellular metabolites remain steady . This implies that the compounds taken in by the cell should either go towards biomass production or towards the formation some byproducts. Overall, the carbon, nitrogen, etc. should be balanced in the determined flux distributions. The FBA approach optimizes any metabolic property, e.g., biomass formation reaction, or other reaction(s) within the constraints provided. In this study, minimization of total cellular fluxes was used as the cellular objective function . For sim−1 see  while th2 intake rate was fixed to 2.05 mmol/(gDCW.h) while the silica uptake rate was varied from 0 mmol/(gDCW.h) to 0.05 mmol/(gDCW.h) and minimization of total flux was used as the objective function.In order to analyse the effect of silica limitation on chrysolaminarin production, a virtual transport reaction was added to the model for transport of chrysolaminarin from cytosol to an ‘extracellular’ compartment. That is, the accumulation of chrysolaminarin is assumed as ‘export’. The COS is the stoichiometry matrix, th value in flux vector.The FBA approach  was used to perform the reaction essentiality analysis. The flux through each reaction was fixed to zero at a time while the flux through biomass reaction (growth rate) was fixed to the experimentally observed value at the time of simulations. A reaction was defined as essential if fixing the flux through the reaction to zero results in an infeasible optimization.Diatoms can be exploited as cell factories . TherefoiThaps987, and the missing reactions were ‘temporarily’ added to the model. The maximum theoretical yields for the heterologous products were predicted by adding the required external reactions and constraining the model as done for indigenous products. The MetaCyc database contains pathways and their respective reactions as mini modules, e.g., pathway for ‘styrene’ biosynthesis contains specific reactions that synthesises ‘styrene’. Using an in-house script, the presence of these specific reactions was checked in S is the stoichiometry matrix, c is the objective function. In this case, the objective function is the exchange reaction(s) for product formation. vBiomass was kept as 80% of the measured growth rate. The maximum yields (x) were obtained as a ratio of product formation to carbon intake rates. The linear programming formulation to perform FBA for the production of the native and heterologous products isT. pseudonana genome) for biomass-precursor biosynthesis. Out of these 62 enzymes, 21 were already present in the PGDB of T. pseudonana while the 14 enzymes were found in P. tricornutum or Chlamydomonas reinhardtii genomes. Also, there are 19 orphan metabolic reactions present in the model that do not have any associated Enzyme Commission (EC) number. A list of gap-filling reactions is summarised in The draft model was subjected to manual gap finding analysis, which identified only 62 orphan enzymes additionally required  numbers. The comparison revealed 483 enzymes are common in both models , followed by lipid metabolism pathways with 29 enzymes. Pathways related to vitamins and co-factor metabolism, pigments, and nucleotides have relatively fewer unique enzymes (The dels see  and 183 ectively a. Furthe enzymes b. L-lactate dehydrogenase (cytochrome), and phosphoenolpyruvate carboxykinase (diphosphate). Sub-pathways of lipids metabolism- glycerophospholipid (8) and fatty acid (8) metabolism have equal number of unique enzymes while arachidonic acid (3) and glycerolipid (1) pathways have lesser number of enzymes. Among the vitamins and co-factor metabolism pathways, almost all sub-pathways have approximately the same number of unique enzymes. Porphyrin and chlorophyll (6) and carotenoids (3) metabolism pathways have more unique enzymes than other sub-pathways of the pigment metabolism pathway. The T. pseudonana.−1 (see 2 and photon uptake rates were predicted to be 1.05 and 13.02 mmol/(gDCW.h), respectively. These uptake rates are within the physiological ranges suggested by previous reports [2 evolution of 1.51 mmol/(gDCW.h). Thus, the O2/CO2 ratio was predicted to be 1.43 which is very close to the value 1.37 obtained with the Phaeodactylum tricornutum model iLB1025 [For the photoautotrophic simulations, the growth rate was fixed to the experimentally determined value of 0.024 h reports ,28. The 2 fixation by the C3 cycle by utilising ATP and NADPH produced during light reactions in chloroplast. The photon directs the photolysis of water and electron flow from water to NADPH involving both photosystems PSII and PSI.The light-driven growth simulations (photoautotrophic growth) showed CODuring the electron flow, protons are pumped in thylakoid lumen which generates an electrochemical gradient across the thylakoid membrane. This gradient provides the force (proton motive force) for plastidial ATP synthesis by ATP synthase complex. The flux distribution reveals that under normal photoautotrophic condition, the cyclic electron flow (CEF), which only allows production of ATP, is inactive. Flux through the linear electron flow (LEF), that facilitates simultaneous and coupled biosynthesis of ATP and NADPH, was found to be 3.4 mmol/(gDCW.h).2 is fixed by the enzyme Rubisco that converts three molecules of CO2 and three molecules of ribulose 1,5-bisphosphate (RUBP) into six molecules of 3-phosphoglycerate (3PGA) which is finally reduced to glyceraldehyde-3-phosphate (GAP). The carbon flux is mainly distributed through GAP to three different routes—i.e., the CBB cycle for the regeneration of RUBP—the pentose phosphate pathway and the upper part of glycolysis in chloroplast. About 0.3% of the carbon goes to lower part of glycolysis to form PEP, PYR, and acetyl CoA while 6.7% forms hydroxypyruvate. The rest 92.7% of the carbon flux goes to form GAP . The flux through pyruvate to alpha-ketoglutarate is 0.03 mmol/(gDCW.h) . Althougl-ornithine to form l-citrulline which is then transported to cytoplasm. In cytoplasm, l-citrulline reacts with l-aspartate to form arginosuccinate which, in turn, dissociates into arginine and fumarate. The flux through all above reactions is same (0.004 mmol/(gDCW.h)). The last reaction of the urea cycle, which converts arginine to urea and l-ornithine, is not active under normal conditions. Only upon increasing the nitrate uptake rate without increasing the growth rate is this reaction activated.Urea cycle was found to be partially active under normal growth conditions. The flux through carbamoyl phosphate synthase, which converts ammonium and carbonate ions (inside mitochondrion) to carbamoyl phosphate, was found to be 0.004 mmol/(gDCW.h). Carbamoyl-phosphate then reacts with T. pseudonana accumulates a significant (20–25%) amount of chrysolaminarin [+ transport was varied and the effect on the flux through chrysolaminarin production was calculated . This range was chosen because the lowest ATPM value of 0.36 mmol/(gDCW.h) has been reported for netarium  while thnetarium , has beeynthesis a and finynthesis a.The effect of ATPM values on various reactions reveal that as ATPM increases, more photons are absorbed and the flux through linear electron flow via the PSII and PSI increases. The cyclic electron flow (CEF) remains inactive. The flux through oxaloacetate–malate valve was also increased b. Simula2 is consumed in mitochondria and more O2 is produced in the chloroplast. The O2 production increases with O2 consumption linearly  of these products were predicted by the model using the FBA approach by fixing the growth rate to 80% of the wild type growth rate and maximizing for product formation. Among the native metabolites, malate, and succinate have same yield of 0.22 while citrate has a yield of 0.14. Among non-native compounds, isobutanol and PHB (polyhydroxybutyrate), have a maximum yield of 0.22 followed by 2-methylsuccinate with 0.17 and styrene with 0.17. Additionally, isobutanol production should require the least metabolic engineering, i.e., introduction of only a single heterologous gene.The model ompounds . The theT. pseudonana across the metabolic pathways suggests the presence of alternative pathways/routes for the biosynthesis of biomass precursors.The reaction-deletion analysis revealed a total of 327 essential reactions under photoautotrophic condition. T. pseudonana was used to interrogate different aspects of metabolism and biotechnological potential of the T. pseudonana in a systems approach. The model iThaps987 is mass balanced and does not contain any thermodynamically infeasible cycles.Diatoms are a major constituent of phytoplanktons and play a central role in marine primary productivity. They can also be exploited as potential feedstock for biotechnological processes. Although a considerable progress has been made in understanding their basic and molecular biology, much remains to be done in the domain of systems biology. In this report, a first genome-scale metabolic model of T. pseudonana. Therefore, the model can be used for improvement of genome annotation. The draft model lacked the “PHOSACETYLTRANS-RXN” reaction but BLAST search revealed that locus “XP_002287976.1” codes for the same enzyme. The reactions “ISOCITRATE-DEHYDROGENASE-NAD+-RXN” is coded by the gene locus “XP_002290637.1”, “MALATE-DEHYDROGENASE-NADP+-RXN” by the gene locus “XP_002296311.1” and “MALATE--COA-LIGASE-RXN” by the gene locus “XP_002286405.1”. All of these gene loci were labelled as “hypothetical protein” or “predicted protein”.During the course of model reconstruction, we identified a number of previously unannotated genes in iLB1025) of another diatom P. tricornutum revealed common and unique features of the model, iThaps987  or by the cyclic electron flow (CEF). The ATP synthesized by LEF is tightly coupled with NADPH production whereas the ATP synthesized by the CEF does not generate any NADPH. The model simulation indicates that the plastidial ATP was synthesized exclusively by LEF. This prediction is also in agreement with the experimental observation by  that theiThaps987 has ‘2-oxoglutarate-malate shuttle’ besides ‘malate–oxaloacetate shuttle’ for exchange of metabolites between mitochondrion and chloroplast. Amongst these, the ‘malate–oxaloacetate shuttle’ plays a major role in translocation of photosynthate from chloroplast to mitochondrion. ‘Malate–aspartate shuttle’ was found to be absent in the iThaps987 model as it was in iLB1025.The model was further utilized to explore the effect of different ATPM values on metabolism b. The in3−) intake does the last reaction of urea cycle get activated to flush out the excess nitrogen in the form of urea.Unlike other unicellular organisms such as bacteria, cyanobacteria, yeast, and green algae, diatoms have a complete urea cycle . HoweverThe simulations for Si-limited condition revealed a trade-off between formation of storage product and biomass production under nutrient-limiting condition. The carbon was partitioning between biomass and chrysolaminarin at various levels of silica. Under stressed conditions (very low silica), most of the carbon goes to chrysolaminarin while at higher silica intake flux, the carbon flux shifts towards biomass formation.iThaps987 with respect to its biotechnological potential revealed iso-butanol as the non-native product that can be produced with the introduction of only a single foreign gene “kivD’. On the contrary, two genes ‘kivD and ADH2′ are required in the P. tricornutum model.The systems analyses of T. pesudonana under different conditions [13C-MFA can be performed in different conditions to validate the predicted FBA solutions.The reconstructed GEM can also be utilized to reveal new systems-level insights about the organism. For example, integration of expression data to the available GEM could provide novel insights into metabolic regulation under different conditions. The model is provided in ScrumPy as well as in mat format that can be used for further studies. Some studies have performed transcriptomic and proteomic analysis of nditions ,54,55. TThalassiosira pseudonana. Several results obtained by the model were found to be in agreement with previous reports. The model was used to probe the metabolic engineering needed to produce various native and heterologous industrially relevant compounds that can be produced by the organism. The model will be a useful tool for systems-level investigation of Thalassiosira pseudonana.This report presents the first publicly available, comprehensive, and manually curated genome scale metabolic model of


Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing  and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions  of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health. The scientific community has only recently begun to realize and fully appreciate the significant role of the microbiome in human health . Increasin silico means of predicting metabolic capacity from nucleic acid sequences would embolden drug discovery by generating testable hypotheses sans cost-prohibitive upstream metabolome profiling analyses.Untargeted metabolomic analyses greatly facilitate the detection and characterization of a wide range of metabolites, affording researchers a comprehensive understanding of the metabolic pathways invoked within a microbial community. Such techniques have bolstered and accelerated mechanistic studies and biomarker identification strategies across a variety of diseases . With maRecent years have seen advances in linking microbiome sequencing data to metabolome data. One such strategy relies on the network of connections linking a given gene to reactions and compounds in a database. These linkages are used to infer molecular compound identities from the genetic information housed within a microbial community. A method called predicted relative metabolomic turnover (PRMT) was used to predict metabolites from a coastal marine metagenomics dataset, and the predicted metabolites correlated strongly with environmental factors . MIMOSA Via MelonnPan, a model trained from paired microbiome and metabolome datasets can be used to predict metabolites from a novel microbiome dataset san a priori knowledge regarding relationships between genes and metabolites. This approach circumvents the limitations of the reference-based methods discussed above and has been used to generate promising results between two inflammatory bowel disease (IBD) cohorts  consisted of samples other than stool or intestinal tissue, (4) had microbiome data other than NGS sequencing, (5) had targeted metabolomics data, and (6) had fewer than 30 microbiome-metabolome paired samples. Notably, as different metabolome generation and processing methods have significant impact on results, we only selected studies which applied untargeted metabolome profiling with multiple liquid chromatography-mass spectrometry (LC-MS) methods or nuclear magnetic resonance (NMR) for a comprehensive view of the metabolite pool. Eighteen studies were retained and six were used in this evaluation due to data accessibility  using USEARCH (de novo OTU clustering) and determined a representative consensus sequence per de novo OTU. Piphillin (Raw sequencing data were either downloaded from NCBI Sequence Read Archive (SRA) for public studies or generated in-house using the Illumina platform . We anal at 0.97 . For 16S USEARCH . We assiiphillin  was thenFor metagenomic and metatranscriptomics reads, adapter sequences and low-quality ends were trimmed with Trimmomatic <Q20; . We then1, and the Chemical Translation Service2 were used to identify and convert metabolites.Detailed LC-MS/NMR conditions, software and library databases for metabolite data generation per each study are listed in A relationship table linking KEGG orthologs (KO) and BioCyc reactions (RXNs) to the compounds housed in KEGG (release date 2019 May) and BioCyc (version 23.0) databases was compiled. Specifically, if KOs have corresponding KEGG reactions which produce or consume KEGG compounds, we directly linked those KOs to KEGG compounds. An example is that K24443 corresponds to two reactions R02428, R02526, which produce or consume compounds C02753, C00001, C00502, C01114, and C00545. Thus, K24443 is linked to those compounds. There are cases when KOs do not have corresponding reactions but KEGG Enzyme numbers, and we would link those enzymes to reactions and further to compounds. An example is that K00046 has no corresponding reactions but the enzyme number EC:1.1.1.69, through which we linked to reactions R01738, R01740 and the reactions produce or consume compounds C00257, C00003, C01062, C00004, C00080, C00006, C00005, thus those compounds are linked to K00046. Manual effort was also made to link transporter KOs (BRITE class: ko02000) to their associated compounds. For the BioCyc database, RXNs and their associated compounds were inferred from “core_description_of_RXNs” directly, which included reactions from both MetaCyc and all PGDBs in BioCyc. This collection of reactions is referred to as “BioCyc reactions” in the remainder of the text. Reaction directionality was not taken into consideration when constructing the relationship table because many biochemical reactions are reversible depending on the reactant and product concentrations.via DESeq2 analysis  between case and control samples 3, we updated the community metabolic network template to the May 2019 version of the KEGG database. Microbiome functional abundance data were normalized by means of MUSiCC using default settings . As the abundance matrix generated with MIMOSA includes both positive and negative values, we elected to apply Wilcoxon rank sum tests with Benjamini-Hochberg FDR correction . Wilcoxon signed rank tests were used to comparatively evaluate precision, recall, and F1 scores across pipelines and databases between paired datasets.Prevalence filtering was applied to measured metabolome data, as well as MIMOSA and MelonnPan predicted metabolome data, and metabolites detected or predicted in fewer than 5% of the samples were omitted from consideration. Data resulting from empirically measured- and MelonnPan predicted-profiles were log transformed after imputing zeros with the minimum non-zero value per metabolite. We then conducted Student’s Random sampling analysis was also performed and compared to the pipeline prediction. For coverage evaluation, the same number as the predicted metabolites were randomly selected from the in-house chemical dictionary and compared with the pipeline predicted results. For differential metabolite identification, gene labels were randomly shuffled prior to DESeq2 and used for prediction for Mangosteen. For MIMOSA and MelonnPan, predicted metabolite labels were shuffled in the predicted matrices, followed by the same procedure for differential metabolite identification. The results were further evaluated against the measured metabolites and performance was represented in precision, recall and F1 score. The procedure was repeated for 99 times.n = 648 from five studies), while a smaller subset (n = 83) arose from intestinal tissue samples of the CRC study. All samples were subjected to metabolomic analysis via either mass spectrometry  or NMR spectroscopy . For microbiome profiling, half of the datasets originate from 16S rRNA gene amplicon sequencing (four datasets), followed by shotgun metagenomic analysis (two datasets), and metatranscriptomic sequencing (two datasets).Among the 6 studies that fit our selection criteria as described in n = 1,273) and NMR the least  identified the greatest number of metabolites . While Mangosteen-B predicted significantly fewer unique microbial functions than Mangosteen-K , it linkMetabolites bearing KEGG identifications were then assigned to their corresponding BRITE classifications . Melonnpp = 0.01) and overall F1 scoring  compared to its BioCyc-referenced counterpart. With respect to ML-based MelonnPan, these two reference databases used for model building performed equally well and yielded no significant differences in precision, recall, or F1 score. We also compared the prediction results to random sampling, which randomly predicted the same number of metabolites, and all pipelines showed significantly better performance regarding precision, recall and F1 score (p < 0.01).Predicted metabolite profiles were compared to empirically measured metabolite profiles. MelonnPan exhibited the greatest precision  in predicting the presence/absence of metabolites in the human gut, followed by MIMOSA (0.08) and Mangosteen , with squared m12 values  compared to random prediction while MIMOSA and MelonnPan did not .We evaluated these pipelines on their ability to identify differentially abundant metabolites between case and control groups as shown in a priori microbiome sequencing data affords researchers the ability to generate hypotheses in a rapid, cost-effective, and relatively reliable fashion. We systematically compared the ability of metabolite predictions by two published tools alongside a newly developed reference-based pipeline using more than 900 paired microbiome-metabolome stool/intestinal tissue samples from six different studies on various human diseases. Resulting metabolite profile predictions were compared to one another and contrasted alongside an empirically measured metabolome dataset, focusing primarily on occurrence  and identification of differentially abundant metabolites.Gaining insight into the metabolite pool of a microbial community is of paramount importance to understand its ecological role(s), not to mention its potential as a source of therapeutics and other invaluable molecules . As an avia these reference-based pipelines likely contributed to the elevated recall values. In addition, any given KO may participate in any number of distinct reactions, bearing the potential to dramatically increase the number of associated metabolites. With regard to reference-based methods, MIMOSA surpassed Mangosteen-K in both precision and F1 score. This speaks to the benefit of considering the directionality as well as the network of connections between reactions within a community. Although Mangosteen showed worse performance in metabolite prediction compared to MIMOSA, it was able to link more metabolites and could be useful for mining metabolites outside of known metabolic network. While the type of microbiome data examined also affects prediction performance, we lacked an appropriate number of independent datasets to evaluate this aspect in our study (one study for metagenomic to metatranscriptomic and another for 16S rRNA amplicon to metatranscriptomic sequencing comparisons). A pipeline’s ability to accurately distinguish differential metabolites between case and control groups is typically a sound foundation from which hypotheses are structured and biomarker and therapeutics discovery initiatives expound. Such evaluations are predicated upon accurate predictions of metabolite abundance, and as such, are much more stringent than occurrence prediction. While we did not observe promising results in any of the pipelines, MelonnPan predictions yielded the highest mean F1 scores (around 0.1 in both MelonnPan-K and MelonnPan-B).When used to predict the presence or absence of given metabolites within a community, the ML-based MelonnPan approach significantly outperformed its reference-based competitors, with respect to overall precision and F1 score. However, the reference-based Mangosteen and MIMOSA pipelines did exhibit high recall despite their lower precision and F1 scores. As recall is calculated based on true-positive and false-negative tallies, the high number of metabolites predicted Machine-learning-based MelonnPan outperformed the reference-based methods in predicting both occurrence and differential metabolites. In contrast to the reference-based pipelines, this powerful technique considers the impact of host metabolism and hitherto undiscovered interactions that do not exist in reference databases. Like all ML strategies, MelonnPan relies heavily on the accuracy of training data. As such, while untargeted metabolome profiling strategies generate the most comprehensive overview of the metabolites present within a sample, coverage is unavoidably limited due to the inherent technical biases associated with either MS or NMR . Hence, While conducting the differential metabolite evaluation, we observed the highest F1 score in the MelonnPan-predicted metagenomic dataset of via conventional untargeted metabolome profiling as well as ML-based prediction , either differential ones between case and control or any features the researchers are interested in. Ultimately, caution and common sense go a long way in interpreting the predicted metabolite profiles resulting from reference-based pipelines.As opposed to ML-based and data-driven approaches, reference-based methods can potentially identify/predict metabolites whose concentration or specific chemical properties precluded them from detection ediction . Referenediction  and BioCediction  are two ediction , but thiediction . There aThere are several limitations in the current evaluation that need to be taken into consideration when researchers try to choose the optimal prediction tool for their research. While we treated the empirically measured, untargeted metabolome as a “gold standard” and directly compared all pipeline prediction results to this profile, biases in technology render even this portrayal imperfect in its attempt to unveil the true and complete metabolite pool . In realPredicting metabolic capacity and metabolite diversity is of paramount consequence to better understanding and manipulating the function(s) of microbial communities, all of which bears immense significance to improving human health and empowering environmental microbiome research . UltimatPRJNA668188.Metadata, 16S rRNA gene amplicon and metatranscriptomic sequencing data generated in this study (SG_IBD) have been deposited in the NCBI database under BioProject ID The studies involving human participants were reviewed and approved by University of Calgary, Conjoint Health Research Ethics Board (ID:18142 and 14-2429). The patients/participants provided their written informed consent to participate in this study.SI, TD, and KD conceived and supervised the project. XY, TA, TD, and SI designed the study. XY, ER, KW, YW, JC, PB, and GK performed data collection and processing. XY conducted the analysis and wrote the first draft of the manuscript. All authors edited and approved the manuscript.XY, ER, KW, YW, JC, KD, TD, and SI are employees of Second Genome Inc., which provides salaries and stock options. Second Genome Inc. is an independent therapeutics company with products in development to treat intestinal disorders and other human diseases. TA is an employee of Altman Analytics LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chlorella sorokiniana (C. sorokiniana) was investigated. At the early stage of mixotrophic growth (day 0–1), autotrophy contributed over 70% of the total metabolism; however, heterotrophy contributed more than autotrophy after day 1 due to the rapid increase in cell density, which had a shading effect in the photo-bioreactor. Heterotrophy continued to have a higher contribution until the available organic carbon was depleted at which point autotrophy became dominant again. Overall, the increase in algal biomass and light conditions in the photo-bioreactor are important factors in determining the contribution of autotrophy and heterotrophy during a mixotrophic culture.The contribution ratio of autotrophic and heterotrophic metabolism in the mixotrophic culture of  Microalgae are a promising source of alternative fuels because they perform photosynthesis by fixing atmospheric carbon dioxide and have high growth rates and areal productivity . MoreoveMany algal species are not only capable of growing autotrophically, using photons of light as a sole energy source, but are also capable of growing heterotrophically, using organic carbon as an energy and carbon source for metabolism . FurtherIn terms of wastewater treatment, the mixotrophic culture is the most efficient culture because not only nitrogen and phosphorus but also organic compounds from wastewater can be removed simultaneously.However, the culture conditions that cause a shift between autotrophy and heterotrophy and the consequent growth efficiency of mixotrophic algae are not fully understood. Thus, in order to use mixotrophic cultures to remove nutrients and organic matter from wastewater, the preferable contribution ratio of organic to inorganic carbon must be investigated.The stoichiometries of autotrophic, heterotrophic and mixotrophic metabolism were revealed by a previous study , which aAutotrophic metabolism:Heterotrophic metabolism:Mixotrophic metabolism:Generally, in autotrophic cultivation, IC consumed by algae can be calculated by measuring the initial and residual concentrations of bicarbonate. Likewise, in heterotrophic cultivation, consumed OC can be determined by estimating the difference between the initial and residual OC concentrations provided. In mixotrophic cultivation, however, IC is produced by heterotrophic respiration (Equation (2)) and is added to the residual IC concentration as an available carbon source. This is the main difficulty in defining the ratio of autotrophic to heterotrophic contributions.Karlodinium micrum and found that it was dominated by heterotrophic metabolism [Chlorella sorokiniana (C. sorokiniana) was used as a microalgae strain instead of Chlorella vulgaris because it was identified as an ideal candidate for mixotrophic cultivation [C. sorokiniana also has a higher specific growth rate, biomass productivity and cell doubling time than Chlorella vulgaris for the post treatment of dairy wastewater plant effluent [Despite the significance of distinguishing between autotrophic and heterotrophic contributions in mixotrophic metabolism, this topic has been rarely studied mainly due to the complex characteristics of mixotrophy compared with autotrophy or heterotrophy alone. A study investigated the balance of autotrophy and heterotrophy in a mixotrophic growth of tabolism . This sttivation . C. soroeffluent .C. sorokiniana (AG20740) was obtained from KCTC .The microalgae strain 3 0.5 g-C L−1 and glucose 0.5 g-C L−1. The flasks were agitated at 120 rpm using an orbital shaker and controlled at 25 ± 2 °C under a continuous photosynthetic photon flux density (PPFD) of 120 μmolm−2s−1 from a light emitting diode measured by a photo-radiometer . The composition of the BG11 medium was NaNO3 (1500), K2HPO4 (40), MgSO4·7H2O (75), CaCl2· 2H2O (36), Citric acid·H2O (6), Ferric ammonium citrate (6), EDTA (1), H3BO3 (2.86), MnCl2·4H2O (1.81), ZnSO4·7H2O (0.22), NaMoO4·2H2O (0.39), CuSO4·5H2O (0.079) and Co(NO3)2·6H2O (0.049) as mg L−1.In order to acclimatize in a mixotrophic culture, the microalgae were pre-cultured in 250 mL Erlenmeyer flasks containing a modified BG11 medium with NaHCOAfter four days of pre-culturing, the microalgae were inoculated in the 2.5 L photo-bioreactor (working volume of 2 L) with the initial inoculum at 0.1 optical density (OD). Other operating conditions are described in a was the concentration of chlorophyll˗a in the extract and OD664, OD647 and OD630 were the corrected optical densities at the respective wavelengths.The residual concentration of glucose was measured with a dinitrosalicylic acid (DNS) reagent . The resThe amount of pigment per unit volume was calculated as follows (Equation (5)):The contribution ratio of autotrophy and heterotrophy during the mixotrophic culture was calculated based on the carbon consumption as below (Equations (6) and (7)):3 and glucose were used as IC and OC sources, respectively, for mixotrophic metabolism, the stoichiometry equation of mixotrophic metabolism can be expressed as follows [As NaHCO follows   and sufficient light penetration. Therefore, photosynthesis was the main metabolism for C. sorokiniana in the culture system. At a low light intensity, the microalgae synthesized photosynthetic pigments to absorb photons more actively [660 0.7) within 24 h. This cell density might be high enough to decrease the light penetration through a photo-bioreactor, resulting in insufficient light intensity for proper photosynthesis. It has been reported that a high chlorophyll-a content and algal density (at concentrations of 105 cells/mL or above) caused a self-shading effect that limited the light for photosynthesis [On day 0–1, the autotrophic contribution was higher due to the low initial cell density . After day 3, as available organic carbon became scarce, autotrophy became completely dominant. The results suggest that increased algal biomass and consequent self-shading are important factors in determining the contribution of autotrophy and heterotrophy during a mixotrophic culture. The study contributed to clarifying the dynamic of mixotrophic metabolism, which is useful for the experts of the sector to understand algae growth.The contribution ratio of autotrophy and heterotrophy changed dramatically during a mixotrophic culture. Autotrophy dominated the growth metabolism during the early culture period (day 0–1). However, increased algal biomass might inhibit factor photosynthesis through self-shading. Heterotrophic metabolism became dominant as


Previous studies have discussed the importance of an optimal range of metabolic activity during preimplantation development. To avoid factors than can trigger an undesirable trajectory, it is important to learn how nutrients and metabolites interact to help launching the correct developmental program of the embryo, and how much the in vitro culture system can impair this process. Here, using the bovine model, we describe a factorial experimental design used to investigate the biochemical and molecular signature of embryos in response to different combinations of morphological features—i.e. speed of development—and external stimuli during in vitro culture—i.e. different oxygen tensions and glucose supplementation. Our analyses demonstrate that the embryos present heterogeneous metabolic responses depending on early morphological phenotypes and the composition of their surroundings. However, despite the contribution of each single stimulus for the embryo phenotype, oxygen tension is determinant for such differences. The lower oxygen environment boosts the metabolism of embryos with faster kinetics, in particular those cultured in lower glucose concentrations. This proposal was further developed2 to introduce molecular components such as “the quiet range” of nutrients turnover, and then revisited many times to include different categories of “quietness” such as levels of intrinsic metabolic activity and loss of quietness due to environmental stress, particularly the oxidative stress often observed in culture systems that use high oxygen tension2. In 2011, Gardner3 suggested that, in human embryos, a significant increase in glucose consumption—indicating a highly active metabolism—is necessary during initial development to sustain embryo viability. Later on, Leese discussed the existence of an optimal range of metabolic activity, or, in other words, the embryo ability to successfully carry out the developmental program with the minimum amount of energy4.Mammalian preimplantation development follows a generally accepted metabolic pattern. Yet, there is no consensus on how we can successfully use metabolism or metabolic biomarkers to predict embryos viability. According to the “quiet embryo” hypothesis5, certain precautions are required. Besides, in vitro based technologies for farm animals exhibit a number of side effects rarely described in other species5, reinforcing the importance of specific studies unraveling the interplay between in vitro culture and metabolism switches in the bovine model.The basis of our knowledge on preimplantation metabolism comes from studies on mouse and human embryos. When extrapolating to large animals, which have a better buffering capacity against metabolic insults6. As embryonic development progresses, glucose consumption gradually increases. Oxidative metabolism has its first mark in the stage of 12–16 cells, when major genome activation occurs8. Within the cells, glucose follows different metabolic pathways, e.g., the pentose phosphate pathway, which generates both the ribose for DNA and RNA synthesis, and the NADPH for lipids biosynthesis and reduction of intracellular glutathione, a powerful antioxidant6. Internalized glucose can also be partially converted to lactate (known as the Warburg Effect), which, in vivo, could facilitate key events such as invasion, angiogenesis and modulation of the immune response during the implantation process6. This plasticity is particularly important for the initial embryo development given that highly proliferative cells have other metabolic requirements beyond the production of ATP. However, studies measuring simultaneously energy efficiency and nutrients uptake/production are still lacking.In the bovine developing embryo, prior to morulae compactation, the production of energy is based on the oxidation of pyruvate, which is preferred due to the low energy requirements at this point9. Such mechanism has been considered responsible for supporting the increase in energy demand for blastulation and hatching, besides contributing to the synthesis of biomass and favoring the redox system10. Numerous evidences show that embryos produced in vitro carry the marks of energetic stress, mainly due to inadequate support offered by existing culture systems11. Thus, providing conditions to foster metabolism, while avoiding, neutralizing or minimizing stress factors, may be the key to ensure proper development.Although it is largely accepted that carbohydrate metabolism  is crucial for embryonic development, the role played by the metabolism of endogenous lipids in this process remains poorly known. Lipids are a potential energy source for the embryo through the β-oxidation of fatty acids12, is a robust predictor of blastocyst formation in humans14. In the bovine, the kinetics of the first 2 cell divisions is associated with distinct phenotypes at the blastocyst stage15. Molecular and biochemical characterization demonstrated that fast-developing embryos exhibit improved ability to trigger adaptive responses17 and distinct profiles of epigenetic reprogramming (more DNA methylation)18.Likewise, the embryonic kinetics seem to be related to the embryos viability and ability to respond to stress. The timing to complete the first cell divisions, in particular from 2 to 5 cellsThe scenario described above allows us to recognize the heterogeneity of embryonic metabolism and responses to external factors when fast and slow embryos are compared. Such heterogeneity should be considered as we explore some overarching questions:(a) Is there a range to up/downregulate embryo metabolism while still sustaining its viability?(b) How does heterogeneity affect metabolism and the ability to respond to environmental stressors?(c) Can we produce embryo with an efficient energy metabolism using the available in vitro culture systems?1. Thus, in the present paper we investigate the biochemical and molecular signature of Fast and Slow bovine embryos in response to combined stressful stimuli during in vitro culture.Avoidance of factors triggering undesirable trajectories should become easier as we learn about the influences of specific nutrients and metabolites on the developmental programming of the embryosBovine embryos were in vitro produced following the experimental design as described on Fig. Bovine ovaries were obtained from a commercial slaughterhouse and transported in warm sterile saline [0.9% (w/v) NaCl] for about 3 h. In the laboratory the ovaries were washed several times again with sterile saline to remove blood and debris. Cumulus-oocyte complexes (COCs) were collected by aspiration of follicles with diameter between 2 and 8 mm using syringe and needle.2 in air and high humidity for 24 h.For in vitro maturation (IVM), groups of 25–30 oocytes were placed in 90 μL drops of the maturation medium  under mineral oil. Maturation of COCs followed in an incubator at 38.5 °C, 5% CO6 sperm/mL. Matured COCs were transferred to 90 μL drops of fertilization media19 and covered with mineral oil. Oocytes and sperm were incubated at the same conditions of air and temperature for 18 h. This study did not require direct handling of animals.Frozen commercial semen straws from 2 bulls were thawed in a water bath at 37 °C for 30 s and their contents were mixed and centrifuged on a discontinuous Percoll gradient (45% and 90%) as a strategy to avoid the influence of the bull in the kinetics of embryo development. Sperm concentration was adjusted to 1 × 10Approximately 18 h post insemination (hpi), remaining granulosa cells were completely removed, presumptive zygotes were transferred in groups of 25–30 to 90 µL drops of Synthetic Oviductal Fluid  and distributed to the study groups following the experimental design . Then, we calculated the necessary quantities to reach the final concentrations of 2 mM and 5 mM of our treatment. At 98 hpi, the embryos were reallocated to these treatments in smaller groups (15–20 per drop).21, where they remained for the last 48 h of culture. Expanded blastocysts and their respective culture media were collected at 168 hpi and kept at − 80 °C for further evaluation.Finally, at 120 hpi, embryos were individually transferred to 20 μL drops of fresh SOFaa inside manually carved microwells, an adaptation from the well of the well system2 [20] or 5% O2 [5]). Final groups are: 0F20, 0S20, 2F20, 2S20, 5F20, 5S20, 0F5, 0S5, 2F5, 2S5, 5F5, 5S5  the amount of glucose in the culture media ; (ii) the speed of first cleavages (Fast [F] or Slow [S]) and (iii) the oxygen tension in the culture system  using the EnzyChrom Pyruvate Assay . The production of lactate was quantified in the same culture media  using EnzyChrom Lactate Assay Kit . Both evaluations are based on enzymatic reactions with fluorometric detection and were performed in accordance with the manufacturer’s instructions.Expanded blastocysts were collected at 168 hpi and analyzed by means of ROS content  with CellROX Green Reagent (CRG) (Thermo Fisher) and MMP  with MitoTracker Red CMXRos (Thermo Fisher). Briefly, blastocysts from all groups were individually transferred to a 50 µL drop of PBS containing CRG (5 µM) + MitoTracker (0.05 µM) and incubated in the dark for 30 min in the same conditions of IVC. After incubation, embryos were washed in PBS, mounted on slides in glycerol drops and immediately evaluated under fluorescence microscopy . Stained blastocysts were individually photographed at 40 × magnification using the Leica Application Suite (LAS) software (Leica Microsystems Brazil).Pools of 2 expanded blastocysts per group were collected at 168hpi and used in 4 replicates for the quantification of ATP using the ApoSENSOR ADP/ATP Ratio Bioluminescent Assay Kit . Briefly, after 5 min in Nucleotide Releasing Buffer, embryonic ATP was converted to light by the catalyzing activity of the enzyme luciferase (ATP Monitoring Enzyme). Luminescence intensity was measured, and a standard curve was used to convert the luminescence output to ATP concentration.22. We selected 3 classes of lipids to include in the present analysis: Free Fatty Acids , Triacylglycerols (99 transitions) and Cholesteryl esters (49 transitions). A total of 178 lipids were monitored and are described in Supplementary Table Lipids were extracted from individual blastocysts  collected at 168hpi using the Bligh & Dyer method and subsequently analyzed by Multiple Reactions Monitoring Mass Spectrometry (MRM-Profiling) following the protocol described inPools of three blastocysts per group were collected at 168 hpi and used in 4 replicates. Gene expression analysis was performed using inventoried TaqMan assays , specific for Bos taurus. We verified the mRNA abundance of 84 target genes  for total RNA extraction and the High-Capacity cDNA Reverse Transcription Kit  for cDNA synthesis. Both protocols were performed following the manufacturer’s instructions. Each RNA sample was quantified with NanoDrop  and final amount was adjusted to 50 ng prior to reverse transcription.23, following the guidelines of the manufacturer . Briefly, samples were submitted to sequence-specific preamplification in a mix containing 1.25 µL assay mix , 2.5 µL TaqMan PreAmp Master Mix (Applied Biosystems) and 1.25 µL cDNA. The reactions were initiated at 95 °C for 10 min followed by denaturing at 95ºC for 15 s, annealing and amplification at 60ºC for 4 min for 14 cycles. The preamplified products were diluted sixfold prior to qPCR.Pre-amplification and qPCR were performed as previously describedThe 96 × 96 Dynamic Array Integrated Fluidic Circuits chip (Fluidigm) was loaded according to the manufacturer's protocol with each sample solution ; and each assay solution . The qPCR thermal cycling was performed in the Biomark HD System  using the protocol TaqMan GE 96 × 96 Standard. All the analysis was performed in four biological replicates and Ct values were calculated from the system’s software .The experimental design proposed for this work originated 12 study groups as previously described. Thus, we opted for a more descriptive approach to analyze the resulting data. When necessary to show the significance of the results, GraphPad Prism 8.0 was used to verify normality and then compare groups by Student t test or ANOVA + Tukey, considering an alpha of 5%. Descriptive results are summarized on Supplementary table Total cleavage rates were obtained from the number of cleaved embryos divided by the total number of presumed zygotes  to set a threshold for the background subtraction; resulting values for integrated density data were used to calculate the fluorescence intensity.The results extracted from the mass spectrometer were processed using a script especially constructed to obtain the absolute intensity of each ion. Then ion intensities from transitions of interest were recovered for all groups and normalized by the total ion current (TIC) to obtain relative ion abundances.24 as stable endogenous controls (GAPDH and PPIA). Gene expression results were expressed as ΔCt and then normalized  for the creation of heatmap graphs.For the relative quantification of the transcripts of interest, we obtained ΔCt values for each sample, calculated from the difference between the Ct values (Cycle Threshold) of each target gene and the geometric mean Ct values of the genes selected by the software NormFinder25, and can be used to organize any kind of comparative data from different species or specimens . The basic requirement for a parsimony analysis is to translate the observed information into characters. In this study, we included 93 characters among molecular and biochemical evidences. More specifically, they are 84 genes , as well as blastocyst rates, were gathered to create a database containing information from a total of 585 blastocysts (or their respective culture media). For every analyzed character, we verified the distribution plot, and embryos were allocated into quartiles. The 1st or 4th quartiles, representing the extreme of phenotypes, were given a score of 0 or 2, as described on Table 2 [Node A] opposing to 5% O2 [Node B]), suggesting this category superimposes the others (glucose concentration and kinetics of development). Groups 5S20 and 5S5 present particular characteristics that prevented them from being included in any of the nodes.The data matrix analysis of the 93 characters from 12 study groups resulted in a single, fully resolved tree , groups cultured in lower glucose concentration (0.6 mM and 2 mM) clustered opposing to those cultured in a high glucose environment (5 mM). On the other hand, in 5% O2 (Node C), the kinetics of development was more decisive. As a consequence, the Fast groups clustered in opposition to the Slow groups regardless of the glucose supplementation.In a second step, we isolated the oxygen tension factor and reanalyzed the matrix. The two resulting dendrograms were fully resolved Fig. . StrikinOur next effort was to identify which attributes were responsible for the clusterization in each of the situations and to understand their roles in the study groups. Thus, the attributes contained in each node and their respective behavior in the main clusters are discussed below.2 presented increased amounts of free radicals  were represented in the groups cultured at a lower oxygen environment, which is also in accordance with the higher relative amounts of cholesteryl esters and TAGs found in those groups Fig. D.2 were submitted to cluster analysis, we observed an evident separation according to the kinetics of development. Out of the 16 attributes responsible for this clustering  from the groups cultured in low or physiological levels (0.6 mM and 2 mM).In terms of gene expression, the cluster analysis indicates a more active transcription in the groups cultured with lower glucose concentrations. In this case, the node formation  were pointing to this direction. A more detailed analysis of fatty acids behavior showed that relative amount of total FFA was increased in groups culture in 2 and 5 mM of glucose , and lipid metabolism towards esterification of fatty acids instead of oxidation, as we propose on the schematics of Fig. 28. Therefore, both extremes received a low score regarding mitochondrial activity. Besides, blastocyst rates were included as a marker for the efficiency of each culture system.It is important to keep in mind that too high or too low levels of mitochondrial membrane potential may induce unwanted loss of cell viabilityRemarkably, when the scores were assigned, groups clustered in a very similar way as previously happened for hierarchical analysis Fig. A. Groups2, this relationship becomes more evident. Offering a low glucose concentration (~ 0.6 mM in this study) in a high oxygen environment may generate fair blastocyst rates, but not without a compromised metabolic efficiency.Confirming the results obtained by cluster analysis, the amount of glucose certainly influences the energy efficiency of the embryos. At 20% O2, there is still some heterogeneity in embryonic response.On the other side, the lower oxygen tension seems to foster the metabolism of fast-developing embryos. In terms of lipid metabolism, these embryos prefer the esterification of fatty acids, which can either be stored inside lipid droplets in the form of cholesterol esters and TAG or enter the phospholipids pathway to help build membrane for cell proliferation. Moreover, whilst there is no difference in blastocyst rates among Fast groups cultured at 5% OFinally, Fast embryos cultured with lower glucose (0.6 mM) in a hypoxic environment were the ones responding more accordingly to a presumable efficient metabolism. These embryos are able to produce a high ATP supply with decreased ROS quantities, thereby indicating functional mitochondria. These results were summarized in Fig. 18. Other studies provided evidences that such differences were related to the metabolic stress caused by the culture system17. Based on these assumptions, the present study was initially designed to investigate how Fast and Slow embryos respond to stressors commonly found during in vitro culture, i.e., non-optimized oxygen tension and high glucose concentration.Although other studies have tackled issues such as how culture under low oxygen produces more efficient embryos and why Fast embryos perform better, the broad approach we address here is unique. Previous reports stated numerous differences in the transcription pattern and DNA methylation profile between bovine embryos developing at different cleavage speeds29. Similarly, the lumen of the female reproductive tract provides an oxygen concentration of 2–8%, differing from the atmospheric level of 20%6. The oviduct also provides an efficient oviduct red/ox system, which guarantees an ideal environment for the passage of the embryo30.The concentration of glucose in the bovine oviduct fluid (~ 2 mM) is lower than in blood plasma (~ 5.5 mM), indicating a differential secretion of this substrate for adaptation to embryonic metabolism31. Therefore, by combining environmental stressors and embryos with different phenotypes, we were able to better visualize this plasticity during initial development. When all the study groups were analyzed together, cluster analysis revealed an evident metabolic signature, showing the modulation of embryonic metabolism is highly related to the oxygen tension in the culture system.The relevance of energy metabolism to control gene expression, since cells constantly adjust their metabolic state in response to extracellular signaling and nutrient availability, was previously demonstrated34. According to these same reports, the exposure to different oxygen concentration generates embryos with distinct transcription profiles. For the bovine embryos herein examined, we observed that not only the profile of gene expression differs, but a greater number of genes are responsible for such differences on groups cultured in high oxygen. This may be related to the activation of adaptive responses, as described by Leese2 for human embryos. Thus, while mammalian embryos are quite susceptible to the effects of high oxygen concentration, having an active metabolism could help them compensate for damages caused by environmental injuries and restore developmental trajectory.Previous studies performed with in vitro produced embryos from different species, including cattle, indicated that lower oxygen tension results in better embryonic development, with increased cleavage and blastocyst rates, and higher post-implantation success rate36 and Heat Shock Proteins (HSF1 and HSP90)37. The exposure to high levels of oxygen is a known source of oxidative stress33 and has also been associated to the activation of pro-apoptotic signaling cascades 34.Furthermore, the genes represented in nodes A and B were mostly related to oxidative and reticulum stress response pathways such as those mediated by GPX1, PRDX12. Among possible damages caused by the excess of ROS, we highlight changes in the expression of transcription factors, such as REST, PAF1, STAT3, CDX2, KEAP1 and TFAM. In general, mRNAs and other nucleic acids are easily targeted by free radicals, which leads to detrimental effects in cellular machinery and protein synthesis2. These results also reinforce recently published data describing the differences in the control of gene transcription mediated by the oxygen tension23.Supporting these findings, an expected higher presence of ROS was confirmed in the groups cultured at 20% O2, which corroborates the representation of glycolysis-related genes (PFKP and PGK1) in those groups. However, this effect was clearly not widespread. Only the groups cultured in physiological glucose concentration (2 mM) presented improvements in mitochondrial activity and ATP amounts, suggesting there is an optimal amount of energy substrates that must be available to the embryos. This is in agreement with Leese et al.4, who proposed that embryos on an intermediary range of pyruvate consumption are more likely to form blastocysts.Along with the metabolism of the embryos cultured in high oxygen, our analysis clearly indicated the importance of energy substrates availability. The results show a higher consumption of pyruvate at 20% O39. Hypoxia also enhances anaerobic energy production. To reduce mitochondrial function, and therefore decrease oxygen consumption and ROS production, hypoxia induced factors block the conversion of pyruvate. Then, the flow of pyruvate into the mitochondria decreases, fueling the production of lactate and reducing mitochondrial metabolic processes40. Such events are evident in the embryos cultured at low oxygen that presented lower pyruvate consumption coupled to an increased lactate production.When oxygen is sparse, cells adapt to hypoxia by reprogramming the expression of a number of genes involved in carbohydrate metabolism2. The excess of intracellularly accumulated free fatty acids can cause lipotoxicity40. To avoid this, cells can convert FFA into neutral TAGs that are stored in lipid droplets and serve as energy depots41. Indeed, the activation of such mechanism was observed in our embryos cultured in low oxygen and it is also typical in tumoral cells. It prevents the formation of free radicals, with the lipid droplets serving as building blocks for the production of essential sterol esters and phospholipids required in high proliferative cells for the biogenesis of new membranes35.Groups cultured in a high oxygen tension also presented differential lipid metabolism. A number of genes responsible for the synthesis of fatty acids were found to be important in those groups. Accordingly, we verified increased relative amounts of total free fatty acids, and particularly of stearic and oleic acids when compared to groups cultured in 5% O2 environment, which is depicted by the higher mitochondrial activity and ATP content when compared to Slow embryos. This suggests a very thin threshold separating two types of embryos with high metabolic demands: (i) embryos with higher activity due to loss of transcriptional control; and (ii) embryos with a certain degree of damage but still able to activate adaptive pathways, surviving even with poor quality, which may lead to future implications.Results indicate that the metabolism of Fast embryos is favored in a 5% O18, we speculate that the loss of transcriptional control and the lower ability to activate adaptive pathways may be explained by perturbations in epigenetic mechanisms.We believe Slow embryos belong to the first type. Despite having a more active metabolism, the conversion rates to blastocyst were lower in all Slow embryos when compared to the faster counterparts. Considering that the DNA from Slow blastocysts is significantly less methylated than DNA from Fast blastocyst42. Therefore, we propose that more viable embryos are those capable of producing more energy with minimum disturbances to the energy production system as whole, i.e. the mitochondria and lipid metabolism.Finally, the score system herein created reflects how the external environment impacts and helps defining the energy efficiency of the embryos. Moreover, it allows us to propose the conditions that better support the development of these embryos in vitro. In accordance with the Dynamic Energy Budget theory, our scores consider “efficiency” in the energy sense, meaning that for any given substrate, there is an optimal range of metabolic activity to support embryonic demand, but when exposed to a challenge or stress, the resulting response will have an energy cost that will make them shift out of this metabolic range43. If further resources are required, development might be compromised as indicated by their metabolic phenotype.Although the high oxygen tension still allows the formation of blastocysts, the energy production of these groups is clearly distressed; hence, they presented the lowest score values. In other words, the metabolism is able to rectify the stress enough to maintain these embryos alive (somatic maintenance), however, it may lead substrates away from other essential processes or even boost additional activity to increase embryonic complexity (i.e. differentiation), as suggested by previous publications2. Under an oxygen condition apparently favoring energy metabolism, the heterogeneous response between Fast and Slow embryos becomes more obvious. The lowest scores belong to Slow embryos suggesting their deficient energy metabolism is consequence of other phenotype-related mechanisms and not driven by external environment. Among the Fast groups, the one more closely matching the expected characteristics of an efficient metabolism is the group 0F5. The combination of lower glucose and oxygen tension offered to this group are probably just the right amount to boost their energy production and sustain the demands of a healthier developmental program.The highest scores belong to the groups cultured at 5% OIt is relevant to consider that these characteristics are representative of the physiology, but do not represent the potential of these embryos to establish pregnancy in the event of a transfer to recipient cows. It is possible that embryos produced in vitro could show some compensation after entering the uterine environment that would allow them to implant and develop into a calf. The metabolic imbalance suffered in vitro could be overcome, but likely not without consequences for the future offspring.In conclusion, our analyses depict how embryonic metabolic response can be quite heterogeneous depending on early phenotypes and composition of the culture media surrounding the embryos. This is a pioneer study that simultaneously shows the capacity embryos have to switch to other substrates when needed, and to modulate molecular machinery, ensuring their survival even in the face of adverse and non-optimized external conditions. We believe that the information presented here may greatly contribute to decision making in terms of choosing the more suitable culture system or performing interventions that favor specific characteristics and rescue embryos that would otherwise perish. Future studies must focus on determining whether these embryos have the same implantation potential and what are the consequences of an inefficient energy metabolism for the blastocyst in a short term, and also for the future offspring in a long term.Supplementary Information.