In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding. Different techniques have emerged alongside it, to make sense of this new knowledge. Through the use of large metabolic databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG)In this context, metabolic networks have been used to study the set of all chemical reactions of organisms in a holistic manner, providing insights into its underlying structure and the different adaptations of these organisms to their environment.nodes—with edges. In the case of metabolic networks, multiple representations can be adopted. Among the most accurate, there is one with metabolites and enzymes as nodes, in which each substrate of a reaction will be linked to the catalysing enzyme, which is in turn linked to the products of the reaction. This is the representation we used when reconstructing our metabolic networks.Networks or graphs are mathematical structures linking objects—4, average path length4, network motifs3, and clustering coefficient3. The second one relies on the analysis of the fluxes of biomass in metabolism and their dynamics. Various constraint-based methods that try to predict steady-state flux distributions exist, such as Flux Balance Analysis (FBA)5. The constraint-based approaches need complete and curated knowledge of fluxes, together with reconstructed state-of-the-art metabolic networks, as it is done, e.g., in ModelSEED6. In this paper, we instead follow the first research direction, and are only interested in topological metrics for graph comparison.In recent years, two main avenues of research have been undertaken to study metabolic networks. The first one studies metabolism structure and properties through network topological measures, such as degree distribution8, oxygen levels9, and the type of habitat10. However, details on the specific metabolic pathways and mechanisms governing the evolution and adaptation to environment of the organisms are not always considered.Several studies reveal that different metabolic network topological measures—metabolism structural properties—are related to different environmental variables, such as optimal growth temperature11. It is based on the concept of network expansion. Starting from seed metabolites from the environment, the network expands if the substrates of the reactions are present in the expanding network, thereby producing its products. This goes on until the network can no longer expand. The scope of the metabolic network is the set of all metabolites and reactions in the resulting network In this study we analyse the scopes of a very large dataset of 5610 prokaryotic metabolic networks that we have derived from KEGG. Crossing with other databases, we assess the relationship between the scope and several environment features. In order to detect this relationship, we use machine learning approaches to predict different evolutionary and environmental conditions  from their scope. Importantly, as the scope can be considered as an interpretable embedding of our metabolic networks, it will allow us to gain insights into the metabolic processes associated with the different environmental variables.The metabolism of living organisms can be described with metabolic networks. In this study, the directed metabolic networks of 301 archaea and 5309 bacteria were derived from KEGG database, with nodes as metabolites and reactions, and edges linking substrates to reaction and reaction to products see “”.9. However, these approaches do not provide any information on the affected metabolic functions.In the field of systems biology, it is necessary to focus on the complex interactions within an organism and with its environment to be able to understand the complete system. Previous works have shown the strong associations between evolutionary and environmental variables and the underlying topology of the metabolic networks of different organisms11. We have described the concept on Fig. potentially be produced from a set of seed metabolites, and does not rely on biomass fluxes. We would like to emphasize that our main interest here is the interaction between potential metabolic functions described in the scope with other taxonomical and environmental parameters . We are not interested in the many other properties obtained from classical metabolic networks, e.g., their flux properties and/or biomass optimization. Such information would limit the number of prokaryotic species we are able to use.To counter this issue, we have chosen to use the scope of a metabolic network, introduced in 2005 by Handorf, Ebenhöh, and Heinrich14, a psychrophilic species15 and a hyperthermophilic species16 are fixed as the seed compounds. A thermophilic medium17 was already included, since the union is used as seed for all species. The single medium—the union of mediums—as seed compounds for the scope allows us to extract discriminating properties between the metabolic networks while mitigating potential species-based bias. We built a matrix of nodes in scope per species, removing the nodes that are in the scope for all species or out of the scope for all species, that is shown on Fig. The union of the metabolites of three chemically-defined growth mediums used to grow a mesophilic speciesFor a union of all nodes of all species (6554 nodes), we obtained 578 nodes in the scope, showing that the seed compounds chosen are in fact rather restrictive, and that the dimension of our data is significantly reduced. However, Fig. In order to visualise and to explore the data, we used a t-distributed Stochastic Neighbor Embedding (t-SNE), and plotted our scope per species matrix in two dimensions. The species are characterized by: clades Fig. a, habitaThe t-SNE visualisation constructs a two-dimensional map that reveals structure at different scales. The method was reported to identify well local dependencies of high-dimensional data, as well as to capture global data structure such as relationships between several clusters. It clearly illustrates the clusters corresponding to all our taxonomic and environmental variables, particularly the clades Fig. a, habitaOur results demonstrate the practical utility and very reasonable predictive performance of the scope, since even though the method reduces the data dimensionality drastically, the embedding still carries sufficient information to visualise , and to explain it.To investigate whether the scope is able to predict the environmental variables and provide us with additional insights into the functional metabolism, we applied other machine learning approaches, described below.We have growth temperature data for 3392 species out of 5610, illustrated on Fig. First, we train a random forest model to predict the growth temperature classes. Growth temperature can be divided into four temperature classes, from hot to cold, we obtained: 76 hyperthermophiles (HT), 188 thermophiles (T), 2910 mesophiles (M), and 218 psychrophiles (P)  (4.3.3.6_1), ABC-type tungstate transporter (7.3.2.6), and Tungstate (C20679) are more specific to species from warm habitats (hyperthermophiles and thermophiles), while others (Glutathione disulfide (C00127), Gamma-L-Glutamyl-L-cysteine (C00669), Glutamate-cysteine ligase (6.3.2.2), Glutathione synthase (6.3.2.3), ...) are clearly more specific to species from colder habitats (mesophiles and psychrophiles).To identify which nodes and pathways are specific to each temperature class, we selected the 17 nodes common to at least 90% of models, and checked the proportion of species that have these nodes in each temperature class, which is shown on the barplot of Fig. We explored the KEGG pathways of these specific nodes. The glutathione metabolism pathway  is associated to species living in cold temperatures, and 2 nodes associated to warm temperatures belong to tungstate metabolism.A feed-forward artificial neural network was trained in order to predict growth temperature directly see “”. In thiFig. We applied the predictive analysis also to other environmental variables, namely, to habitat and oxygen tolerance.We explore the habitat, a simplified variable consisting of a “Symbiont” class with 554 bacteria living in a host, an “Environment” class, with 395 free-living bacteria, and a “Mixed” class, with 235 bacteria that can live in a host or freely in the environment see “”.We also use the oxygen tolerance information, where we have 917 prokaryotes in the “Aerobe” class, 782 in “Facultative”, and 532 in the “Anaerobe” class see “”.In the same way as for growth temperature classes, we built a 300-fold cross-validated weighted model for each environmental variable. The results are shown on Fig. For the habitat, the average maximal tree depth was 28, with an average F-measure of nce Fig. c, and alnce Fig. a, with vWe identified the nodes of interest for the classification of the habitat and the oxygen tolerance, by choosing the 50 most predictive nodes in each fold of the cross-validation. On Fig. We identified the pathways to which the most important nodes belong to, and we found that the most important KEGG pathways for the habitat are aminoacid metabolism pathways , followed by Methane metabolism and Nitrogen metabolism. For the oxygen tolerance, the pathways with the most chosen nodes are Glutathione metabolism, Carbon fixation pathways in prokaryotes, Alanine, aspartate and glutamate metabolism, and also Citrate cycle (TCA cycle).Although we noticed that some pathways are recurrently present, since the corresponding nodes are selected frequently, some pathways, on the contrary, are specific to particular environmental variables.This study introduces metabolic networks on a wide-scale of 5610 prokaryotic organisms, and discusses how it is possible to infer environmental and taxonomical information from the topology of the network.The topological analysis of the networks presented is based on the scope of metabolic networks from the union of four chemically-defined mediums. The scope can be understood as an interpretable embedding with strict constraints, due to the rules of the scope expansion. As demonstrated by our t-SNE projections and the results of the predictive analysis, although the scope significantly reduces data dimension, the reduced data still carries sufficient information to reasonably predict the environmental and taxonomical variables: the taxonomical classification, growth temperature values and classes, habitat, and oxygen tolerance.interpretable. Predicting temperature classes, we identified a pathway that is prevalent in species from cold habitats —Glutathione metabolism—, and nodes prevalent in species from hot habitats are involved in tungstate metabolism.An important remark is that the scope is able to provide us with metabolic information helpful for the prediction of the environmental classes, and at the same time be 18. Some hyperthermophilic archaea are even vitally dependent on tungsten18.In the case of tungstate metabolism, metabolite tungstate (C20679), and consequently ABC-type tungstate transporter 7.3.2.6), were identified as very specific to species from hot environments, especially hyperthermophiles , glutathione synthase (6.3.2.3), glutathione disulfide (C00127), and glutathione-disulfide reductase (1.8.1.7). These nodes constitute two glutathione biosynthesis subpathways. In the first one, Glutamate-cysteine ligase (6.3.2.2) takes aminoacids l-glutamate and l-cysteine and makes gamma-l-Glutamyl-l-cysteine (C00669). Glutathione synthase (6.3.2.3) then catalyses the reaction that transforms gamma-l-Glutamyl-l-cysteine (C00669) and glycine into glutathione.Glutathione (GSH) metabolism was also found to be a pathway of interest containing 5 nodes specific to cold species: glutamate-cysteine ligase (6.3.2.2), gamma-The second glutathione biosynthesis subpathway takes glutathione disulfide  and reduces it to glutathione, with the glutathione-disulfide reductase (1.8.1.7).These two subpathways are not present in hyperthermophilic bacteria and archaea, with none of the species having the genes encoding the enzymes of the first one, and only 2 hyperthermophilic archaea out of 65 having the gene for the enzyme in the second subpathway Fig. c.For thermophilic species, few species have the enzymes of the first subpathway, even though the first enzyme (6.3.2.2) is slightly more prevalent in the bacterial graphs . The second subpathway enzyme is present in respectively 13% and 3% of thermophilic archaea and bacteria.20, generally preferring other intracellular thiols to respond to oxidative stress, such as coenzyme A (CoA)22. Indeed, CoA has been shown to be more stable than glutathione at high temperatures22, and may therefore exhibit a functional role equivalent to glutathione in thermophilic and hyperthermophilic organisms21, explaining glutathione specificity for colder temperatures in our models. In the case of hyperthermophilic bacteria, the absence of glutathione is not as clearly shown in the state-of-the-art as for hyperthermophilic archaea, however, Hummel, Lancaster and Crane21 do hypothesize this, pointing to future work focused on bacteria. Our results would, therefore, bring confirmation, going in the same direction.Multiple hyperthermophilic archaea are reported to miss glutathione (GSH and GSSG forms) in cell extracts as well as glutathione synthetase genes23. It is known to be absent in some gram-positive bacteria of anaerobic or microaerophilic sources23, which also supports the results we found in the case of oxygen tolerance, where glutathione metabolism pathway was found to be the most important pathway to distinguish oxygen tolerance classes.On another hand, glutathione is synthesized in almost all gram-negative bacteria, but it is more diversified for gram-positive bacteriaHowever, it is important to keep in mind that although the number of prokaryotic species studied is high, as they were taken from the complete list of species of the KEGG database, they have the same biases found in the database: a very low number of extremophiles when compared to mesophiles, a bigger number of bacteria studied than archaea, as well as some very well studied species, where multiple strains are considered, compared to less studied species. We handled this problem by subsampling mesophiles in the growth temperature analysis.Another important remark is that the scope is a property completely based on topology, which allows its ease of use on a wide range of species, but it ignores stoichiometry and biomass dynamics. It is also strongly dependent on the input metabolites and the completeness of our networks, which greatly impacts the size of the scope. However, the results found still give sufficient information and, as we have just mentioned, are corroborated by what is found in literature.For the sake of completeness, we tested whether the presence or absence of enzymes could also accurately predict the growth temperature using similar methods. This approach is akin to a classical differential genomics analysis without going through the network derivation and scope evaluation . In Supplementary Figure In this work, we have mainly focused on a generic medium for our scope, built on different species examples from each temperature class. As it happens, it also encompasses the various oxygen tolerance classes. As a potential research avenue, it would be interesting to also test a medium based on the different habitat classes, even though it is not a trivial task, as even a single habitat class may display a large variability.Overall, our results suggest that large scale datasets of prokaryotic species can be compared using interpretable embedding that also reduces dimensionality. Our use of the scope of metabolic networks provided key metabolites and pathways that are characteristic to environmental pressure that have somewhat been validated (or at least hinted) by wet lab experiments. This works can be extended—using other databases—to different environmental aspects and other prokaryotic communities. Our work suggests that this can be a powerful embedding tool to bridge the gap between metabolic processes and environmental impact.The Python code reproducing our results, as well as the networks and metadata is publicly available on GitHub.All information on the species was extracted from the databases in November–December 2019.1. This represents almost all prokaryotic species in the database as of November 2019. Among these, 301 are archaea and 5309 are bacteria. We obtained further taxonomic information from the National Center for Biotechnology Information (NCBI) Taxonomy database24.Our data set contains 5610 prokaryotic species from KEGG databaseA synthesis of the data set and the associated metadata can be found on Fig. 25.We obtained the growth temperature values for 3392 species (190 archaea and 3202 bacteria). The temperature information originates from BacDive databaseAmong the 3392 species with available temperature, only 693 have optimal growth temperature. For the 2699 species left, we found temperature values at which the species grow, but they are not necessarily the optimal ones. When several temperature values or an interval were given for a species, the mean of the values was taken.Using the growth temperature, we divided our species into four temperature classes. We considered hyperthermophilic species as species whose optimal growth temperature is above 26, which stores functional data and metadata for 1374 bacteria. This database divides habitats into the following categories: ’Fresh water’, ’Marine’, ’Soil’, ’Other’, ’Human’, ’Host’, and ’Multi’ (a combination of the others). These categories may be further detailed with the specific habitat where the species has been found.We extracted the habitat information from the FusionDB databaseIn order to simplify the variable so as to focus on the hypothesis that free-living and non free-living species are metabolically different, we created an ’Environment’ class, consisting of the free-living categories: ’Fresh water’, ’Marine’, ’Soil’ categories and ’Multi’ category combining the other three. The second class created was ’Symbiont’, consisting of ’Human’ and ’Host’, representing the species living in other organisms. The last class was ’Mixed’, consisting of the ’Other’ category, and the ’Multi’ categories that combine a ’Symbiont’ category and an ’Environment’ one.We obtained 554 bacteria in the ’Symbiont’ class, 395 in the ’Environment’ class, and 235 in the ’Mixed’ class.27, completed with FusionDB database26. There are 6 different categories, from mandatory oxygen to mandatory lack of oxygen: ’Obligate aerobe’, ’Aerobe’, ’Microaerophilic’, ’Facultative’, ’Anaerobe’, ’Obligate anaerobe’. However, as classes ’Obligate aerobe’, ’Microaerophilic’, ’Obligate anaerobe’ amount to only 8% of oxygen tolerance information, we removed them. We, therefore, have data for 2103 bacteria and 128 archaea, consisting of 917 ’Aerobe’, 782 ’Facultative’, and 532 ’Anaerobe’.We acquired the species’ oxygen tolerance information from the Genomes OnLine Database (GOLD)There exist a number of ways to produce metabolic networks from chemical reactions. The nodes of such a metabolic reconstruction can be metabolites , or enzymes. The analysis in this paper is based on a representation using both metabolites and enzymes as nodes. There is a directed edge between substrates and the catalysing enzyme, and the catalysing enzyme is linked to each product.1. We started with the first KEGG brite entry for each species. The brite entry lists gene names and the annotated enzyme commission (EC) codes for a given organism. From each entry we extracted all EC codes if they were found in complete form, i.e., no hyphen was present in the code. We then retrieved all Reaction Numbers (RN), found in the KEGG enzyme entries, consisting of the reactions catalysed by the enzymes. From the KEGG reaction entries, we extracted all substrates and products, and we built the directed edges between each substrate and each enzyme, and each enzyme and each product. Each reaction of a single enzyme was treated separately, with “_0”, “_1” and so on after the enzyme name to differentiate them.In order to have a very extensive dataset and maintain database consistency, our networks were directly derived from the KEGG database28. The average distance of the shortest paths between two metabolites is greatly shortened by the currency metabolites, since they are used in a great number of reactions, and, therefore, short circuit the actual relevant biochemical pathway when studying the network topology, making it difficult to obtain meaningful functional information. The ubiquity of these metabolites is another reason why they will not give us specific biological information, and are, therefore, commonly omitted28. Furthermore, removing these nodes does not separate the network into multiple subnetworks: the integrity of the network is kept for almost all reactions28.Some ubiquitous metabolites present in a big number of reactions cause an important impact on a network structure. A common practice is to exclude them to make the network more relevant biologically. Indeed, very widely available and used currency metabolites such as water, ATP, ADP, and so on will greatly impact the structure in regard of network topological properties such as the path length (number of steps in a pathway)29. We used 14 ubiquitous metabolites8: There is no strict consensus on ubiquitous metabolites, but the metabolites used as electron carriers and other metabolites transferring common functional groups are usually considered as ubiquitous metabolitesThe default direction of the KEGG reaction was used to orient the edges, which is the direction of the catalytic reaction. It is the direction in which the flow of biomass is expected.11. It relies on the sequential nature of chemical reactions in the metabolism. The concept is illustrated in Fig. The scope of a metabolic network is based on the concept of network expansionThe expansion starts from a set of seed compounds, a medium ses Fig. .32 to predict the growth temperature classes, habitat, and oxygen tolerance. The classes in each model were weighted according to the observation frequencies of each class. We performed a 300-fold cross-validation in order to increase the stability of our results. 10% of the species for which we have environmental information were kept as a test data set, an unbiased evaluation of the model. The other species were divided into a 66% training data set and a 34% validation data, on which the model was trained and the model parameters adjusted. The criterion for the quality of split in the decision trees was the Gini impurity. Our optimal random forest models have 1000 trees.We built random forest classifiersIn the case of temperature classes, mesophiles were reduced to 300 in order to further balance the growth temperature classes. For each model learned within the 300-fold cross-validation, 300 mesophiles were randomly chosen, and the data set was then divided into training, validation and test data sets.In order to predict not only the growth temperature class, but also the growth temperature values, we built a feed-forward neural network.We again selected 300 random mesophiles to reduce the temperature classes unbalance. 66% of species were used as the training data, and 34% for test. We normalised the growth temperature values (T) as follows: 33. The output neuron takes the output of the hidden layer, multiplies it by the weights, and applies a sigmoid function.Our network has three layers: an input layer, with as many input neurons as the number of compounds, a hidden layer of size 1000, and an output layer with a single neuron, which returns the predicted growth temperature. The hidden layer takes as input a linear function of the weights and input values, coupled with a dropout of probability 0.2 to prevent overfitting34. We developed our own mean squared loss function to account for the unbalanced temperature distribution. We applied a learning rate of https://pytorch.org/) framework to declare and train the network.The weights are found by optimizing the mean squared loss with the Adam optimizerSupplementary Information.


The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux. Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition. While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state. We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement. In several disease conditions, metabolic reprogramming serves as the epicenter for loss of homeostasis in the host cell–4. MetabIn the context of the current COVID-19 pandemic, several reports highlight the deregulation of multiple metabolic pathways in the host cell in response to SARS-CoV-2 infection–17. CaseDespite the advancement in metabolic modeling, most of the work has focused on elucidating altered reactions based on differentially incorporated reactions in condition-specific metabolic models. Such an approach, allows us to map the effects on gene expression or enzyme production on reactions qualitatively. Our ability to find the degree of alterations at the level of fluxes in such context specific models will enable us to understand the activities of different pathways in the cell. Fluxes are a consequence of interactions between several reactions, the structural features of the metabolic pathways and the nutrient uptake rates. LimitedWhile such steady state approaches, allows us to predict reaction fluxes under given conditions, it doesn’t speak much about the mechanism of flux regulation. To understand the regulation of reactions, it becomes imperative to conduct integrative analysis of metabolic flux analysis with other omics approaches. Omics datasets such as protein-protein interaction networks and phosphorylation variations can help us in inferring the prospective causative factors behind alterations in metabolic flux. Therefore, our ability to develop pipelines for integrated analysis of drivers  and effects (fluxes) will enable us to obtain better insights into disease mechanisms.In this study, we developed a Genome-Scale Differential Flux Analysis (GS-DFA) that leverages flux sampling and stringent statistical framework to report affected metabolic modules in diseased conditions. We developed a context-specific integrated GEM of SARS-CoV-2 infected NHBE cells leveraging the publicly available gene expression data. We apply the GS-DFA to the context specific GEM of normal human lung cells or tissues (NHBE and Lung Biopsy) and SARS-CoV-2 infected human lung cells. We show that several expected pathways are affected and significant number of altered pathways have associations with viral proteins through PPIs or/and altered phosphorylation landscape. Several pathways such as fatty acid oxidation, arachidonic acid metabolism (eicosanoid metabolism), fatty acid biosynthesis, cholesterol biosynthesis and amino acid metabolism is affected in diseased state. Some of these pathways i.e. arachidonic acid metabolism are associated with the production of immune-regulatory molecules like Leukotriene and Prostaglandins which mediate cell-to-cell signaling and inflammatory response management. SARS-CoFlux balance analysis requires a biomass equation which would serve as an objective function for the underlying linear optimization problem. The bioPhosphatidylcholine and Phosphatidylethanolamine occupied the highest lipid molar fraction in the membrane whereas cholesterol occupied the lowest molar fraction . The bioWe further integrated the Cov2VBOF into the context specific models of NHBE cells in infected state. We showed the Cov2VBOF helps in accurately simulating the growth kinetics of the virus upon infection. By varying the coefficient of each precursor by 50% (an extreme variation), we were able to show the predicted growth rate shows no significant difference with the experimental reported growth rates of the virus  . As seveIn the case of mammalian cells, it becomes particularly important to constrain the reactions in the generalized HumanGEM metabolic model based on expression levels in the cells to define the metabolic model of a given cell type. This eliminates reactions that are lowly expressed in the specific cell/tissue while keeping reactions having high expression or are required for basic metabolic functionality. We used task based INIT Integra was inteThe Cov2VBOF was leveraged to measure the specific growth rate of the virus in the context specific model of the infected NHBE cell. We used Flux Balance Analysis (FBA) on the context specific model constrained by HAM media using Cov2VBOF as an objective function. We repeated the FBA for several perturbations in the Cov2VBOF (to account for effect of uncertainty in biomass composition on growth rate) and small alterations in the uptake rates of nutrient in the infected conditions (Accounting for effect of uncertainties in uptake rates of media components). We compared the predicted specific growth rate to the experimental growth rate as reported in earlier. We coulFurthermore, we tried to capture the effect of specific uptakes rates (mmol/gdw/hr) of nutrients on the fitness of the virus in infected cells through phenotypic phase plane analysis. Such an analysis allows to visualize the effects of alterations in uptake rates (Phenotype 1) on the growth of the virus i.e. fitness (Phenotype 2) . It was Context specific metabolic models of NHBE cells and lung biopsy helped us to catalogue altered metabolic reactions between normal and infected condition. The network properties of the metabolism and the gene-protein-reaction association captured by the HumanGEM model combined with the gene expression data allowed us to analyze altered metabolic phenotypes in diseased state. These models can be further leveraged to reveal more biological insights into the metabolic state of the diseased cell using powerful tools of metabolic flux or network analysis.Integration of gene expression data into the highly curated HumanGEM pruned non-essential reactions while enriching important reactions. This helped us to generate context specific models of normal and diseased cells. We further aimed at understanding the alterations in metabolic flux in an infected cellular state. Such an analysis would pinpoint specific reactions which show altered behavior in diseased state. While inferring altered metabolic pathways have been conventionally done using differential gene expression analysis, we show the limitations of such analysis. The presence of either isozymes (multiple enzymes having same function) or multi-subunit complex (multiple subunits contributing collectively towards a function) makes it difficult to infer the effects of altered gene expression on metabolism. It is vMetabolic flux analysis allows us to understand the flux distribution in a model under a given set of condition. While tools like FBA enable us to obtain possible flux values for reactions throughout the metabolic model, it doesn’t guarantee uniqueness of the solution and necessitates an objective function. Flux vaFlux sampling allows us to screen through all the possible flux values and generate a probability distribution of the values that a given reaction can undertake. This caFor NHBE cell context specific models i.e. iNHBE and iNHBECov2, cholesterol biosynthesis (Kandutsch-Russel Pathway and Bloch pathway), fatty acid oxidation, beta-oxidation of di-unsaturated fatty acids (mitochondria), fatty acid activation, carnitine shuttle pathway (mitochondria and cytosol), arachidonic acid metabolism and few amino acid biosynthesis pathway were severely altered between the normal vs. diseased state . MajoritSeveral pathways highlighted in this analysis  are alsoIt has been reported earlier that most RNA viruses modulate the metabolism and physiology of the host cell through post-translational modifications rather than transcriptional changes. We wereThis directly indicates the modification of host metabolism induced by the viral proteins through post-translational modification. We also acknowledge the presence of complicated pathways of modifications through host signaling pathways which we might have missed in our analysis. Phosphorylation and allosteric modification are two significant pathways through which metabolic machinery is regulated at the post-translational level. Inhibitors against the viral proteins mediating such molecular modifications can be used as a therapeutic option to restore metabolic homeostasis in the cell. Nevertheless, fatty acid metabolism appears to be one of the most affected pathways in the host which can be targeted for therapy.In this study, we propose a computational method for conducting a differential flux analysis of metabolism at a genome-scale. This method allowed us to identify altered reactions in the metabolic network of NHBE cells infected with SARS-CoV-2 virus. This method comprises of a stringent statistical framework which enables us to filter out reactions which occur by chance due to the fundamental behaviour of linear optimization problems. This method is independent of objective function used and hence can be leveraged for understanding the metabolic flux alterations under given set of conditions in the case of mammalian cells.Here, we leveraged flux sampling method ACHR  to samplKolmogorov-Smirnov test compares between the probability distribution of fluxes across several conditions and highlight metabolic modules that are altered. Furthermore, we use multiple-hypothesis testing to ensure the reported significances don’t contain false positives. The enriched set of reactions are further subjected to hypergeometric enrichment test for metabolic subsystems in the model. This is to make sure the reported pathways that are affected are not due to chance. Using this method, we find that several metabolic subsystems affected between diseased and normal state belong to fatty acid pathway in the case of SARS-CoV-2. Interestingly, there are already several reports which point at the severe deregulation in fatty acid metabolism confirmed at the level of clinical surveys under controlled trails and metabolomics studies, 40, 49.Fatty acid are not only important energy precursors which might be expended by the virus to grow rapidly but also the biochemical precursor for several important signalling molecules. Leukotriene biosynthesis which begins from the oxidation of arachidonic acid has also been inferred to be one of the affected pathways between diseased and normal state. Leukotriene is an important autocrine and paracrine signalling molecule which allows cells to attract immune cells when abnormalities arise is homeostatic state of the cells. It is pWe would also like to mention some of the limitations of the current analysis and the corresponding pipeline. First, proteomics data would be more useful for constraining the metabolic models compared to transcriptomic data. But it wouldn’t make much difference to the current pipeline as tINIT has been extensively used with proteomics data. It is iWe consider this method to be a significant advancement in the field of metabolic flux analysis. This would allow us to probe into differences in flux apart from differences in network structure among several conditions. This would help us to design experiments to measure the effects of infections on specific metabolic modules in the cell. Such an analysis along with other multi-omics datasets like metabolomics, phospho-proteomics and interactomics will allow us to understand the molecular mechanistic principles of infections. Integration of multi-omics data is expected to reveal several mechanistic phenomena of diseases and will help us design better therapeutics leveraging the power of systems biology.All the calculations for nucleic acids, proteins, carbohydrates and lipids are conducted per gram dry weight (GDW) of the virus. The nucleotide content was calculated from the single stranded positive RNA sequence which is present as a single copy. The contribution of transcriptome towards nucleotide was also considered. Briefly, the number of transcripts per gram dry weight of the virus was calculated from the proteomics and translational efficiency data. The traThe contribution of amino acids to viral biomass was calculated from a proteomics dataset publishe2 as discussed in earlier studies[N-acetyl Glucosamine (NAG) content of the biomass was calculated from the PDB file (PDB ID: 5SZS) of S protein. Briefly, the number of NAG molecules present in the crystal structure of a single S protein subunit was calculated. We obtained 63 NAG molecules per subunit of the virus in this way. The total number of NAG molecules studded on the S protein was calculated using the copy number information obtained above. The lipid composition was derived from previous studies. Brieflyr studies.The total virus molecular weight was calculated from the sum of mass of all macromolecules present in the virus . The uniThe gene expression data was derived from GSE147507. The triplicate data for mock infected NHBE cells and SARS-CoV-2 infected NHBE cells was considered. The information regarding the transcript isoform was obtained from Mammalian Transcriptomic Database . The dataset was normalized to TPM (Transcripts per Million reads) using the transcript lengths specific for NHBE cells . Custom The latest version of tINIT (version 2.0) was used to integrate the gene expression data into HumanGEM model. The steThe resulting context specific models was constrained using the uptake rates from media components as reported in several literature . Flux BaHenceforth, we will call the context specific model for normal NHBE cells and SARS-CoV-2 infected NHBE cells as iNHBE and iNHBECov2. For calculation of Hamming distance, we constructed a reaction Boolean vector for each model. Briefly, we constructed a Boolean vector of length 13417  for each model with 1s and 0s i.e. whether a reaction from the base model was incorporated into the context-specific model or not.It was important check for the sensitivity of the specific growth rate prediction with respect to variations in coefficient (mmoles/gdw) or fractional composition of biomass precursors. We varied the coefficients of biomass precursors one at a time by ±10% and calculated the growth rate by flux balance analysis. The FBA was set-up on the modified SARS-CoV-2 biomass equations with coefficients increased by ±10% one at a time as follows:c* = . Here, the coefficient for cn biomass precursor has been manipulated by ±10% whereas the coefficient of other biomass precursors remain the same.Here, c* is the modified biomass coefficient vector such that IBM CPLEX solver was used to conduct the flux balance analysis. The context-specific model of NHBE infected with SARS-CoV-2 i.e. iNHBECov2 was considered for the analysis constrained with HAM media.ACHR  was used to sample fluxes from iNHBE and iNHBECov2. A thinning factor of 100 was used to obtain sparse and un-correlated fluxes which helps to span the whole flux space. IBM CPLEX was used for the optimization underlying ACHR flux sampling. The pipeline was executed in an i5 8th Generation Intel Core processor with 8 GB RAM. 10,000 sample points were drawn for each model from the sampling object. Custom scripts were written in Python 3.7 for the flux sampling estimation.infected and Suninfected are the flux distributions for a given reactions in infected and uninfected conditions. The bar indicates the arithmetic mean of the distribution which is the most represented flux from the distributions. The flux change was calculated for each reaction as follows:Two-sample Kolmogorov Smirnov Test (KS Test) was used to differentiate between the flux distribution between iNHBE and iNHBECov2 models. A significance level of 0.05 was used for filtering the altered reactions. Moreover, we calculated the change in flux between two models from the sample means of the distributions. The flux mean in the case of infected and uninfected cells is the statistical mean of the flux distribution of each. SThe denominator is used to normalize the magnitude of flux change. This is avoid biased filtering out of reactions with low flux but of important biological consequence. A Flux Change (FC) of 0.82 (corresponding to 10 fold change in flux) was used to filter reactions with insignificant flux change. For reactions exclusively present in one model, the flux through the same in the model where it is absent is assumed to be zero. To compare the flux distribution, we used bootstrapping to estimate the 95% confidence interval and then checked if zero is contained within the same. All reactions, where zero i.e. flux through the absent reaction, is outside the 95% confidence interval were considered to have a differential flux and taken for further analysis. A Benjamini-Hochberg FDR Multiple hypothesis correction with α = 0.05 was used to correct the p-values for all the resulting differentially altered reactions. The adjusted p-value was used along with the FC cutoff of 0.82 for filtering out the reactions which would also occur by chance.Hypergeometric enrichment was used to obtain HumanGEM subsystems S2 Data which arHere, P(X = k) is the probability that we find k reactions just by chance in a given subsystem. K is the total number of reactions belonging to a given subsystem, N is the total number of reactions in the model and n is the total number of reactions that show altered flux as calculated by differential flux analysis. For each subsystem in the model, we conducted a hypergeometric enrichment test for overrepresentation. If P(X = k)<0.05, then it is likely that the over-representation of the subsystem is due to the high number of altered reactions in the pathway rather than by chance. The resulting p-values for pathways were again subjected to multiple-hypothesis correction using Benjamini-Hochberg method using False Discovery Rate (FDR) with α = 0.05. From this, we were able to obtain the pathways in the HumanGEM model which are most affected by the infection.The PPIN dataset was obtained from recently published literature on host-virus interactome. It is tThis set of reactions was compared to the set of reactions which were found to be differential altered in term of flux. A hypergeometric test was conducted to check if the enrichment of PPINs and DPs affected reactions in the set of reactions having differential flux were due to chance or not. The hypergeometric test was conducted as follows:Here, P(X = k) is the probability that we find k reactions affected by PPINs or DPs just by chance in a given subsystem. K is the total number of reactions affected by PPINs or DPs, N is the total number of reactions in the model and n is the total number of reactions that show altered flux as calculated by differential flux analysis. For each subsystem in the model, we conducted a hypergeometric enrichment test for overrepresentation. If P(X = k) <0.05, then it is likely that the over-representation of PPINs or DPs affected reactions are due to real effects of PPIN and DP on metabolism rather than by chance.S1 FigA) The electron micrographs of SARS-CoV-2 (Credit: NIAID-RML) B) Number of spike proteins calculated from intensity measurements of the micrograph along the circumference of the virus.(TIF)Click here for additional data file.S2 FigThe processed final model is used for differential flux analysis.(TIF)Click here for additional data file.S3 FigThe uptake rates were varied and a point optimization program was used to calculate the specific growth rate. The fitness change is reported as the ratio of specific growth rate under perturbation to the specific growth rate under normal uptake rate. All the uptake rates were negative.(TIF)Click here for additional data file.S4 FigThe coefficients of biomass precursors were varied by ±10% taken one at a time and the specific growth rate was calculated by FBA.(TIF)Click here for additional data file.S1 DatasetThe sheets in the dataset comprises of stepwise calculation of SARS-CoV-2 amino acid composition, nucleotide composition, carbohydrate and lipid composition. The last sheet comprises of the final viral biomass equation.(XLSX)Click here for additional data file.S2 DatasetThe description of each dataset within (XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions  to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of  There is an increasing demand for protein pharmaceuticals, especially monoclonal antibodies. Chinese Hamster Ovary (CHO) are currently the leading production host due to their ability to perform human-like post-translational modifications. However, it typically takes several months of trial-and-error approaches to develop a high-producer cell line. Metabolic modelling has the potential to make cell line and process development faster and cheaper by predicting targeted modifications to the cell line genome, cultivation medium or bioprocess. In fact, genome-scale metabolic reconstructions of CHO are already available, and ready for use in cell line development. However, in order to successfully use these models, we need to make sure that they are able to accurately predict metabolic phenotypes. Here we use genome-scale metabolic models of CHO to evaluate the models’ ability to correctly predict intracellular flux distributions. We find that a crucial key ingredient for the correct estimation of central carbon fluxes is the non-growth associated maintenance energy (mATP). We estimated mATP computationally and confirmed it experimentally. Adding this single constraint leads to significantly better predictions of intracellular fluxes, especially in glycolysis and the tricarboxylic acid cycle. Chinese hamster ovary (CHO) cells are currently the leading production host for the synthesis of complex biopharmaceuticals with human-like post-translational modifications . ProductIn 2016, a community-derived, consensus genome-scale metabolic model (GSMM) of CHO was published  and seveTo successfully use modeling for the rational design of new engineering strategies, accurate predictions of cellular phenotypes are essential. Previously we showed that a GSMM can accurately predict growth rates if supplied with accurate exchange rates of  amino acids (AAs) and correctly determined biomass composition –15. Howe13C metabolic flux data sets across producer and non-producer cell lines in different media and culture modes  extracted from six publications [In this work, we compare fluxes predicted by parsimonious flux balance analysis (pFBA), using the GSMM of CHO , againstications –23. We fR_biomass_cho and R_biomass_cho_producing. The main difference between these two reactions is that R_biomass_cho has lower protein content (56% compared to 70% in R_biomass_cho_producing), but higher lipid, DNA and RNA content. Uptake and secretion rates of extracellular metabolites and the recombinant product from 20 datasets (six publications) were used as constraints [−1 h −1 using dry cell masses provided in the publications. If not available, the average mass of CHO (264 pg) was used [13C metabolic flux analysis (MFA) were used. If the data was provided only as plots, it was extracted using WebPlotDigitizer (https://apps.automeris.io/wpd/). Oxygen uptake was left unconstrained.PFBA  was perfstraints –23  to the GSMM of CHO iCHO1766 [In order to compare predictions made by the GSMM of CHO to the results of iCHO1766 . MetaboliCHO1766 –23, see 13C model could be mapped to several reactions in iCHO1766, the fluxes from these reactions were summed up or subtracted, depending on the direction.If one reaction in a In case of multiple equivalent reactions occurring in several compartments, their individual contributions predicted by PFBA were summed up and only the total was used for comparison. This approach disregards cellular compartments.13C models, several reactions are often lumped into one; therefore, the net flux of the corresponding reactions in the GSMM was calculated and compared to the flux of the lumped reaction.In the In case of producer cell lines, reactions for the synthesis of the recombinant protein were added comprising the AA composition provided in the publications and the energy demand for the polymerisation from   was constrained to a range of different values of mATP (0 -40 mmol g−1 h−1 or until the simulation was no longer feasible). For each value of mATP, the agreement between experimental and predicted fluxes was evaluated and the value that lead to the lowest median relative error was chosen as optimal. Reactions with the experimental fluxes less than 1% of the maximum flux (To estimate maintenance energy (mATP), PFBA was run for every individual dataset, where growth rate was maximized and mATP hydrolysis  and the essential uptake rates to the predicted or experimental values (see above). The flux distributions were obtained by performing pFBA with minimization of an uptake rate  as the objective.lm. In case of intracellular flux predictions, the inverses of the experimental confidence intervals were used as weights for the linear fitting. The relative error of the fluxes was calculated with Statistical analysis was carried out in R version 4.0.2. Linear correlations between experimental and predicted data were calculated with R function χ2 analysis.To check whether the addition of mATP constraint has a significant effect on the fits, mATP was added as a categorical predictor  and an interaction term was included in the model . If the term is significant , we conclude that mATP has a significant effect on the slope. We also compared the models without or with mATP predictor with 2 and 85% humidity and passaged every 2–4 days. Mycoplasma contamination was regularly checked with MycoAlert Mycoplasma Detection Kit . The cell concentration and viability were determined with Vi-CELL XR  calibrated with ViaCheck Concentration Control .Suspension CHO-K1 cells (ECACC CCL-61) were grown in CD-CHO medium  supplemented with 0.2% (v/v) Anti-Clumping Agent  and 8 mM L-Glutamine . Cells were cultivated in 125 mL non-baffled Erlenmeyer flasks at 37°C at 140 rpm with 25 mm throw, 7% CO2 and 7.5% (w/w) NaHCO3. pH was also checked with an external pH probe (Mettler Toledo) at least twice per week to correct potential pH drifts. Dissolved oxygen was measured with DO sensor VisiFerm  and maintained at 30% with a cascade . Cells were inoculated at a seeding density of 1.6 × 105 viable cells/mL at a working volume of 300 mL. At the end of the exponential phase, the culture was switched to the continuous mode and maintained at a constant volume of 270 mL. The flow-in pump was set to a constant rate and the amount of medium pumped into the bioreactors was monitored using Mettler Toledo balances MS6002TS (readability 0.01 g) to calculate an accurate flow rate into the bioreactors. The tube for flow-out was positioned at the height corresponding to 270 mL and the flow rate was set to a higher value than flow-in to prevent overflow. The feed medium was kept at room temperature and protected from light with aluminum foil. Due to the instability of glutamine, the medium was exchanged every 5–7 days. During this time frame, the glutamine degradation was shown to be negligible  in DS0700ODSS vessels at 37°C and agitated with a marine impeller at 80 rpm. pH was monitored with EasyFerm Plus PHI K8 225 pH Electrode  and maintained at 7 ± 0.05 with COible see .lm. If the 95% confidence interval of the slope contained zero, the parameter was considered stable. For seven out of eleven dilution rates, all parameters were stable, including a dilution rate where only 2.5 volume exchanges were reached due to contamination (DR3). For three dilution rates After changing the dilution rate, the cultures were left to equilibrate for at least five volume exchanges . Lactate and ammonium measurements were corrected with 4-point calibration curves made in CD-CHO medium. As glucose was already contained in the CD-CHO medium, a calibration curve was not done. Instead, the average measured concentration of the standards was used for the calculation of the uptake rates in For two dilution rates (DR1 and DR2), the AA concentrations were quantified by Biocrates with a commercial AbsoluteIDQ p180 kit. Briefly, AAs were derivatized with phenyl isothiocyanate in the presence of internal standards. The quantification was performed by liquid chromatography-mass spectrometry (LC-MS/MS) using a 4000 QTRAP  and a Xevo TQ-S micro  instrument with an electrospray ionization source.2HPO4:10mM K2B4O7 (Merck) pH 8.2 as solvent A and acetonitrile:methanol:water  (Merck) as solvent B, AAs were excited at 230 nm and the fluorescence signal was detected at 450 nm for OPA derivates and 266 nm and 305 nm for FMOC derivates, respectively. Samples were quantified using an internal standard calibration.For the remaining dilution rates, AAs were analyzed using a high-performance liquid chromatography method. Briefly, samples were diluted, internal standards 3-(2-thienyl)-DL-alanine (Fluka) and sarcosine (Sigma) were added and subsequently filtered using a 0.2 μm filter unit (Sartorius). In an automated pre-column derivatization method, free primary AAs reacted with ortho-phthalaldehyde  and proline and hydroxyproline with 9-fluorenyl-methyl chloroformate  and were then separated on a ZORBAX Eclipse Plus C18 column (Agilent) at 40°C using a flow rate of 0.64 mL/min. After gradient elution with 10 mM KThe metabolite concentrations in the medium measured by both methods were compared to the patent for CD-CHO medium  to make sure the results are in the expected range and comparable between the two methods.D was calculated with F is the flow rate into the bioreactors  and V = 270mL is the volume of the medium in the bioreactors. The growth rate μ at steady state was calculated with Nv/Nt denotes fraction of viable cells. Steady state exchange rates qi of extracellular metabolites were calculated with σ) of the rate was calculated according to the mathematical rules of manipulation with standard deviations—For all calculations, the average of all data points in each steady state was used. Dilution rate R_biomass_specific) were fixed to the experimental values ± SD. Due to high experimental noise, the uptake rates of some essential AAs were too low to sustain the experimental growth rate. Therefore the minimal uptake requirements were estimated as in [The GSMM with cell line specific biomass composition from  was useded as in  and, if R_DM_atp_) for each dilution rate. Total ATP production was calculated by summing up fluxes of all reactions in the GSMM that produce nucleoside triphosphates  and plotted against growth rates. Linear fit was done in R (function lm). The intercept represents the estimated non-growth associated energy consumption (mATP) and 1/slope is biomass yield per mole of ATP corrected for mATP , see For 6 out of the 20 datasets growth could be predicted with an error of less than ±25% .R_DM_atp_c_) such that the median relative error across all fluxes was minimal. −1 h−1. Across all cell lines, mATP averages at 5.9 and 6.4 mmol g−1 h−1 for R_biomass_cho and R_biomass_cho_producing, respectively .Additionally we estimated a mean maintenance energy by fitting mATP across all datasets. Mean maintenance was very close to the average mATP determined for each individual dataset , leading to a significant change of the slope from 0.36 to 0.98 for R_biomass_cho and from 0.27 to 1 for R_biomass_cho_producing .Adding the estimated mATP value as a constraint strongly decreases the prediction errors in the intracellular fluxes for all but one dataset, see Not only intracellular fluxes, but also growth rates were better predicted . Now tenWhile the predictions largely improved for TCA and glycolysis fluxes, PPP became inactive and the agreement with experimental data became worse . HoweverR_biomass_cho and R_biomass_cho_producing, respectively, Recently, minimizing uptake of non-essential nutrients (rather than maximizing growth) was suggested to be a more suitable modeling objective for CHO . Thus, wThe prediction accuracy of the intracellular fluxes after the addition of the mATP constraint was comparable with biomass objective function (BOF) for all objective functions  and asparagine . However, the uptake rates of the remaining AAs were not predicted well  [In this work, we evaluated the agreement between experimentally measured intracellular fluxes from 20 datasets –23 and pATPases) . As no sR_biomass_cho and R_biomass_cho_producing, respectively). This might be the result of the experimental errors of the metabolite exchange rates. As seen in 13C flux measurements which often had considerably big confidence intervals. The differences might also stem from differences in the cell lines, cultivation conditions or productivities. However, it was not possible to differentiate between biological effects on mATP estimation  from the effects caused by the differences in labelling and quantification  and modelling approaches . Nevertheless, the average mATP values were very similar when estimated with different biomass equations  or NADP+-dependent isocitrate dehydrogenase (NADP-ICDH). Indeed we found higher activity of NADP-ME and NADP-ICDH in some datasets, but not consistently in all. This points to a possible lack of actual NADPH demand in the model. Anabolic pathways that require NADPH, such as synthesis of lipids or nucleotides [Fluxes of PPP got worse after the addition of a mATP constraint, which points at alternative NADPH sources connected to the TCA, e.g. NADPleotides  are presleotides  or maintleotides  are not all amino acid exchange rates are accurately measured, growth rate predictions are accurate too as these rates essentially determine the energy metabolism even without considering mATP. Here we encountered several data sets where the inclusion of mATP improved growth predictions. However, since accuracy estimates on the exchange rates were sometimes missing in the original publications, we are unable to exclude simple measurement inaccuracies as the reason for this observation.In our previous contribution  we obserWe also investigated the effect of alternative objective functions  suggested by Chen et al. . The estThe choice of the appropriate objective function might depend on the availability of experimental data. In case of using BOF, highly accurate uptake and secretion rates are needed in order to obtain accurate predictions, especially for essential AAs , 14. If cell-1 h-1 to mmol g−1 h−1 with a literature value for cell dry mass [The experimentally determined mATP for CHO-K1 was comparable to the average computational estimate but much lower than the estimated mATP for CHO-K1 from Nicolae et al.  Fig 2A)Fig 2A). dry mass . As an edry mass .Furthermore, the uncertainty of the experimental estimate was quite high due to the technical difficulty of running continuous fermentation and the unstable nature of CHO cells. Long cultivations lead to cell clumping, which complicated cell counting. It is also known that CHO cells are unstable during long term cultivations , 40. Fur−11 mmol cell-1 d-1  [−1 h−1 with a mouse cell dry mass (660 pg/cell) or 2.4–3.6 with a range of CHO dry masses [2. However, mammalian cells in culture will also use glutamine and other AAs as energy source [In literature there is only a small amount of data for mammalian maintenance energy and no data for CHO. Mouse cells require a maintenance energy of 1.7 × 10th rate) , which cy masses . Howevery masses  was quity source .−1 h−1 [Depending on the experimental/computational methods used, maintenance energy of cancer cells was estimated to be within 1.6 and 3.7 mmol g−1 h−1 , 51. The−1 h−1 .−1 h−1 [In other organisms, estimated/measured values widely vary and often depend on the cultivation conditions. In bacteria, the reported values range between 3.15–18.5 , 52–54, −1 h−1 , 55–57. −1 h−1 .−1 [−1 depending on the carbon source [−1, also depending on the carbon source [The estimated  g mol−1  up to 9.n source . Values n source . To our iCHO1766 by adding carbon availability constraints. In another study, the predictions of intracellular fluxes were improved by adding constraints based on enzyme kinetic information [Finally, it is important to note that GSMMs have a large solution space. By computing a parsimonious flux distribution the space of possible solutions is reduced. That space can be further constrained by imposing mATP demand. However, some variability will remain . Hence, adding even more constraints could be beneficial for model performance. As an example, Lularevic et al.  reduced ormation . This alIn this work we evaluated the prediction accuracy of CHO GSMM with pFBA. The intracellular fluxes were largely underestimated due to low energy demand and the missing non-growth associated maintenance energy was identified as the main reason for the bad flux predictions. The computationally estimated maintenance energy largely improved the predictions of central carbon metabolism and it was consistent with experimentally determined maintenance energy and with literature values for other mammalian cell lines. Adding this simple constraint to the model leads to a big improvement in the flux prediction accuracy and should not be neglected in constraint-based metabolic modeling of CHO.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 FigR_biomass_cho_producing as the objective function. RE—relative error. The legend in panel (A) indicates the publication and the used CHO cell line (if the information was available). Empty symbols indicate non-producers.Data is shown for biomass equation (TIF)Click here for additional data file.S2 FigR_biomass_cho.mATP was gradually increased and the agreement between experimental and predicted fluxes was evaluated at each step. The mATP value that lead to the smallest median relative error of the fluxes was chosen as the optimal value. Data is shown for the dataset SV-M3 from Templeton 2017  for biom(TIF)Click here for additional data file.S3 FigR_biomass_cho as the objective function. RE—relative error. Several outliers can be observed, which in most cases belong to a specific dataset. For example in glycolysis +mATP, the two most overestimated points belong to the “K1” dataset. In TCA +mATP, four underestimated points again belong to “K1” dataset, one to “early” dataset. In pyruvate metabolism +mATP, two underestimated points belong to “K1”, two to “early” datasets. In AA metabolism, the outliers belong to various datasets and sub-pathways.Results are shown for (TIF)Click here for additional data file.S4 FigR_biomass_cho as the biomass reaction. RE—relative error. The legend indicates the publication and the used CHO cell line (if the information was available). Empty symbols indicate non-producers.Results are shown for (TIF)Click here for additional data file.S5 FigR_biomass_cho as the biomass reaction. RE—relative error.Results are shown for (TIF)Click here for additional data file.S6 FigThe concentration was measured with Bioprofile 100Plus . The degradation rate (slope of the linear fit) during this time frame is not significant .(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file. 14 Apr 2021Attachment113168_1_rebuttal_2025570_qqfbsv.pdfSubmitted filename: Click here for additional data file. 28 Apr 2021Dear Mr. Ruckerbauer,We are pleased to inform you that your manuscript 'Inclusion of maintenance energy improves the intracellular flux predictions of CHO' 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. 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HaughDeputy EditorPLOS Computational Biology*********************************************************** 1 Jun 2021PCOMPBIOL-D-21-00677 Inclusion of maintenance energy improves the intracellular flux predictions of CHODear Dr Ruckerbauer,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. 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Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale Based on the basic GSMM model iHL1210 of A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with The online version contains supplementary material available at 10.1186/s12934-021-01614-2. Aspergillus niger (A. niger) is widely used in industrial fermentation for producing citric acid and glucoamylase as it is approved as Generally Regarded As Safe (GRAS) [A. niger has been proposed and continuously updated [A. niger, such as exploring the relationship between environmental pH and acid production [A. niger, great progress has been made for proteomics study on A. niger [Saccharomyces cerevisiae has been proven effective [e (GRAS) –4. In th updated –8, and hoduction , predictoduction , 10, etcA. niger –13. Enzyffective .Bacillus subtilis, the allocation of bacterial protein resources was accurately and effectively predicted [yeast, Bacillus subtilis, and Escherichia coli [Initially, enzyme constraints to GSMM were implemented mainly based on transcriptome data, i.e., using mRNA level instead of protein level as upper bound for corresponding reaction flux . Or implredicted . Recentlredicted . This moredicted . GECKO uhia coli , 20, 21.A. niger—the enzyme kinetic parameters  and multifunctional enzymes  are the same as general enzymes, it should be noted that for the reversible enzymes, the reversible reactions have been splited into two irreversible reactions, with the same enzyme assigned with two different he COBRA  and the he COBRA , and theA. niger, we can easily introduce new protein concentration data into eciJB1325. In addition, the experimental value of total enzyme concentration could be integrated with the model using the function addCouplingConstraint [Here, we do not apply additional constraints on those enzymes that no abundance data was available. Because the upper bound constraint of total enzyme concentration measurements as the sum of this portion of the reaction flux is too broad, and we hope to refine the model through more specific constraints of protein on reaction. Combined with the development of absolute quantitative proteomics of nstraint , which cA. niger can be obtained from the database PAXdb [A. niger, and for proteins with no abundance value for A. niger, we searched for the homologous proteins’ abundance of organisms in the order of the same genus, the same family, the same order, the same class, and selected the maximum value among them as the upper bound of the reaction flux. According to the rule, 1255 enzymes have been matched and assigned with abundance values as model constraints, and the abundance of core proteome in eukaryotes has been proved to be conservative [The protein abundance data of se PAXdb . We retrervative . In thisThe entclass1pt{minimaSince the ntration  Eq. 3)33\documenThe actual catalytic rate of enzyme,We used Cytoscape  to illusSv = 0) and a linear programming (LP) problem is solved [A. niger, the corresponding experimental data obtained from the literature [i.To examine the biomass specific growth rate under different growth conditions, we maximized the biomass growth rate as the objective, and the constraints were the uptake rate of carbon source and oxygen, the secretion rate of products and byproducts, and the production rate of carbon dioxide.ii.To examine the secretion rate of the product in the chemostat culture, the biomass specific growth rate was fixed to the dilution rate under the chemostat condition, and maximizing the citric acid production rate was taken as the objective. Other measured reaction rates were added as extra constraints to the model.iii.Similar method was used to examine substrate uptake rate in chemostat culture.iv.The above three FBA calculations were based on eciJB1325 and compared with the results of iJB1325.The mathematical analysis of GSMM is usually based on flux balance analysis (FBA), i.e., the mathematical representation of metabolism is based on steady-state mass balance equations  of the 1325 mutants under the same experimental conditions and calculated the biomass specific growth rate ratio (grRatio) of the mutant to the wild type by Eq.  and part from Cryptococcus neoformans (10.66%), which were relatively close to A. niger.To provide a maximum constraint on the flux of the enzyme participating in the catalysis, we retrieved the enzyme abundance data from the database PAXdb . Since tThrough the integration of enzyme kinetic parameters and enzyme abundance information, we built the enzyme-constrained model eciJB1325. However, we found that the model behaved with a very serious over-constraint problem. Some reactions were blocked directly when we checked the results. It is found that these reactions were constrained with too small nditions , the maxFigure To calculate ue Table . In addiThe core of GSMM model is the metabolic network. Metabolite networks are often used to calculate the connectivity of metabolic networks –36. The For the metabolites with a high node degree in the network, it means they participate in more reactions and often play a more important role in the metabolism. Therefore, we further analyzed metabolite connectivity for both iJB1325 and eciJB1325 by comparing the node degrees of metabolites, and we found that currency metabolites possess the highest node degree value, which means they play vital roles. The node degree of intermediate metabolites in fatty acid synthesis and metabolism is also large in the network because this process always shares the same precursors, e.g., AcCoA. And the connectivity of substances such as glutamic acid/carnitine in the metabolite network was larger which are mainly involved in protein synthesis and fatty acid metabolism Fig. .Fig. 6MeAs 1255 enzymes in eciJB1325 were considered as reactants, we further analyzed the network connectivity of enzymes, which helped to dissect the importance of different enzymes. We found that enzymes with relatively large connectivity mainly got involved in the fatty acid synthesis and metabolism pathways. These pathways show high connected reactions and multifunctional enzymes Fig. , Table 5−1, consistent with the literature reported value under the same condition [A. niger was not limited in the model with the increase of carbon source and oxygen source, which was not true in reality. However, eciJB1325 simulated a value of 0.1805 h−1 for the biomass specific growth rate when glucose was utilized, which was slightly greater than the literature value for the same strain (approximately 0.15 h−1) [One of the main functions of GSMM is to predict microbe behaviors. The main purpose of integrating more information and constraints into the model is to improve the prediction accuracy of the model and enable the model to simulate a more real cell phenotype , 38. Comondition , and theondition , 39 resu.15 h−1) . TherefoA. niger was simulated using iJB1325 and eciJB1325, respectively. The effects of each mutation on biomass specific growth rate of A. niger were analyzed. The results showed that eciJB1325 predicted more gene knockouts which can affect cell growth but do not block growth than iJB1325. This indicates the new model improved accuracy for simulating gene mutation than the ordinary model.GSMM has great value on guiding target gene identification for specific property enhancement of strain, such as simulating the change of growth phenotype caused by gene knockout , 42. IndA. niger has a special alternative oxidase pathway [Moreover, eciJB1325 can also explain phenotypic changes at the protein level with growth-related metabolism caused by gene knockout. Further thoroughly investigate the genes identified by eciJB1325, which show growth limiting without blocking effects, get involved mainly in six pathways , can be released by more expression of isozyme gene (An16g02500).The integration of the enzyme constraints can reduce the flux solution space of the model. We performed a flux variability analysis (FVA) for all reactions of iJB1325 and eciJB1325, respectively, solving for the minimum and maximum fluxes achievable by the reactions and their flux variability . For all the reactions in iJB1325 and eciJB1325, the median of FV in eciJB1325 was much smaller than that in iJB1325, and the total FV of the model was reduced by 37.24% compared with iJB1325, and the distribution of FV also changed significantly . From the results of reduction in FV of each reaction of eciJB1325 versus iJB1325, the FV of 1215 reactions was reduced 40.10%) and that of 1100 reactions was reduced by more than 75%, accounting for 36.30% of the total number of reactions . Therefore, up-regulation of enzymes related to arabinose synthesis and metabolism could be observed from the model, as well as significant up-regulation of enzymes related to ribose metabolism   Fig. B. BesideA. niger utilizes maltose [We also simulated the condition with maltose as a carbon source using eciJB1325. It has been reported that glycosidase is overproduced when  maltose . Model pA. niger iJB1325. Unlike small-scale integration that only adds enzyme constraints to several specific reactions [Based on the method of GECKO , we inteeactions , eciJB13−1, which is slightly larger than the reported 0.15 h−1 [The integration of enzyme constraints improves the model’s ability to predict metabolic phenotypes. Compared with the original iJB1325, eciJB1325 showed higher accuracy in phenotype prediction, which was shown by closer prediction with cell growth rate, substrate uptake rate, and product secretion rate compared to the experiment results. Especially, robustness analysis showed that the original model predicted an unbounded cell growth rate with excess unlimited nutrients concentration, while the enzyme constraints corrected this. The eciJB1325 gave an upper bound of the cell biomass specific growth rate, 0.181 h0.15 h−1 . In addiA. niger used maltose as substrate [The proposed eciJB1325 model has more practical application values. For example, with quantitative proteomics data, flux upbound of reaction can be accurately simulated and various individual terature . And eciubstrate . This isfor A. niger are scarce, here in this work we applied available data of other strains instead for that are missing in A. niger. Furthermore, the There is also a limitation for the current eciJB1325 model. Implementation of enzyme abundance constraints depends on accurate kinetic parameters and abundance data of enzymes . Howeverdatabase . From modatabase , ETFL modatabase , etc. Aldatabase  and unstdatabase , 48 intoA. niger, which significantly improved the model's ability to predict metabolic phenotypes and narrowed down the model's flux solution space. The integration of enzyme abundance constraints to GSMM model widened the application of proteomics data in systems biology. With the help of proteomics data, the newly formed model can not only improve the prediction ability but also can be used for detecting target genes for metabolic engineering. The model also showed good ability to be used to estimate the enzyme turn over number