Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering. Growth-coupled designs for chemical production are limited by native metabolic networks' optimality for growth. Here, the authors introduce pathway orthogonality as a measure of the independence of biomass and chemical production pathways, identify metabolic valves that allow substrate utilization to be switched between the two, and demonstrate advantages of orthogonal designs. C-methyl-D-erythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate (MEP/DOX) pathway than the mevalonate pathway. Simultaneously, the expression of chemical production pathways is not sufficient to overproduce chemicals because cellular objectives are in competition with a chemical production objective34It is well established that pathway selection for chemical production plays an important role in metabolic engineering, for example, isopentenol has a higher pathway efficiency on a basis of yieldHowever, growth-coupled production has many biological challenges owing to the complex nature of metabolism. From an evolutionary perspective, metabolic pathways in cells have been optimized to convert sugar to biomass. The design of these pathways may, partially at least, be explained by optimality principles relating their structure to their function. Examples of such descriptions can be found in literature4678910Since structure is inexorably linked to function, it follows that a network function supporting chemical production and satisfying the key metrics stated above should exhibit a different structure from a wild-type cell and therefore also obey different principles of optimality. For example, in one recent study, the structure of the central metabolism was described as a ‘minimal walk' between the input substrate and the 12 requisite precursors for biomassHere we argue that to optimally convert the input substrate to the target chemical, one has to analogously generate a biosynthetic network that is largely independent of the natural metabolism but still capable of synthesizing biomass. We define such pathways as orthogonal pathways and examine the orthogonal properties of natural and synthetic metabolic networks that are designed for chemical production. This approach is in contrast to the approach taken in contemporary metabolic engineering design where a network optimized for biomass is augmented by deletions. Instead, we seek to determine the design characteristics of an orthogonal network that is optimal for the production of a target chemical as opposed to biomass. In doing so, we also present an algorithmic approach to engineer orthogonal pathways and develop a method based on cut sets to identify metabolic control reactions  that can be manipulated to allow or disallow cell growth. A metric for optimality that we developed helps to identify pathways that are optimal in the context of a set of minimal cellular interactions.Our analysis also leads us to consider substrates beyond the sugars that are naturally used by organisms, and to identify substrates such as ethylene glycol that are inherently better suited to produce target chemicals. We show that reworking the metabolic network structure to meet design specifications and designing networks by considering substrate–product pairs has implications for two-stage fermentation design. The tradeoff between growth and metabolite production in such orthogonal strategies can be effectively addressed through the design and identification of enzymes, such as phosphoenolpyruvate carboxykinase, to act as control valves between product and biomass production. Finally, we believe that the approach provides a new paradigm of metabolic engineering strategies for chemicals, in contrast to the existing growth-coupled strategies, which can be difficult to implement in practice. In doing so, it provides an improved framework for industrial strain design and the selection of substrate utilization pathways and strongly suggests that orthogonal strategies can lead to reduced cellular interactions when compared to native pathways that are instead optimized for growth.Orthogonal pathways are growth-independent pathways optimized for the production of a target chemical. These pathways are characterized by the minimization of interactions between the chemical-producing pathways and the biomass-producing pathways. Physiologically, this means in perfect orthogonal networks (i) the product pathway shares no enzymatic steps with cellular pathways that are responsible for the production of precursors required for biomass and, (ii) only a single metabolite serves as a branch point from which product and biomass pathways diverge. Hence, by design these pathways not only minimize interactions between the cell's biomass-producing pathways and the chemical-producing pathways, but also obey a minimal-walk optimality principle for product formation. The ideal structure of this type of network is shown in Escherichia coli. These pathways, the Embden–Meyerhof–Parnas (EMP) pathway, Entner–Doudoroff (ED) pathway variant and the methylglyoxal (MG) bypass shown in Glycolysis, including its many variants, consumes glucose through a highly connected metabolic network. We hypothesized that these points of connectivity, often described as redundancies that make cells robust to perturbations in their environment12Since analysis of pathways requires a substrate–product pair, we used succinic acid production from these pathways as a case study for our analysis as succinic acid is a well-studied, industrially produced biochemical made from sugars. Using our orthogonality analysis framework , we showThe orthogonality score for succinate production for each of the natural pathways is shown in St, We find that, within the natural pathways, the difference in orthogonality arises from the degree to which the glucose utilization pathways overlap with elements of the metabolism that support biomass. Both the MG shunt and the less connected ED provide routes that bypass several biomass precursors. This can be determined by analysing the elementary flux modes (EFMs) that only produce the target chemical set St,  and calcWhere possible, the orthogonality scores of A second observation was that orthogonality and redundancy are negatively correlated. Since orthogonality quantifies the shared nature of the biomass and the chemical-producing pathways within a metabolic network, we find that increasing the redundancy of the network decreases its underlying orthogonality. As an example, the inclusion of phosphofructokinase, which is typically unique to the EMP pathway, in the network of the ED pathway, reduces the orthogonality score (0.43). Hence, increasing the number of redundant reactions common to product and biomass synthesis decreases the orthogonality of the two objective functions. This result is expected as the goal of orthogonal networks was to share the least number of reactions and thereby reduce redundancies in the network. Hence, as hypothesized, by eliminating shared redundant pathways between the product and biomass precursors, well-designed synthetic pathways can reduce the complexity of supporting two distinct production objectives relative to the wild-type network. Next, given the popularity of growth-coupled strategies, we analysed the impact of growth-coupled strain design on the orthogonality between production and growth.Sx in Sx), the orthogonality score is undefined (as there are no biomass production modes left) and it can no longer be calculated using In growth-coupled strain design, cell growth is linked to product formation by identifying reactions such that biomass-producing EFMs  that eliminates the biomass production pathways and enforces production above a threshold yield.The MCS algorithm allows for the identification of designs with a non-zero production of the target chemical that lead to orthogonality by removing all the growth-dependent pathways (whether coupled or not), resulting in zero growth. We use this algorithm and develop a novel approach  to identify valve reactions that permit orthogonal pathway design. Specifically, the MCS algorithm is used to search for cut sets that guarantee theoretically viable product yields when the growth rate is zero. By applying these cut sets to the metabolic network, and then searching for reactions in the cut set that, when active, can restore growth rates above a desired threshold , it is possible to identify metabolic valves. If the reaction can restore biomass growth, then the cut set can be considered as a candidate for a branched structure. By calculating its orthogonality score when the valve is considered on, permitting flux , the cut sets' suitability can be assessed from the perspective of orthogonality. This approach necessitates a dynamic metabolic engineering (DME) strategy consisting of a growth phase and a subsequent production phase. We provide a detailed explanation of the approach in Methods section.First, since earlier results indicated that glycolytic pathways for consuming glucose and producing biomass were not orthogonal to succinic acid production pathways, we tested the ValveFind methodology on the natural glycolytic metabolism. In this case, we sought to determine whether a branched structure for succinate production could be derived through a set of gene deletions in a network characterized by a low orthogonality score, thereby effectively raising its score. This is akin to the method used to obtain strain designs for metabolite production albeit without demanding growth as is common in these methods. We identified 99 cut set strategies capable of producing succinic acid independent of growth. Of these, only 38 contained a reaction that could serve as a metabolic valve not linked to nutrient limitation . TCA cycle reactions such as isocitrate dehydrogenase and fumarase reactions were among the most commonly identified valve reactions. An example of one of those designs is the deletion set encompassing reactions phosphoenolpyruvate carboxykinase, a transketolase and both malic enzymes. Restoring isocitrate dehydrogenase could restore growth above 90% of wild type . This saWe then calculated the orthogonality scores for all 38 sets and manually verified that none exhibited an obvious branched structure. We initially expected all cut sets to be lower than the wild-type score of 0.41 since we expected that branched structures would be absent from the natural metabolism. Instead, we found that the scores varied considerably between cut sets, and the maximum was 0.43 and the minimum was 0.28. In hindsight, it is intuitive that orthogonality scores can be both greater and less than the unmodified network score. Removing reactions that contribute to redundancy in biomass space increases the orthogonality score if those reactions support biomass synthesis, as shown earlier. In contrast, if those reactions disproportionately remove EFMs that support product formation, it is possible for the score to decrease. Hence, the results suggest that cut set-based design strategies can be selected rationally to minimize the interactions between biomass and chemical production EFMs, even in natural metabolism where branched structures are not apparent. Low scores can be disregarded as they are not suitable for the primary objective of orthogonal metabolism, which is the minimization of interactions.High scores, however, do not necessarily guarantee branched structures, although branched structures, as we will describe below, do result in high scores. In the example, controlling isocitrate dehydrogenase as a metabolic valve prevents cell growth, but a zero flux through that reaction does not preclude the synthesis of most of the individual component metabolites of biomass. Specifically, synthesis of 11 of the 12 biomass precursors is possible even when biomass as a whole cannot be synthesized. The orthogonality score captures this dependence that the individual components of biomass have on network interactions, which can be indiscernible by simply examining individual valve reactions. The relatively lower-orthogonality score for this case (0.41) compared to the synthetic pathways in ldhA, adhE, ackA). These five consist of the gluconeogenic enzymes phosphoenolpyruvate synthase, both malic enzymes as well as succinyl-coa ligase . Whereas, for the synthetic pathways, the same reduction in yield can be achieved with a valve flux that is 10–25% of the wild-type flux. These results further highlight the value of orthogonal design for modulating flux and the differences in the orthogonality between the native and the synthetic substrate utilization pathways. We extend this analysis to the xylose metabolism of E. coli as well to show that these principles appear to be consistent . However, conversion of xylose to succinic acid was highly orthogonal for the non-native Weimberg pathway  it provides non-phosphorylated routes to assimilation, which bypass regulation in the metabolism and, (2) it allows the substrate's entry point into the metabolism in a way that bypasses highly connected nodes of natural metabolism. Both xylose to succinate and ethylene glycol to succinate are examples of these types of pairings. For example, in the context of orthogonality, glucose conversion is not as well suited to succinate production compared to ethylene glycol, which has a substantially higher score. In addition, we wanted to evaluate whether the orthogonality results obtained for succinate production could be extended to other products. Hence, we examined a total of nine different pathways and five additional products  from four substrates to support the generality of the findings in this case study. See Flux through metabolic pathways for biomass and product synthesis are determined by, among many factors, hierarchical cellular regulation that favours biomass synthesis over product synthesis. However, this flux is also a function of the driving force available through that pathway, expressed as changes in the Gibbs free energy and the kinetic parameters of the enzymes in the pathway. Coupling product formation to growth overrides the cell's regulation, that in the presence of a driving force, permits product synthesis and provides a basis for rational engineering to increase that flux.Since orthogonal pathways exist outside any regulatory framework, only a driving force is required to support flux through them. By examining the energetics of their path, we can understand how cell factories can support flux through these pathways. Hence, we calculated the various components of the protein cost: the kinetic, thermodynamic and saturation cost19−1. The synthetic glucose pathway was the most expensive—two orders of magnitude greater than the natural EMP pathway. This difference occurs as a result of poor kinetics of a single enzyme that dehydrates glycerate to pyruvate. In general, these results show that the difference in the cost of supporting flux can vary depending on the pathway. However, not all orthogonal pathways have high protein costs. For example, the orthogonal ethylene glycol pathway has costs of 47 g s mol−1 and the Weimberg pathway has a cost of 110 g s mol−1.We calculated the protein costs of the synthetic pathways and the natural glycolytic pathway . The proWe find an interesting observation when looking at the three components that make up the protein cost. A feature of orthogonal pathways is that they involve non-phosphorylative reaction steps that are orthogonal to the phosphorylative steps that are typically found in the metabolism of biomass pathways. The synthetic pathways have an overall higher cost because the thermodynamic advantage from non-phosphorylating reactions is detracted by a higher kinetic penalty for using inefficient enzymes. For example, the synthetic pathway has almost no thermodynamic penalty while the glycolytic pathway has a 10% penalty. This suggests the possibility that successful enzyme engineering or screening of non-phosphorylating enzymes with better kinetic parameters might lead to orthogonal pathways capable of supporting a higher flux than natural pathways due to their thermodynamic advantage. Taken together, these results reasonably suggest that cell factories that utilize non-natural pathways for substrate utilization may be able to more efficiently support flux for chemical production.de novo strain design. This idea of orthogonality is closely related to modularity, which has been well studied for metabolic networks21222425E. coli is highly connected and robust, elements of it do behave as modular subsystems. Amino-acid biosynthesis control is one such example that allows the cells to be stable in the presence of varying environmental conditionsIn this paper, we provide an alternative perspective to the problem of designing pathways and strains for metabolic engineering. In contrast to the prevalent approach of growth-coupled designs, we suggest that orthogonal pathway design coupled with DME might be effective for When traditional metabolic engineering aims to repurpose cellular metabolism for chemical production, it does so within the evolutionary disposition for growth known as growth coupling. However, the organization of this network structure follows principles of optimality different from those that metabolic engineers would attribute to be optimal for chemical production. We have shown efficient chemical production requires an optimality principle outside the scope of a cellular growth objective, which, akin to elements of metabolism such as amino-acid biosynthesis, require modular and independent subsystems in the cell, and a robust control mechanism over them. In this work, these subsystems can be measured by the ability of the metabolic network to perform two separate tasks . The orthogonality score measures this ability by calculating a ‘distance' metric in the metabolic flux space for these two tasks.A determinant of orthogonality is the overlap of the reactions that support biomass production and the chemical production pathways. A key finding of our work is that native glucose utilization pathways are not orthogonal for succinate and several other products  due to this overlap. Further analysis reveals that this non-orthogonality is largely due to the generation of phosphorylated metabolites and the individual biomass precursor metabolites in these native pathways that are valuable for biomass production, but are not essential for substrate utilization in the chemical production modes. In the We found that, by contrast, the catabolism of most orthogonal pathways lacked phosphorylation reactions. We found both glucose and xylose to be structurally more efficient for product formation when they were not phosphorylated. These types of non-phosphorylated pathways are sometimes observed naturally in microbes, although they are not common. These pathways typically do not involve substrate-level phosphorylation, are less energy-efficient and dissipate more free energy, thereby providing a higher thermodynamic driving force than conventional pathways. This is an important aspect of the flux capacity of metabolic pathways.zwf. (2) Orthogonal pathways implicitly bypass regulation as the biomass precursors tend to be highly regulated. For example, fructose-1-6-bisphosphate has been demonstrated to be a metabolic ‘flux sensor' important to the control of glycolytic fluxThere are two significant benefits for bypassing biomass precursors: (1) Pathways produce higher yields because they avoid carbon losses associated with precursor synthesis. For example, the generation of metabolites of the pentose phosphate pathway results in carbon loss through The significance of a flux sensor in natural metabolism is an important consequence for metabolic engineering. Glycolytic flux during stationary phase often ceases due to the accumulation or draining of intracellular metabolites, which are recognized by these flux sensors, and play a role in reducing glycolytic fluxde novo for a given substrate–product pair is a better approach to metabolic engineering than depending on pathways that consume glucose for any and all biochemical products. The diversification of feedstocks away from glucose, syngas, methane, methanol and glycerol, supports our idea30313233We also uncovered that orthogonality principles rest on the pairing of an input substrate and the product. Accordingly, engineering pathways Our work also has important applications for DME. Conceptually, DME has gained quite a bit of attention353738394041424339It is worthwhile noting that nutrient-based valves can exist, and there have been demonstrations of such valves including the use of oxygenThe recent focus in metabolic engineering has been the design and use of complex synthetic circuits to control gene expression . Each branch should contain reactions entirely devoted to the production of either the product or biomass. The common metabolite serves as an intermediate compound from which biomass precursors as well as the desired biochemical can be produced in the two different branches. We provide a metric, the orthogonality score, that quantifies the network's ability to convert a substrate to a product with as few shared nodes as possible, between the reactions that are responsible for producing biomass and those that are responsible for the conversion of the substrate to the product.St and Sx, respectively, the orthogonality score measures the degree of separation of Sx and St. It also captures the complexity of moving between the St and Sx subspaces as a function of the average number of reactions that need to be turned ‘on' or ‘off' in EFMs to move between subspaces. This measurement is akin to the Euclidean Norm measuring the distance between any two points in n-dimensional space. Geometrically, the score characterizes the complexity of separating the reactions that contribute to product production from the reactions that contribute to biomass formation. Accordingly, by measuring the average similarity (AS) between two shared parts of the same network, orthogonality enables one to make decisions regarding the ability to uncouple biomass from product production.If the stoichiometric solution subspace of reactions contributing to product production and biomass production can be represented by Sx and St containing their respective EFMS, The calculation of the orthogonality score uses EFMs55Sx and St  is designated as a candidate for the metabolic valve. The cut set is then identified as a possible candidate for having a branched network topology since it contains metabolic valve reaction. Its orthogonality score is then calculated. The scores calculated for the resulting network(s) can help confirm its orthogonality and eventually rank them as such. When the MCS algorithm is used in combination with the orthogonality score, the output is a strain metric that attempts to maximize growth-independent chemical production, and a set of genetic interventions required to establish it. This metric allows ranking the designs to determine the best combination of cut sets and metabolic valve reactions, which can then be systematically evaluated for the presence of a branched network topology as shown in et al.We used the methodology described by Flamholz kcat and Km obtained from in vitro enzyme assays data in the literature.The estimated cost accounts for the thermodynamics and the kinetics of the enzyme with respect to its interaction with the substrates and products of the reaction, and is also a function of the forward flux flowing through the reaction. We used the linear programming (LP) formulationThe data that support the conclusions of this study are available from the authors on request.http://www.labs.chem-eng.utoronto.ca/mahadevan/the-lab/downloads/All relevant codes to run the algorithms are publicly available at How to cite this article: Pandit, A. V. et al. Redesigning metabolism based on orthogonality principles. Nat. Commun.8, 15188 doi: 10.1038/ncomms15188 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references.This table contains over 168 strain designs and metabolic control valves for the succinate production in Escherichia coli.


The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk. Clostridium difficile infections [The human gut is colonized since birth with complex microbial communities, mainly consisting of bacteria with millions of unique genes that show substantial interpersonal variation in adult life . This cofections . HoweverGenome-scale metabolic models (GEMs) are mathematical representations of the knowledge on an organism’s metabolic capacity and have been previously applied in bacterial systems for a variety of purposes, including the design of cultivation media, phenotypic characterizations, metabolic engineering, drug discovery, and to study interspecies interactions. For an overview of common GEM applications, we would like to refer to these reviews , 8.Bifidobacterium adolescentis L2-32, taken up by Faecalibacterium prausnitzii A2-165 and used to produce butyrate from sugar. This enabled the prediction of F. prausnitzii acetate requirements for butyrate production and how this relates to its low abundance in cases of Crohn’s disease [Strong developments in both GEMs and gut microbiome research are bound to facilitate moving from correlation studies to gaining mechanistic insights. GEMs can integrate knowledge on the metabolism of one or more gut microbes and predict how this metabolic system functions in different niches in the gut. The gut environment includes nutrient gradients both along the length of the GIT, as well as along the mucosal gradient and villi, and have strong effects on the microbial function , 10. GEM disease , showingIn the remainder of this review, we will discuss the use of GEMs in gut microbiota research and how GEMs can advance gut research toward the understanding of gut homeostasis and (dys)function. We will focus on the metabolic reactions of the microbes in the gut, on their growth, on their interactions, and on the metabolites produced. These are either primary products of microbial metabolism or breakdown products of our diets or host compounds, having a plethora of functions, ranging from SCFAs that fuel enterocytes and have specific signaling and immune functions, to vitamins and other host growth-promoting compounds . Most ofThe basis of GEM construction is the genome annotation of the microbe of interest since this predicts the enzymes a microbe encodes, and thereby provides a list of chemical reactions the microbe can perform. This list of chemical reactions forms the draft metabolic model, which is often far from complete . TypicalAfter gap-filling, GEMs are expected to be able to sustain in silico growth of the modeled organism. Growth is modeled as the formation of biomass in a complex organism-specific reaction involving a large number of biomass precursors such as DNA, RNA, proteins, lipids, ATP, NADPH, and various small molecules. The use of biomass precursors in bacterial GEMs has recently been thoroughly explored resulting in a shortlist of universally essential, as well as organism-specific biomass precursors . If all GEMs are typically evaluated by comparing predicted growth phenotypes for both wild type and mutant strains to the available experimental data. This experimental data usually consists of growth measurements for a large number of media containing different carbon, nitrogen, phosphorus, and sulphur sources. For the comparison, both the experimental data and the GEM predictions are discretized to the two states ‘growth’ and ‘no growth.’ This binary discretization leads to two different types of inconsistencies: (i) growth predicted by the GEM but not experimentally found, and (ii) growth that is experimentally validated but not predicted by the GEM. In the first case, the GEM overestimates the microbe’s abilities, suggesting it may include reactions that the microbe cannot perform. In contrast, the other case suggests that the GEM is missing reactions. This comparison can thus be used to evaluate both the annotation and the gap-filling process that underlie the GEM construction. For example, if the removal of a single reaction from the GEM results in a large improvement of GEM predictions, this suggests that this reaction was erroneously added and should be considered for removal. This process of using experimental data to find incorrect GEM predictions and subsequently making changes to the GEM has also been combined into algorithms, such as GrowMatch , that wiThe established manual GEM reconstruction process ultimately results in high-quality GEMs, but is extremely time-consuming . The advAkkermansia muciniphila that was used for a human safety study [Oscillospira spp. that are receiving considerable attention [The basis of classic microbiology is the ability to culture bacteria in a pure culture on a well-defined medium. Such a well-defined medium is required for detailed metabolic analyses, growth optimization, and finally also in a feedback loop with the GEM itself to optimize the metabolic model. Moreover, well-defined media devoid of animal-derived compounds will be needed when intestinal microbes that are therapeutically effective are to be cultured and used in therapeutic settings. An example is the recently developed medium for ty study . Finallyty study , which wty study , 32. Howty study . A numbety study , which httention –37. A maThere are three main challenges in the use of GEMs for the design of defined growth media: (i) The in silico biomass composition is an influential aspect of the GEM as it defines all metabolites required for growth . The omiLactobacillus plantarum WCFS1 [L. plantarum WCFS1 was automatically constructed based on its genome sequence and subsequently extensively manually curated [6, as well as for the amino acids arginine, glutamate, isoleucine, and tryptophan. The incorrect predictions pinpointed errors in both the GEM construction process and in the experimental procedures, and also pinpointed distinct metabolic features of L. plantarum WCFS1, for example (i) the incomplete folate biosynthesis pathway in the GEM was in part due to a missing EC number for a correctly annotated gene, as well as no reactions in Metacyc for another EC number. (ii) The GEM lacked a complete isoleucine biosynthesis pathway, but growth was observed in the isoleucine omission experiment. This turned out to be a result of isoleucine contamination in the other amino acids. (iii) A missing reaction for thiamine biosynthesis was assigned to a gene involved in molybdopterin biosynthesis. In Enterobacteria, these reactions are carried out by two paralogs, but it appears that both reactions are carried out by a single enzyme in L. plantarum [Despite these challenges, GEMs have proven to be useful in the design of chemically defined growth media, as has been shown for the lactic acid bacterium um WCFS1 . Lactic um WCFS1 . Therefoum WCFS1 , 41, as um WCFS1 , 43. The curated , 44. Thelantarum . These rLactococcus lactis IL1403, was constructed and used to remove all non-essential metabolites from a rich medium in order to design a minimal medium for physiological studies [L. lactis, and that glutamate and glutamine are not [L. lactis IL1403 GEM, a compound was essential if its omission reduced the specific growth rate below 0.01/h. In the omission experiment, a compound was considered essential if the final OD dropped below 40% of the final OD in the rich medium. This introduces a certain level of ambiguity and, for example, if the experimental threshold would instead be at 20%, asparagine and serine would not have been considered essential.A GEM of a different lactic acid bacterium,  studies . This ex are not . At firsStaphylococcus aureus N315, a pathogen that frequently infects hospitalized patients [S. aureus N315 has no intrinsic auxotrophies for amino acids, but that some particular isolates do require some amino acids [S. aureus can indeed synthesize these amino acids. This study showed how a GEM can aid in omitting nutrients from a known defined medium.The ability to culture pathogens and probiotics is important to study them in isolation and to determine their role in the gut microbiome. Therefore, a GEM was used to design a minimal growth medium for patients . The GEMno acids . This diThese three case studies show that GEMs are a good starting point for designing minimal media. In fact, the ability of GEMs to design growth media was recently emphasized by the development of the Minimal Environmental TOol (MENTO) . MENTO pF. prausnitzii, a prevalent and potential beneficial gut microbe that is commonly grown on the chemically undefined YCFAG medium [F. prausnitzii. This curation involved changing the biomass reaction, updating reaction directionalities, adding species-specific pathways, and filling gaps. The curated GEM was then used to predict a chemically defined growth medium called CDM1. CDM1 did, however, not facilitate in vitro growth and was subsequently supplemented with additional nutrients to form an extended medium CDM2, which did facilitate in vitro growth. The researchers then used LC-MS to identify what metabolites in CDM2 are net consumed, and what metabolites are net produced. The metabolite consumption and production data was then used to improve the GEM and the corresponding genome annotation. Ultimately, the researchers were able to design a refined and chemically defined medium CDM3 that facilitated both in silico and in vitro growth, albeit that growth was still rather poor and unreliable [Such a manually curated ModelSEED GEM was recently used for minimal medium design for G medium . The autreliable .Bacteroides caccae ATCC 34185 was successfully used to design the first chemically defined medium supporting in vitro growth for this gut microbe [The requirement for manual curation of ModelSEED  GEMs pri microbe .Metagenomic studies  and singMost microbes have versatile and complex metabolic pathways. Often, many alternative pathways are available for the conversion of the available substrate to all biomass components. GEMs can be used to explore all possible phenotypes for a wild type or mutant strain in a given environment. In addition, GEMs can be used to interpret experimental data that is difficult to directly connect to metabolic rates, such as transcriptomics and proteomics data . GEMs, wA main challenge in the use of GEMs for the prediction of phenotypes of gut microbes is that these models are—traditionally—restricted to metabolic activities. They do not explicitly include regulation nor the synthesis of mRNAs or individual proteins. Hence, GEMs can accurately predict growth phenotypes that are related to the optimal conversion of substrates to biomass components , but do Salmonella in a mouse model system. The GEM describes a very versatile metabolism that enables Salmonella to utilize 31 host nutrients, allowing it to grow fast within the host cell. The GEM predicted the pathogenicity of phenotypes and was accurate in 92% of the cases [Salmonella show similarities in host dependency for growth substrates and biosynthesis to other pathogens. Like Salmonella, other pathogens are also capable of degrading purine nucleosides, pyrimidine nucleosides, fatty acids, glycerol, arginine, N-acetylglucosamine, glucose, and gluconate. Similarly, it was hypothesized that comparisons of metabolic patterns between Pseudomonas aeruginosa and non-pathogenic relatives could yield insight into opportunistic pathogenic phenotypes of this species [Burkholderia species [P. aeruginosa also showed a versatile metabolic pattern and accounted for virulence inducing pathways, such as exopolysaccharide alginate synthesis [GEM-driven exploration of the metabolic capacities of pathogens has been explanatory for pathogenic phenotypes. For example, a GEM was used to predict virulence of he cases . In addi species , as has  species . The metynthesis .Enterococcus faecalis, a human gut pathogen [In more recent research, highly quantitative proteomics and metabolic measurements were used to impose pH-dependent constraints on the GEM of pathogen . The pH-Lactobacillus reuteri, with potentially opposite effects on the human immune system, were analyzed by visualizing the data on two GEMs. The analysis revealed that both strains produce vitamins, essential amino acids, and mucosal binding proteins, but that they differed in their production of potential inducers of tumor necrosis factor [Transcriptomics and proteomics experiments aim to discover what an organism is doing, but the data is often difficult to analyze because there are no one-to-one relationships between expression levels, protein quantities, enzyme activities, and fluxes , 67. GEMs factor . The pres factor . The evas factor , which dEscherichia coli, Mycobacterium tuberculosis, and Mycoplasma genitalium have been elucidated and incorporated in metabolic models [A different approach to find out what an organism is doing, rather than what it can do, is by combining GEMs with other models, such as regulatory networks –74. The c models –78. Basec models , also esThese examples show how GEMs can be used to explore possible phenotypes, and to predict actual phenotypes based on ~omics data or regulatory models. However, we highlight the need for a thorough evaluation on methods for the integration of ~omics data and regulatory networks with GEMs to predict the phenotypes of gut bacteria in vitro and ultimately in vivo. This will be an important stepping-stone in predicting the role of bacteria under different gastrointestinal conditions, on which also other microbial species have a big influence.Within the gut microbiome, there are numerous microbial interactions and networks. Three types of simple multispecies interactions have been described and modeled before: mutualism, commensalism, competition, and neutralism –82. GIT-Multispecies modeling using GEMs is complicated through the aforementioned phenotype prediction challenge regarding secondary metabolites, but also by two other challenges: (i) the vast majority of GEM analysis methods rely on a steady-state assumption, but microbial interactions via signaling molecules are inherently dynamic. (ii) Flux prediction methods are based on computational optimization with regards to a single metabolic goal, usually the maximization of biomass production; a reasonable goal for an individual microbial species. However, when multiple microbes are modeled simultaneously, it is not a reasonable assumption that all work together to maximize total biomass production. The examples discussed hereafter provide a rough overview of different approaches that have been taken to minimize or circumvent these issues.Desulfovibrio vulgaris and archaeon Methanococcus maripaludis S2 into a single model with a shared extracellular environment [D. vulgaris ferments lactate, and M. maripaludis consumes the fermentation products formate, dihydrogen, and acetate. In this work, the aforementioned issue on optimizing for biomass production was evaluated by applying distinct weights to the different types of biomass. In other words, the mathematical optimization would prioritize one type of biomass over the other in order to explore how this would affect overall flux predictions. The predicted biomass production for D. vulgaris was practically independent of the relative weights, whereas the M. maripaludis biomass production increased if it received higher weights. This is due to the sequential nature of the interaction between these bacteria, where D. vulgaris effectively ‘feeds’ M. maripaludis. However, this approach is not suitable if the community members exhibit cross-feeding or substrate competition.The pioneering work in GEM-based multispecies modeling was directly combining two GEMs for the mutualistic bacterium ironment . In thisE. coli, while cooperation was more evident in Clostridia species that were able to degrade lignin and cellulose, which releases free sugars to other bacteria. This type of macromolecule degradation is highly important in degradation of host dietary compounds and thus directly relates to gut health.A similar approach was taken to identify media that stimulate commensal or mutualistic relationships between each possible pair of seven well-known microbes . This nuInstead of looking into the details of the interactions between a few species, GEMs have also been used to elucidate general properties of the co-occurrence of microbes. Specifically, there are two main mechanisms driving species co-occurrence: (i) habitat filtering: microbes occupy a similar nutritional niche and compete, and (ii) species assortment: microbes have complementary metabolisms and cooperate. A recent study aimed to identify which of these two mechanisms is the driving force behind the co-occurrence of microbes in the human gut . TherefoE. coli and Salmonella enterica [E. coli under cooperative conditions, but more robust to genetic perturbations in S. enterica under competing conditions [In order to understand how gut communities form and change, it is also important to consider spatial and temporal effects. The novel modeling framework COMETS —Computatenterica  on compeenterica . Interesnditions . These rE. coli. However, as a mucus glycan gradient was imposed using diffusion on the 2D grid, the glycan-degrading Bacteroides thetaiotaomicron became dominant in the mucosal layer, while the lumen represented a more varied community still dominated by E. coli.A conceptually similar framework is BacArena . BacArenB. thetaiotaomicron affect its metabolism [B. thetaiotaomicron model was constructed using ModelSEED [A multispecies interaction of particular interest is the interaction between gut microbes and their host. The host is not only an important environmental factor for gut microbes, but is also metabolically active itself. Additionally, host behavior such as diet intake has a great and reproducible influence on the microbiota composition . GEMs hatabolism . A B. thodelSEED  and, aftodelSEED .Host-microbe interactions have also been modeled using a single ‘supra-organism model’  to repreThe use of GEMs to predict multispecies interactions and to study the influence of perturbations in environmental factors and communities is a valuable asset in microbiota function prediction. In this way, it can be predicted how individual species contribute to healthy and diseased conditions. The increase in tools for the prediction of multispecies interactions highlights the importance of this application. Moreover, these predictions were instrumental in the prediction of diets to improve the metabolic function of gut microbiota . UltimatAfter a few decades of characterizing gut microbiota composition many gut microbes have been sequenced , 105. OvHere, we reviewed three ways in which GEMs contribute in elucidating gut microbiome function. We described how GEMs are used to (i) culture bacteria, (ii) predict bacterial phenotypes under changing conditions, and (iii) study the interactions both among the bacterial species and with their host.We have shown that recent advances in automated generation of GEMs , 29, sinwhy species are present and what they do, instead of who they are, as was the focus in the last decades. We expect that GEMs will contribute to elucidate the mechanisms behind known probiotics, as well as in identifying new probiotics, and understanding the role of different bacteria in complex ecosystems. Ultimately, GEMs can contribute to the design of controlled interventions that steer gut composition and activity to improve human health.GEMs allow understanding


Prochlorococcus marinus thrives by adopting all three strategies and a fourth, previously unknown strategy. By generating a detailed model of its metabolism, we found that strain MED4 has evolved a way to reduce its dependence on phosphate by minimizing the number of enzymes involved in phosphate transformations, despite the stringency of nearly half of its metabolic genes being essential for survival. Relieving phosphorus limitation, both physiologically and throughout intermediate metabolism, substantially improves phosphorus-specific growth rates.Microbes are known to employ three basic strategies to compete for limiting elemental resources: (i) cell quotas may be adjusted by alterations to cell physiology or by substitution of a more plentiful resource, (ii) stressed cells may synthesize high-affinity transporters, and (iii) cells may access more costly sources from internal stores, by degradation, or by petitioning other microbes. In the case of phosphorus, a limiting resource in vast oceanic regions, the cosmopolitan cyanobacterium  Prochlorococcus marinus thrives. Physiological and regulatory control of phosphorus acquisition and partitioning has been observed in HLI both in culture and in the field; however, the optimization of phosphorus metabolism and associated gains for its phosphorus-limited-growth (PLG) phenotype have not been studied. Here, we reconstructed a genome-scale metabolic network of the HLI axenic strain MED4 (iJC568), consisting of 568 metabolic genes in relation to 794 reactions involving 680 metabolites distributed in 6 subcellular locations. iJC568 was used to quantify metabolic fluxes under PLG conditions, and we observed a close correspondence between experimental and computed fluxes. We found that MED4 has minimized its dependence on intracellular phosphate, not only through drastic depletion of phosphorus-containing biomass components but also through network-wide reductions in phosphate-reaction participation and the loss of a key enzyme, succinate dehydrogenase. These alterations occur despite the stringency of having relatively few pathway redundancies and an extremely high proportion of essential metabolic genes . These strategies are examples of nutrient-controlled adaptive evolution and confer a dramatic growth rate advantage to MED4 in phosphorus-limited regions.Inorganic phosphorus is scarce in the eastern Mediterranean Sea, where the high-light-adapted ecotype HLI of the marine picocyanobacterium IMPORTANCE Microbes are known to employ three basic strategies to compete for limiting elemental resources: (i) cell quotas may be adjusted by alterations to cell physiology or by substitution of a more plentiful resource, (ii) stressed cells may synthesize high-affinity transporters, and (iii) cells may access more costly sources from internal stores, by degradation, or by petitioning other microbes. In the case of phosphorus, a limiting resource in vast oceanic regions, the cosmopolitan cyanobacterium Prochlorococcus marinus thrives by adopting all three strategies and a fourth, previously unknown strategy. By generating a detailed model of its metabolism, we found that strain MED4 has evolved a way to reduce its dependence on phosphate by minimizing the number of enzymes involved in phosphate transformations, despite the stringency of nearly half of its metabolic genes being essential for survival. Relieving phosphorus limitation, both physiologically and throughout intermediate metabolism, substantially improves phosphorus-specific growth rates. Prochlorococcus marinus is the numerically dominant photoautotroph in the vast oligotrophic gyres, where it often contributes a majority of carbon fixation  network reconstructions represent a cornerstone of systems biology, serving as both a knowledge base for contextualizing physiological and multiomics data types and as a framework for computational approaches, such as constraint-based modeling , 18. GEMiJC568) and used the model to simulate growth in a variety of defined media to describe its metabolic capabilities and quantify fluxes associated with conditions of phosphate-, carbon-, and light-limited growth . The imprint of adaptive evolution under phosphate-depleted conditions was found throughout the MED4 metabolic network, with implications for global cellular elemental turnover and energy metabolism. To explore MED4 metabolism, we first described fundamental properties of the metabolic network in relation to a diverse selection of microbial GEMs (referred to below as the ensemble). We then used FBA to compute its metabolic capabilities and compared model simulations with experimental data from both the laboratory and the field. Finally, we discuss genome-wide alterations to phosphorus metabolism and implications for the PLG phenotype.Here, we discuss how we reconstructed a GEM for MED4  ; Ensemble Models, table summarizing ensemble models ; iIntracellular P, table of intracellular phosphate concentrations in P-replete and P-limited media for Prochlorococcus marinus MED4, Synechococcus WH7803, Escherichia coli MG1655, and Saccharomyces cerevisiae; Succinate costs in mutants, table of NAD(P)H costs associated with de novo succinate synthesis for each of the strain variants . Values for NAD(P)H-consuming reactions represent the difference between fluxes in the steady-state solution and fluxes in the forced-accumulation solution. Download Data Set S2, XLS file, 0.4 MB.Accompanying data sets used and produced in the manuscript. g et al. , and theCopyright © 2016 Casey et al.2016Casey et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the iJC568 biomass objective function  composition, mass and energy budgets, elemental stoichiometry, and standard enthalpies were calculated and compared with reported experimental data. The elemental stoichiometry of the BOF composition was within the standard error of reported values for carbon, nitrogen, and phosphorus ratios under balanced-growth conditions   and Saccharomyces cerevisiae (21 kJ g DW−1)  . The res−2 s−1 blue light . The short-term [14C]bicarbonate primary production measurements fell between model net and gross primary production for most of the light cycle , growth yields, exchange fluxes , and central carbon metabolism metrics (phosphoglycerate kinase/phosphoglycerate mutase flux and anapleurotic CO2 fixation) were in close agreement with those reported for strain MED4, where available, and for Synechocystis sp. strain PCC6803 , consistent with the loss of many regulatory functions (iJC568 belong to the strain-independent “core” of the Prochlorococcus pan-genome (compared with 65% of the whole genome), nonlethal genes were enriched (17%) in strain-dependent “flexible” genes compared with the amount of essential genes (8%). A similar pattern was seen for gene essentiality for mixotrophic growth in supplemental medium  to 38% (Synechocystis strain PCC6803) of metabolic genes, reinforcing the adaptive gene loss hypothesis for Prochlorococcus  (Haemophilus influenzae strain Rd KW20 (47% of the whole genome)  is significantly higher than the proportions in the GEM ensemble   and Haem genome) .iJC568 metabolic network. A fundamental attribute of the stoichiometric matrix S  is the connectivity of the column and row space, defined here as the degree distribution of the undirected bipartite graph. Metabolite participation  was assessed for iJC568 and the ensemble by normalizing the degree distribution to the number of nonexchange and transport reactions of each network. For example, the obligate anaerobes Thermotoga maritima and Methanosarcina barkeri strain Fusaro had oxygen metabolite participation values near zero. The patterns of metabolite participation generally clustered together according to taxonomic group  is useful for interrogating network properties and for comparing the structural features and dynamic coupling of networks. Among the numerous factorization methods for analyzing the unconstrained solution space of the stoichiometric matrix, the most intuitive is the singular value decomposition (SVD), given by S = TUΣV. The m × r column-space (U) and r × n row-space (V) matrices contain the linearly independent orthonormal eigenvectors of the four fundamental subspaces of the m × n matrix S, and the singular values σ = diag(Σ) that define the set {σ1, … rσ}, where r is the rank of S, are measures of the distortion induced by each linear transformation. In this way, the first mode (σ1) is the weight given to the first systems reaction, a linearly dependent set of reactions forming a basis pathway that is effectively a spanning set of S. For iJC568, and typical of other networks, the first systems reaction maps to a set of reactions involving proton translocation, due to the central role of the proton motive force in the electron transport chain and photosystems. The first systems reactions correspond quite closely to the metabolites with the highest flux sums , with the exception of the photon flux, which participates in the 3rd and 6th modes in iJC568. In other organisms, subsequent modes vary in composition according to the metabolic capabilities of the organism  redox, and the proton motive force] were similar for iJC568, phosphate transformations were associated with the 8th mode, compared with the 4th mode of Escherichia coli, Haemophilus influenzae, and Helicobacter pylori (iJC568 (0.9%) than in other phyla    cycle reactions d-glyceraldehyde-3-phosphate:NAD+ oxidoreductase and sedoheptulose 1,7-bisphosphate 1-phosphohydrolase. The majority of the remaining 60% of the P turnover was shared between reactions with phosphorylated central carbon metabolites, nucleic acid intermediates, and dinucleotide energy carriers, implying that much of the difference in turnover is due to phosphate participation.The implications of a diminished role for phosphate in MED4 were investigated by comparing the elemental turnover of intermediate metabolism based on flux sums and quotas for hydrogen, carbon, nitrogen, oxygen, phosphorus, and sulfur with that in the high-quality reconstruction (iTO977)  for Sacc10.1128/mSystems.00065-16.2Figure S2 iJC568 and iTO977. Download Figure S2, EPS file, 0.01 MB.Elemental flux sums and turnover comparison of Copyright © 2016 Casey et al.2016Casey et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Prochlorococcus is known to have an extremely flexible elemental stoichiometry, perhaps a key to coping with the variable supply of nutrients and extended periods of nutrient starvation typically encountered in the oligotrophic surface waters. Populations of Prochlorococcus in the periodically P-limited Sargasso Sea exhibited a wide range of particulate C/P ratios (120:1 to 350:1), varying latitudinally (4+/H2PO4− ratio = 800:1), the MED4 particulate C/P ratio increased to 464:1 ± 28:1, compared with 121:1 ± 17:1 under balanced growth (molar NH4+/H2PO4− ratio = 16:1) , and the soluble pool (BioPool) . The remaining P quota (2%) is found in lipids and in protein fractions. Since the discovery that P-limited MED4 and other Prochlorococcus strains have virtually eliminated phospholipids  in favor of sulfolipids and glycolipids   requires that all of these fractions must be capable of drastic reductions. Accounting for the 33% increase of the C quota under P limitation (P = 0.71) to the coefficients of variation (CV) within any particular biomass composition (12% to 14%). In contrast, the growth rates increased 370% ± 12% over the allowable C/P range for the PLG phenotype, with smaller compositional variations . To identify which biomass components would yield the highest growth rate gains, we performed a brute-force sensitivity analysis (Ψk) , cell wall (Ψk = 0.15), and the soluble pool (Ψk = 0.13). Within the cell wall crude fraction, di-trans-poly-cis-undecaprenyl diphosphate and lipid A disaccharide were responsible for 74% and 26% of the sensitivity, respectively. Within the soluble pool, most (70%) of the sensitivity was due to nicotinamide dinucleotides.udinally . When gr = 16:1) . The parmitation . Over thZ scores for each reporter metabolite, we identified a set of metabolites which were associated with up- or downregulated proteins from differential expression (λ)  can be used to infer the degree to which the production of certain metabolites is limiting growth. By constraining a suboptimal upper bound on the Pi transport rate and calculating the shadow prices, a set of 28 metabolites were determined to be negative, or growth limiting. These include phosphate esters, nucleotides, cell wall precursors, NADH, and the phosphorylated central carbon metabolites 3-phosphoglycerate and 2-phosphoglycolate. A comparison of reporter metabolites and shadow prices showed agreement between Z scores and the relative magnitude |− λ| . By ranking pression  and chanpression  of phosp− λ| see . These q10.1128/mSystems.00065-16.3Figure S3 Figure S3, JPG file, 1.8 MB.Reporter subnetworks identified from differential expression in P-replete and P-limited media. Data are from reference Copyright © 2016 Casey et al.2016Casey et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in silico PLG phenotype in iJC568, with an increase in the PSI/PSII photon absorption ratio at the maximum growth irradiance (Imax) as a nonlinear function of the Pi transporter flux, converging on a new steady state for ATP and reductant for the PLG phenotype. We compared growth and key photosynthetic fluxes between the LLG and PLG conditions by phenotype phase plane (PhPP) analysis, varying light and Pi uptake rates  pathway begins with PSII, cytochrome b6f (Cytb6f), and PSI and ends with ferredoxin-NADP+ reductase (FdR). LEF is linked by the oxidation and reduction of the plastocyanin (Cu2+PC/Cu+PC), ferredoxin (Fdox/Fdred), and plastoquinone (PQ/PQH2) pools. A set of alternative electron flow (AEF) pathways include cyclic electron flow (CEF) around PSI via NADPH dehydrogenase type 1 (NDH) or via ferredoxin:quinone oxidoreductase (FQR), pseudocyclic electron flow (PCEF) around PSII via cytochrome oxidase bd , the Mehler reaction, and photorespiration. The activities of AEF pathways affect a number of fundamental fluxes, including the ATP/NADPH ratio, photosynthetic efficiency, quantum yield, and the photosynthetic quotient. Under optimal growth conditions , the ratio of PSI to PSII absorption was 2.3, with the entirety of the PSI flux split between CEF around PSI via NDH to prevent overreduction of the PQ pool and to NADPH via FdR to maintain the optimal ATP/NADPH ratio of 1.30. Under LLG conditions (above the LO), the PSI-to-PSII absorption ratio increased to 2.4, with the PSI flux mostly diverted to NADP+, at the expense of CEF, via NDH. Under PLG conditions (below the LO), ATP deficits resulted in a PSI-to-PSII absorption ratio of 0.8, with excess reductant diverted to PCEF around PSII via COX and LEF to NADPH from PSI. CEF around PSI was diverted to FQR from NDH under PLG conditions.In culture, P stress induced changes not only in acquisition mechanisms and biosynthetic pathways but also in central carbon metabolic pathways and the photosynthetic apparatus. Following the initiation of P stress, photosystem II (PSII) was degraded, with a concomitant decrease in carbon fixation proteins, although photosystem I (PSI) and subunits of the ATP synthase complex remained intact . This fi10.1128/mSystems.00065-16.4Figure S4 in silico SDH knock-in mutant. The center panel is a detailed view of the iJC568 photosystem, including the transport of protons across the thylakoid membrane (orange text), cofactors associated with each reaction (black arrows and numbers), and the stoichiometry of metabolites associated with each reaction (blue arrows and numbers). Reactions belonging to the LEF (pink), CEF (yellow), PCEF (orange), and succinate dehydrogenase knock-in (black) include photosystem II (PSII), ferredoxin-NADP+ reductase (FdR), photosystem I (PSI), ferredoxin:quinone oxidoreductase (FQR), cytochrome oxidase bd (COX), cytochrome b6f, NADPH dehydrogenase type 1 (NDH), ATP synthase, and succinate dehydrogenase (SDH). Reactions catalyze oxidations (toward the left) and reductions (toward the right) of ferredoxin (Fdox/Fdred), plastoquinone (PQ/PQH2), NADP+/NADPH, and plastocyanin (Cu2+/Cu+). Arrows in the condition-specific panels  are scaled by the flux of electrons, based on the individual fluxes, the stoichiometry of each metabolite, and the number of valence electrons exchanged, normalized to the incident number of photons absorbed. Download Figure S4, TIF file, 2.8 MB.Illustration of changes to photosynthetic electron flow under optimal growth conditions, light-limited growth conditions, phosphorus-limited growth conditions, and phosphorus-limited growth conditions for the Copyright © 2016 Casey et al.2016Casey et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2+PC at the cost of PQH2 via Cytb6f. Furthermore, all cyanobacteria have a branched TCA cycle, lacking 2-oxoglutarate dehydrogenase; however, Prochlorococcus marinus and marine Synechococcus spp. also lack the recently discovered analogous enzymes 2-oxoglutarate decarboxylase (2OGDC) and succinic semialdehyde dehydrogenase (SSADH), which are necessary to regenerate succinate from 2-oxoglutarate H costs of four in silico variants grown photolithoautotrophically: wild type (WT), +2OGDC+SSADH, +SDH, and +2OGDC+SSADH+SDH. NAD(P)H costs were calculated as the change in flux sums for each variant after forcing a net accumulation of succinate . For both the WT and the +2OGDC+SSADH mutant, the NAD(P)H cost of succinate synthesis was primarily due to the CEF enzyme NDH and the Calvin-Benson-Bassham pathway enzyme triosephosphate dehydrogenase. For both the +SDH and +2OGDC+SSADH+SDH mutants, electron flow largely bypasses NDH, reducing the cost of succinate synthesis. However, for the +SDH mutant , additional NAD(P)H costs were required for precursor synthesis via malate dehydrogenase. In summary H in the absence of SDH, while the reverse is true for MED4 WT, in which the cost decreases from 21 to 14 molecules of NAD(P)H in the absence of SDH. These findings are qualitatively in agreement with the findings for wild-type and –SDH, –2OGDC, and –SSADH mutants of Synechococcus sp. PCC7002 (2 reduced [mol photons]−1) over the range of PLG phenotypes.It is plausible that persistent changes to the optimal path of electron flow under P-limited conditions have resulted in a restructuring of the MED4 photosynthetic apparatus. MED4 lacks the genes encoding succinate dehydrogenase (SDH), which catalyzes the succinate-fumarate couple and directly links the oxidation of TCA cycle intermediates to the reduction of the PQ pool in the photosynthetic electron chain. Succinate oxidation forms an AEF pathway which supplies reductant to Culutarate  and havelutarate . To quan PCC7002 . As a coProchlorococcus PLG phenotype in an effort to characterize its adaptation to low-phosphate marine environments. In addition to sulfolipid substitution and utilization of organophosphorus to satisfy P demand, we uncovered three additional strategies that reduce the influence of phosphorus control over optimal growth flux topology: (i) choreographed reductions in enzymes reliant on orthophosphate as a substrate across the entire metabolic network, (ii) reductions in phosphorus-rich biomass constituents, and (iii) alterations to photosynthetic and respiratory electron flow. Phosphorus, the “staff of life”  for phosphate from a network perspective, suggesting that low phosphate availability may have guided gene loss during the evolution of MED4, streamlining the loss of genes associated with phosphate transformations. Low-phosphate-reaction participation in iJC568 contributed to decreased total elemental phosphorus fluxes in a comparison with iTO977, resulting in slower turnover and possibly lowering soluble phosphate concentration requirements to maintain optimal fluxes throughout the network. This prediction is supported by experimental determinations of intracellular phosphate concentrations and their responses to phosphate availability  in the network. In a comparison between s WH7803 . Importarevisiae , althougin silico methods to identify where the additional 98% of P quota reductions were to be found. Sensitivity analysis of the BOF composition suggested that reductions in RNA and the cell wall were likely candidates. The selective reductions in phospholipid, cell wall, and RNA synthesis were also observed by the method of reporter subnetworks from differential gene expression under balanced- versus phosphate-limited-growth conditions. Our exhaustive search algorithm predicted a set of feasible biomass compositions for a range of C/P ratios, with optimal growth corresponding to a phosphorus composition for DNA/RNA/lipid/BioPool/cell wall of 1:2.8 ± 0.5:4.7 ± 0.9:1.6 ± 0.4:3.0 ± 0.5. This optimal biomass composition was partially validated in our culture experiments with a shift in the DNA/cell wall ratio from 1.51 under CLG conditions to 2.75 under PLG conditions, assuming LPS remains proportional to cell wall content. It is unclear what physiological effects such a dramatic reduction in cell wall might have; even under conditions of rich medium growth, MED4 has a reduced cell wall thickness (19 nm), compared with 34-nm thickness in a strain isolated from deeper in the euphotic zone where phosphate limitation is less prevalent : how can genome replication be feasible when a fully replicated genome alone translates to a C/P of 264:1? Based on comparison of the phosphatidylglycerol contents of P-replete and P-limited cultures , substit4:1: how eHL-I has shed SDH. The loss of SDH creates an unexpected link between photosynthetic quantum yield and phosphorus-limited growth, and it is at least suggestive that the gene coding for the A subunit of SDH, sdhA, is the one annotated gene that differentiates the high-light ecotypes eHL-I, which dominates the P-limited Mediterranean Sea, and eHL-II, which dominates the predominantly N-limited Atlantic and Pacific Oceans.Consistent with the theme of low-phosphate-guided gene loss, the conspicuous absence of SDH in MED4 and other ecotype HL-I strains prompted us to investigate the role of this otherwise ubiquitous enzyme under a variety of growth conditions. The reversible succinate-fumarate couple and its catalyst, SDH, are found in all three domains of life, including the last universal ancestor, and were probably conserved throughout organismal evolution . AdditioNearly three decades after its isolation, MED4 has very likely undergone considerable laboratory evolution; however, its metabolic potential remains imprinted with the signature of the chronically phosphorus-depleted surface waters of the eastern Mediterranean Sea. Metabolic network reconstruction and constraint-based modeling revealed previously unknown evolutionary strategies for organisms perpetually coping with low phosphate availability. These strategies include a redesign of the metabolic network to alleviate metabolic control by a single substrate, global control of phosphorus partitioning in biomass components, and optimization of photosynthetic electron flow.http://biomet-toolbox.org). Hidden Markov models (HMM) of protein sequences for each KO were retrieved and queried against the MED4 reference genome (NCBI GenBank: BX548174.1). Metabolic genes which were excluded from HMM hits were individually examined using different resources . General and unbalanced reactions were excluded, and extensive manual curation was performed for the gap-filling and balancing process, due in part to the conservative reaction assignment criteria, as well as the incomplete genome annotation (30% of open reading frames [ORFs] were assigned to putative or unknown functions), which is typical of marine cyanobacteria . Draft model reactions were checked for elemental and charge balance, for known substrate and cofactor specificity, and for directionality. Reaction directionality was determined by thermodynamic favorability  . In all cases, the presence/absence of the reaction was scored for evidence according to the guidelines in reference Gaps were identified by iteratively examining dead-end metabolites and associated blocked reactions and returning to the literature for evidence of synthesis, degradation, secretion, or uptake of associated metabolites. Conserved domains from the resulting orphan reactions were then queried against the MED4 genome by protein homology using BLASTp. The resulting well-connected network was then queried for futile cycles, and transport and exchange reactions were added. Several exchange reactions were added for protein complexes  which are not explicitly synthesized by the network, though these carry no flux and are included only for modeling purposes. Fake exchange reactions were also added for dead-end metabolites not included in the biomass objective and for which no transporters are annotated . These reactions can carry flux and are considered analogous to diffusive transport. A tunable ATP sink was introduced, also for modeling purposes, to account for costs associated with photodamage above an experimentally determined irradiance (549 µmol photons m−2 s−1) , though in silico metabolic reconstruction is, historically, a series of iterative improvements whereby the model grows in size and complexity, often with detail added to specific pathways as experimental data become available. Open code and computational design are essential to this process, and we have made efforts to enable community contributions. The model is fully MIRIAM compliant and is available in standard formats  were calculated using the elemental matrix E, constructed for hydrogen, carbon, nitrogen, oxygen, phosphorus, and sulfur from metabolite molecular formulas as follows:FBA and several related approaches were employed in the manuscript. In the dynamic state, FBA seeks to maximize or minimize a metabolic function, such as biomass growth or ATP dissipation, subject to constraints on fluxes as follows:m are assigned to steady-state constraints, and variables 1q and 2q are assigned to the flux constraints vLB and vUB, respectively, as follows:Sensitivity analyses were based on so-called shadow prices of the dual solution to the LP problem according toP values and fold changes from a differential expression data set  where available. Our BOF includes the protein amino acid composition, lipid profiles, pigment content, cell wall composition, carbohydrate content, DNA nucleotide fraction, RNA nucleotide fraction, and mineral and trace element composition, for a total of 121 compounds. However, detailed biochemical composition data are lacking for intracellular metabolite concentrations in MED4, and our BOF lacked information on free nucleotides, free amino acids, and the soluble pool (BioPool) concentrations, which were instead taken from the more completely characterized cyanobacterium Synechocystis sp. PCC6803. Cumulatively, these three pools make up less than 5% of ash-free dry weight (DW) and correspond to 4% of the variance of the growth rate under optimal growth, mostly (59%) due to spermidine and nicotinamide dinucleotides (see k) was evaluated by brute force, analogous to the calculation of shadow prices, as follows:SBIO with variable composition k. This is accomplished by varying either a pool of biomass precursors, where BIO is the index of the biomass reaction and ka is a vector of ones with an element of variable magnitude corresponding to a crude fraction , or a specific compound within that crude fraction . These targeted elements for each biomass precursor pool or compound were varied by an arbitrarily small interval (−1 ppm ≤ Δka ≤ 1 ppm), and FBA was then performed to quantify the resulting change in growth rate (ΔZ).A detailed biomass objective function (BOF) is essential to any high-quality GEM. QC is the number of carbon atoms and QP is the number of phosphorus atoms of each compound (k) in each biomass precursor pool (l) and mth target C/P composition  was grown in 30-ml batches in 70-ml borosilicate glass tubes in modified PRO99 low-nutrient-seawater-based medium , and the pellets resuspended in 100 µl of 0.2-µm-filtered seawater containing 0.2% paraformaldehyde. Aliquots of harvested cells were allowed to fix in the dark at 4°C for 30 min prior to analysis of lipopolysaccharide by the Limulus amebocyte lysate spectrophotometric method (Axenic d medium . P-limitytometry . Cells wc method .


In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective  single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments. It is well known that the metabolism of cancer cells differs from the metabolism of normal cells. The Warburg effect (aerobic lactic fermentation) has been known to be a characteristic of cancer cells for almost a century . Metabol13C labeled substrates, are limited to a relatively small number of fluxes in the central carbon metabolism [GSMMs , 8 are ctabolism . Experimtabolism , 11. Howtabolism  is likelUsing RNA-seq data from healthy differentiated tissues and cancer cell lines, we estimated metabolic flux distributions and compared them in order to identify cancer metabolic hallmarks. Remarkably, already well described metabolic hallmarks such as higher glycolysis, glutaminolysis etc. were well reproduced in the estimated flux distributions, which supports the validity of the method presented here.www.proteinatlas.org), and microarrays are available  at CellMiner (https://discover.nci.nih.gov/cellminer/). The largest exchange metabolic fluxes of cancer cells are glucose and glutamine uptake and lactate secretion. We will benchmark our method based on its ability to quantify these fluxes. Experimental uptake rates for the selected cell lines are available in the literature [In order to assess if our constraint based modeling approach (pyTARG) can provide realistic estimations of metabolic fluxes and compare it to the existing method PRIME , we haveterature . The fluterature . Both PRterature , from thThe main difference between pyTARG and PRIME is that the first one constraints all the metabolic reactions based on the expression levels of their associated genes, while PRIME only constrains the reactions whose associated genes showed significant correlations with experimental growth rates. This leads to many reactions being left unconstrained (including reactions whose genes are completely non-expressed and which should be constrained to have a zero flux). Less constrains lead to more feasible solutions for the same biomass production rates and less accuracy in the prediction of metabolic fluxes.www.proteinatlas.org). A t-test was used to identify reactions showing statistically significant  differences in metabolic fluxes . In the same way differentially expressed genes  were identified. Differentially used reactions associated to one or several differentially expressed genes were selected as cancer metabolic hallmarks. The list of identified metabolic hallmarks  is presented in the By using RNA-seq data, putative flux distributions for 34 different cancer cell lines and 26 healthy tissues were computed as described in materials and methods. RNA-seq data were obtained from the Human Protein Atlas  was downregulated in cancer cells, which is consistent with less water production due to lower activity of the respiratory chain. A concomitant increase in both gene expression and metabolic flux was observed in all the steps of glycolysis with the exception of pyruvate kinase. The expression of glycolytic enzymes is known to be induced by the PI3K/Akt signaling pathway, whose constitutive activation is one of the most common features in spontaneous human cancers . IncreasThe expression levels and predicted metabolic fluxes for the glycolytic enzymes PGAM4 (phosphoglycerate mutase) and TPI1 (triosephosphate isomerase) are shown in Figure Overall we can conclude that constraining a human GSMM using RNA-seq data is a valid approach to compute metabolic flux distributions that mimic well known features of cancer metabolism. Therefore, CBM could be expected to be a suitable tool for predicting the effects of blocking certain metabolic reactions .The main challenge to find new anticancer drug targets is the existence of deleterious effects on healthy cells, in particular on dividing healthy cells such as stem cells and progenitors. In order to test if the previously identified reactions can be targeted achieving negative effects on cancer cells while having lower effects on healthy cells, the metabolic flux through each target reaction was forced to have a maximal value equal to 0.1 times its original value . A new flux distribution was computed after imposing the mentioned constraint and the impact on growth on each cell line was reported as the ratio between the new estimated growth rate and the original one. A ratio of 0.1 means that biomass production is fully coupled to the targeted reaction. In the opposite extreme, a value of 1 would mean that there are alternative pathways able to fully compensate the drop in activity of the targeted reaction . This was performed on the 34 cell lines previously used and 6 mesenchymal stem cell lines (3 from bone marrow and 3 form placenta) . Mesencht-test was carried out in order to identify reactions that have significantly higher effects on cancer cells compared to the 6 mesenchymal stem cells. The results  for each of the tested reactions are reported in the A The effects of targeting MVD  and glucose uptake are represented in Figure in vivo [in vivo, expression profiles of NPC1L1 and LPL were downloaded from the TCGA Research Network (http://cancergenome.nih.gov/). Tumors are composed of many different cell types, including cells from tumor associated stroma, therefore using bulk gene expression data has large limitations, which are likely to be overcome with the development of high throughput single cell sequencing techniques [Deregulation of the mevalonate pathway and cholesterol metabolism in cancer, has already been described . Overexpin vivo . High amin vivo . The potin vivo . Preclinin vivo , 30. Thiin vivo  it is pochniques . HoweverEfficient cancer treatments will require finding therapeutic windows that allow targeting selectively cancer cells while keeping the damage on healthy proliferating cells as low as possible. These therapeutic windows are likely different for each patient and should be determined by analyzing individual gene expression profiles . In this work, we have developed a heuristic algorithm implemented in the python programing language and contained in the library pyTARG . This algorithm finds sets of metabolic reactions that, targeted simultaneously, lead to a reduction in the predicted biomass production rate down to a half of its initial value, while keeping as mild as possible the effects on a second cell type chosen as a reference.In order to test pyTARG we found reaction sets targeting selectively each of the 34 cancer cell lines used in this article. The placental mesenchymal stem cell line PL10087 was used as a reference. This cell line was chosen because it is predicted to be sensitive to blocking cholesterol biosynthesis , therefore the algorithm will output targets outside the cholesterol synthesis pathways.Figure Interestingly only 18 metabolic reactions appear in 34 different combinations despite the fact that the model contains several thousands of gene associated metabolic reactions. This means that the use of a rather limited set of enzyme specific inhibitors or small interfering RNAs, combined in a patient specific manner, could constitute the core of a metabolism-based therapeutic solution to cancer. All the reaction sets found by pyTARG include at least one of the glycolytic reactions identified in our previous analysis. This result is consistent with the Warburg effect and the current interest in developing glycolytic inhibitors as anticancer drugs . NeverthThe reaction that appears most commonly among the sets of targets if fumarate hydratase (FH), one of the steps in the TCA cycle which was also revealed  to carry higher metabolic fluxes in cancer cell lines compared to healthy tissues. Interestingly, the FH gene has not been described as an anticancer target but as a tumor suppressor gene  whose loThe reactions in the sets found by pyTARG need to be targeted simultaneously in order to decrease the predicted proliferation rate down to 50% of its initial value. This means that the loss of activity of one single reaction in the set can be compensated by the increased activity of one or several of the remaining members of the set. Therefore it is reasonable to expect that tumors with a very low expression level of FH, will tend to show higher expression of one or several glycolytic enzymes.http://cancergenome.nih.gov/), corresponding to the cohorts BRCA (breast cancer), PRAD (prostate cancer) and LUSC (lung squamous cell carcinoma). From each cohort, we selected the 20 samples with higher and lower expression levels of FH and performed a t-test for differential expression of glycolysis related genes. Significant compensatory effects  were observed for the glucose transporter gene SLC25A1 in the BRCA cohort and for the glucose transporter gene SLC2A9 in the PRAD cohort. No significant results were observed in the lung cancer cohort.Despite the already mentioned limitations of using bulk tumor RNA-seq data, we tested if the hypothesized compensatory effects are detectable in tumor samples. We downloaded RNA-seq data from the TCGA Research Network  was observed in the three considered cancer types, compensating the loss of AQP5 (aquaporin 5). In the LUSC cohort, LDHA appeared to compensate also the downregulation of AQP2, 3 and 4. Our previous analysis  showed tLDHA also appeared to compensate the downregulation of carbonic anhydrases: CA3 in breast and lung cancers, CA4 in prostate cancers and CA12 and 13 in lung cancers. The membrane bound CA9 and CA12 are the carbonic anhydrases that have received most attention as promising anticancer targets . The purThe described compensatory effects are shown graphically in Figure It has been shown that human GSMMs constrained using RNA-seq data can be used to compute metabolic flux distributions that reflect well known metabolic hallmarks of cancer. The effects of blocking metabolic reactions in cancer cell lines and mesenchymal stem cells have been also tested computationally leading to the conclusion that cholesterol biosynthesis could be a highly selective therapeutic window to be targeted using statins or other drugs. An algorithm to identify personalized therapeutic windows have been developed, leading to the identification of groups of metabolic reactions with cytostatic effects (for 34 different cancer cell lines). These sets of reactions frequently involve glycolytic reactions combined with non-glycolytic ones such as fumarate hydratase, carbonic anhydrases or aquaporins. Compensatory effects were observed in tumor samples for some of the identified pairs of reactions. We can conclude that the use of stoichiometric metabolic models integrated with personalized gene expression data, could play a key role in the design of patient specific therapeutic strategies against cancer.We have used a manually curated version of the Human Metabolic Reactions (HMR) database . The modwww.proteinatlas.org in the form of a comma separated file that contains the gene expression of each gene in each cell line (given as RMPK). This file was parsed using a customized python script (available upon request). RNA-seq fastq files for 6 different mesenchymal stem cell lines (3 from placenta and 3 from bone-marrow) were downloaded from https://usegalaxy.org/u/cic19/h/mesenchymal-stem-cells-rnaseq the reads were aligned on the complete list of human transcripts obtained from Ensembl BioMart using Bowtie2. The resulting alignment files were analyzed using a customized python script that is available upon request.Gene expression data for cancer cell lines (BioProject accession number PRJNA183192) as well as the healthy tissues included in the Human Proteome Atlas were downloaded from https://discover.nci.nih.gov/cellminer/) corresponding to the NCI-60 cell lines. A false discovery rate of 0.05 was used. The selected reactions are reported in the p-values.Upper bounds for the reactions whose associated genes are significantly correlated with growth rate were computed as described in the PRIME algorithm . The reahttps://github.com/SergioBordel/pyTARG. The function takes as input the model stored in BioModels after being imported using the python library COBRApy [The model is constrained using the function fullconstrain from the pyTARG library. This library can be downloaded from  COBRApy . The RNA-1h-1 times the expression level of the most abundant enzyme associated to each reaction (measured in RPKM). The boundaries were set in a discrete way (by rounding up the expression levels to their upper multiple of 10), this helped to avoid numerical problems while performing linear optimization.Each gene-associated reaction in the model is constrained to have a maximal value  proportional to the expression level of its associated gene. When a reaction is associated to several genes the highest expressed gene is chosen. Reactions not associated to any gene are left unconstrained. The proportionality constant between the gene expression and the upper boundary for the fluxes is a pure phenomenological constant chosen to reproduce the experimental growth rate of the cell line A549. The chosen proportionality constant was 0.0027 mmol g-DWAfter constraining the model and setting an objective function, the flux distribution can be computed using the function flux of the pyTARG library, which itself relies on the COBRApy library. The computed flux distribution corresponds to maximizing the objective function while keeping the total sum of metabolic fluxes as low as possible.In this article we have always used the rate of biomass production as objective function. Biomass production is linked to proliferation rate, which is the main phenotype relevant to cancer that can be directly linked to a metabolic flux. On the other hand, the effects of blocking a metabolic reaction on other relevant phenotypes such as ATP production rate or NADH production rate (which could be linked to apoptosis for example) will be also observable on the biomass production rate. Interestingly, 26 of the healthy tissues form the Human Proteome Atlas were able to produce all the biomass building blocks necessary to proliferate. This does not mean necessarily that they proliferate, which would require that these building blocks are actually assembled into macromolecules and all the checkpoints of the cell cycle are being transited by the cells. Indeed we observed that most of the genes whose expression is correlated with growth rate are not metabolic genes but cell cycle components and ribosomal proteins . Neverthblock of pyTARG constrains the fluxes of one or several metabolic reactions to 0.1 times their original values and computes a new flux distribution after imposing the constraints. It outputs the ratio between the value of the objective function after and before imposing the new constraints. A ratio of 0.1 would mean that the objective function is fully coupled to at least one of the constrained reactions while higher values mean that the metabolism of the cell is theoretically able to compensate the new constrain by using alternative pathways. We chose to constrain the targeted reactions to 0.1 times their original value and not to zero, in order to account for the fact that in a real setup the reaction would not be fully blocked . Also in some cases, the metabolic flux of essential reactions  can be strongly reduced without drastic effects on the objective function.The function The function personal of the pyTARG library implements a heuristic algorithm that aims to find a set of metabolic reactions with cytostatic effects on a target cell type while minimizing the effects on a healthy reference cell line. The inputs to the function are two models in COBRApy format, constrained using RNA-seq data for the target and the reference cell lines respectively. The algorithm starts by computing flux distributions for each of the two cell types. For each reaction in the model we take the difference between the flux in the target cell and the flux in the reference cell. The reactions are ranked in decreasing order starting from those with larger differences. Once the reactions have been ranked, the algorithm constrains the first reaction to 0.1 times its original flux in both cells. If the relative growth rate (in case the objective function is biomass production) on the target cell line is lower than 0.9 and the difference between relative growth rates between the target and the reference is higher than 0.05, the reaction is selected and the flux constraint is kept. If the conditions are not satisfied, the constraint is released and the same is repeated with the second reaction. The algorithm proceeds testing reactions until the relative growth rate of the target cell line drops to 0.5. The output of the function is a python list with the identifiers of the selected reactions. The chosen parameters of the algorithm have led to results involving relatively few reactions (from 1 to 5) as it is shown in Figure