The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs’ efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure. Antimicrobial resistance (AMR), the ability of a microorganism to resist the action of one or more antimicrobial agents, is one of the major public health problems of this century . AntimicFor bacteria, the current antimicrobials inhibit a narrow spectrum of cellular processes  . Their aOverall, antimicrobials can affect bacterial metabolism in three ways. First, they have a direct effect on metabolism that affects their efficacy, which seems to be different depending on whether the antimicrobial is bactericidal or bacteriostatic  ,5. The action of bactericidal antimicrobials on their primary targets causes damage to other essential macromolecules  within the cell, resulting in the induction of stress response pathways. Stress responses increase metabolic activity to meet the corresponding energy demands. This results in the production of toxic metabolic byproducts such as reactive species, which damage macromolecules, and leads to the induction of additional stress response pathways. Once again, the overall cellular metabolic load results increased. The alteration of the metabotype, therefore, creates a cyclical process that ends with cell death ,13,14,15Bacteriostatic antimicrobials, instead, inhibit protein biosynthesis or transcription in certain contexts, leading to a decrease in metabolic activity and subsequent cell stasis, thus, again resulting in an altered metabotype ,18. As mOn the other hand, the metabolic state of the bacterial cells can affect the antimicrobials’ efficacy in various ways. It must be stated that, in addition to the classical molecular mechanisms of antimicrobial resistance, bacterial cells can counteract antimicrobials in several ways, each of which relies on a general metabolic downregulation. Indeed, the term resistance is usually used to “describe the inherited ability of microorganisms to grow at high concentrations of an antimicrobial, irrespective of the duration of the treatment, and is quantified by the minimum inhibitory concentration (MIC) of the particular antimicrobial” . If thisThe metabolic state of bacterial cells  give rise to an intricate set of interconnections. Haemophilus influenzae [One of the most powerful tools to study cellular metabolism at the system level is represented by genome-scale metabolic models (GSMMs) . The stafluenzae , severalfluenzae ,62.GSMMs, however, have been progressively applied to many other areas of research. Recently, several works have illustrated the tools for the development of metabolic networks of Gram-negative pathogens and the metabolism of priority pathogens reported by the World Health Organization, the possible drug targets for them , and the awareness of the spread of antimicrobial resistance pathogens ,64,65. HWe have performed an operational classification of available case studies on the use of GSMMs to study antimicrobial (AM) exposure/resistance into antimicrobials: non-informed vs. informed (AM-non-informed vs. AM-informed) works. In the first group, we include all those works that did not use experimental data obtained from cells exposed to antimicrobial compounds to constrain and refine the model predictions. Conversely, all the studies that used experimental data (mostly -omics data) obtained from cells exposed to the antimicrobial molecule(s) to constrain the GSMM of the corresponding microorganisms  will falAll the metabolic reconstructions cited in this work and that have been used in anti-microbial resistance studies are reported in Acinetobacter baumannii’s AYE GSMM was reconstructed and used in combination with a filtering framework of essential metabolites (EMFilter) to predict the most effective drug targets [A. baumannii AYE case study, 179 metabolites remained after this step. Second, it selects essential metabolites that are surrounded by at least two metabolite-consuming reactions, in order to enhance the drug impact (97 metabolites remained). Third, it prunes essential metabolites that participate in human metabolism as well (22 metabolites remained). Fourth, it removes essential metabolites that are being consumed by human homologous enzymes, in order to avoid the possible interaction between the drug and the human  drug target. After all the steps of the filtering pipeline, the enzymes involved in the consumption of nine essential metabolites were predicted as final drug-target candidates. As a confirmation, some of the enzymes identified by this approach have been previously reported as drug targets.Studying the metabolism of bacteria with the aim to prevent or fight AMR can be carried out by performing analyses such as gene/reaction or metabolite essentiality  in order to identify possible points of metabolic weakness of the cell. This approach removes a gene/reaction  from the metabolic network and then simulates growth with FBA. In other words, every time a gene, reaction, or metabolite is deleted, an FBA is performed using the biomass production as the objective function. In 2009, in one of the first works of this kind, the  targets . The firA. baumannii AYE case study, Jenior’s group [Clostridioides difficile. They reconstructed the GSMMs of two strains (630 and R20291), and after the GE analysis, they compared the prediction of essential genes for growth in silico and in vitro, obtaining 89.1% and 88.9% accuracies, respectively. This group identified the minimum subset of metabolites necessary for growth, and subsequently, the carbon sources needed. Since C. difficile utilizes phase variation to generate phenotypic (rough- or smooth-edge colony morphology) and metabolic heterogeneity to maximize its fitness and virulence expression, Jenior’s group aimed to characterize the differences between in vitro and in vivo conditions using context-specific metabolic reconstructions derived from expression data. They found differences in alanine transport and utilizations, as well as in glycolysis’ transporters and metabolic reactions during periods of increased virulence expression. With the gene essentiality analysis, they found numerous reactions that were essential only for the smooth phenotype , and surprisingly, no unique essential reactions for the rough phenotype. Finally, by comparing the predicted core metabolic activity of high- and low-sporulation conditions, Jenior’s group described how both N-acetylneuraminate and cytidine are being consumed during infection and decrease sporulation. These findings could help to find solutions to AM use in order to mitigate C. difficile infections. Analogously to the ’s group  decided H. influenzae. They refined its previous GSMM, and after the GE analysis, they found 11 genes of the fatty acid biosynthesis pathway (FASII pathway), 6 genes for the phospholipid biosynthesis pathway, and 12 genes encoding enzymes involved in lipid A biosynthesis. Since the FASII pathway generates acyl carrier proteins , these authors proposed that genes related to this pathway could work as a drug target, specifically, beta-ketoacyl-ACP, which initiates fatty acid elongation cycles.López-López’s group  also perPseudomonas aeruginosa to different mucins. Mucins are polymeric, highly glycosylated proteins produced by airway epithelial cells and submucosal glands that play a key role in the microbe clearance and infection prevention. Even though these proteins are not considered as antimicrobial agents, it is clear that the mucin–drug interaction may have a remarkable impact on drug absorption since mucus is the first barrier that drugs must overcome to be adsorbed [P. aeruginosa (iPau21) that has 40 genes, 24 metabolites, and 76 reactions more than the previous model, iPau1129. This new high-quality model was constrained using transcriptomic data from the literature of four different conditions: casamino acids (no mucin exposure), MUC5AC, MUC5B, and mucin-glycans, all of which are present in different body parts prone to P. aeruginosa infection. Since the experiments were carried out with strain PAO1, its genes in the transcriptomic dataset were mapped to PA14 orthologs, and then the data were integrated with the iPau21 model. The integration of the data was performed using RIPTiDe [P. aeruginosa. Thus, they proposed AMs that target proteins for fumarate and propionate metabolism in order to combat mucin-resistant bacteria. Dahal et al. [P. aeruginosa model (iSD1509) and added a whole new pathway for ubiquinone-9 biosynthesis, required for anaerobic growth. They validated the model in its totality using experimental data from different works, including Dunphy’s [To link the (complex) carbon sources that one microbe might encounter in vivo and the overall metabolic reprogramming during the infection process, Payne et al.  studied adsorbed . This im RIPTiDe , a tool l et al.  recentlyDunphy’s . In ordeDunphy’s  performeDunphy’s , supplemKlebsiella pneumoniae using polymyxin B in low doses, plus exogenous metabolites as adjuvants, which have a lower risk of resistance development. Chung’s group reconstructed the GSMM of four different K. pneumoniae strains, one polymyxin-resistant, one polymyxin-susceptible, and two polymyxin-susceptible but MDR. They studied seven different metabolites that were previously identified as significant metabolites perturbed by the combination with polymyxin B, at the level of both gene expression and metabolism of the same pathway. In silico predictions using flux sampling  revealed that the presence of at least three of these exogenous metabolites (3-phosphoglycerate (3PG), ribose 5-phosphate (R5P), and uridine 5′-diphospho-N-acetylglucosamine (UACGAM)) enhanced the central metabolism , increasing bacterial growth. Since the growth rate is one of the factors that determines the phenotype of susceptibility to antimicrobials, with a slow growth rate being associated with low antimicrobial activity, 3PG, R5P, and UACGAM were good candidates for a non-antimicrobial plus polymyxin treatment. Chung’s group confirmed these results with time-kill studies, where bacteria displayed a decrease in the growth rate when using polymyxin B plus 3PG, R5P, or UACGAM, but not for the rest of the metabolites. Ultimately, this study provided evidence of the possibility that an altered metabolism could change the antimicrobial efficacy, also promoting the use of state-of-the-art modeling approaches to fully comprehend molecular adaptations to antimicrobial exposure.Just as Payne et al. addressed the environmental–drug interaction scenario, evaluating how mucins change the metabolic state of bacteria, Chung et al.  proposedK. pneumoniae through the constraint-based analysis of its GSMM. They simulated three host microenvironments by setting specific bounds to each reaction flux and set the biomass production as the objective function. Combining GE and ME analyses on this GSMM, Cesur’s group performed a preliminary screening that led to the identification of possible enzymatic targets, for which they identified the cellular compartments/function and performed a “druggability” assessment, i.e., to estimate the affinity of the protein to bind drug-like chemical compounds. Out of this selection procedure, one enzyme (encoded by the kdsA gene) stood out. For this target, using the computer-aided drug design approach (SBVS), Cesur’s group obtained two possible compounds, one of which showed to be quote promising as it is a derivative of coumarin with low acute toxicity and already proven high antimicrobial activity. Nazarshodeh and colleagues [E. coli. To this aim, they used a GSMM-PRO, i.e., a genome-scale metabolic model integrated with protein structures. Combining GE analysis (biomass production as the objective function) with a search for informative protein–ligand complexes, they obtained 70 essential genes, accounting for 92 protein–ligand complexes, which were proposed as drug targets. To perform SBV screening, the authors used the 3D structures of FDA-approved drugs and ranked them against each essential target . Finally, their pipeline suggested eight known antimicrobials as possible drugs to target the essential genes found, proposing a new therapeutic indication for these reported drugs (drug repurposing).An additional methodology to look for potential drug targets in different organisms is the combination of flux simulation-based techniques  with protein structure-based ones, such as structure-based virtual screening (SBVS). The latter is one of the most promising in silico techniques for drug design due to its robustness, and it is capable of predicting the most likely interaction between two molecules to form a stable complex , e.g., tlleagues  also useA possible advancement for this work is represented by the simulation of drug epistasis, as drug combinations can illuminate new possible therapeutic strategies that might be inaccessible to single-target drugs, possibly eliminating functional redundancies exhibited by metabolic networks. In 2016, Krueger’s group  studied A. baumannii [A. baumannii nicely underlines two main features of genome-scale metabolic modeling: (i) the scalability and versatility of GSMMs to be used with different experimental (-omics) datasets , and (ii) the possibility to constantly update and (possibly) improve pre-existing models once new data are available. In 2017, to address new possible solutions for the treatment of infected patients and to overcome the appearance of resistant phenotypes, Presta and colleagues [A. baumannii ATCC 19606 to perform a systems-level study of the antimicrobial response in this bacterium. Specifically, to propose new targets for AM drugs, they performed GE analysis. Since the exposure to the antimicrobial might alter the metabotype  [A. baumannii AYE strain model (iCN718) [A. baumannii and to shed light on some key points to optimize polymyxin combination therapy.The model reconstructed in this work (iLP844) led to further studies of CC19606)  and for (iCN718) . Both gr(iCN718)  used it Chromobacterium violaceum, an opportunistic human pathogen, is exposed to antimicrobials. They reconstructed C. violaceum’s model (iDB858). After experimentally measuring several cell rates, they constrained different uptake values of the model  to represent three different conditions: wildtype and exposure to chloramphenicol and to streptomycin, both together and independently. Using the biomass production as the objective function, performing FBA, FVA, and GE, they described a rewired central and redox metabolism in the presence of each antimicrobial; specifically, high levels of NAD recycling provided by pyruvate, suggesting a reprogramming of metabolism to compensate for the stress consequences.Another example is that of Banerjee and Raghunathan , who stuP. aeruginosa’s GSMM [P. aeruginosa: a lab-evolved phenotype, and piperacillin- (PIP), tobramycin- (TOB), and ciprofloxacin (CIP)-resistant strains. Each strain had been grown on 190 unique carbon sources, but there were only 14 growth-supporting carbon sources common for all four lineages, revealing a strong metabolic reprogramming following the exposure to each of the tested compounds. Since in Yen’s work they found that the PIP-evolved lineage contained a large deletion of 343 genes, much higher than for the other lineages, Dunphy’s group decided to analyze the role of each gene deletion and its impact on the metabolic functions and cellular growth. They did so by performing a GE analysis in silico using the biomass production as the objective function and repeated it on M9 minimal media and 42 different carbon sources, independently. The next step was to look for the intersection between predicted essential genes and the mutated genes in the PIP-evolved strain in order to predict resistance-specific essential genes and to identify incorrect model predictions. In conclusion, although the iPau1129 GSMM did not include several AMR genes, it was possible for Dunphy’s group to identify potential mutations impacting growth phenotypes, such as the loss of L-leucine catabolism in PIP-evolved metabotypes. Researchers can take advantage of the already developed GSMMs for the organism under investigation. In this case, the integration of -omics data for the reconstruction of context-specific models (representing different metabotypes) can be performed in a more or less straightforward fashion. This is the case of Dunphy’s group , who usea’s GSMM  and lab-a’s GSMM  to conteEscherichia coli. In 2017, Zampieri et al. [E. coli metabolic reconstruction (iJO1366) [E. coli’s GSMM, and systematically maximized or minimized fluxes for each reaction of the model, i.e., they iteratively changed the objective function to calculate the shadow prices . Negative shadow prices mean limiting metabolites for the reaction. In this way, reactions with overrepresentation of altered limiting metabolites could be those with an active role in the evolution of resistance. They observed that most of the evolved metabolic characteristics were present in pathways not directly affected by the antimicrobial. For the chloramphenicol-glucose-evolved populations, the evolved metabolic characteristics involved sugar transport, oxygen uptake, and CoA formation, while for the ampicillin-glucose-resistant populations, they involved nucleotide metabolism, serine biosynthesis, and cell wall recycling. The authors found an important difference between the ampicillin-glucose-evolved and ampicillin-acetate-evolved populations, with the first one being eight times more sensitive to fosfomycin, and the latter more tolerant to it. Thus, apparently, metabolic adaptation to antimicrobial exposure strongly depends on the nutritional context.Not surprisingly, most of the research concerning the effect of the cellular metabolic state on antimicrobial resistance emergence and/or antimicrobial efficacy has been carried out on i et al.  used theiJO1366)  in orderMycobacterium tuberculosis (TB) and built a genetic variant matrix (G) containing the information of all drug-tested TB strains and all their allelic variants. The abundance of AMR genes in the latest TB model (iEK1011) [E. coli. Using a set of unique E. coli genomes to analyze the AMR phenotypic variability, they developed a ML-integrated k-mer and SNP approach: they scanned entire genomes against selected phenotypes, allowing for the identification of arbitrary numbers of genomic features ranked on the strength of the correlation with the AMR and the susceptible phenotype (named the Gradient Boosting Classifier). These genetic determinants, such as AMR genes and specific mutations and alleles to specific phenotypes, were assessed using the GSMM: they used FBA to constrain the model and predict the effects  in order to broaden the space of predictions. ML is a method that handles the automatic learning of machines without explicit programming (black box) and has been widely used in the field of bioinformatics, and recently, in systems biology as well . For exaiEK1011)  justifieiEK1011) . These aE. coli’s and M. tuberculosis’ GSMMs, Chung’s group assessed their methodology using several scenarios with multiple drugs and clinical trials, inferring a set of differentially regulated genes by calculating the differential fitness (from chemogenomic data) and expression (from transcriptomic data). Then, they assigned four scores for each condition and trained ML models to associate the described profiles to drug combination outcomes, as interaction scores. After comparing the predictions with experimental data (-omics and literature) and assessing the performance for synergy and antagonism scenarios, the authors’ approach revealed to be accurate in predicting combination therapy outcomes. Afterwards, they assessed how a pathogen adapts to a first drug and how this, in turn, influences its sensitivity to a subsequent drug. Using data from evolved E. coli in single-drug treatments over different timespans and subsequent treatments, CARAMeL once again yielded robust predictions, also when addressing the model sensitivity to a broad range of drug combinations and growing media. A problem that is sometimes difficult to address is the impact of the combinations of different drugs and/or how the drug efficacy (or the efficacy of drug combinations) changes depending on the growth conditions. To address this issue, Chung et al.  proposedEven when there have been other methodologies that do not use GSMMs , the joiE. coli was systematically perturbed (through the simulation of single-gene knockouts), and its flux distribution was analyzed to identify targets predicted to increase ROS production, i.e., genes whose removal from the WT would increase the intracellular ROS concentration. Remarkably, such computationally predicted targets were experimentally validated and demonstrated to confer increased susceptibility to oxidants and to killing by antibiotics. A schematic representation of the overall pipeline used in this work is reported in Lastly, we would like to refer to a case study that used GSMMs predictions to enhance the killing activity of antibiotics. Brynildsen et al. ,99 exploE. coli metabolic model and deriving an “antimicrobial-specific objective” function that provides novel insights regarding the metabolic basis of the mutations involved in antimicrobial resistance. This was achieved by determining the set of potential constraints imposed by an allele through discretization of the flux solution space; thus, deriving the metabotypes that more closely resemble the stressful conditions faced by cells. To tackle the same issue, i.e., the definition of a proper objective function under stressful conditions, Montezano et al. [M. tuberculosis bacterium when exposed to mefloquine, and integrated condition-specific proteomic data with its GSMM model to derive an antimicrobial-informed biological objective function, i.e., the actual cellular overall objective when facing antimicrobial-induced stress.The use of genome-scale metabolic modeling has improved our understanding of the intertwined relationship between metabolism and antimicrobials. We believe that the most useful application of GSMMs resides in using them as scaffolds for the interpretation of -omics datasets, as this helps in reducing the space of possible solutions, for example, after an FBA simulation, and to identify the most likely set of active reactions in a specific condition. Indeed, the speed and ease-of-use of constraint-based simulations comes at the cost of many  constraints that must be imposed on the model to make it mathematically solvable. Among them, the one that most impacts the use of genome-scale metabolic modeling for studying the response of microbial cells to cellular perturbation is probably the identification of a proper objective function, i.e., the reaction in the metabolic network that describes the primary objective of the cell in a specific growth condition . While io et al.  studied When experimental information on the cellular state in the presence of antimicrobials are not available, the exploration of the possible solution space through sampling methods Population-level metabolic heterogeneity  is known to play a key role in the response of microbes to antimicrobial stress , and sevE. coli and M. tubercolosis. It is important to stress that the relevance of such hybrid models  resides in their capability of providing a mechanistic understanding of how biological circuits (metabolism in this case) respond to environmental stresses/fluctuations, and which strategies are the most promising in preventing antimicrobial resistance/tolerance/persistence [As we saw in the case studies, the modeling of antimicrobial-induced metabolic changes has recently been boosted by the combination of ML methods with GSMMs. There are (at least) three different ways for integrating ML with constraint-based metabolic modeling (see Sahu et al.  for a thsistence .


E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.Since Jacob and Monod’s discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as “metabolic reprogramming.” The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of  Escherichia coli which led to the discovery of the lactose operon and the lactose repressor protein , the activity of which was believed to be rate limiting for that pathway . Figure Once techniques became available that made it possible to examine changes in rates of gene expression, and to estimate the concentrations of enzyme isoforms in yeast, it became obvious that the Crabtree effect has a genetic component too. About 40 min after glucose addition, major changes in rates of gene expression occur that result in the wholesale replacement of the isoforms of the glycolytic enzyme that were present before glucose became available with different isoforms . Further13C magnetic resonance spectroscopy (MRS) revealed that the glycogen and trehalose synthesis pathways, which we will refer to as the glycogen and trehalose shunts, and futile cycling pathways are very active early in the transition phase  had not come to maturity at about the same time. MCA originated in the mid 1970s, and since that time it has been a major contributor to the development of a rigorous, quantitative framework for understanding the properties of metabolic systems . Prior tTwo of the several general theoretical and experimental principles discovered using MCA are relevant here. First, the flux through pathways is almost always controlled collectively by the activity and kinetic properties of many of the enzymes involved, not just one, and that is why mutations that alter the activity of PFK can have so little impact on the glycolytic flux . Second,The role played by the shunts during the initial transition phase was not understood until quite recently. Using an advanced form of MCA called Supply and Demand Analysis , it was After approximately 30 min, a new metabolic steady state is achieved, as shown in The reason these enzymatic alterations have selective value is that the shunts consume ATP that can be better used for other purposes. Futile cycling and glycogen synthesis, which are necessary for maintaining homeostasis during the first stage of the transition state, come at a cost. When yeast cells are operating this way, glycolysis results in no net ATP production. In addition, these alterations allow all of the glycogen synthesis flux to be used for ethanol production, which is believed to provide yeast species that exhibit the Crabtree effect a competitive advantage over other microorganisms .But how does the cell suppress the shunts while still maintaining homeostasis of branchpoint intermediates, and why is a whole sale replacement of glycolytic enzyme isoforms required? Using supply and demand MCA , we recealpha pathway, which regulates transcription of enzymes in the glycolytic and lipogenesis pathways in response to carbohydrate feeding and is directly regulated by G6P and F 2,6P2 and pentose phosphate pathway intermediate levels . This is surprising because it ignores an important problem that bacteria undergoing this adaptation cannot: where is the supply of energy and metabolites they need to survive, and ultimately to synthesize lac permease and beta galactosidase to come from once they run out of glucose? Fortunately, over the past 60 years, a lot has been learned about what happens during the 45 min transition phase of the glucose to lactose adaptation, and we now know that even this adaptation is not entirely top-down.As already noted, the transition phase that is such an obvious feature of diauxic growth was neglected in the original model for the glucose to lactose adaptation in Glc, (see Glc is a component of the phosphotransferase system (PTS) that is responsible for the active transport of glucose into E. coli cells . When cAMP is bound to it, it binds with high affinity to a specific site at the upstream, promoter end of the lac operon. Prior to the working out of the PTS, it was believed that CAP played a key supplementary role in the regulation of gene expression along with lactose and allolactose  by affecting the concentration of allolactose present, and 2) by changing the efficiency with which the lac operon is transcribed. The lac operon is not autonomously regulated. Its regulation is mediated by the cell’s metabolic system. It is bottom-up.Thus the state of phosphorylation of EIIAGlc ceases, and 3) the concentration of cAMP rises because the increase in the concentration of the phosphorylated form of EIIAGlc activates adenyl cyclcase This system displays positive feedback  the dilution caused by growth stops, 2) the inhibition of lac permease by EIIAfeedback . As soonfeedback . The glye levels . In addie effect . This isNote that when growth resumes, growth-related dilution will again come into play, and the concentrations of all of the molecule involved in lactose metabolism will be determined by the ratios of their rates of synthesis/uptake to their rates of dilution/destruction. It is important to realize that the mechanisms that repressed lactose expression during the initial state, and were not operational during the transition phase, play a role in determining the characteristics of the new steady state. They are activated because the hydrolysis of lactose catalyzed by beta galactosidese generates glucose directly, as well as galactose, which is ultimately converted to glucose. Therefore, even in the final state, metabolism is still having an effect on gene expression.E. coli . We suggest that the lethal increased flux through the lactose permease is a consequence of the EIIAGlc system, which integrates information from multiple substrates and metabolic pathways in the cell, not having the necessary inputs to inhibit lactose permease. It is an example of the case in which the metabolic plasticity of the initial transition state is insufficient for the cell to survive the environmental change.The importance of metabolic plasticity in a transition state, and how successful long term adaptation depends upon it, is demonstrated by the phenomenon referred to as lactose killing . In thisSince the time of Mendel, if not before, biologists have realized that organisms have long term memory systems, i.e., genomes, and by the middle of the 20th century, it was realized that they are made of nucleic acid. The sequence information in an organism’s genome is remarkably stable, and largely insensitive to changes in its environment or to its lifetime experiences. Genomic changes, i.e., mutations and/or rearrangements, are rare, and largely random events, but they can have consequences that affect the fitness of an organism and its progeny into the indefinite future. If it were true of all metabolic adaptations that the physiological properties of cells that have undergone any particular metabolic adaptation were independent of the properties they displayed in their initial steady states, and that they could be accurately predicted using genomic information alone, the contributions made by metabolism to adaptations, however interesting they might be, would be of mechanistic interest only. The metabolically determined vs. genetically determined distinction made earlier would then be a chimera because it would then be possible to argue that outcomes are 100% genetically determined.The issue of whether genetics fully explains all adaptations was addressed in the summary paper Jacob and Monod wrote for the 1961 Cold Spring Harbor Symposium in Quantitative Biology . Many ex“. . for instance, in the system shown below  a regulaSystems like these could make cells “remember” what the conditions were that they had experienced earlier, but would be genetic in nature, rather than metabolic.E. coli.Since the early 1960s, there has been a tremendous increase in our knowledge of non genetic mechanisms that make the responses of cells to environmental challenges dependent on their prior histories. We have alluded to only a small fraction of them here, e.g., see . FuthermAlthough epigenetics is still considered by many to be the major mechanism that makes cellular adaptations history-dependent, recent studies have revealed that post translational modifications of proteins and cellular organelles can have the same effect. For example, long term conditioning of muscle mitochondrial metabolism by exercise has been shown to be regulated independently of both gene expression and epigenetic modifications of the genome . The posAs shown in E. coli, lactose activates systems that produce long lasting alterations in lac operon activity by acetylating chromatin , or to provide G6P for ATP production while transcription of the lac operon is being upregulated (E. coli). Recently, mathematical simulation led us to conclude that if ancestral organisms did not have the metabolic plasticity provided by the glycogen shunt, the evolution of the Crabtree effect would not have occurred because mutations that led to increased activity of glycolytic enzymes proximal to F 1,6P2 would result in excessive increases in glycolytic intermediates as well as depletion of ATP levels that the cells could not survive (Proponents of the phenotypic plasticity concept have primary identified the source of this plasticity as being determined by the range of gene expression an organism is capable of, which may be expanded epigenetically . We prop survive . We advoE coli lactose metabolism demonstrates that neither the traditional views of genetic driven control (Analysis of the adaptations phases for the Crabtree effect and  control , nor mor control , epigene control , and met control  can full control . HoweverGiven the failure of models for metabolic adaptation that are either purely genetic or purely metabolic to fully describe even the simplest examples of metabolic adaptation, it seems to us that it is high time to stop trying to generate models of either type. Neither should be pursued to the exclusion of the other either in the introductions and/or discussions of papers, let alone in educational materials such as text books. Instead, explanations of adaptive processes should acknowledge the roles played by metabolic phenotype and plasticity as well as changes in gene expression. Although the models for adaptation that emerge will be more complex than they would be otherwise, they will force both investigators and students to confront the dynamic interactions that occur between the environment, a cell’s metabolism, and its genome, the understanding of which is the true challenge for those concerned with adaptive processes in biology. A similar conclusion applies to the integration of signaling pathways and epigenetic and post translational modifications.One of the goals of this paper is to call attention to the importance of metabolic plasticity for both short-term and long term metabolic adaptation. Despite the advances that have been made in identifying metabolic mechanisms such as the glycogen and trehalose shunts that confer plasticity, and the many direct feedback and feed forward control mechanisms that make it possible for metabolic systems to influence gene expression, the number of publications that include metabolism in their final descriptions of adaptive control is small. Perhaps even more regrettable, the dynamic interplay between the environment, metabolism, signaling pathways, and gene expression that occurs during transition phases is largely omitted. Instead, no matter how complex and interdependent the processes described in some publication, the explanation given for its control all too often focusses on regulation by a single or small set of genes, or equivalently signaling pathway signaling pathway proteins and receptors.The question remains how best to quantitatively and conceptually describe the complex, time-dependent interplay of metabolic, genetic, signaling, and epigenetic factors in adaptation. We hope that by encouraging researchers, and educators, to drop the view that adaptation is controlled at a single level, whether it be genomic, epigenetic, signaling or metabolism, we will expedite achievement of this goal.


The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situThis review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.The online version of this article (10.1007/s11306-023-02031-9) contains supplementary material, which is available to authorized users. Central carbon metabolism is critical for energy production and includes the glycolysis pathway, the pentose phosphate pathway and the tricarboxylic acid (TCA) cycle. Increased understanding has revealed that the roles of these metabolic pathways are not limited to cellular energetics, but they are also involved in the assembly of the most important cellular building blocks, including nucleotides and amino acids , which has itself been the target of many studies, because it is overexpressed in various types of cancer cells  techniques are used to study the rates of metabolic transitions that are critical for identifying key regulatory nodes in metabolic pathways, based on changes in rates under intra/extracellular perturbations. Understanding the metabolic dynamics and fluxes through these techniques serves as the foundation for comprehending disease mechanisms/phenotypes and developing novel therapies  under glucose depletion. The researchers found that P493 cells rely on a glutamine-driven TCA cycle independent of glucose and that inhibiting GLS curbs tumour cell proliferation both in vitro and in vivo. However, intervention or inhibition of the glutamine pathway does not always result in a therapeutically effective outcome such as apoptosis. Alternative metabolic pathways can be activated to compensate for the required energy deficiency  or non-viable cells (steady-state quantification). Figure Biochemical assays are commonly used due to their affordability, reproducibility, and simplicity  (measuring the rate of glutamine degradation from the medium provides information about the rate of glutamine uptake by cells in a culture system) has a dampening effect on the metabolism of gastric cancer cells , which is used to quantify proton production and serves as a substitute marker for lactate production  For instance, Rupprecht et al. determined both ECAR and oxygen consumption rate (OCR) to analyse the influence of glucose and glutamine deficiency on the metabolic activity of N18TG2 cells   and the early onset of disease , while the near-IR region (~ 780 nm to 2500 nm) can be used to measure vibrational overtone and combination modes, which are intrinsically orders of magnitude weaker. The weaker absorption in this region gives rise to greater sampling depths, which makes the technique of near-IR spectroscopy popular in, for example, food science (‘Near-Infrared Spectroscopy in Food Science and Technology—Google Books’ n.d.). Mid-IR absorption spectroscopy is well established in analytical chemistry, as a first port of call for materials characterisation and identification and has increasingly found applications in, amongst others, the pharmaceutical sector , Raman spectroscopy quantifies the intensity of inelastically scattered monochromatic light by a molecule at varying frequencies. The frequency shift is due to the change in energy of the photon (which corresponds to the energy of a molecular vibration) as it couples to the vibrations of the molecule  in in A549 and Calu-1 lung cancer cells has been demonstrated Below is the link to the electronic supplementary material.


Microbial communities experience continuous environmental changes, with temperature fluctuations being the most impacting. This is particularly important considering the ongoing global warming but also in the “simpler” context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of their possible adaptations to a changing environment. In this work, we investigated the mechanisms through which metabolic homeostasis is maintained in a cold-adapted marine bacterium during growth at temperatures that differ widely (15 and 0°C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction, and to provide a systemic understanding of cellular adaptation to growth at 2 different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes, despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates  and in the cross talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.IMPORTANCE This manuscript addresses a central and broad interest topic in environmental microbiology, i.e. the effect of growth temperature on microbial cell physiology. We investigated if and how metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely and that match measured changes on the field. Our integrative approach revealed an extraordinary robustness of the central metabolome to growth temperature. However, this was counteracted by deep changes at the transcriptional level, and especially in the metabolic part of the transcriptome. This conflictual scenario was interpreted as a transcriptomic buffering of cellular metabolism, and was investigated using genome-scale metabolic modeling. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the use of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations. Microorganisms are able to colonize virtually every environmental niche on Earth . They ha4–in situ Eulerian temperature range by up to 10°C . This bacterium  is known to experience such broad seasonal temperature fluctuations. Indeed, rather than studying the consequences of a cold shock event (a circumstance that rarely occurs in nature), we here focus on the comparison of the main cellular networks in cells growing at 2 different temperatures. This was performed through the integration of metabolomic and transcriptomic data unitedly with genome-scale metabolic modeling of the Antarctic bacterium acterium  has beenacterium –19.We show that different growth temperatures induce broad transcriptional changes that involve genes of many key metabolic pathways. This transcriptional rewiring, however, is scarcely reflected at the level of the core metabolism, as most key central metabolites show overlapping trends at the 2 tested temperatures. The obtained -omics data were used to compute the flux distributions sustaining growth at low and high temperature, and this provided a mechanistic understanding of the possible adaptation strategies to temperature fluctuations.PhTAC125 cells in a bioreactor and sampled the 0 and 15°C growth curves at 5 different time points (−1 and 0.016 (s.d. 6.4e−05) h−1 at 15° and 0°C, respectively. The same calculation was repeated and limited to the exponential phases of the two cultures, yielding 0.27 (s.d. 0.006) h−1 and 0.027 (s.d. 0.0009) h−1 at 15° and 0°C, respectively. The samples obtained were used for intracellular and extracellular metabolites quantification through nuclear magnetic resonance (NMR) . For 2 of these time points .Overall, we assigned and analyzed the concentration of 34 intracellular metabolites in the 2 growth curves. These metabolites represented key intermediates of central metabolic pathways, such as the TCA cycle, amino acids biosynthesis, glycolysis, Pentose Phosphate Pathway (PPP), and nucleic acids biosynthesis. Our NMR metabolomic approach was not able to discriminate between the oxidized and reduced forms of NAD and NADP, so we generally refer to NADX and NADPX in the rest of the manuscript. First, we monitored the overall trends of their intracellular concentrations with respect to the beginning of the growth experiment  by compue < 0.05 ). StrikiP values = 0.74 and 0.89, respectively) suggesting that, despite the large difference in growth temperatures, the overall dynamics of intracellular core metabolites were maintained, pointing to an apparent structural metabolic robustness to growth temperature. This robustness was also conserved quantitatively, as the average concentration of each metabolite was maintained similar across the 2 different growth experiments , no other average metabolite concentration showed a significant difference at 0 and 15°C  and an adjusted P value of 0.05 as thresholds. Overall, we identified 607 differentially expressed genes in the comparison between growth at 15° and 0° C, with an almost identical amount of up- and downregulated genes at 15°C . The outliers of up- and downregulated genes are shown in Differentially expressed genes (DEGs) were identified using a logPhTAC125 share an amino acid sequence identity ranging between 63.8% and 65.6% with the mesophilic cspE of E. coli. Furthermore, these proteins contain highly conserved RNA-binding motifs, RNP1 (K-G-F-G-F-I) and RNP2 (V-F-V-H-F) , and further experiments are required to understand their role in the adaptation to growth at warmer temperatures. Two of them (PSHA_RS07950 and PSHA_RS06965) were annotated as TonB-dependent receptors, suggesting their involvement in the uptake and transport of large substrates, possibly siderophores complexes and/or vitamins. The remaining gene (PSHA_RS11830) is annotated as NAD-dependent succinate-semialdehyde dehydrogenase, responsible for the conversion of succinate-semialdehyde to succinate. The downregulation of this gene is in line with the lower intracellular concentration of succinate at 15°C, as discussed later in the text. Strikingly, among the over-expressed genes during growth at 15°C, we found a prevalence of cold shock related proteins (4 out of 5) and a RNase R encoding gene (PSHA_RS14645). Although no functional studies have been done on cold shock responses and cold shock proteins (CSPs) from psychrophilic bacteria, similarities with the CSPs that are produced in mesophiles have been observed. In particular, the over-expressed CSPs in F-V-H-F) , 21, ind10.1128/msystems.01124-22.6FIG S4FIG S4, PDF file, 0.09 MB.Sequence alignment of four psychrophilic CSPs with the mesophilic CspE protein. Gaps indicated by hyphens were introduced to improve alignment, and identical amino acids are indicated with asterisks. The RNA-binding motifs RNP1 and RNP2 are boxed in black. Download Copyright © 2023 Riccardi et al.2023Riccardi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the http://pfam.xfam.org/family/PF00313), which is annotated as RNA chaperone/anti-terminator. Moreover, the NCBI identifies both genes PSHA_RS14635 and PSHA_RS14640 as encoding for the same protein (WP_011329604.1), while the others encode for proteins with a different identifier but virtually same sequence. Cold shock proteins would normally counteract the deleterious effects of temperature drop, enabling the cells to grow at low temperatures  domains near the N-terminal region of the protein, an S1 domain and a highly basic C-terminal region (rnr gene is co-transcribed with flanking genes as an operon induced under cold shock in E. coli (locus tag b4179) , spaced by an average distance of 216 nucleotides. The homology to a variety of domains involved in stress response, together with our observation of a strong upregulation during growth at 15°C, may indicate that PhTAC125 strives to mediate the elimination of detrimental secondary structures and a temperature rise promotes the expression of enzymes that are required for the correct processing of rRNA precursors.PSHA_RS14630, PSHA_RS14635, PSHA_RS14640 and PSHA_RS16715 contain 1 highly conserved nucleic acid-binding domain, called cold shock domain  . There iExtending the analysis to a broader functional level, we identified significant differences in 12 COG categories. Specifically, intracellular trafficking, secretion and vesicular transport, signal transduction mechanisms, cell motility, cell wall/membrane/envelope biogenesis, replication, recombination and repair and cell cycle control, cell division, and chromosome partitioning functional categories were found to be over-represented among upregulated genes during growth at 15°C. This matches the results from recent works on the characterization of the evolutionary pathways responsible for thermal adaptation, and the overall notion that one of the main effects of temperature increases on cell physiology consists in the disruption of membrane integrity caused by increased fluidity , 28. OvePhTAC125 motility that had shown a reduced swimming capability of this strain at 0°C . In these pathways, the number of downregulated genes strongly outpaced that of upregulated genes , and resulted in 0.10 and 0.08 mmol/gCDW*h−1 at 0°C and 0.47 and 0.62 mmol/gCDW*h−1 at 15°C.A genome-scale metabolic reconstruction exists for the strain PhTAC125 . We, thu−1) were in line with the measured growth rates (0.027h−1). Conversely, at 15°C a discrepancy was observed between the experimental growth rate and the one predicted in silico, 0.27 versus 0.13 h−1, respectively. Although there could be many possible explanations for this, we hypothesize that constraining the model only based on the uptake rates of the provided nutrients may not be enough to correctly represent the metabolic phenotype of the cells exposed to different temperatures. For this reason, and to provide a more realistic picture of how metabolic homeostasis is maintained at the level of the central metabolism, we combined metabolomic and transcriptomic data with the genome-scale modeling of PhTAC125 metabolism. We decided to focus on T1  as this time point should better resemble the cellular physiological state in which FBA assumptions hold the most . Then, we computed the log2 fold change (log2FC) of central intracellular metabolites in the 2 conditions  (2FC > 1 (thus being more abundant at 15°) whereas PEP, succinate, UDP-GlcNAc, thymidine, acetate and X1- methyl-nicotinamide displayed a log2FC < -1 (thus being more abundant at 0°).As shown in nditions . Most (7 < 0.05) . More sp2FC of each of the 34 internal metabolites, accounting for how many times the model correctly predicted the increase (or decrease) of its internal concentration. Overall, we found an SMC of 71% between simulated and measured metabolic data, revealing that the model is capable of accounting for most of the central metabolome rewiring in respect to growth temperature. Then, we focused on those metabolites that showed a marked change between growth at 15 and 0°C  .We then constrained the model using all available experimental data obtained in this work using REMI  see Mat and C weC | > 1) . Figure FC | > 1   than those resembling metabolism at 15°C . A sustained activity of PPP at 15° is in line with the increased internal concentration of gluconate and ribose (2 key PPP intermediates) at this temperature , both PPP and fatty acids metabolism showed significant differences in their fluxes between 0° and 15° growth simulations. As for fatty acids metabolism, fluxes representative of growth at 0°C were significantly higher  and the reaction converting 2-oxaloacetate to PEP . Also, ATP:pyruvate, HOverall, our experimentally constrained simulations seem to suggest a working model for the metabolic adaptation to growth at different temperatures. Considering growth at 0°C, an increased level of intracellular PEP observed experimentally might be functional to its conversion to acetyl-CoA and its consequent tunneling into fatty acids metabolism , and migE. coli, only 4  of the 9 CSP genes are cold-induced  and by evaluating gene expression during the initial stages of its exponential phases. To our surprise, the top 5 upregulated genes at 15°C contain a cold shock domain, which is usually recruited to counteract the deleterious effects of temperature drop. A possible explanation for the apparent paradox where CspA is activated, not only following cold stress, but also under non-stress and other stress conditions which entail a downregulation of bulk gene expression and protein synthesis is presented in . CSPs ha-induced . Two of pression , 34. Furpression –37. In PPhTAC125 regulatory network to buffer the temperature shift to produce strikingly similar metabolic phenotypes, despite the temperature gap. This is also reflected in the conservation of the overall metabolic network structure   . Remarkabolites) , and/or bolites) . This is network . A few mcis to trans fatty acids thus, ultimately, through the modulation of fatty acids metabolism (as observed in this work). This also validates and extends previous findings on general cold-adaptation strategies  were used to constrain a genome-scale metabolic reconstruction and derive the most-likely metabolic phenotypes at these 2 temperatures. This approach provided evidence that most of the metabolic adaptation is probably played at the level of the phosphoenolpyruvate (PEP)–pyruvate–oxaloacetate node and specifically involves the increase of TCA fluxes and their redirection to PEP production. Consistent with this idea, is the overall reduction of key TCA intermediates at 15°C  and the rategies , 43, 44,PhTAC125 displays strong propensity to form more biofilm at 15°C versus 0°C when grown in the same minimal medium used in this work. Also, we found a remarkable fraction of overexpressed biofilm-related genes  had mainly mutations in proteases-encoding genes or in genes involved in chromosome copy number reduction, energy production and conversion and cell wall biosynthesis. Thus, apparently, the metabolism of PhTAC125 was not the primary target of mutations that led to an increased fitness in the tested conditions. This, in turn, may indicate the presence of an already optimized and plastic metabolism that allows growth in a broad range of temperatures, in line with the metabolic robustness that we have characterized in this work.Very recently, Macarena Toll-Riera et al. published  a work iThe aim of our experiments was to evaluate the effect of growth temperatures on cellular homeostasis, particularly at the metabolic level. We have shown that a cold-adapted marine bacterium expresses 2 very similar metabolic phenotypes in response to 2 widely different temperatures, by adjusting the expression of key enzymes and fine-tuning the intracellular concentration of key intermediates. This illuminated on the possible molecular mechanisms that marine microbes may use to adapt to broad  temperature changes. In particular, we showed that specific changes at the level of regulatory circuits can buffer such variations, and maintain the underlying metabolic network robust to temperature fluctuations. In the future, it will be interesting to investigate whether similar mechanisms are at play in natural microbial assemblages.PhTAC125 ; the cells were washed twice with fresh medium, and then used to inoculate the bioreactor with a starting OD600 of 0.2, in aerobic conditions , in stirring (500 rpm at 15°C and 250 rpm at 0°C). The bacterial culture was carried out at 15°C for 40 h or at 0°C for 240 h. Each culture condition was repeated three times. Cell growth was monitored, measuring the OD600 about every 2 h in the experiments at 15°C, and every 8 h at 0°C. Three different measurements were performed at each time point for each biological replicate. For intra- and extracellular metabolites analysis, the samples were taken in triplicate at 5 different time points during the growth in GG medium at 2 different temperatures . For the analysis of extracellular metabolites, aliquots (1 mL) of cell cultures were harvested during the growth and centrifuged for 15 min at 13,000g at 4°C; the supernatant was recovered, filtered  and stored at −80°C. The analysis of intracellular metabolites was performed on 60 OD600 pellets recovered during the growth by centrifuging for 20 min at 6,000 rpm at 4°C.PhTAC125  cells wePhTAC125  in a Sti600 pellets were recovered during the exponential growth phase (~1/1.5 OD/mL) at 15°C and 0°C by centrifugation . Cell pellets were washed in RNase-free PBS three times and stored at −80°C. Total RNA was isolated from the cells using the Direct-zol RNA Kit (Zymo Research) following the manufacturer’s instructions. Contaminating genomic DNA was then removed through treatment with RNase-free DNase I (Roche).For the RNA-Seq experiment, 1 ODRNA concentration was measured using Quant-IT RNA assay kit-high sensitivity and a Qubit Fluorometer (Life Technologies), and its quality and integrity assessed with the Agilent 4200 Tapestation System (Agilent Technologies). Indexed libraries were prepared starting from 400 ng of total RNA according to Universal Prokaryotic RNA-Seq Library Prep kit (Tecan). Final libraries were sequenced at a concentration of 1.7 pM/lane on the NextSeq 500 platform (Illumina Inc) in paired mode 2 × 75bp. For each experimental condition, 3 biological replicates were prepared.PhTAC125’s transcriptome free of tRNA and rRNA sequences, providing the entire genomic sequence as background decoy to account for possible underlying DNA contamination. CDS and genomic FASTA were retrieved on the NCBI, using assembly accession GCF_000026085.1. Moreover, CDS and genomic files were concatenated with data from pMEGA plasmid (NZ_MN400773.1) and pMtBL plasmid (NZ_AJ224742.1). The transcriptome quantification step was performed using the –validateMappings flag, to ensure a sensitive selective alignment of the sequencing reads. The resulting mapping rates ranged between 56.8% and 66.21% (average of 62.47%), whereas roughly half of the reads represented ribosomal DNA sequences . The integration of transcript-level abundance estimates from Salmon with the data analysis pipeline was performed using R package tximport . The testing of changes in the overall transcriptional output was handled through the statistical engine in R package DESeq2, with an expected proportion of false positives set at 5%. The volcano scatterplot showing statistical significance versus magnitude of change for 607 differentially expressed genes was produced using the library EnhancedVolcano from the R Bioconductor 3.14 suite.The 12 samples were assessed for base call quality and adapter content using fastp , allowin1H NMR-based metabolomic analyses were performed on cell lysates and growth media to monitor the intracellular metabolites, and the uptake and release of the extracellular metabolites, respectively, by measuring their concentration levels in samples collected at different time points during the cell growth.2HPO4, 100% (vol/vol) 2H2O, 10 mM sodium trimethylsilyl propionate (TMSP), pH 7.4), and 540 μL of each growth medium.Medium samples were prepared in 5.00 mm NMR tubes by mixing 60 μL of a potassium phosphate buffer (1.5 M K2H2O and 540 μL of samples. All the NMR spectra were recorded using a Bruker 600 MHz spectrometer (Bruker BioSpin) operating at 600.13 MHz proton Larmor frequency, and equipped with a 5 mm PATXI 1H-13C-15N and 2H-decoupling probe including a z axis gradient coil, an automatic tuning-matching (ATM) and an automatic and refrigerate sample changer (SampleJet). A BTO 2000 thermocouple served for temperature stabilization at the level of approximately 0.1 K at the sample. Before measurement, samples were kept for 5 min inside the NMR probe head, for temperature equilibration at 300 K.Cell lysate samples were prepared in 5.00 mm NMR tubes by mixing 60 μL of 1H NMR spectra were acquired with water peak suppression and a standard NOESY pulse sequence using 128 scans, 65536 data points, a spectral width of 12019 Hz, an acquisition time of 2.7 s, a relaxation delay of 4 s, and a mixing time of 0.1 s.The raw data were multiplied by a 0.3 Hz exponential line broadening before applying Fourier transformation. Transformed spectra were automatically corrected for phase and baseline distortions. All the spectra were then calibrated to the reference signal of TMSP at δ 0.00 ppm using TopSpin 3.5 (Bruker BioSpin srl).1H NMR spectral library of pure organic compounds , stored reference NMR spectra of metabolites, and spiking experiments. Matching between new NMR data and databases was performed using the Assure NMR software (Bruker BioSpin). The relative concentrations of the various metabolites were calculated by integrating the corresponding signals in defined spectral ranges, using in-house developed R 3.0.2 scripts. Similarly, downstream data analysis was performed using R. Raw data, post-Raw data, and post-processing codes are made available at https://github.com/combogenomics/MetRob015.The metabolites, whose peaks in the spectra were well resolved, were assigned, and their levels analyzed using a dedicated R script developed in-house. In total, 34 and 17 metabolites were identified and quantified in the cell lysate and in the growth medium spectra, respectively. The assignment was performed using an internal μ) and the biomass yield (λ). The former was computed as:Uptake rates of gluconate and glutamate at the 2 different temperatures were computed as the ratio between the average growth rate between T1 and T3  at the 2 different temperatures, and averaged this number of reactions by the total number of reactions included in that pathway. From this latter analysis, we excluded those reactions that (i) had a flux equal to zero (were inactive) in both conditions and (ii) had a different sign  at the 2 temperatures.PRJNA886636.The authors confirm that the data supporting the findings of this study are available within the article and/or its supplemental materials. Collection of sequence data produced in this study is available under the BioProject


Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of ‘-omics’ datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions. Haemophilus influenzae RD [Since the first genome-scale metabolic reconstruction in 1999 for enzae RD . Genome-enzae RD , 4 and ain silico gene knockout experiments [The applications for metabolic reconstructions of microbial species are wide-reaching within biology, including but not limited to the design of metabolic engineering approaches , the moderiments , studyineriments , 16 and eriments .During their lifestyle as reproducing cells, dormant cells, commensals or pathogens, microbes encounter multiple different environmental perturbations, both externally and within a host niche, to which microorganisms must adapt accordingly. Such perturbations are often difficult to study in a laboratory, both technically and financially.in vitro with the aim of identifying just how microbes alter the way they metabolize and respire in order to best survive, colonize a host, induce virulence factors, evade the host’s immune system or produce products of biotechnological interest.The ever-increasing availability of ‘-omics’ data for microbial species has allowed the integration of such data with GEMs to create context-specific metabolic reconstructions , detailiSince the late 2000s, high-throughput sequencing technologies have become more frequently applied in the biology field and have been continually developing. Since first-generation Sanger DNA sequencing technologies , next-geab initio, gene prediction methods [DFAST, MicrobeAnnotator and Prokka all utilize homology-based annotation pipelines whereby reference databases or genomes are used to identify sequence similarity facilitating the prediction of protein coding genes . In cont methods , 31, 36. methods , 37. All methods . The out methods , while M methods . While aDue to the revolution in the sequencing field, an enormous number of species have been sequenced and annotated , 39. HigWith whole genome sequencing techniques becoming more accessible, the number of sequenced genomes has been growing exponentially since the first bacterial genomes were sequenced in 1995 . As a remerlin [merlin have inbuilt genome annotation tools  associations, and therefore gene expression data obtained through transcriptomic experiments can be integrated into networks by adjusting reaction fluxes. Gene expression data are mapped onto GPR associations to define inactive and active reactions in the condition of interest, and from this definition context-specific models are extracted . SeveralTo ensure that context-specific models are representative of the organism’s metabolism in a particular context, it is vital to evaluate expression scores while mapping to GPR associations , 121. ThThe GIMME algorithm can also be extended to include metabolomics: the Gene Inactivation Moderated by Metabolism, Metabolomics and Expression (GIM3E) algorithm . IntegraIntegration of microbial GEMs with ‘-omics’ data can enhance our understanding of microbial metabolism during infection such as deciphering the metabolic changes induced in pathogens by antibiotic treatment , exploriEscherichia coli and Saccharomyces cerevisiae integrated with transcriptomics data, pFBA was the most accurate at predicting reaction fluxes compared with experimentally measured fluxes [For models of d fluxes . Howeverd fluxes  when comGenome-scale metabolic models are a valuable tool to investigate the lethality and downstream metabolic effects of gene knockouts or mutations in species , 95, 129Genome-scale essentiality studies have become increasingly popular in the last 20 years, but they are expensive and not always feasible, particularly for less well-studied species. Such datasets are often used to validate phenotypic predictions generated from model reconstructions , 131. Hoin silico can be particularly helpful to determine the effects this genetic perturbation has on metabolism. For example, simulation of in silico single and double mutant strains of Mycoplasma pneumonia, using an experimentally validated GEM, revealed that biomass synthesis in this pathogen has very limited adaptability [Neurospora [Generating mutant bacterial strains, gene knockdowns or knockouts, can be challenging and elucidating any downstream effects can be even more challenging. Creating reconstructions of mutant strains tability . Metabolurospora .Streptomyces coelicolor increase antibiotic production [Streptomyces hygroscopicus [Strain-design algorithms can be implemented with GEMs in metabolic engineering approaches to identify optimal conditions for the maximal production of a particular metabolite of interest. Such approaches have been implemented to identify that the overexpression of genes encoding the enzymes acetyl-CoA carboxylase and branched-chain α-keto acid dehydrogenase in oduction . Similarscopicus , 7. Guidscopicus   is the most common method to predict fluxes across reactions in metabolic reconstructions, but has limitations; therefore, several other methods have been developed to expand upon this methodology, including dynamic FBA, geometric FBA and parsimonious FBA.Microbes will adapt their metabolism in response to several genetic and environmental perturbations, and simulating these perturbations through the integration of ‘-omics’ datasets with metabolic reconstructions and the use of other constraint-based analysis approaches can aid in the elucidation of such adaptations.


Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.The purple non-sulfur bacterium  Rhodopseudomonas palustris actively participates in the carbon and nitrogen cycles by fixing carbon dioxide (CO2) and molecular nitrogen (N2), as well as transforming nitrate into N2. This photosynthetic bacterium has been extensively studied and is considered a model microorganism for elucidating carbon and nitrogen fixation, H2 production, denitrification, aromatic compounds degradation, anoxygenic photosynthesis and the production of complex polymers like polyhydroxyalkanoates. Constraint-based metabolic models are systems biology tools capable of predicting an organism’s phenotype under several growth conditions. The metabolic model of R. palustris Bis A53, iDT1294, accurately predicts growth rates of over 350 carbon and nitrogen sources  under different oxygen and light conditions. Furthermore, iDT1294 precisely simulates how R. palustris switches between photoheterotrophic and photoautotrophic metabolic modes over time, depending on nutrient availability and light conditions. The model represents the most comprehensive M-model for a purple non-sulfur bacterium to date, thus aiding the elucidation of R. palustris’ metabolic roles in different environments. Rhodopseudomonas palustris is a photosynthetic Gram-negative purple non-sulfur bacterium (PNSB) of the family Bradyhizobiaceae. It can attain a wide range of metabolic states and is considered one of the most versatile microorganisms  [2 as nitrogen sources, achieving a minimum accuracy of 74% and a maximum value above 98%.c growth . To expldustries ,72. BaseiDT1294 was further validated using growth with organic acids and glycerol under anaerobic conditions in the light, employing experimental data from Govindaraju (2019) [10P) were calculated comparing the experimental growth and consumption rates against the model estimations across the time.u (2019)  to evaluu (2019)  was applu (2019) . To caliiDT1294 demonstrates a strong correlation R2 coefficient (98.6%) for the growth rate samples. For most of the conditions analyzed, the model accurately predicted lag and exponential phase trends with clear mismatches of the starting point of the stationary phase. Additionally, substrate consumption across the conditions was predicted correctly according to the dynamic simulation time points generated through dFBA model analyses (R2>97%). In some specific cases , the consumption of carbon sources was incorrectly estimated since the COBRA Toolbox algorithms assume constant uptake fluxes, ignoring the biological regulation and uptake restrictions of the organism. Surprisingly, iDT1294 was capable of accurately predicting growth rates after depletion of the available carbon sources. When the carbon source is depleted, R. palustris utilizes CO2 as the carbon source (carbon fixation). For instance, under malate and malate-lactate conditions nutrients were depleted after 100 hours and CO2 became the sole carbon source available; iDT1294 accurately predicted the change in growth rates for these scenarios. Based on the statistical correlation parameters for growth rate and substrate consumption values, our comprehensive model is established as a suitable tool to estimate and analyze the dynamic metabolic behaviors of R. palustris Bis A53 under photoheterotrophic conditions.R. palustris under four distinct metabolic modes. We generated a high-quality, manually curated, meticulously validated metabolic model of R. palustris Bis A53. Initially, the R. palustris model was reconstructed based on three principal model references from BiGG [Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942) and a well-known Gram-negative bacterium (Escherichia coli K-12 substr. MG1655). BLASTp parameter values were optimized by reconstructing multiple draft models employing the three template models. While there is no clear consensus on which BLASTp criteria optimize the metabolic reconstruction process, previous studies have reported similar BLASTp cut-offs to reduce the amount of false positive (wrong gene assignations) and false negative (missing genes) calls in the GPR associations [We have reconstructed a comprehensive genome-scale metabolic model for a well-studied PNSB using semiautomatic strategies. This work represents a systems biology modeling approach to elucidate the metabolic capabilities of rom BiGG  selectedciations ,54,55. Hciations , the corciations , KBase [ciations  and CarvR. palustris Bis A53 M-model contains 2,721 reactions and 2,123 metabolites associated with 1,294 genes. The resulting model was highly curated using semiautomatic and manual strategies, including GPR-specific compartment associations. iDT1294 was successfully validated using over 350 different carbon and nitrogen sources under several growth conditions varying O2 content (aerobic and anaerobic) and presence of light (light and dark), achieving the highest growth phenotype accuracy , is the first M-model capable to predict steady state and dynamic phenotypic data utilizing a wide range of carbon and nitrogen sources under the four R. palustris metabolic modes in three cellular compartments.Our accuracy  comparedtal data , demonstiDT1294 accurately predicts the growth rate values of R. palustris Bis A53 using 190 carbon and 95 nitrogen sources under aerobic chemoheterotrophic dark conditions . The model contains all required reactions and constraints to simulate the BOF representing the growth of the organisms successfully. We have designed an R. palustris-specific BOF, which includes the principal metabolic constituents of a PNSB, employing experimental and theoretical calculations based on genomic and proteomic annotations. BOF optimization was tested under the four metabolic modes of this organism, demonstrating the model elasticity to precisely predict the metabolic mechanisms using the available nutrients in the environment. The resulting predictions were confirmed by experimental validation using Biolog plates  and aromatic compounds assimilation for steady-state conditions and organic acid substrate consumption to validate dynamic growth and metabolites assimilation datasets. We estimated statistical parameters from the high-throughput phenotypic data validation, achieving values higher than 90% regarding phenotype growth accuracy. Compared to other R. palustris metabolic models, iDT1294 showed higher precision in true positive and negative predictions for both carbon and nitrogen sources. With this information, we identified metabolic pathways employed by this bacterium to consume different types of carbon and nitrogen compounds. Despite the "Swiss army knife metabolism", R. palustris could not metabolize several amino acids as carbon and nitrogen sources; almost 50% of the true negative growth estimations were amino acids. While the organism contains general ABC branch-amino acid transporters to transfer these amino acids inside the cell, it lacks the required enzymes to transform these amino acids into core metabolic intermediates and thereby utilize them as an energy source. In some cases , the necessary enzymes and transporters can be produced by the model. The discrepancy can be explained by non-metabolic mechanisms , which are out of the scope of the metabolic model capabilities. Compared to other prokaryotic metabolic models’ accuracy, iDT1294 displays similar statistical parameter values for both carbon and nitrogen analyses with a global accuracy of 90% and MCC maximum value of 0.86 [R. palustris Bis A53 M-model was curated to consume aromatic compounds under anoxygenic photosynthesis. Most of the resulting simulations achieved a quantitative accuracy close to 80%, demonstrating quantitative and qualitative precision using uncommon carbon sources. These estimations are of particular interest since R. palustris can be found in many wastewater and bioremediation consortia which contain such varied metabolites. While the M-model accurately predicted utilization of several carbon and nitrogen sources under changing O2 concentrations and light conditions, it still possesses some inherent limitations due to the composition behind the metabolic model. For instance, metabolic models cannot predict the partial expression of the nitrogenase complex and changes in enzyme activity due to O2 concentrations. These tightly regulated processes, e.g., transcriptionally, translationally, or allosterically regulated, are out of the scope of GEMs but could be partially recapitulated by including all the expression and allocation features in a subsequent model of metabolism and gene expression (ME-model) of the organism [iDT1294 can be employed to understand how R. palustris Bis A53 metabolism varies over time under several different conditions. of 0.86 ,54,59. Forganism ,75. FinaiDT1294 successfully predicts qualitative and quantitative growth behavior of R. palustris under a wide range of conditions, such as with or without oxygen present, in the presence or absence of light, under molecular nitrogen-fixing conditions, in the presence of aromatic compounds, and metabolizing a variety of different carbon and nitrogen sources. Moreover, the model predicts combinations of these conditions to determine possible metabolic fluxes and pathways employed by the bacterium. Thus, iDT1294 represents a valuable computational tool to elucidate how R. palustris can successfully establish itself in so many different environments, such as wastewater or plant roots, and manages to metabolize varies carbon and nitrogen sources, as well as aromatic compounds, in the presence or absence of light.R. palustris Bis A53 was obtained from The NCBI Reference Sequence database . Protein sequences were aligned to build the initial draft model using the BLAST algorithm for protein homology. This first draft was reconstructed using The COBRA [R. palustris Bis A53 metabolism, four reference models were selected according to the proteome comparison and metabolic capabilities from the BiGG Database [Synechocystis sp. PCC 6803, model iJN678 [Synechococcus elongatus PCC 7942, model iJB785 [Escherichia coli K-12 substr. MG1655, model iML1515 [Azotobacter vinelandii DJ, model iDT1278 [iDT1278 was mainly employed as a template model to obtain the nitrogen metabolism. The initial draft model from the photosynthetic organisms and iML1515 was built using optimized BLAST parameters . A sensitivity analysis was performed to determine which specific BLAST parameters’ values maximize the number of true positive homology calls and minimize the number of false negative and positive calls in the Gene-Protein-Reaction (GPR) associations. The determination of the optimal values involved the assessment of each BLAST parameter and the effect of the three parameters interactions. The resulting optimal BLAST parameter values were employed to determine the GPR associations of the Gram-negative template models. Template models contained reactions associated with carbon and nitrogen fixation, photosynthesis, amino acid catabolism, lipid metabolism, nucleotide pathways, and sugar degradation. The generated draft model also contained genes from the template models, which were later removed during the model refinement stages.The genomic sequence of he COBRA  and The he COBRA  ToolboxeDatabase . Templatl iJN678  and Synel iJB785 . The sec iML1515 , and Azo iDT1278 . iDT1278Model refinement included manual curation of the GPR associations using bioinformatics databases and experimental information followed by gap-filling of new metabolic reactions not found in the BIGG Database based on genomic associations and experimental evidence.R. palustris metabolism using data from bioinformatics databases . Exogenous proteins assigned during the reconstruction stage were replaced with homologous sequences of R. palustris Bis A53 using the Enzyme Commission (EC) number as a reference to identify the metabolic function of the protein. For every subsystem of the model with exogenous proteins, the GPR associations were manually reviewed using KEGG, BioCyc, NCBI and PATRIC. When the sources disagreed about which protein should be associated to a reaction, a unique GPR was chosen by following the consensus among the databases and annotations. Additionally, reactions lacking any R. palustris Bis A53 proteins were checked through BLASTp. Candidate proteins from the R. palustris proteome were identified via BLAST against the proteins assigned to the GPR associations for the reaction in other microorganisms in the BiGG database. The BLASTp criteria were an e-value ≤ 1e-10, query coverage ≥ 80% and identity percentage ≥ 40%. A second step of manual curation was executed to confirm the correct assignation of the GPR associations [R. palustris. All validated reactions, metabolites, and GPR associations were distributed in three different compartments . Metabolites were labeled according to the corresponding position in the cell. Metabolites obtained from the photosynthetic model references were carefully renamed and properly allocated to the corresponding compartment of R. palustris Bis A53. Duplicated metabolites generated during the renaming and reallocation of photosynthetic metabolites were merged and integrated into a unique metabolite identifier. Transport reactions were added using the TransportDB database [In the first step of the manual curation process, reactions in the initial draft model with exogenous proteins in the GPR associations were identified and verified in ciations ,54,55. Tdatabase . Metaboldatabase . ReactioGap filling was performed in two steps, (1) gap filling of metabolic pathways already present in the manually curated draft model, and (2) gap filling by adding new metabolic pathways into the model obtained from different bioinformatic databases or experimental evidence from the literature.Gap analysis was performed to recognize which compounds were disconnected in the M-model and which reactions were lacking in the analyzed pathways. Initially, disconnected metabolites were determined through dead-end analysis algorithms. The dead-end metabolites were classified according to their disconnection motive . Subsequently, the involved reactions were identified to establish suitable connections of dead-end metabolites. Disconnected reactions were manually added using different bioinformatic databases . After these analyses, gap filling was employed to connect pathways  through the data retrieved. Finally, dead-ends were identified through KEGG and BioCyc pathway modules and synthesized using COBRA gap filling algorithms . A finalR. palustris Bis A53 from Machado et al 2018 and Navid et al., 2019 [R. palustris Bis A53-specific features, like bacteriochlorophyll biosynthesis and pathways for aromatic compound degradation, literature information using experimental evidence was employed to reconstruct these specific metabolic subsystems. Pseudoreactions with unspecific intermediates or used to represent transformations with no known pathway were not included in the M-model to avoid inaccurate predictions. All pathways of aromatic compound utilization were curated based on experimental evidence from Harwood and Ma [In the second step of gap filling, new pathways were added to the refined model using semiautomatic algorithms. Specifically, carbon (aerobic and anaerobic degradation of aromatic compounds and PHB production), nitrogen , and photosynthetic  metabolisms and their annotation. The names of new reactions and metabolites were assigned according to the BIGG database; reactions and metabolites with no information in BiGG were added to the model according to the EC Number information (through BRENDA ) and biol., 2019 ,68. For d and Ma ,72. A dein-silico single gene-deletion simulations (in-silico gene knockouts) to verify if the GPR associations are properly assigned using the COBRA and RAVEN Toolboxes. Next, we performed Mass and Charge Balance simulations of the COBRA Toolbox on the model to check for unbalanced reactions added during the model refinement. Unbalanced reactions were fixed by adding the correct formula and charge of each metabolite. Stoichiometric values assigned in every reaction were carefully reviewed and corrected for unbalanced reactions. Ultimately, the final model was analyzed by looking for ATP, NADH, and NADPH free energy production, by removing media nutrients (exchange reactions) and checking that they had zero flux.The final quality check was accomplished to guarantee correct GPR associations and suitably balanced reactions and metabolites. We performed R. palustris Bis A53 was purchased from the American Type Culture Collection (ATCC BAA-1125). R. palustris Bis A53 was grown in mineral medium [4Cl, 2.7 g; KH2PO4, 0.5 g; MgCl2 · 6H2O, 0.33 g; CaCl2 · 2H2O, 0.05 g; NaCl, 0.4 g; 9.5 ml Pfennig and Lippert’s mineral solution; and 8.5 ml vitamin solution [R. palustris was cultured in aerobic medium for 3 days at 30°C under dark conditions. Optical density (OD) measurements were taken at 600 nm (OD600). A solution based on OD600 readings was prepared for the Biolog plates experiments. After the inoculation, Biolog plates were incubated aerobically at 30°C in the dark (chemoheterotrophic condition).l medium  in lightsolution . R. paluR. palustris were tested for growth on 190 different carbon sources using Phenotypic Microarray (PM) plates 1 and 2A from Biolog, Inc.  following the company’s instructions. Briefly, cell suspensions from a 10-day old culture (see above) were washed and resuspended in Biolog’s IF-0a GN/GP Base (1.2X) inoculating fluid (#72268) up to an optical density of 0.025 at 600 nm ). The suspentions were further supplemented with ATCC trace minerals and vitamin supplements (ATCC-MDTMS and ATCC-MDVS). 100 uL of these washed cell suspensions were inoculated into each well of the Biolog plates PM1 and 2A (a full list of compounds including a blank well with no carbon source (negative control) can be found here). Plates were incubated at 30°C without shaking with lids coated with an aqueous solution of 20% ethanol and 0.01% Triton X-100 (Sigma) to prevent condensation [R. palustris as an OD600 increase > 0.02  over a 96 hour incubation period.Bacterial suspensions of ensation . We defiThe bacterial suspensions for the nitrogen use assays were prepared in the same way as for the carbon source but were washed in IF-0a GN/GP Base (1.2X) inoculating fluid (#72268) containing 5 mM potassium phosphate monohydrate, pH 6 (Millipore-Sigma), 2 mM sodium sulfate (Millipore-Sigma) and 20 mM sucrose as the carbon source. The rest of the steps and determination of substrate utilization as a nitrogen source were similar to the ones described above for the carbon source assay.2 exchange flux. The constraints related to mineral requirements were set according to the BOF estimations. Growth and internal fluxes simulations were performed in the COBRA Toolbox for MATLAB [R. palustris Bis A53 [2 was tested as the nitrogen source (diazotrophic capabilities) in combination with other soluble nitrogen compounds for 14 anaerobic conditions [R. palustris Bis A53 under autotrophy and heterotrophy. Reaction flux distributions were analyzed to identify how pathways participate according to the medium conditions. Specifically, pathways involved in energy production, amino acid, nucleotide and lipid metabolism, pollutants degradation (aromatic compounds), photosynthesis, polymer production (PHB), carbon fixation, and cofactor and vitamin synthesis were analyzed according to the participation of each subsystem in all the different conditions. For experimental conditions with no specific uptake rates or experiments with only qualitative results (growth or non-growth), the validation process was reduced to true positive, true negative, false positive, and false negative calls. Additionally, the model was validated through experimental data generated in this study, with a total of 190 carbon sources (Biolog plates PM1 and PM2) and 95 nitrogen sources (Biolog plates PM3). For carbon source assessment, ammonium assimilation was not fixed to a specific value (non-diazotroph conditions). The experimental results from Biolog plates were matched with data retrieved from the literature to determine and evaluate model precision during the simulations. During nitrogen condition simulations, sucrose was used as the main carbon source. Statistical parameters were calculated according to the comparison between the M-model predictions and the experimental values. All Biolog plates conditions were tested and simulated in the model under chemoheterotrophic conditions (photons and CO2 assimilation constrain set to 0). Carbon and nitrogen sources without available or well-defined metabolic pathways for assimilation and metabolic integration were not mapped into the metabolic model. Additionally, nitrogen fixation metabolism and N2 assimilation constraints were fixed to 0 since the experiments were performed under aerobic conditions. The model accuracy from the Biolog plates results was compared with the in-silico predictions of other R. palustris’ models to determine the quality of model simulations. Statistical parameters were calculated for each model to determine model precision, accuracy, sensitivity, positive and negative predictions, and MCC. These statistical parameters were calculated using the formulas employed in the confusion matrices to evaluate model performance previously described [Experimental conditions from the literature were employed to determine specific medium constraints of the M-model. For all heterotrophic growth conditions, carbon and nitrogen uptake rates were calculated depending on the values obtained from results in the literature. For phototrophic (photoautotrophic and photoheterotrophic) conditions, photon fluxes were set to a maximum uptake rate value of 1000 and limited based on the COr MATLAB ,83 usingr MATLAB . The sto Bis A53 . Mineral Bis A53 ,80,81. A Bis A53 ,17,30. Snditions . The reaescribed .R. palustris M-model, kinetic studies were employed to determine the prediction capabilities of the metabolic model. Carbon source consumption and growth rate values were obtained from the literature [iRpa940) [iRpa940 for all nine experimental conditions and the results were compared to our M-model predictions. Statistical parameters and correlation values were calculated for growth rates and substrate consumption data using experimental time points as a reference. Correlation values were estimated employing the Pearson correlation algorithm of Python based on the experimental observations and predicted values.For in-depth validation of the terature . A totaliRpa940)  was modiS1 Material(XLSX)Click here for additional data file.S2 Material(XLSX)Click here for additional data file.S3 Material(XLSX)Click here for additional data file.S4 Material(XLSX)Click here for additional data file.S5 Material(XLSX)Click here for additional data file.S6 Material(XLSX)Click here for additional data file.S7 Material(XLSX)Click here for additional data file.