Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients , minerals and chemical elements . We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids  involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of  Escherichia sp., Akkermansia muciniphila (A. muciniphila), and Subdoligranulum variabile (S. variabile)  , and lowrtlettii , as wellrtlettii . HoweverClarifying complex metabolic responses and relationships between gut microbes and host metabolism requires an analysis of large and highly intertwined reaction networks. GEnome-scale Metabolic models (GEMs) allow for the analysis of such complex networks and have successfully been applied to clarify the mechanisms underlying insulin resistance  and to iEscherichia sp., A. muciniphila, S. variabile, and I. bartlettii to analyze relationships between the bacterial metabolism and the extracellular environment, as well as predicting the survivability of the bacteria against nutritional alterations  on the 27th of January 2017, for A. muciniphila ATCC BAA-835 and S. variabile DSM 15176 on the 2nd April 2018. Details regarding microorganism AGORA reconstructions are accessible in Supplementary Table S1. The models were manually curated to ensure biological functionality. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX).AGORA  model reconstructions  were dowThe RAVEN  Toolbox  was usedSupplementary Table S2) were further validated by constraining methods, setting lower and upper bounds to zero, with biomass maximization defined as objective function. Single lethal reactions were determined and treated as essential reactions for cell growth. Double lethal reactions were considered as those pairs of reactions that induce no growth when blocked simultaneously but not individually. Exchange reactions were determined using default RAVEN functions, and only those involving nutrient exchange with the extracellular space are reported . This permits the identification of essential exchange reactions, which are the nutrients required to be uptaken from the environment by the organism in order to guarantee cell survival.Lethality analysis was performed by adapting the Fast-SL algorithm  from theSupplementary Table S5) and unclassified pathways were ignored. We applied modeling-constraints (lower and upper bounds set to zero and objective function defined as biomass maximization) going through each of the single and double lethal reactions  and non-essential exchange reactions. Pathway sensitivity to changes was determined based on the proportion of reactions that presented absolute flux changes above 0.01 mmol/gDW/h relative to the respective flux in the reference model where no constraints were set on lower/upper bounds. This value was conservatively considered based on the observation that FBA-based approaches often use 0.001 mmol/gDW/h as threshold for identifying reactions that have fluxes  and non-essential exchange reactions, and compared to a reference output without applied constraints on lethal neither exchange reactions. Only solutions on flux variation that achieve ≥90% of the reference solution were considered. Using the minimum and maximum fluxes determined for each reaction, we computed the mean and ranges for all reactions in each subsystem.Escherichia sp. under aerobiosis. A biomass flux threshold of 10-5 was defined as minimum to consider cell growth.We have employed a novel approach which allowed us to identify which are the minimal sufficient nutrients that when combined are capable of providing cellular growth when uptaken by the organism. In order to identify which nutrients are on the first line promoting cellular growth under environmental limited conditions, the target reactions of this approach were non-essential exchange reactions. No constraint was applied for single essential exchange reactions to ensure that growth inhibition was not due to the block of required essential nutrients. Cellular intake through non-essential exchange reactions was blocked with lower bounds set to zero. Based on FBA methods, non-essential exchange reactions were blocked one-by-one, two-by-two, and three-by-three. Future work should test how this approach compares with existing methods for determining minimum growth conditions e.g., . This waSupplementary Table S7). Thus, the predicted maximal growth  was used as lower bound constraint for biomass together with minimal media conditions and under anaerobic conditions (with oxygen exchange constrained to zero in both models).We developed a novel approach to assess the contribution of each bacteria for nutrient production and consumption under the maximum growth rate permitted under minimum media conditions. Specifically, we determined the maximal rate of secretion or intake of each metabolite when the organism is at its highest growth yield by individually setting each metabolite of interest as objective function at a time, therefore maximizing its production or consumption. Maximum organism growth was determined based on FBA under minimal media for each organism . These models comprise the entire known metabolic reaction networks of these organisms, and contain 1757, 1095, 1125, and 1057 reactions, and 1267, 730, 592, and 1313 genes, respectively. Using the FAST-SL algorithm  on these four organisms. Through this approach, we revealed the influence of an inhibited  reaction on the metabolic network. This allowed for the identification of single essential reactions , and those combinations of reaction pairs that become lethal when blocked simultaneously but not individually . In total, this represents between 559,153 to 1,544,403 different conditions (including single and double reaction combinations) tested.To assess growth requirements of ed AGORA  models flgorithm  based onEscherichia sp., I. bartlettii, A. muciniphila, and S. variabile respectively displayed 211, 153, 142, and 130 exchange reactions. I. bartlettii was the bacteria with higher proportion of exchange reactions, while S. variabile was the organism with higher proportion of single lethal reactions. Escherichia sp. was the bacteria with lower proportion of essential exchange reactions . These four organisms commonly shared 46 single lethal reactions. A. muciniphila presented 182 organism specific single lethal reactions and 45 additional single lethal reactions shared with Escherichia sp. Among all exchange reactions, 10 single-lethal were shared by these four organisms: environmental exchange of calcium, chloride, carbon dioxide, copper, potassium, magnesium, manganese, sulfate, zinc, and ferrous (Fe2+) iron . Escherichia sp. did not present organism-specific essential exchange reactions, whereas I. bartlettii, S. variabile, and A. muciniphila respectively had 1, 2, and 6 single-lethal exchange reactions found only in these organisms. Both A. muciniphila and S. variabile presented shared single-lethal exchange reactions with I. bartlettii, where exchange of ferric iron (Fe3+) was essential in the three organisms. Exchange of vitamin B5 and tryptophan were essential exchange reactions found in I. bartlettii and S. variabile, whereas exchange of hydrogen phosphate is commonly essential in I. bartlettii and A. muciniphila.Additionally, this approach allowed for understanding the consequences of unavailability of environmental compounds  on cell growth by inhibiting transport reactions with the extracellular environment . A. muciniphila presented the highest proportion of double lethal reaction pairs and pairs that include at least 1 exchange reaction. I. bartlettii was the organism with higher number of organism-specific lethal reaction pairs with ≥1 exchange reaction, followed by A. muciniphila . There were no lethal reaction pairs (≥1 exchange reactions) exclusively shared by the two organisms. However, ornithine exchange comprised 7 and 4 organism-specific double lethal reactions in I. bartlettii and A. muciniphila, respectively. Among those combinations found together with ornithine exchange, the urea cycle was the only common pathway between the two bacteria, where arginine and proline metabolism are specific for A. muciniphila, and alanine and aspartate metabolism, pyrimidine synthesis, citric acid cycle are specific for I. bartlettii.When considering all possible pairs of combinations, +/NADPH became lethal when combined with riboflavin or diaminoheptanedioate exchange in I. bartlettii. In turn, intracellular reactions involving NADP+/NADPH together with environmental exchange of vitamin B5, the fatty acid laurate or thymidine were synthetic lethal reaction pairs in Escherichia sp., but not in I. bartlettii. Escherichia sp. displayed the lowest proportion of double lethal reactions, as well as the lowest proportion of double lethal reactions with ≥1 exchange reactions, and the lowest number of organism-specific lethal reaction pairs with ≥1 exchange reactions. Among these pairs with ≥1 exchange reaction which involved fatty acids, Escherichia sp. and A. muciniphila respectively displayed 20 of 21 shared reaction pairs involving laurate exchange and an intracellular reaction associated with fatty acid synthesis or oxidation. Simultaneous inhibition of acetate exchange and acetate kinase or phosphotransacetylase reactions were lethal in A. muciniphila.Intracellular reactions involving NADPS. variabile (three pairs) and I. bartlettii (four pairs). Lethal pairs involving nicotinamide mononucleotide (NMN) exchange were also found for S. variabile (three pairs) and Escherichia sp. (two pairs), where one pair involves nicotinate-nucleotide adenylyltransferase in both organisms. Reactions involved in hypoxanthine exchange and purine synthesis were found in 10 double lethal pairs exclusive of S. variabile.Several double lethal pairs involving nicotinate exchange were present in L-lysine exchange simultaneously with diaminopimelate decarboxylase reaction was the only lethal pair found in the four organisms. Reaction pairs including exchange of arginine, alanine, asparagine, aspartate, isoleucine, lysine, tyrosine, valine, thymidine, or thiamine (vitamin B1) were synthetic lethal in one or more organisms. S. variabile, A. muciniphila, Escherichia sp., and I. bartlettii respectively had 4, 2, 1 and 1 double lethal pairs involving 2 exchange reactions. Simultaneous inhibition of NMN exchange and nicotinate, or L-tyrosine coupled with glycyl-L-tyrosine, or phosphate paired with glycerol 3-phosphate and uracil paired with succinate became lethal in S. variabile. In A. muciniphila inhibiting the exchange of L-asparagine together with glycyl-L-asparagine or thiamin led to lethality. Notably, simultaneous blocking of exchange of oxygen with ferric iron (Fe3+) or nicotinate, respectively prevented growth in Escherichia sp. and I. bartlettii. While I. bartlettii is an obligate anaerobe (Supplementary Table S1), the observation that O2 exchange is present in this model could indicate that the model failed to describe its aerotolerance. However, the two following points indicate that the model predictions are robust to O2 availability. First, the Spearman correlation between model fluxes in presence vs. absence of O2 was very high . Second, the reactions catalyzed by antioxidants against reactive oxygen species (hydrogen peroxide reductase) showed activity in a model encompassing oxygen exchange, but not in its absence (Supplementary Table S3). We finally, removed oxygen exchange from the I. bartlettii model and repeated the lethality analysis for the entire reaction network. The comparison of synthetic lethality analysis under aerobic versus anaerobic conditions changes the number of single lethal reactions from 80 to 85, and from 124 to 171 lethal pairs (Supplementary Table S4), respectively. However, only one additional single lethal exchange reaction (methionine exchange) was identified in the I. bartlettii model. These observations reinforce the confidence in the predictions of the model in terms of environmental dependency or synthetic lethal reactions.The inhibition of Escherichia sp. displays 73 metabolic pathways, I. bartlettii displays 66, A. muciniphila has 68 and S. variabile displays 63, of which 54 are commonly present in the four organisms (Supplementary Table S5). Considering the entire metabolic network and sets of single, paired essential reactions, and non-essential exchange reactions individually and coupled in pairs, we computed the proportion of reactions that are altered in each pathway in comparison with each bacteria’s reference model, i.e., the model with no reaction blocking. To do so, we used FBA to identify flux distribution between pathways while maximizing for bacterial growth, i.e., “pathway sensitivity” to reaction blocking. Additionally and to complement this methodology, we employed FVA (Supplementary Table S6). We observed  that several pathways show significant alterations in >50% of their reactions. For instance, cholesterol  synthesis but not other reactions involved in cholesterol metabolism, were highly perturbed by essential reactions and partly by environmental exchange reactions in Escherichia sp. In turn, cholesterol metabolism was highly perturbed by essential and environmental exchange reactions under the same constraints in A. muciniphila but not in I. bartlettii and S. variabile.We investigated which pathways were mostly altered by single and double synthetic lethal reactions  and non-essential exchange reactions. Akkermansia muciniphila showed the most significant cellular pathway alterations in response to essential reactions including N-glycan synthesis, exclusive to this bacteria . Lipopolysaccharide (LPS) biosynthesis and nucleotide sugar metabolism were metabolic pathways highly perturbed in A. muciniphila and Escherichia sp., but not in S. variabile. I. bartlettii showed  substantial (>50%) alterations in metabolism of propionate, phenylalanine, alanine but no change in chloroalkane and chloroalkene degradation, a species-exclusive metabolic pathway. In turn, metabolism of butyrate and vitamin B2 showed substantial (>50%) alterations in metabolism in Escherichia sp. . Oxidative phosphorylation, a metabolic pathway found in the four organisms, was highly perturbed in S. variabile when any of its lethal or non-essential exchange reactions were inhibited. The same was observed in I. bartlettii, however mainly when double lethal reactions were blocked simultaneously. The metabolism of sulfur and energy were equally highly perturbed in S. variabile in response to inhibition of any of its essential or non-essential exchange reactions, while sulfur metabolism was poorly affected in other species and energy metabolism was only considerably perturbed in A. muciniphila.Escherichia sp. and 30 reactions in A. muciniphila), butyrate metabolism (9 reactions in I. bartlettii and Escherichia sp.), or phenylalanine metabolism (25 and 10 reactions in Escherichia sp. and S. variabile). Importantly, these trends were also observed when blocking single or pairs of essential exchange reactions, and for many of the non-essential exchange reactions, indicating the strong effect of nutritional availability in these pathways. Metabolic pathways were more sensitive to inhibition of essential  reactions that are intracellular and environmentally exchanged, comparatively to non-essential exchange reactions in the four organisms. FVA showed qualitatively similar results, though it indicates that more pathways were sensitive to perturbations than FBA.While some of the pronounced changes exhibited by some pathways reflect their small size , other pathways showed substantial changes though they comprise more reactions. This is the case of LPS biosynthesis . We questioned if pathway utilization may differ not only in response to nutrients but also in response to oxygen availability . Such differential nutritional responses may present an added selective advantage over anaerobic bacteria.Bacteria present different growth requirements, and thus may present selective advantages and disadvantages. Among the four bacteria tested here, all are strict anaerobes with exception to Escherichia sp., and determined the minimum growth requirements for the four organisms. We developed an approach complementary to those used above for assessing pathway sensitivity . Briefly, from the entire metabolic reaction network, we selected those involving exchange reactions and blocked all non-essential single exchange reactions identified above, whereas the single-lethal exchange reactions identified above are unblocked. All non-lethal exchange reactions are firstly blocked, and then unblocked one by one, two by two, etc. The synthetic lethality approach employed above optimized for cell growth, and thus allowed for identification of those exchange reactions that most penalize cell growth and whose blocking prevents cell growth using otherwise unconstrained models. In turn, the approach used here optimizes flux distribution in a tightly constrained model and permits identifying those combinations of exchange reactions that, when simultaneously unblocked, promote cell growth. This additionally permits identifying those pathways showing the greatest changes while conferring the greatest increments to cell growth by comparison with the reference fully unconstrained model.We investigated pathway response to oxygen availability in Escherichia sp. when combinations of two-by-two non-essential reactions were allowed. In combinations of three exchange reactions, oxygen and iron exchange is always present as one of the necessary reactions for growth (results not shown). Unblocking combinations of two non-essential reactions in A. muciniphila and S. variabile provided significant cellular growth. Notably, employing this approach yielded no growth in I. bartlettii using combinations of 1, 2, and even 3 unblocked non-essential exchange reactions (results not shown), suggesting that more nutrients must be available in order to permit growth. The Escherichia sp. model shows growth with as few as 12 exchange reactions , whereas the model for I. bartlettii does not grow with 18 exchange reactions . Additionally, A. muciniphila shows growth with only 23 exchange reactions , while S. variabile shows growth with only 14 exchange reactions .None of the four gut bacteria under study displayed cellular growth when unblocking any single exchange reaction. Only pairs comprising either oxygen or iron exchange resulted in growth for Escherichia sp. displays substantial pathway changes  in LPS, squalene and cholesterol biosynthesis, nucleotide sugar metabolism , purine and butyrate metabolism , as well as metabolism of histidine, tryptophan, valine, leucine, isoleucine, aspartate, alanine, and lysine (>70%). Glutathione and nitrogen metabolism tend to be mostly unchanged (<2%). Additionally, we observed that Escherichia sp. responds differently depending on oxygen availability. As expected from aerobic growth, ROS detoxification is significantly active when O2 exchange is unconstrained versus no changes when Fe3+ is unblocked but O2 exchange is blocked . Slight increased fluxes are also identified under aerobic conditions in energy metabolism (mean pathway reaction changes >21% aerobic vs. 16% anaerobic), pentose phosphate pathway (57 vs. 50%), starch and sucrose metabolism (14 vs. 3%), and metabolism of vitamins B6 (64 vs. 57%) and B2 (71 vs. 64%). Oxidative phosphorylation shows substantial increases (>66%) in some entries under aerobic conditions, but not under anaerobic conditions, when exchange of some compounds and amino acids is unblocked . In turn, sulfur metabolism (18 vs. 25%) and glyoxylate/dicarboxylate metabolism (29 vs. 35%) are slightly altered under anaerobic conditions. Practically all nutrients confer highest growth rates under aerobic than anaerobic conditions, with exception to nitrate exchange that elicits similar growth rates under aerobic and anaerobic growth. NMN, glutamate, aspartate, nitrate, and nitrite exchange confer the most substantial increases to growth under aerobic and anaerobic conditions . Interestingly, tyrosine, phenylalanine, tyramine, and vitamin B6 uptake allow for cell growth under aerobic but not anaerobic conditions.Supplementary Table S7, see section “Materials and Methods”), since the human intestinal environment is predominantly anaerobic , while both Escherichia sp. and I. bartlettii revealed to compete for ribose, deoxyribose and cysteinylglycine, as well as for aspartate and phosphate, which were both products of S. variabile .We also determined how amino acids, short chain fatty acids, and other nutrients important for host and bacterial metabolism  were pronaerobic . We obseA. muciniphila and S. variabile , while consumed by Escherichia sp. and I. bartlettii. Phenylalanine produced by the three other bacteria may be consumed by I. bartlettii, which in turn is predicted to secrete phenylacetate. Proline and glutamine were produced by A. muciniphila, I. bartlettii and S. variabile and consumed by Escherichia sp. Finally, Escherichia sp. was involved in the production of the gasses hydrogen and, together with A. muciniphila, both may produce hydrogen sulfide; whereas I. bartlettii produced methanethiol . Because Escherichia sp. is a facultative aerobe we repeated these analyses under aerobic conditions, and observed some differences in comparison with the results under anaerobiosis, specifically in the secretion of amino acids  and nucleobases .Potential commensal behavior may occur, since some of these compounds may be produced by Altogether, our results demonstrated that the four bacteria displayed substantial differences in substrate requirements for growth, as well as metabolic responses to nutritional changes in the environment. As a consequence of their metabolisms, these four organisms differently contributed and competed for nutrients in the gut, which among those were short chain fatty acids, amino acids, and gasses.Escherichia spp., A. muciniphila, S. variabile but lower of I. bartlettii , and nucleotide sugar metabolism.The mechanism of action of metformin on glucose metabolism was suggested to be mediated through the bacterial production of SCFAs, where local LPS-triggered inflammation and lower intestinal lipid absorption are side effects of the drug . A. muciEscherichia sp. growth both under anaerobic and aerobic growth are NMN and nitrate exchange, as well as tyrosine, phenylalanine, and tyramine uptake under aerobic conditions. NMN exchange is involved in the coenzyme nicotinamide adenine dinucleotide (NAD) salvage pathway I  uptake prevents growth of Escherichia sp, whereas ferrous iron (Fe2+) uptake is by itself essential. Although iron and other metals may be toxic due to radical formation by reaction with reactive oxygen species , it is reaction . In the c matter , and itsc matter . Althouglsewhere , and is in silico observations suggest commensal and competing behavior in the production of extracellular compounds including short chain fatty acids and amino acids, among which the metabolism of Escherichia sp., A. muciniphila, S. variabile, and I. bartlettii may explain the observed features in metformin-treated type 2 diabetes patients. These observations remain to be experimentally tested, though the above observations indicate good agreement with previously known features of these organisms; multiple studies have shown that growth predictions by FBA and gene essentiality prediction are in good agreement with experimental observations (Overall, our rvations . MicrobiAM conceived and supervised the study. DR, RB, GB, and CZ designed the experiments. DR performed the experiments. SS assisted in model acquisition and refining of the model. DR and RB analyzed the data and wrote the manuscript. All authors have revised and contributed to the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods. Genome-scale models of human metabolism have facilitated numerous exciting discoveries regarding human physiology and therapeutics. The accuracy of results from such studies requires that models capture the tissue or cell-type specific metabolism. In hopes to obtain accurate models, several algorithms have been developed to extract cell- or tissue-specific metabolic models. Each algorithm has provided useful insights into the metabolism of specific cell and tissue types. However, since each of these methods use different assumptions to guide reaction inclusion and removal, they result in considerable differences in size, functionality, accuracy, and ultimate biological interpretation, even when using the same data set. To overcome this, the enclosed research proposes an approach to infer the functionalities of a cell or tissue from omics data, and then protect these functions to guide the construction of a context-specific model. Through this study, we highlight the value of using experimental data to help infer the set of metabolic functions that should be included in a model, in an effort to obtain greater consensus across existing extraction algorithms. This study further provides guidelines for the development of the next-generation of data contextualization methods. Genome-scale metabolic models (GeMs) have been widely used for model-guided analysis of large omics datasets, since they provide cellular context to these data by establishing a mechanistic link from genotype to phenotype. GeMs include all reactions in an organism. Since not all enzymes are active in each cell type or culture condition, algorithms have been developed to build context-specific models using omics data to recapitulate the metabolism of specific cell types under specific conditions ,2. TheseThe poor consensus in generated models requires increased caution in the interpretation of model-derived hypotheses of how metabolism is used under specific environments. Indeed, most generated models, upon construction, will be missing known metabolic functions and this varies considerably for models built using different approaches . To gainTo overcome this, we propose an approach to infer the functionalities of a cell or tissue from omics data, and then protect these functions to guide the construction of a context-specific model. To this end, we curated and standardized published lists of metabolic tasks ,14, resuWe built models from Recon 2.2  and iHsaTo assess the relative impact of algorithm and data source on model content, we conducted a principal component analysis (PCA) of the reactions in all models for each reference GeM. As observed previously ,11, the Model reaction content is often evaluated to compare context-specific algorithms. Recently, approaches to benchmark models with their functionalities have been proposed ,12,21. CMetabolic tasks can be used to compare the performance of models extracted from different reference GeMs. As observed at the level of the reaction content, the extraction method strongly influences the model functions , the number of reactions included in the extracted model was not considerably influenced by the protection of the metabolic task, while the number of active tasks clearly increases Fig C in . We perfBeyond model content, we evaluated how the protectionist approach influenced model predictions. Thus, we analyzed the influence of protecting inferred tasks on gene-essentiality predictions (i.e. prediction of the genes whose knockdown leads to a growth impairment). We systematically deleted each gene in all generated models, and then used flux balance analysis to test models for normal or impaired growth. Gene deletions associated with impaired growth are considered as essential. We observe that task protection reduces the number of genes predicted to be essential for all thresholds considered  for the various extraction methods used on both reference models .We further evaluated the accuracy of essentiality predictions by comparing these to CRISPR-Cas9 loss-of-function screens for 20 cell lines –27. In tTo further assess the identity of these true positives provided by task protection, we compared our model simulations to a collection of known anti-cancer drug targets  30]. In. In30]. Here we generated hundreds of models for 44 cell lines from the NCI-60 panel using multiple MEMs and two reference GeMs (Recon 2.2 and iHsa) using standard approaches or by protecting metabolic tasks that have been directly inferred from transcriptomic data. We presented a comparative analysis of these two sets of models. As previously observed, the analysis of the first set of extracted models  indicated that the choice of model extraction algorithm significantly influenced the model content at the reaction level ,2,11. ThWe provided here a curated list of 210 tasks that were used to compare the functionalities of the extracted models. The evaluation of metabolic tasks has emerged as a valuable practice in metabolic modeling studies ,22,32–35a priori knowledge or assumptions of the metabolic functions that should be included when building a cell or tissue specific model. Therefore, this list of metabolic tasks provides a framework for modelers to develop more physiologically accurate models by inferring the activity of metabolic tasks directly from omics data. Thus, key reactions that need to be included in a model can be protected, without requiring one to know what the cell does. However, the resulting models should still be curated to evaluate expected functionalities, such as for example auxotrophies.One challenge in the evaluation of metabolic models is the difficulty of comprehensively defining metabolic functions from a manual search of the literature. Thus, another strength of our approach is that it decreases the need for Our protectionist approach can be implemented with diverse model extraction algorithms since it only requires the algorithms to prevent the removal of active metabolic tasks during the extraction process. However, some algorithms will require modifications to ensure the protection of all reactions related to a task. Current implementations of the GIMME-like and iMAT-like families do not favor this type of protection. By minimizing flux through reactions associated with low gene expression, GIMME-like extraction methods may remove low expression reactions one would want to retain for a validated metabolic task if there are high expression reactions that allow for growth. The iMAT-like methods are similar as they rely on finding an optimal trade-off between removing reactions associated with low gene expression, and keeping reactions whose genes/enzymes are highly expressed. Thus, modified implementations of these algorithms will be needed to allow the protection of reactions based on experimental observations. Finally, this approach can also be extended to any type of network complexity reduction that have been developed in the metabolic modeling field, such as the MILP-based approaches developed to tailor models based on exometabolomic data ,38.In our work, we also demonstrated that the models built with the protectionist approach are able to better capture cell-type specific metabolism and accurately predict many essential metabolic genes. Thus, these models may be invaluable for drug development strategies. The emergence of experimental techniques to assess the genetic vulnerabilities of a cell  allows researchers to identify sets of genes that should be essential for growth maintenance. These essential genes can further be used to evaluate the capacity of models to represent the interdependence between down-regulation of a gene and the concomitant impairment of growth. Thus, models can be used for interpreting the mechanisms underlying metabolic vulnerabilities that may be invaluable for new drug discoveries. Furthermore, many of the metabolic changes occurring in certain diseases, such as cancer, can be captured by the current list of tasks. Since many of the metabolic tasks are shorter paths, cases where the metabolic flux is redirected due to disease-related metabolic perturbation might be captured by the specific collection of tasks computed using our method. Finally, for the rare cases where a mutation to a specific enzyme leads to a change in the metabolic reaction catalysed by the enzyme change, such as mutations in isocitrate dehydrogenase leading to the production of oncometabolites –41. WithFinally, the list of tasks presented in this study was constructed based on existing repositories. However, a community effort could be undertaken to extend the scope and the definition of these metabolic functions, including the development of tasks seen in plants and microbes and tasks associated with secondary metabolism and microbial gene clusters ,43. FurtConclusively, context-specific extraction methods are powerful approaches that provide insights in the metabolic state of a cell in specific environments. However, the underlying assumptions used to tailor the GeM based on omics data vary across algorithms, with the consequence that drastically different models can be obtained based on the same data. The poor consensus in generated models may limit the use of context-specific methods for data-driven hypotheses. The definition of metabolic tasks can help with these concerns. Our curated list of tasks and computational framework will allow users to infer metabolic functions directly from transcriptomic data using the whole genome-scale model, and drive the development of improved context specific models. Such models will pave the way toward a better consensus between existing context-specific extraction algorithms, and facilitate the application of models for novel biomedical and engineering applications.th percentile of the overall gene expression value distribution and lower or equal to the 75th percentile. The gene score is computed as follows:RNA-Seq data for the 44 cell lines from the NCI-60 panel were downloaded from . We procminimum expression value amongst all the genes associated to an enzyme complex (AND rule) and the maximum expression value amongst all the genes associated to an isozyme (OR rule) [These gene scores are mapped to the models by parsing the GPR rules associated with each reaction. The gene score for each reaction is selected by taking the OR rule) . Note thOR rule) .createTissueSpecificModel available in the COBRA Toolbox 3.0 [Model extraction methods (MEMs) employ diverse algorithms to extract cell line- or tissue-specific models from a GeM. The MEMs we have considered can be categorized into three families: “GIMME-like” , “iMAT-like”  and “MBA-like” , as proposed previously . The GIMlbox 3.0 . We descFASTCORE [—The core reactions set (options.core) is determined by all the reactions associated to a gene score superior to 5log(2). Note that the biomass reaction was added to the core reactions sets.FASTCORE —The coreGIMME [—The implementation of GIMME requires two parameters: the gene scores (options.expressionRxns) and a threshold value, the reactions associated with a gene score value below this threshold will be minimized (options.threshold = 5log(2)). Note that we manually attributed a gene score of 10log(2) to the biomass reaction to ensure its inclusion.GIMME —The impliMAT [—Three parameters need to be provided to run iMAT: the gene scores (options.expressionRxns), a lower threshold value  and a upper threshold value . To simplify the comparison across algorithms, we set both thresholds to the same value: options.threshold_lb = options.threshold_ub = 5log(2), as done in a previous benchmarking study (1). Note that we manually attributed a gene score of 10log(2) to the biomass reaction to ensure its inclusion.iMAT ,47—ThreeINIT [—The implementation of INIT requires attributing positive weights (options.weights) to each reaction with high expression and negative weights for the ones with low expression. All the reactions associated with a gene score below 5log(2) have been assigned a weight of -8 while the weights of remaining reactions were defined as the ratio between the gene score for each reaction and 5log(2). The weight associated with the biomass reaction was put to the maximum of obtained reaction weights.INIT —The implMBA [—The implementation of MBA requires the definition of two set of reactions: high confidence (options.high_set) due to their expression and others with medium confidence (options.medium_set). The set of reactions with high confidence is defined as reactions with a gene score above the 75th percentile of the distribution of all gene scores and the medium confidence set by all the reactions presenting score above 5log(2) and below the 75th percentile of the distribution of all gene scores. Note that the biomass reaction has been manually added to the high confidence set of reactions.MBA —The implmCADRE [—The implementation of mCADRE requires a score quantifying how often a gene is expressed across samples (options.ubiquityScore) and a literature-based evidence score (options.confidenceScores). Since the confidence score identification used in the original paper is difficult to transpose in this study, we did not define the confidence score as preformed in the tutorial presenting the implementation of mCADRE in COBRA Toolbox 3.0 (46). Furthermore, as the gene scores are computed based on the knowledge of the gene expression of a gene across all samples, we used the gene scores as ubiquity scores.mCADRE —The implThe curation has been done by first taking the union of previously published lists of metabolic tasks ,14. We rmetabolic task as a nonzero flux through a reaction or through a pathway leading to the production of a metabolite B from a metabolite A. The metabolic capacity of the network was demonstrated by testing nonzero flux values for these metabolic tasks. For each of the simulations, a steady-state flux distribution was calculated. Each metabolic task was optimized individually by choosing the corresponding reaction in the model, if present, as objective function and maximized the flux through the reaction”.“In parallel, Agren and coworkers presented an alternative framework to compute the metabolic tasks present in a model within their RAVEN toolbox [In its original version, Thiele and coworkers (2013)  define a toolbox . They decheckMetabolicTasks, has been contributed to the COBRA Toolbox3.0 [We also propose to define a metabolic task as the capacity of producing a defined list of output products when only a defined list of input substrates is available. However, we modified the way to implement it from the RAVEN toolbox. Instead of relying on the relaxation of the steady-state assumption, we take an approach more similar to that proposed by  by imposolbox3.0 .We tested the list of tasks using published genome-scale models of human ,17,22,23minimum expression value among all the genes associated to an enzyme complex (AND rule) and the maximum expression value among all genes associated with an isoenzyme (OR rule)). Therefore, each reaction involved in a task is associated with a reaction activity level (RAL) that corresponds to the preprocessed gene expression value of the gene selected as the main determinant for this reaction. Finally, the metabolic score can be computed as the mean of the activity level of each reaction:We developed a computational framework for attributing a score to each metabolic task in order to extend the application of the concept beyond the model benchmarking scope. If a task successfully passes in a model, one can compute the list of reactions associated with this task and, in doing so, access the list of genes that may contribute to the acquisition of this metabolic function based on the GPR rules. To this end, we used the parsimonious Flux Balance Analysis (pFBA) algorithm to define the set of reactions and associated genes required to pass a task within a specified model . Thanks Doing so, a metabolic task will be considered as active if its MT score has a value greater than 5log(2). The list of active metabolic tasks for each of the 44 cell lines from the NCI-60 panel is available in We used the list of active metabolic tasks  to deterFASTCORE—The set of reactions associated with the metabolic tasks defined as active based on the transcriptomic data has been manually added to the core reactions set (options.core).GIMME & iMAT—A gene score of 10log(2) (options.expressionRxns) has been attributed to all the reactions associated to the metabolic tasks defined as active based on the transcriptomic data.INIT—The weights (options.weights) for all reactions associated with the metabolic tasks defined as active based on the transcriptomic data were put to the maximum of obtained reaction weights.MBA—The reactions associated with the metabolic tasks defined as active based on the transcriptomic data have been manually added to the high confidence set of reactions.mCADRE—A ubiquity score (options.ubiquityScore) of 1 has been attributed to all the reactions associated to the metabolic tasks defined as active based on the transcriptomic data.2 scaled to percentages. The same procedure was used to perform the PCA on the model functionalities except that the binary matrix of reactions was replaced by the binary matrix representing the list of metabolic tasks that are successfully passed in each extracted model. The attributes of all extracted models  are available in For the reaction PCAs, a binary matrix is constructed in which each row represents an extracted model and each column represents a reaction, with each element representing the presence (1) or absence (0) of a reaction in a model. Reactions in all or no models were removed from the matrix. Similarly for the metabolic function PCA, the matrix had each row as an extracted model and each column as a metabolic task, with each element in the matrix representing if the task is present (1) or absent (0) in a model. For the PCAs, the matrix was centered to have zero mean within each row. PCA was done on this matrix. The variance explained by the different factors  within each of the principal components is calculated as follows. Within one factor, the maximum Pearson correlation coefficient (R) of the component scores and categories is calculated across all possible orderings of the categories. Reported is the RsingleGeneDeletion.m [To predict gene-essentiality, FBA was used to optimize biomass production following the removal of each reaction in the cell line-specific models that would be affected by gene removal based on the GPRs. The function used to perform this deletion analysis is available in COBRA Toolbox 3.0, letion.m . To testletion.m –27. In tletion.m . A 1-taiS1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.


Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of  Depending on the aims, such inoculants should maintain and reproduce for longer-term inside the resident community or only deploy their metabolic capacity transiently.4Microbiome engineering is an upcoming discipline that aims to manipulate, complement or restore the functionality of existing damaged communities, e.g., contaminated soils, by adding specific new metabolic capabilities.5 However, even the simplest inoculations and N + 1-strategies are rarely effective because it is insufficiently understood what inoculants need to establish successfully within a (new) existing community, and how they need to adjust their physiology to meet the requirements of the new environment and degrade the desired toxic compound(s). Modeling strategies based on the integration of a variety of (nowadays more easily) accessible condition-specific omics data, would help to better understand and predict how cellular regulation and physiology at different growth conditions and environments interplay. However, the impact and advantage of such integrative analysis are not yet explored to its full extent.6 We propose and demonstrate here that combining comprehensive genome-wide transcriptomics, exometabolomics and metabolic modeling can better predict physiological adaptation.Inoculation of preselected strains has been widely practised for pollutant bioaugmentation, using bacteria with particular metabolic capabilities that enable them to efficiently degrade and grow on common pollutants such as toxic aromatic compounds.7 GEMs encompass metabolites, metabolic reactions, and genes coding for the enzymes catalyzing the reactions. Together with FBA, GEMs predict steady-state fluxes,9 and therefore, they can predict cellular physiology. While the genome specifies the complete set of biochemical reactions which the cell can potentially carry out, the actual enzymatic capacity at each physiological condition is orchestrated by regulatory networks in the cell. GEMs do not explicitly consider regulation, whose effects are better reflected in the global transcriptome and the metabolome.14 FBA approaches have been extended with RNAseq and metabolomics data to capture cell regulation and more accurately describe cellular metabolic behavior.15 For example, transcriptional regulation of gene expression has been linked to GEMs, either by taking into account the absolute expression values, scoring genes and subsequently reaction fluxes as active or non-active based on their expression,18 or by incorporating relative gene-expression.18 Use of relative gene expression is assuming that the relative changes between two conditions correlate with the resulting differential flux profiles. Both approaches can lead to condition-specific GEMs that are more effective for inferring the actual biochemical activity and the observed physiology of the microorganism.Metabolic modeling has largely advanced through the development of GEnome-scale Metabolic models (GEMs) and constraint-based modeling techniques such as Flux Balance Analysis (FBA). GEMs can be built from the annotated genomes and they describe an organism’s metabolism as completely as possible, linking genotype to metabolic phenotypes.Pseudomonas veronii 1YdBTEX2. Strain 1YdBTEX2 is capable of degrading a variety of mono-aromatic hydrocarbons such as benzene, toluene, ethylbenzene, and m- and p-xylene (BTEX).22 The ability of P. veronii 1YdBTEX2 to grow in contaminated environments makes it a promising candidate for rational complementation of microbial communities in contaminated soils.23 Based on an available manually curated high-quality genome,22 we reconstructed the first GEM for P. veronii 1YdBTEX2 (iPsvr). Genome-wide transcription changes and exometabolome compounds were measured during growth of P. veronii on toluene, in exponential and in stationary phase. Transcriptome and exometabolome data were integrated into the iPsvr using the recently developed tool REMI (Relative Expression and Metabolomics Integrations).19 REMI integrates gene expression, thermodynamic, and metabolite abundance data into metabolic models, and then maximizes the consistency between differential gene expressions, metabolite concentrations and levels of metabolic fluxes for any given pair of conditions. Therefore, by building a context-specific model for each condition, it allows elucidating metabolic states and predicting cellular phenotypes and flux distributions of condition-specific differences e.g., wild type vs mutation. Two obtained metabolic models representing exponential and stationary physiologies were then used to evaluate growth rates and the production of biomass precursors, and model predictions were compared to the experimentally observed values. Although the temporal variations of the growth rate cannot be predicted using GEMs,24 we showed that introducing the additional regulatory information from gene expression and metabolomics data into GEMs allows for consistently estimating growth rates at different growth phases. Finally, we incorporated into iPsvr previously published transcriptomics data of P. veronii transits from liquid culture to sand22 to understand its physiological adaptation in soil. Our work shows strong consistency of model outputs with the experimental data, manifesting that integration of condition-specific omics data into a curated GEM constitutes a major improvement for prediction of metabolic reprogramming during adaptation.As a study system for predicting physiology from an integrated GEM-transcriptome-metabolome approach, we here use P. veronii strain 1YdBTEX2 (iPsvr), gap-fill missing parts of the metabolism (compounds and reactions), complement genome annotation using the transcriptomics and exometabolomic data and estimate the steady-state growth rate using FBA  Quantify the cellular states at each phenotype, by genome-wide transcriptomics and exometabolomic data from spent media composition Fig. ; (2) ConFBA Fig. ; and (3)P. veronii growing on toluene, iPsvr-EXPO and iPsvr-STAT, which correspond to the exponential and the stationary phase, respectively, and employ them to predict quantitative and dynamic readouts of P. veronii metabolism in the two conditions  generate two growth-phase-specific models of ons Fig. ; and , 4 h  and 24 h  after inoculation.Whole-genome gene expression profiles and metabolite formation in the spent medium were analyzed in P. veronii 1YdBTEX2 on toluene, we identified 4h and 24 h to be representative time points for P. veronii 1YdBTEX2 metabolic activity at the early exponential growth and late stationary phase, respectively. Genome-wide gene expression was quantified by mapping Illumina 100 nucleotide long single–end sequencing reads from deeply sequenced cDNA libraries to the protein coding genes in P. veronii genome (read numbers indicated in Table S1). For each sampling time point, four replicates clustered closely together, with slightly higher variability observed among the T24h replicates . Out of these 1241, 300 (21%) were significantly differentially expressed in EXPO vs. STAT phase cells (FDR < 0.05) (Table S2). The transition to STAT phase in bacteria is characterized by growth arrest in response to several factors, such as carbon and nutrient depletion, the accumulation of toxic compounds and environmental stress, which decrease ribosomal activity and therefore protein synthesis. This is consistent with toluene becoming depleted in STAT phase. As anticipated, enriched GO terms for the category “Biological Process” among the differentially expressed genes between EXPO and STAT included “protein folding” (GO:0006457), “tRNA aminoacylation for protein translation” (GO:0006418), “intracellular protein transmembrane transport” (GO:0065002) and “regulation of transcription, DNA-templated” (GO:0006355) (Table S3), thus indicating cells to be more active in EXPO phase, as expected. Consistent with nutrients becoming depleted in STAT phase, the terms “benzoate catabolic process via hydroxylation” (GO:0043640) and “tricarboxylic acid cycle” (GO:0006099) (Table S3), important for aromatic compound catabolism, were under-represented in the STAT phase transcriptome.The untargeted metabolomic analyses of the spent medium detected 1630 (positively charged) and 3509 (negatively charged) distinct ion species or metabolite features. Unsupervised principal component analysis yielded three distinct clusters indicating metabolic phenotype differentiation over time, from inoculum to stationary phase Fig. . Similar25 showed a significant increase in the spent media over time of the majority of the metabolites implicated in the toluene and benzene degradation pathways and central carbon pathways, including glycolysis, purine and pyrimidine metabolism and amino acid metabolism  matchingism Fig. . This imism Fig. . This suism Fig. .P. veronii genome22 by using the RAVEN toolbox26 .A draft GEM was generated from the curated x26 Fig. . The draP. veronii 1YdBTEX2 incubated with 26 different carbon substrates to further curate and refine the model. In comparison to a no-carbon control, Biolog indicated growth of strain 1YdBTEX2 on 15 out of the tested 26 substrates as the sole carbon source (Table S4). We confirmed the experimental observations using iPsvr for 20 substrates, whereas the model could not grow on the remaining six substrates . We hence gap-filled the model to include these additional metabolic tasks (Table S4). This gap-filling improved the metabolic network connectivity, and it resulted in a more versatile model that can be used for studies on an extended range of substrates.We used Biolog growth data of P. veronii genes with RAVEN but had a lower score than the ones chosen as a baseline for the draft reconstruction of iPsvr. The gap-filling algorithm introduced in total 71 new metabolic reactions together with their corresponding 28 genes to iPsvr  to decompose the iPsvr metabolic network into its main subnetworks of 1370 reactions and 23 blocked reactions/pathways of different lengths, with the longest blocked pathway consisting of seven reaction steps (Table S5). Out of 191 blocked reactions/pathways, we identified those associated with differentially expressed genes between the two growth conditions and the ones whose participating metabolites were present in the exometabolomic data. The identified reactions/pathways were next unblocked by gap-filling as described in the Methods section. Interestingly, we identified gap-filling reactions that had been annotated to P. veronii strain 1YdBTEX2 in minimal medium with toluene as the sole carbon source was in the range of 0.25 h−1 to 0.35 h−1. In absence of proper empirical measurements of toluene uptake rate, we estimated a maximal uptake rate of toluene from a constrained maximal specific growth rate to 0.35 h−1 in the thermodynamics-based flux balance analysis (TFA).32 We computed that a toluene uptake rate of 0.45 mmol gDW–1 h–1 is required to achieve this growth rate.The experimentally determined maximal specific growth rate of P. veronii 1YdBTEX2, we manually curated the predicted toluene -3-methylcyclohexa-3,5-diene-1,2-diol to 3-methylcatechol, which is further degraded, according to the KEGG pathway database, through two pathways until the central carbon metabolism compounds are reached . Note that the default e-value in RAVEN is 10−50, which is why the reaction was not initially captured in the model from the genome annotation. Therefore, the missing reaction (2.8.3.6) was added to iPsvr, and the toluene pathway , added to iPsvr and could thus link both metabolites. The next step (1.14.13.-), yielding homogentisate  was used to integrate the relative gene-expression and metabolite abundance data between EXPO and STAT into iPsvr. We explored three scenarios, depending on the type of data integrated into iPsvr: (i) REMI-TGex for the integration of relative gene-expression data only; (ii) REMI-TM for the integration of metabolomics data only; and (iii) REMI-TGexM for the simultaneous integration of both the gene-expression and metabolite abundance data into a thermodynamically curated model. The “T” in the three methods stands for the inclusion of thermodynamic constraints. Contextualized metabolic models, iPsvr-EXPO and iPsvr-STAT, coherent with omics data obtained at the exponential and stationary growth phase, respectively, were built, and the growth rates were again simulated  was insufficient to predict a reduction in growh rate at the STAT phase. Both REMI-TGex and REMI-TGexM correctly estimated near-zero growth rates of 0.0056 h-1 and 0.0028 h-1, respectively, for the stationary phase . In this study, the alternative solutions allow investigating different flux distributions that result in the same simulated growth rate. Since REMI-TGexM can integrate the highest number of available experimental data as constraints Table , we usedREMI analysis revealed that there were 40 alternative solutions with the maximal consistency score (MCS) of 279 . Biosynthesis of amino acids (KEGG map01230) with 41 deregulated reactions and metabolites scored as the most deregulated pathway. This was followed by Carbon metabolism (KEGG map01200) with 28 and Purine metabolism (KEGG map00230) with 21 deregulated reactions and metabolites. The complete list of the deregulated pathways is provided in Table S7. This analysis suggests that the deregulation of central carbon meatbolism and amino acid synthesis plays a critical role in growth arrest in the transition from exponential to stationary phase.We used the KEGG mapper tool to study those pathways that deregulate during the transition from exponential to stationary phase.To further understand the underlying mechanisms of growth reduction when cells transit from EXPO to STAT, we identified the biomass precursors that may become limiting in the stationary phase, therefore leading to growth arrest. The 62 biomass precursors were grouped into seven groups of biomass building blocks (BBBs), namely carbohydrates, cofactors and vitamins, DNA nucleotides, lipids, minerals, amino acids, and RNA nucleotides. For each metabolite, we calculated the fold-change of their maximal production between EXPO and STAT using REMI-TGexM (Table S8).For the BBB analysis, we used REMI-TGexM and investigated the variation of maximum possible BBB production across all the 40 alternative solutions  and 0.44 h−1 to 0.161 ± 0.002 h−1 (succinate). This suggests that cells have to adapt to soil as their new environment and have to reprogram their physiology before resuming growth.Integrating previously obtained transcriptomic data from liquid versus sand exposureP. veronii, we performed the analysis of alternative solutions. In the case of succinate, among the 195 alternative solutions, 152 reactions were in common and 88 reactions were varying between them, whereas for toluene, 134 reactions were common and 97 reactions varied across 84 different alternatives (Table S9). The KEGG mapper tool34 was used to identify the most deregulated pathways within the transition from liquid to sand, for both succinate and toluene (Table S9). Similar to the previous pathway analysis of transition from EXPO to STAT, the two most deregulated pathways were Carbon metabolism (KEGG map01200) and Biosynthesis of amino acids (KEGG map01230), for both Toluene and Succinate (Table S9). Fatty acid metabolism (KEGG map01212) was the third most deregulated pathway (with 20 deregulated reactions) for toluene, and the fifth (with 8 deregulated reactions) for succinate. Interestingly, fatty acid metabolism was not amongst the 25 top ranked deregulated pathways in transition from EXPO to STAT. Another difference from the EXPO to STAT pathway analysis was the deregulation of 2-Oxocarboxylic acid metabolism (KEGG map01210) as the third and the sixth deregulated pathways in toluene and succinate sand exposures, respectively. This suggests that although lower growth is observed in both transitions, i.e., from EXPO to STAT and from liquid to sand, the deregulated pathways underlying the growth reduction are not necessarily the same.To understand how the transition from liquid medium to sand impacts the intracellular metabolic fluxes and growth of We further investigated the production of BBBs in both growth environments (Table S10). In contrast to the EXPO to STAT transition results, in the case of cells exposed to sand, the production of the majority of precursors was higher in sand compared to liquid media . This suggests that although cells grow much less in sand than in liquid, they still actively produce BBBs, and the maximum production can be even higher in sand than liquid media for certain BBBs (Table S10). The higher production of lipids and amino acids in sand for both toluene and succinate might indicate that cells reshape their membrane content and protein metabolism upon transition to sand.P. veronii 1YdBTEX2 from exponential growth to stationary phase, and during adaptation from liquid to a sand environment.This work demonstrates how a metabolic-model-based (multi)omics data integration approach can accurately capture cell physiology and adaptation during growth or environmental transitions. Metabolic models are typically used for predicting optimal cell growth, but not for growth transitions and adaptation. We show here the usefulness of models with comprehensively integrated transcriptomics and exometabolomic to capture changes in growth rate during adaptation. As example, we studied transition of the toluene-degrading bacterium P. veronii, iPsvr. iPsvr is useful for a wide range of applications in biological and biotechnological research, such as the integration of omics data, investigation of relationships among species in microbial communities, bioremediation strategies, design of metabolic engineering strategies, hypothesis-driven discovery, and analysis of metabolic network properties. As such, iPsvr may represent a valuable resource for the study of complex microbiomes and microbiome engineering.For the purpose of this study, we reconstructed and curated the first genome-scale metabolic model of 36 For example, in the most recently and best-curated published E. coli model, iML1515, 10% of the reactions are blocked.37 We identified the blocked reactions/pathways in iPsvr and introduced a novel omics-based gap-filling approach, which targets the gap-filling of reactions/pathways by looking at differentially expressed genes or metabolites. We showed that the gap-filling step improved the metabolic model connectivity, thus increasing the number of functional pathways that can carry flux. The curation also increased the consistency of the metabolic model with the omics data, expressed as the number of genes/metabolites with available omics data that can be integrated into iPsvr. We illustrated through two gap-filling examples on toluene and phenylalanine metabolism how we could add the missing parts of metabolism and increase consistency. Gap-filling using metabolomics data not only improved the network connectivity, but also allowed integrating metabolites and uncovering missing reactions that must be part of the metabolic network but are missing due to unannotated and misannotated genes as well as promiscuous enzymes.38GEMs usually contain inconsistencies that manifest as blocked reactions, i.e., the reactions that carry zero flux. Most of the available metabolic models are gap-filled to obtain a functional model that produces all the BBBs. Nevertheless, many reactions in GEMs are not directly connected to biomass formation and may remain blocked even after routine gap-filling. Roughly, 20–50% of the reactions of published GEMs are blocked reactions.Statistical analysis of gene expressions and metabolite levels revealed significant variations across the exponential and stationary phase, exemplifying the known distinct physiologies under these conditions. However, the system’s level physiology and the underlying adaptation mechanisms remained unrevealed.19 a method for the integration of omics data into metabolic models and for building context-specific models, produced predictions of P. veronii growth rates under dynamic conditions. Integration of the transcriptomic data (REMI-TGex and –TGexM) proved to be vital for predictions of growth rate dynamics, i.e., higher growth rate at exponential versus almost a growth arrest at the stationary phase, whereas inclusion of metabolomic data alone (REMI-TM) was insufficient. This might be expected, because only a small subset of metabolites could be putatively identified, such that their impact on the predictions was comparatively smaller. Overall, these results clearly demonstrated that integrating multi-omics data in GEMs significantly increases the consistency of the model predictions with experimental observations.It should be highlighted that in FBA, the growth rate is evaluated under the assumption that all metabolic fluxes in the cell are geared towards the maximal production of biomass at each moment of the cell growth and under any environmental condition. Nevertheless, inclusion of REMI,P. veronii growth by identifying the metabolic pathways whose fluxes were consistently changed across all alternatives thus identifying where the fluxome was mostly rewired. Interestingly, although the same growth reduction phenotype was observed for both the transition from EXPO to STAT and liquid to sand, the deregulated pathways were not completely identical.The analysis of alternative outcomes generated by REMI allowed us to better characterize the intracellular state of P. veronii adapts to STAT phases primarily by limiting the metabolic fluxes for the production of the majority of cofactors and vitamins required for growth, with metabolism reshuffling being regulated at the transcriptional level. In addition, an increased production of specific amino acids in the STAT phase suggests the cell is preparing for starvation and survival. In contrast to the EXPO to STAT transition, REMI analysis of iPsvr-LIQUID and iPsvr-SAND indicated that, surprisingly, the maximum production of most of precursors from the cofactors and vitamins, and the BBBs amino acids actually increased in cells transited to sand, consistent for both sand with toluene or succinate as the sole carbon source. We hypothesize that the production of these BBBs and the function of their associated metabolic pathways are rewired to adapt to the local sand conditions. This finding resonates with previous work of Morales et al.,22 who concluded from Gene Ontology terminology that P. veronii cells inoculated in sand readjusted their metabolism during the first hour of contact.REMI analysis of iPsvr-EXPO and iPsvr-STAT suggested Collectively, our results demonstrate the importance of integrating into metabolic models the contextualization of condition-specific gene-expression and metabolite-abundance data. This increases the value of growth rate predictions and improves the assessment of the relative changes of measurable metabolic phenotypes. The method as demonstrated here is thus an important advancement to explain, quantify or predict cellular responses to environmental or genetic perturbations, which is crucial for microbiome engineering.P. veronii 1YdBTEX2 was grown on solid 21C mineral medium39 with toluene in the vapor phase at 30 °C for 3 days. Colonies were inoculated into three 100 ml screw-cap conical flasks containing 25 ml of the mineral medium amended with 0.5 ml of a 1:19 mixture of toluene:tetradecane . Flasks were incubated at 30 °C and at 180 rpm on an orbital shaker until a culture turbidity (OD600) of 0.7 . At this point, the three cultures were pooled, and the bacterial cells were harvested by centrifugation . The cell pellet was resuspended in the mineral medium and centrifuged again, as described above, to remove any residual carbon. After that, the cells were resuspended in mineral medium, diluted to obtain a starting OD600 of 0.16 and transferred into four replicate flasks, to which the toluene:tetradecane mix was added, as described above. The cultures were incubated at 30 °C and 180 rpm and regularly sampled for OD measurements. For transcriptomics, ~1 × 109 cells were harvested from an appropriate volume of the culture by centrifugation at 3500 g, for 6 min at 30 °C; snap-frozen in liquid nitrogen and stored at −80 °C until RNA extraction. Three quadruplicate sample sets were produced: the inoculum at time 0 (T0h), that is cells starved for ~30 min at 25 °C in mineral medium without a carbon source; exponentially growing cells ; stationary phase cells (at OD600 ≈ 1.8–1.9 harvested after 24 h incubation). To study the composition of the exometabolome (metabolites in the culture medium excreted or leaked out of cells), 1 ml of the culture was sampled at the same time points. Cell culture was transferred into a 1.5 ml polypropylene microcentrifuge tube, which was clarified by 20 min centrifugation at 21,100 × g and 4 °C, after which 0.5 ml of spent media was transferred to a new polypropylene tube, stored at −80 °C and shipped on dry ice to the metabolomics facility.P. veronii 1YdBTEX2 was plated from –80 °C on LB agar and incubated at 30 °C for 16 h to obtain isolated colonies. A single colony was subsequently restreaked on a minimal medium agar plate, which was incubated in a closed glass jar with a vial of toluene. Single colonies grown on toluene were transferred to closed glass bottles with 20 ml minimal medium to which 20 µl of toluene dissolved in 1 ml of tetradecane was added. Cultures were incubated with agitation at 30 °C and cells were harvested after 24 h by centrifugation, removing tetradecane carefully. Cells were resuspended in minimal medium to a density of 106 per 75 µl. Wells of BIOLOG EcoplatesTM containing triplicates of 31 carbon sources and one no-carbon control were filled with 75 µl minimal medium, after which 75 µl of P. veronii cell suspension was added and mixed. In total five Ecoplates were inoculated, covered with a sterile seal and incubated in the dark for 16 days at room temperature. Absorbance at 590 nm, representative for the respiration activity of the cells, was measured by spectrophotometry every second day.Total RNA from the frozen cell pellets was extracted using the RNA PowerSoil Total RNA Isolation Kit (MoBio Laboratories) as recommended by the manufacturer. Contaminating genomic DNA was removed by two cycles of TURBO DNase (Invitrogen) digestion and RNeasy MinElute Cleanup kit (QIAGEN) column purification. The quantity, purity and integrity of RNA samples were assessed using agarose gel electrophoresis, NanoDrop spectrophotometer (ThermoFisher Scientific) measurements and Agilent 2100 Bioanalyser (Agilent Technologies) profiling.22 Next, 4 µg of each RNA sample were depleted from ribosomal RNAs, converted to the reverse-complement stranded Illumina sequencing library using the ScriptSeq Complete Kit  and indexed with ScriptSeq™ Index PCR primers set 1  following the standard protocol. The resulting directional RNA-seq libraries were sequenced using single-end 100-nt read chemistry on an Illumina HiSeq 2500 platform (Illumina) at the Lausanne Genomic Technologies Facility.The completeness of DNA removal was verified by PCR using primers Pv_chr2_fw, ATCGGCTGTCCGACATCGGACG and Pv_chr2_rev, TCGAAGAGCTCCACCGAGAGCCGCC) and 1 pg of genomic DNA as a positive control, as described previously.4OH in water at pH 9.3 and B = 100% acetonitrile. The linear gradient elution ran from 90% (0–1.5 min) to 50% B (8–11 min) down to 45% B (12–15 min). Finally, the initial chromatographic conditions were established during a 9 min post-run for column re-equilibration. The flow rate was 300 μL/min, column temperature 30 °C and sample injection volume 2 µl. ESI source conditions were set as follows: dry gas temperature 290 °C and flow 14 L/min, fragmentor voltage 380 V, sheath gas temperature 350 °C, nebulizer 45 psi and flow 12 L/min, nozzle voltage 0 V, and capillary voltage 2000 V. The fragmentor voltage of 380 V has been optimized in both negative and positive using pure chemical standards in order to improve metabolite signal and minimize in-source fragmentation. Thus, this value is found as the best compromise between sensitivity and in-source fragmentation.The spend media (100 µL) samples collected at different time points were extracted with 400 µL of ice-cold methanol to quench the metabolism, precipitate proteins and extract a broad range of polar metabolites. The extracted media were analyzed by HILIC-HRMS using an electrospray ionization source operating in both positive and negative mode. Pooled QC samples (representative of the entire sample set) were analyzed periodically (every 4 samples) throughout the entire analytical run in order to assess the quality of the data, correct the signal intensity drift and remove the peaks with poor reproducibility (CV > 30%) that can be considered chemical or bioinformatic noise. Data were acquired using a 1290 UHPLC system (Agilent Technologies) interfaced with a 6550 iFunnel Q-TOF mass spectrometer operating in a full-scan MS mode. In addition, pooled QC samples were analyzed in auto MS/MS mode  to acquire the MS/MS data for metabolite identification. In positive mode, chromatographic separation was carried out using an Acquity BEH Amide, 1.7 μm, 100 mm × 2.1 mm I.D. column . The mobile phase was composed of A = 20 mM ammonium formate and 0.1% formic acid in water and B = 0.1% formic acid in acetonitrile. A linear gradient elution from 95% B (0–1.5 min) down to 45% B (17–19 min) was applied followed by 5 min for column re-equilibration to the initial gradient conditions. The flow rate was 400 μL/min, column temperature 30 °C and sample injection volume 2 µl. ESI source conditions were set as follows: dry gas temperature 290 °C and flow 14 L/min, fragmentor voltage 380 V, sheath gas temperature 350 °C and flow 12 L/min, nozzle voltage 0 V, and capillary voltage 2000 V. In negative mode, a SeQuant ZIC-pHILIC  column was used. The mobile phase was composed of A = 20 mM ammonium acetate and 20 mM NHm/z), MS acquisition rate of 500 ms, and MS/MS acquisition rate of 500 ms.In the MS-only mode, the instrument was set to acquire over the m/z range 50–1200, with the MS acquisition rate of two spectra/s. Targeted MS/MS data for dysregulated metabolite features were acquired using the inclusion list with narrow isolation window . Genes were called significantly differentially expressed between two EXPO and STAT when their false-discovery rate was <0.05 and their fold-change >2 and were subsequently interpreted using Gene Ontology (GO) analysis. GO terms of P. veronii genes were inferred using the program BLAST2GO.44 The same software was then used to analyze GO datasets of significantly differentially expressed genes in each pair-wise comparison, under the TopGO “Weight” algorithm.Read mapping, sorting and formatting of the raw reads was done with Bowtie2p-value (<0.05) and signal intensity (>1000 ion counts). The remaining table of metabolite features together with the most significant ion features selected from the loadings plot of the multivariate models in positive and negative ionization mode were subjected to metabolite identification as described below.Raw LC–MS data were converted to mzXML files using ProteoWizard MS Convert. mzXML files were uploaded to XCMS for data processing including peak detection, retention time correction, profile alignment, and isotope annotation. Data were processed as a multi-group experiment, and the parameter settings were as follows: centWave algorithm for feature detection , obiwarp settings for retention time correction (profStep = 1), and parameters for chromatogram alignment, including mzwid = 0.015, minfrac = 0.5 and bw = 5.5. Preprocessed data  were filtered according to the 25 based on accurate mass (AM) with Δppm = 10. The list of hits was further manually curated by taking into account the biological relevance of the hit (endogenous vs. exogenous metabolites) and a presence of the “true” peak shape (using the interactive XCMS Online interface45). Short listed ions of interest together with the metabolites identified by the in-house database were further subjected to targeted MS/MS validation. The metabolite identifications were validated by matching the MS/MS46 data acquired in the pooled samples  against the in-house PCDL database, METLIN (https://metlin.scripps.edu/) standard metabolite database47 or mzCloud (https://www.mzcloud.org/). Otherwise, if the MS/MS data quality did not allow for metabolite ID confirmation, the metabolite IDs remained putative, based only on accurate mass matching.In the first instance, putative metabolite identification was performed by accurate mass and retention time (AMRT) matching against an in-house database . For that, raw data files (.d) were processed using Profinder B.08.00 software (Agilent Technologies) with the following parameter settings: mass tolerance 10 ppm, retention time tolerance 0.2 min, height filter 1000 counts, and peak spectrum obtained as an average of scans at 10% of the peak. In parallel, the XCMS output table of significantly different metabolite features was matched against the Human Metabolome Database (HMDB)P. veronii 1YdBTEX2 genome-scale metabolic reconstruction process combines the automated draft metabolic network process with several manual refinements and curation procedures, in total involving four main steps: (i) functional annotation of the genome: functional annotation of the genome is required prior to the reconstruction of the GEMs, and consequently, the quality of a GEM highly depends on the availability of a gapless genome. The protein sequences (FASTA files) of P. veronii 1YdBTEX2 were acquired from a previously published study22 and were annotated to identify the associated reactions that enzymes catalyze to determine the stoichiometric matrix, done using the RAVEN Toolbox.26 The generation of the draft metabolic network followed the protocol detailed previously,49 and the output of this annotation process is summarized in Table S6. Version 1.07 of the RAVEN Toolbox and the version of KEGG database as of September 2017 were used. (ii) Compartmentalization, the definition of biomass reaction and uptakes and secretions: In the absence of information or experimental evidence about the compartmentalization and the cell content in P. veronii 1YdBTEX2, the cell compartment information , the transport mechanism between the compartments and the extracellular environment, the uptakes and secretions, and the biomass composition were obtained from two available models of closely related Pseudomonads: Pseudomonas putida29 and Pseudomonas stutzeri.30 The biomass reaction in a GEM designates the metabolic precursors that build the BBBs, i.e., DNA, RNA, amino acids, lipids and carbohydrates and their corresponding stoichiometric coefficients. (iii) Thermodynamic curation: In a thermodynamically curated model, the standard Gibb’s free energy of a reaction and consequently the directionality of the reactions, i.e., reversible or irreversible, are associated with the reaction as additional constraints, which allow the performance of TFA. We followed the established protocol to thermodynamically curate metabolic models32 to add thermodynamics constraints to iPsvr, where we could estimate the standard Gibb’s free energy of formation of 76% metabolites, using the group contribution method (GCM)50 and the standard Gibb’s free energy of 84% of metabolic reactions in iPsvr. This allowed us to perform TFA, simulate the growth and determine whether iPsvr was functional, with further evaluations if it correctly predicted the expected growth-associate phenotypes. (iv) Gap-filling of iPsvr: The draft metabolic network of iPsvr did not contain all the necessary reactions for the production of all the biomass building blocks, and thus, the model did not show any growth. This is a very common observation in the reconstruction process of GEMs, since the (automatic) genome annotations are often incomplete or erroneous, with significant proportion of predicted proteins having no functions attributed.51 Therefore, reactions without associated genes were included in order to obtain a functional model that simulates non-zero growth, done using a procedure called gap-filling. We followed the gap-filling procedure introduced previously,48 wherein the production of each biomass precursor is defined as a metabolic task26 and a mixed-integer linear programming (MILP) formulation is used to generate alternative groups of minimal number of reactions (borrowed from KEGG) that enable the production of the biomass precursor. The draft GEM was gap-filled by iterative manual curation until a model was obtained that was able to carry non-zero flux through the biomass reactions at steady state . Although gap-filling is a routine procedure in the curation of GEMs and most of the available GEMs are gap-filled to obtain a functional model that is able to grow under defined conditions, gaps in pathways not involved with biomass production are, however, mostly overlooked in GEM analysis. We performed a complementary second gap-filling step apart from the biomass reaction, where as a result, the consistency of iPsvr with the obtained experimental data on gene expression and metabolomics was increased. Available GEMs usually contain a large well-connected subnetwork, which encompasses the most of the central carbon metabolism and a part of the secondary metabolism, and many isolated reactions (or sets of reactions) are probably disconnected from the rest of the network because of misannotations or insufficiently known pathways. Such isolated reactions/pathways are blocked, i.e., cannot carry flux under any condition, and therefore one or more reactions must be added to connect the blocked reactions with the rest of the metabolic network. To this end, the metabolic network structure of iPsvr was decomposed to the main component and to the isolated (disconnected) reactions/pathways using a MATLAB graph-based built-in function (conncomp). Using the same gap-filling approach, the blocked reactions/pathways that were associated with differentially expressed genes  or measured metabolites in exometabolomic data were gap-filled to become functional in the model (carry non-zero flux). (v) Quality control of iPsvr: To assure the quality of the model, especially reaction balances, we used Memote,52 an automated genome-scale metabolic model test suite. Memote pinpointed reactions that were not balanced, and we manually balanced them, and commented the reasons for the reactions’ imbalance (Table S11).The 19 was used for the integration of transcriptomics and exometabolomics data into iPsvr. REMI assumes that reaction fluxes associated with genes that are significantly differentially expressed are deregulated. Moreover, REMI also considers that the in vivo metabolite abundance ratios between the two conditions, e.g., the two growth phases can be used to constrain reaction fluxes associated with the metabolites that are differentially regulated. Expression/abundance-based ratios between the two conditions are formulated as flux perturbations for each reaction and are imposed as constraints on individual fluxes. Based on the data used, it translates into three different methods: (i) REMI-TGex allows the integration of relative gene expression data into a thermodynamically curated GEM, (ii) REMI-TM allows the integration of metabolomics data and (iii) REMI-TGexM integrates simultaneously both the gene expression and metabolite abundance data as additional constraints into the metabolic model.REMIvs reference), REMI considers a separate metabolic model for each condition. Then, to integrate differential expression, REMI enforces a higher flux through a reaction in the perturbed condition (perturbed model) as compared to a reference condition (reference model) if the genes of the reaction are upregulated. For downregulated reactions, REMI enforces a lower flux as compared to a reference. To integrate extracellular metabolite concentrations, REMI assumes that if a metabolite is upregulated, then the production of the metabolite is forced to be higher as compared to a reference condition, and similarly, a lower production of a metabolite is forced if a metabolite is found to be downregulated. Then, an optimization problem is formulated to maximize the number of constraints imposed by the relative gene expression and metabolite abundances that can be integrated into the model while preserving a growth phenotype. Two scores are calculated: a theoretical maximum consistency score (TMCS), representing the number of genes/metabolites with available omics data, and the maximum consistency score (MCS), representing the number of genes/metabolites whose relative omics data are consistent with relative network fluxes and therefore can be integrated in the model.REMI aims to maximize consistency between differential expression and fluxes as well as differential metabolite concentrations and fluxes. To study condition-specific differences in metabolism between two conditions  from a given set of constraints. The most consistent models are built by activating constraints that are overlapping and consistent between all the alternatives. Alternative solutions were computed for the maximal consistency score with the MILP solver in IBM CPLEX 12.7.2, and the time limit for the optimization was set to 3 h.We used the transcriptomics and/or exometabolomics datasets measured at the exponential and stationary phase and also at the sand versus liquid environment, to derive additional flux constraints for the TFA problem, which were applied using REMI.31 We perform TFA for estimating the growth rate before and after the integration of omics data.To determine fluxes and subsequently the growth rate at different growth and environmental conditions using iPsvr, we employed FBA, the most widely used constraint-based modeling technique for studying biochemical networks and cellular physiology. Previous studies show that the integration of appropriate thermodynamic constraints leads to more accurate metabolic model predictions and also a significant reduction in the ranges of the predicted fluxes (solution space).We further identified the biomass building blocks (BBB), wherein a low/zero production limits growth upon the transition of cells from the exponential to stationary phase or from liquid to the soil environment (two examples that were discussed in this work). Each BBB was tested by defining the TFA objective function as the maximum production of that metabolite under the defined media condition . Then, the BBB production was compared and the limiting BBBs for each case were identified.SupplementaryInformationnr-reporting-summaryData Set 1