Here, we describe the construction of iEC2812, a genome scale metabolic reconstruction of endothelial cells and its application to describe metabolic changes that occur following endothelial dysfunction. Metabolic gene expression analysis of three endothelial subtypes using iEC2812 suggested their similar metabolism in culture. To mimic endothelial dysfunction, an in vitro sepsis endothelial cell culture model was established and the metabotypes associated with increased endothelial permeability and glycocalyx loss after inflammatory stimuli were quantitatively defined through metabolomics. These data and transcriptomic data were then used to parametrize iEC2812 and investigate the metabotypes of endothelial dysfunction. Glycan production and increased fatty acid metabolism accompany increased glycocalyx shedding and endothelial permeability after inflammatory stimulation. iEC2812 was then used to analyse sepsis patient plasma metabolome profiles and predict changes to endothelial derived biomarkers. These analyses revealed increased changes in glycan metabolism in sepsis non-survivors corresponding to metabolism of endothelial dysfunction in culture. The results show concordance between endothelial health and sepsis survival in particular between endothelial cell metabolism and the plasma metabolome in patients with sepsis.Endothelial dysfunction contributes to sepsis outcome. Metabolic phenotypes associated with endothelial dysfunction are not well characterised in part due to difficulties in assessing endothelial metabolism  The estimated annual cost of sepsis hospitalization in the US is around $15 billion4, with mortality rates around 30%5. An NIH panel recently redefined sepsis as “severe endothelial dysfunction syndrome in response to intravascular and extravascular infection”7. All blood vessels of the body are lined with a single layer of endothelial cells. These cells are the contact-point between blood and the body and as such they play a pivotal role in sepsis. Endothelial dysfunction refers to the inability of the endothelium to produce appropriate concentrations of vasorelaxants, particularly nitric oxide (NO), to properly control blood pressure7. Furthermore, endothelial injury due to sepsis leads to oedema and plays a role in dysregulation of blood clotting10. Patient response to treatment is somewhat endothelium dependent and highly variable11. Altered metabolism has been described in sepsis patients12. Metabolism in sepsis is reflected in both treatment guidelines and broader studies identifying metabolic fingerprints for sepsis outcome prediction16. Plasma metabolites in sepsis patients are similar to those of healthy lipopolysaccharide (LPS)-treated volunteers18, suggesting that LPS provides a useful proxy for sepsis studies. Interferon γ (IFNγ) has been shown to potentiate LPS stimulation in cultured human umbilical vein endothelial cells (HUVECs)19.Sepsis is a life-threatening systemic inflammatory condition caused by the body’s response to infection, leading to fever, increased heart and breathing rates and septic shock is the subset of these patients with hypotension and altered serum lactate21. Endothelial permeability is dependent on fatty acid oxidation22 and arachidonic acid, ceramide, tryptophan, and arginine metabolism have all been shown to be important in sepsis progression28. Addressing endothelial metabolic changes may present novel avenues for specific treatment of sepsis. However, the full functional relevance of endothelial metabolic changes in sepsis are not fully understood. This knowledge gap is partly due to the difficulty of sampling directly from the endothelium in vivo and therefore of separating endothelial metabolism from that of other cell types29. To address this problem in vitro and in silico techniques are needed to analyse endothelial metabolism.Studies have implied a role for endothelial metabolism in many diseases, specifically glycolysis and glycosylation of surface proteins30. GEMs have previously been used to analyse the metabolism of cells in silico and applied to diverse problems, including biomarker identification31 and patient stratification of treatment33. A proteomics based GEM has recently been built for the endothelium and identified a role for carnitine palmitoyl transferase in endothelial permeability22. These developments present an intriguing possibility for the treatment of sepsis, given the variable patient response to treatment29. The quality of GEMs is dependent on manual curation by literature review and the coverage of context specific data34.Genome scale metabolic models (GEMs) offer a framework for understanding metabolism through integrated analysis of gene expression, protein abundance and metabolic flux35, allowing endothelial metabolism in these patients to be inferred on the basis of a metabolic network reconstruction. We demonstrate that iEC2812 bridges a gap between in vitro and clinical research and represents a platform for the future systems analysis of endothelial metabolism.Here we describe the construction of a cell specific endothelial metabolic model, iEC2812, and its application to study septic endothelial dysfunction. We built and manually curated GEMs of three common endothelial subtypes and compared their output. We then focused our study on metabolic changes in cultured HUVECs stimulated with combinations of LPS and IFNγ to parametrize the model to endothelial dysfunction. Finally, we utilized iEC2812 as a framework to analyse publically available plasma metabolomics data from sepsis patientsWe built a metabolic reconstruction of endothelial metabolism called iEC2812 based on RECON1 Fig. . iEC281240. All of the models were capable of synthesising nucleotides from fatty acids which has been confirmed in endothelial cells41. The endothelial sub-type models were investigated by comparing essential genes and reactions using the RECON1 biomass function as the objective43. The majority of essential genes were found within nucleotide or complex lipid metabolism and were shared by all models , human pulmonary artery endothelial cells (HPAEC- iHPAEC2588) and HUVECs (iHUVEC2552)  between untreated and LPS treated HUVECs. This change did, however, translate to a predicted difference in the proportion of lactate produced via glycolysis vs the pentose phosphate pathway in LPS treated cells (68% vs 70%). This prediction is based on the theoretical distribution of labels in a purely glycolytic or pentose phosphate pathway system (Supplement Ultra-high pressure liquid chromatography mass spectrometry (UPLC-MS) analysis of spent media after 24 hours of culture defined the baseline metabolism of HUVECs and the perturbations from LPS and IFNγ. Small differences in metabolism between LPS treated and untreated HUVECs were observed  results in Supplement 45. Differences in gene expression and nutrient utilisation of LPS stimulated HUVECs were applied as constraints to reaction flux in iHUVEC2552 . Fatty acid metabolism was the second most perturbed area of metabolism. Clustering of reaction fluxes across the four HUVEC models showed that the control and IFNγ models clustered together separate from the LPS and IFNγ/LPS models. Greater functional changes in the IFNγ/LPS treated cells are thus reflected in greater metabolic differences. Cluster analysis of reaction fluxes revealed 12 clusters of similarly regulated reactions identified as clusters A through L in Fig. 43. Reactions of transport, fatty acid, and amino acid metabolism were the most common essential reactions. Peripheral and fatty acid metabolism have the most essential reactions unique to one context, indicating that these areas of metabolism differentiate stimulated and unstimulated cells. Inhibiting flux through carnitine palmitoyl transferase inhibited growth in the control model but not in the LPS treated model, suggesting a more robust metabolism with alternatively active fluxes in the LPS model.We subsequently identified the genes and reactions essential to an active biomass reaction in each model Fig. . The bioet al.22 we found that the control model and the IFNγ model are capable, on optimization, of producing more acetyl-coA from palmitate than from glucose, by around 10%. The LPS and LPS/IFNγ model are equally capable of producing acetyl-coA from palmitate as glucose.In agreement with Patella in vitro experiments  are limited by the context of the in vitro experiments. The in vitro experiments and therefore the GEMs do not include the many types of inflammatory cell and their signalling pathways involved in human sepsis. This lack may be particularly important with respect to lipid inflammatory mediators which may be detected in the plasma of sepsis patients. The case of prostaglandin D2 highlights the limitations of this modelling approach as it does not properly account for metabolic changes due to short term signalling. Further work in vitro and in vivo is necessary to fully assess the impact of complex signal cascades mediated by other cell types in this context. Nonetheless our models are in agreement with many previously described aspects of endothelial metabolism in sepsis and may be used it to further our understanding of endothelial dysfunction.The biomass function used to predict essential genes and reactions in these analyses is the same for all models and may therefore underestimate biological differencesOur analysis of context specific models has demonstrated the critical role of glycan metabolism and confirmed alterations in fatty acid metabolism in endothelial inflammatory activation. We have also shown that patients who survived sepsis had more metabolic features shared with less activated endothelium. The endothelial biomarker profiles predicted by our models broadly corresponded with the changes expected upon endothelial dysfunction caused by sepsis. This demonstrates that analysing plasma metabolic data from patients with an endothelial GEM provides results with practical relevance. The metabolic models are available as a resource to study the many other critical roles which endothelial cells play in human disease.et al. as previously reported63. The cells were harvested by collagenase digestion and seeded in 25 cm2 flasks with 5 mL of Endothelial Growth Medium (EGM) , containing 2% foetal bovine serum and antibiotics . Cells were incubated at 37 °C in humidified air with 5% CO2. Medium was changed 24 hours after seeding and every 48 hours thereafter confluence was reached (~5 days), they were then used or sub-cultured. For use cells at passage 1–3 were seeded onto 35 mm2 culture plates in 2 mL EGM. At confluence control media or media containing 50 ng/mL IFNγ , was added and the cells were incubated for 3 hours. Then control media or 10 ng/mL, 100 ng/mL or 1000 ng/mL LPS was added  and incubated for either 4 or 24 hours. After incubation the cells and media were collected and stored at −80 °C prior to analysis. These experiments were performed in accordance with approval granted by the National Bioethics Committee of Iceland and Icelandic Data Protection Authority, following their guidelines on informed consent.Primary HUVECs, obtained from Landspitali University Hospital, Reykjavík, Iceland, were harvested by a modification of the method of Jaffe 13C labelled glucose  with and without 10 ng/mL LPS. Medium was collected as above after 4 hours and 24 hours incubation with 13C glucose.HUVECs were cultured to confluence as above and then had their medium changed to EGM plus with added 5 mM normal glucose or 5 mM 1,2-et al.64. Additional glucose and lactate measurements were performed in an ABL 90 gas analyzer . Missing values were replaced by the minimum positive values detected for each parameter. The data was first batch normalized by dividing each variable of each batch by the square root of the sum of squares of original values of that batch. We then used a standard metabolomics analysis pipeline following the MetaboAnalyst online tool65. This dataset was normalized by constant sum, log transformed and scaled using unit variance scaling method (mean-cantered and divided by the standard deviation of each variable). Data normalisation and heat mapping of metabolites measured in medium of HUVECs and a comparison of extracellular metabolites by were performed in the online Metaboanalyst tool65 using heat mapping for data visualisation and a two way ANOVA test ANOVA (Two-way (between subjects) ANOVA with type I, weighted means) with false discovery rate correction to assess the effects and interaction of the LPS and IFNγ treatment on HUVEC metabolism. For isotopologue analysis, IsoCor33 corrected for natural isotope abundance providing the percentage of heavy isotopes that exceed natural abundance and the corrected isotopologue distribution.UPLC-MS Analyses were performed with an UPLC system  coupled in line with a quadrupole-time of flight hybrid mass spectrometer  as described in Paglia 66. Readings were made at excitation wavelength of 555 nm, emission wavelength 565 nm to image the heparan sulphate proteoglycan 2, excitation wavelength of 495 nm, emission wavelength 519 nm to image the wheat germ agglutinin, and excitation wavelength of 340 nm, emission wavelength 488 nm to image DAPI. Quantification of the fluorescent areas was performed using ImageJ software67 and normalized to area. Results were analysed using a two sample two tailed equal variance t-test and considered significant at p < 0.05.HUVECs were seeded at 100 000 cells/ml in 400 µl of media into an 8 well chamber slide  coated with gelatine. The cells settled for 72 hours. The media was changed 24 hours to remove unattached cells. After 72 hours (at confluence) the media was replaced with control media or media containing 50 ng/mL IFNγ and incubated for 3 hours, this was then removed and replaced with control or 10, 100, or 1000 ng/mL LPS containing media and incubated for 4 hours. This media was discarded and the cells washed with phosphate buffered saline and then fixed with 10% formalin. Cells were then blocked with 5% bovine serum albumin (BSA), on ice, before staining with 1:100 dilution of mouse anti-heparan sulfate anti-body  in BSA and goat anti-mouse secondary anti-body conjugated to Alexa fluor 555 (Goat anti-mouse IgG(Alexa Fluor 555) ab150114, Abcam, Cambridge UK) at a 1:300 in BSA dilution then wheat germ agglutinin 5 µg/mL diluted in PBS , and finally Fluoromount-G diamidino phenylindole (DAPI) . Cells were imaged using a Leica DMI6000B microscope and CTR 6500 electronics box  with an EXi Blue Fluorescent Microscopy Camera  controlled by Micro-Manager software68. HUVECs were seeded onto Transwell cell culture inserts  coated with 0.1% gelatine. HUVECs were grown to confluence (~72 hours), as above. Media was then removed and replaced with either control media or media containing 50 ng/mL IFNγ and incubated for 3 hours. Cells were then stimulated with 1000 ng/mL LPS for 4 hours the medium in the upper chamber was then removed and replaced with EGM containing FITC-dextran at 1 mg/ml (Sigma) and LPS. Tumour necrosis factor α  was used as a positive control. The plate was incubated for a further 45 minutes and medium from the lower chamber assayed for fluorescence at excitation wavelength of 488 nm and emission wavelength of 520 nm. Results were analysed using a two sample two tailed equal variance t-test and considered significant at p < 0.05.Endothelial permeability was estimated by measuring the flow of fluorescently labelled dextran- fluorescein isothiocyanate–dextran (FITC)42. RECON1 is a comprehensive genome scale metabolic reconstruction of humans. A genome scale metabolic reconstruction is a stoichiometric matrix representing metabolic reactions. This is linked to gene rules describing the genes that need to be transcribed to produce the enzymes needed for each reaction. This allows the integration of genomic or transcriptomic data with metabolic data. To create iEC2812, transcriptomic data from ArrayExpress (E-GEOD-21212) was used to prune RECON170 using Fastcore71 to obtain a consistent model which was then manually curated to include reactions of tryptophan metabolism and extra transport reactions from RECON272  that ensured that no gene occurred in both up and down regulated sets. Constraints on these reactions were then multiplied by 2 or 0.5 respectively. This value was chosen as it was close to the mean change up or down in the transcript levels. Additional endothelial subtype specific metabolic flux constraints were then added manually based on data from the literature, see , GSM530363-66 combined Human microvascular endothelial cells (HMVEC), GSM530371-74 combined human pulmonary artery endothelial cells (HPAEC))76) descriptive of transcriptomic changes in cultured HUVECs following LPS stimulation, in addition to our metabolomics. Affymetrix IDs were converted to Entrez IDs using DAVID then genes separated into up and down regulated genes (untreated sample as baseline). This produced a new base model that was used to create the LPS and LPS/IFNγ combination treated models while the original model was used as the base for a control model and the IFNγ treated HUVEC model. Following this constraints on uptake and secretion reactions were applied based on metabolomics data from the medium of HUVECs cultured as above . Uptake and secretion of 20 metabolites and data from cells grown with 1,2-13C labelled glucose to constrain the ratio of flux through the reactions of glycolysis vs pentose phosphate pathway  were used for data visualisation.The models were analysed using flux balance analysis, flux variability analysis, random sampling, and essential reaction and gene features  in the Cobratoolbox in MatlabData is available in the supporting information or upon application to the authors.Umbilical cord vein sampling was approved by the National Bioethics Committee of Iceland and Icelandic Data Protection Authority.Supplement ISupplement IISupplement III


Organismal adaptation to a new environment may start with plastic phenotypic changes followed by genetic changes, but whether the plastic changes are stepping stones to genetic adaptation is debated. Here we address this question by investigating gene expression and metabolic flux changes in the two-phase adaptation process using transcriptomic data from multiple experimental evolution studies and computational metabolic network analysis, respectively. We discover that genetic changes more frequently reverse than reinforce plastic phenotypic changes in virtually every adaptation. Metabolic network analysis reveals that, even in the presence of plasticity, organismal fitness drops after environmental shifts, but largely recovers through subsequent evolution. Such fitness trajectories explain why plastic phenotypic changes are genetically compensated rather than strengthened. In conclusion, although phenotypic plasticity may serve as an emergency response to a new environment that is necessary for survival, it does not generally facilitate genetic adaptation by bringing the organismal phenotype closer to the new optimum. Phenotypic plasticity has been suggested to facilitate survival in new environments and subsequent adaptation. Here, the authors reanalyze transcriptomic data from experimental evolution studies in combination with computational metabolic network analysis and show that genetic adaptation tends to reverse plastic changes in order to recover fitness. Specifically, it is suggested that plastic phenotypic changes are often necessary for organismal survival in a new environment11, which is essential because no adaptive evolution is possible if the environmental shift kills all individuals. Furthermore, it is suggested that genetic adaptations in the second phase are eased by the PCs in the first phase2. For example, plasticity can move the phenotypic value of an organism closer to the adapted state in the new environment and serve as a stepping stone to adaptation7  during the adaptation to the new environment could be in the same direction toward the optimal phenotypic value in the new environment. In this case, the PC is reinforced by the adaptive GC and hence is considered adaptive15 Fig. . The PC  15 Fig. . Because21. Second, it is necessary to examine if the finding from gene expression traits applies to other phenotypic traits. Third, organisms acquired at the end of experimental evolution are usually partially rather than fully adapted to the new environment, making the distinction between reinforcement and reversion less certain. Fourth, in experimental evolution, it is unknown whether an observed gene expression change is beneficial, neutral, or even deleterious. For example, an expression change accompanying organismal adaptation could be responsible for, a result from, or even unrelated to the fitness gain. Some authors assume that expression changes observed in replicate experiments are beneficial15, but it is also possible that they are consequences of adaptation and have positive, zero, or negative fitness effects. Thus, not all expression changes observed in experimental evolution are relevant to the hypothesis that plasticity is a stepping stone to genetic adaptation. By contrast, in the metabolic network analysis, all flux changes observed in the maximization of fitness are required and therefore are beneficial. It has been shown, for instance, that upon the maximization of fitness, alteration of any non-zero flux would be deleterious22. Last and most importantly, because the regulatory and evolutionary mechanisms of gene expression changes are not well understood, it would be difficult to discern the mechanistic basis of expression level reinforcement or reversion. By contrast, patterns of computationally predicted flux changes can be understood mechanistically by the metabolic model used in the prediction. We thus test whether plasticity facilitates adaptation by computational metabolic flux analysis of the model bacterium Escherichia coli. Our analyses of transcriptome and fluxome changes in numerous adaptations consistently show that phenotypic reinforcement is not only no more but actually less prevalent than reversion, indicating that plasticity is not a stepping stone to genetic adaptation. More importantly, we uncover the underlying cause of the preponderance of phenotypic reversion.More importantly, five considerations prompt us to expand the analysis from gene expression levels to another set of traits—metabolic fluxes. First, it is desirable to test the hypothesis across diverse environmental shifts, but experimental evolution studies with transcriptome data are currently limited in this aspect. By contrast, fluxes in well-annotated metabolic networks can be computationally predicted with reasonably high accuracy under a wide range of environmentsE. coli adapting to a high-temperature environment17, 6 replicates of another strain of E. coli adapting to a high-temperature environment18, 7 replicates of E. coli adapting to a glycerol medium16, 7 replicates of E. coli adapting to a lactate medium16, 1 replicate each of 12 different yeast (Saccharomyces cerevisiae) strains adapting to an xylulose medium19, and 2 replicates of guppies (Poecilia reticulata) adapting to a low-predation environment15. In total, we analyzed 44 cases of adaptation.We identified five studies that conducted six different adaptation experiments and collected transcriptome data suiting our analysis. These six experiments included 10 replicates of Lo, Lp, and La, respectively. In each experiment, we first identified genes with appreciable PCs in expression level by requiring PC = |Lp–Lo| to be greater than a preset cutoff. We also identified genes with appreciable GCs in expression level by requiring GC = |La–Lp| to be greater than the same preset cutoff. For those genes showing both appreciable PCs and appreciable GCs, we ask whether the two changes are in the same direction  or opposite directions  as the cutoff in the above analysis. The fraction of genes exhibiting expression level reinforcement (CRI) is smaller than the fraction of genes exhibiting reversion (CRV) in 42 of the 44 adaptations, and the difference between CRI and CRV is significant in 40 of these 42 cases . The same trend is evident when the cutoff is altered to 0.05Lo , shortly after their exposure to the new environment (p for the plastic stage), and at the conclusion of the experimental evolution in the new environment  to predict the optimized fluxes of fully adapted organisms in the original (stage o) and new (stage a) environments, respectively, under the assumption that the biomass production rate, a proxy for fitness, is maximized by natural selection20. FBA predictions match experimental measures reasonably well for organisms adapted to their environments29 and are commonly used in the study of genotype–environment–phenotype relationships37. When predicting plastic flux changes upon environmental shifts (stage p), we employed minimization of metabolic adjustment (MOMA) instead of FBA because MOMA better recapitulates the immediate flux response to perturbations21 (see Methods). We treated the flux of each reaction in the metabolic network as a trait, and modeled environmental shifts by altering the carbon source available to the network. There are 258 distinct exchange reactions in iAF1260, each transporting a different carbon source. We therefore examined 258 different single-carbon source environments.To assess the generality of the above finding and understand its underlying cause, we expanded the comparison between phenotypic reinforcement and reversion to metabolic fluxes (see Introduction). Because our metabolic analysis is not meant to model the above experimental evolution or expression changes, the parameters used are unrelated to the experimental evolution. Specifically, we computationally predicted plastic and genetic flux changes during environmental adaptations using iAF1260 construction23. We then considered the adaptations of E. coli to 257 new environments each with a different single-carbon source. We found that these new environments are naturally separated into two groups in the MOMA-predicted biomass production rate, a proxy for the fitness at stage p (fp)  in each adaptation. The chance probability that all 50 adaptations show CRV > CRI is 1.8 × 10−15 , but found that CRV still exceeds CRI  that each has more than 20 adaptable  new environments . CRI/CRV is expected to be [0.5(1 − q)]/[0.5(1 − q) + q] = (1 − q)/(1 + q) < 1. In other words, as long as PC > TC for a few traits, reversion is expected to be more frequent than reinforcement (under no other bias).Our finding that phenotypic reinforcement is not only no more but actually much less common than reversion is unexpected and hence demands an explanation. The observation of this trend in both transcriptomic and fluxomic analyses suggests a general underlying mechanism, which we propose is the occurrence of PC > TC. Geometrically, it is obvious that when PC > TC, the GC must reverse the PC  and that after adaptation to a new environment (fa), relative to that in the original glucose environment, for each of the adaptations to the 50 new environments. In all cases, fp < 1 . Second, our model assumes an association between flux changes and fitness changes22. Across the 50 adaptations from the glucose environment, there is a strong negative correlation between fp and mean PC  are adaptable by at least 20 of the 500 random networks. We thus analyzed the adaptations of random networks to each of these 20 new environments, with the glucose environment being the original environment. For each new environment, the median CRV of all random networks that can adapt to this environment is generally around 0.1 , in 30 of which CRV significantly exceeds CRI  and the computationally predicted fluxomes of E. coli in hundreds of different environmental adaptations, we showed that genetic adaptations to new environments overwhelmingly reverse, rather than reinforce plastic phenotypic changes. Our fluxome analyses have several caveats worth discussion. First, because MOMA minimizes the total squared flux difference from the original flux, PCs could have been underestimated, but this bias would only make our conclusion more conservative. Second, a bias could exist owing to potentially different accuracies of MOMA and FBA that are respectively used to predict plastic and genetic flux changes. To tackle this problem, we designed a MOMA-based algorithm to infer both PCs and GCs, but found the results to be qualitatively unchanged , but found our conclusion unaltered  in the definition of GCs , and CRV significantly exceeds CRI in each of these 28 cases  and hindering PCs (CHIN) in each adaptation. In 32 of the 44 cases of experimental evolution, CHIN exceeds CFAC, demonstrating an overall preponderance of hindering plasticity  but no appreciable GC (GC < 0.2Lo) in the transcriptome analysis of Fig. It is also possible that the PC of a trait can move its phenotypic value to the optimal state in the new environment such that no GC is needed. But, we found that the fraction of traits with an appreciable PC , reinforcement is no more prevalent than reversion , further refuting the stepping stone hypothesis. It is worth stressing, however, that a trait with TC < 0.2Lo may have also experienced adaptive evolution, because it could have a large PC reversed by a large GC that is beneficial. In other words, traits with similar values in stages o and a may have had cryptic adaptations unrevealed due to the lack of information about stage p. Hence, the observation that a trait looks similar among organisms living in different environments does not necessarily mean that it experiences no adaptive changes in organismal adaptations to their respective environments.Evolutionary biologists may contend that they are interested only in traits that differ between organisms living in different environments, because these traits have most likely experienced adaptive evolution. We showed that even for such traits  experienced by the organisms in the recent past, it is possible that mutations conferring plastic phenotypic changes that are beneficial in these environments have been fixed and there is no controversy that adaptive plasticity can evolve under this scenario.48 and accommodation6, which refer to the evolutionary process by which a phenotype induced by an environmental stimulus becomes stably expressed even without the evoking environmental stimulus. Because the experimental evolution data analyzed do not contain information on the phenotypic plasticity of the organisms adapted to the new environment, our study cannot test genetic assimilation or accommodation. A related hypothesis that we did not test regarding the role of plasticity in adaptation is that upon an environmental shift, organisms with a relatively high plasticity adapt faster or are more likely to adapt than organisms with a relatively low plasticity. It would be interesting to test this hypothesis in the future when comparable organisms with contrasting levels of plasticity become available for experimental evolution studies.The importance of plasticity in adaptation has also been discussed in theories of genetic assimilationDue to the limitation of the available data, our transcriptome and fluxome analyses focused primarily on unicellular microbes (with the exception of guppies). Compared with unicellulars, multicellulars are more complex because of differential gene expressions among cell types and because the biomass production rate of a cell type may not correlate well with organismal fitness. Therefore, it will be important to confirm the generality of our findings in the future when more data sets from multicellulars become available.Transcriptome data sets from six experimental adaptations were acquired from five studies. For each replicate of each adaptation, the data included gene expression levels of ancestral organisms in the original environment (stage o), ancestral organisms in the new environment (stage p), and evolved organisms in the new environment (stage a). For each data set, we removed genes with any missing expression levels and then normalized gene expression levels such that the mean expression level of all genes is the same across all data sets.E. coli K-12 MG1655 in a 42 °C medium with 10 replicates17. The authors performed RNA sequencing (RNA-seq) using (i) the ancestral line at 37 °C, (ii) ancestral line at 42 °C, and (iii) 10 parallelly evolved lines at 42 °C, and these data were respectively used to estimate the Lo, Lp, and La of 4341 genes. All expression levels measured in FPKM were available in their Dataset S3.The first data set came from the experimental evolution of E. coli B REL1206 in a 42 °C medium18. The authors performed RNA-seq using (i) the ancestral line at 37 °C, (ii) ancestral line at 42 °C, (iii) two evolved lines at 42 °C, and (iv) four lines each carrying a distinct adaptive mutation at 42 °C. We respectively used (i) to estimate Lo, (ii) to estimate Lp, and both (iii) and (iv) to estimate La of 4202 genes. All expression levels measured by DESeq were provided by the authors.The second data set came from the experimental evolution of E. coli K-12 MG1655 in glycerol and lactate medium, respectively16. The authors used Affymetrix E. coli Antisense Genome Arrays to profile the transcriptome of (i) the ancestral line in glucose, (ii) ancestral line in glycerol, (iii) ancestral line in lactate, (iv) seven parallelly evolved lines in glycerol on day 21, (v) seven parallelly evolved lines in glycerol on day 44, (vi) seven parallelly evolved lines in lactate on day 20, and (vii) seven parallelly evolved lines in lactate on day 60. Each line has three replicates, except that profile (iii) has six replicates. We averaged gene expression levels across replicates for each line. For the adaptation to the glycerol medium, we respectively used (i) to estimate Lo, (ii) to estimate Lp, and (v) to estimate La. For the adaptation to the lactate medium, we respectively used (i) to estimate Lo, (iii) to estimate Lp, and (vii) to estimate La. Transcriptomes of (ii)–(vii) were downloaded from Gene Expression Omnibus (GEO) with the accession number GSE33147, whereas that of (i) was provided by the authors. In total, 3745 genes were considered.The third and fourth data sets came from the experimental evolution of S. cerevisiae in a xylulose medium19. The authors performed RNA-seq using (i) 12 ancestral lines in a glucose medium, (ii) 12 ancestral lines in the xylulose medium, and (iii) 12 evolved lines in the xylulose medium. Each line has two replicates, and the averaged expression levels of the two replicates were used. We respectively used (i) to estimate Lo, (ii) to estimate Lp, and (iii) to estimate La of 2235 genes. All expression levels in terms of UMI scoring normalized counts were downloaded from GEO with the accession number GSE76077.The fifth data set came from the experimental evolution of 12 different strains of P. reticulata guppies originating from streams with high numbers of cichlid predators (high predation (HP) environment) in cichlid-free streams (low predation (LP) environment)15. The authors performed RNA-seq of brain tissues from (i) guppies caught in HP, (ii) guppies caught in HP but reared in LP, and (iii) two populations of guppies in LP after experimental evolution. We respectively used (i) to estimate Lo, (ii) to estimate Lp, and (iii) to estimate La of 37,493 genes. All expression levels in terms of TMM-normalized counts measured by edgeR were provided by the authors.The sixth data set came from the experimental evolution of E. coli metabolic network model iAF126023 was downloaded from BiGG49 and parsed by COBRA50. All linear and quadratic programming problems in this study were solved by the barrier method using Gurobi optimizer with MATLAB (method = 2). Numerical differences smaller than 10−4 were ignored in the analysis. The codes are available upon request.The SMBL file of the E. coli network when it is fully adapted to an environment. FBA assumes a metabolic steady state and maximizes the rate of biomass production20. Mathematically, FBA is a linear programming question in the following formv is a vector of reaction fluxes that need to be optimized, cT is a transposed vector describing the relative contributions of various metabolites to the cellular biomass, S is a matrix describing the stoichiometric relationships among metabolites in each reaction, α is a vector describing the lower bound of each flux, and β is a vector describing the upper bound of each flux.We used FBA to estimate the fluxes of the iAF1260 includes 258 exchange reactions, each of which allows the uptake of one carbon source. In the estimation of the fully adapted flux distribution in one environment, the uptake rate of the focal carbon source was set at 10 mmol g DW−1 h−1, which follows the setting in a previous study for a glucose-limited medium23, while the uptake rates of other carbon sources were set at zero. The uptake rates of non-carbon sources such as water, oxygen, carbon dioxide, and ammonium were set as in the previous study23. Note that some reactions are simple diffusions between different cellular compartments. Because these reactions do not have dedicated enzymes and are not “mutable”, we excluded them from the list of phenotypic traits considered. In total, 1811 reactions were considered.The model E. coli is shifted from one environment to another21. The mathematical form of MOMA isv is the vector of all reaction fluxes upon the environmental shift and is the variable to optimize, v0 is the vector of all reaction fluxes in the original environment and are predetermined using FBA. S, α, and β are the same as described for FBA.We used MOMA to estimate plastic flux changes when 21. MOMA assumes that cells attempt to maintain the metabolic homeostasis as much as possible in the face of an unexperienced situation, may it be the loss of a reaction (due to gene deletion) or a change in the environment. In theory, an environmental change can be very similar to a gene deletion. For example, moving cells from the glucose medium to an unexperienced medium containing a different carbon source is equivalent to deleting genes for glucose transportation. Indeed, metabolic fluxes of E. coli respectively experimentally measured in lactate51 and in galactose52 correlate well with the fluxes predicted using MOMA  were used. The relative flux of each of the nine reactions was calculated by the value underneath each box in Fig. lldD box. Metabolic fluxes experimentally determined in the galactose medium were from Fig. 52. The mapping from gene names to reaction names was also based on the annotation in iAF1260. Note that we considered the flux measured for mae (MAL > PYR) as the combination of reactions ME1 and ME2 in iAF1260. In total, 26 measurements were used, and their relative fluxes were calculated by their values divided by the galactose uptake rate (2.17). For the relative fluxes predicted by MOMA, normalization was performed by using the estimated uptake rate of the corresponding carbon source in MOMA solutions.In the above investigation of MOMA performance, metabolic fluxes experimentally determined in the lactate medium were from Fig. v and parameters v0, S, α, and β are the same as described for MOMA, and b is the FBA-predicted biomass production rate in the new environment. This optimization problem is still a quadratic programming problem and its solution can differ from that of FBA.To ensure that our results are not artifacts of different optimization accuracies of FBA and MOMA, we designed MOMA-b and used it to predict the fluxes in organisms adapted to new environments. In addition to having the same objective function and constraints as in MOMA, MOMA-b has a biomass constraint. Specifically, we set the biomass production rate in MOMA-b to be the same as what FBA predicts for organisms adapted to the new environment. The mathematical form of this new optimization question is53 to generate 100 environments with multiple carbon sources. In each environment, we generated a random number g from an exponential distribution with a mean of 0.1 for each of the 258 carbon sources. Here g is the probability that the carbon source is available. The actual presence or absence of the carbon source is then determined stochastically using g. These random environments have a mean of 28 and a median of 21 carbon sources per environment.In addition to using single-carbon source environments, we followed a previous studyAll relevant data are available from the corresponding author upon request.Supplementary InformationPeer Review File


Here, I describe the rewiring of the metabolic pathways in cyanobacteria to better understand the endogenous carbon flux and to enhance the yield of heterologous products. The plasticity of the cyanobacterial metabolism has been proposed to be advantageous for the development of S2C and S2F processes. The rewiring of the sugar catabolism and of the phosphoketolase pathway in the central cyanobacterial metabolism allowed for an enhancement in the level of target products by redirecting the carbon fluxes. Thus, metabolic pathway rewiring can promote the development of more efficient cyanobacterial cell factories for the generation of feasible S2C and S2F platforms.Photoautotrophic cyanobacteria have been developed to convert CO Together with the development of integrated bio-electrochemical systems2. Recent reviews on the development of S2C and S2F platforms using engineered cyanobacteria have focused on general perspectives for cyanobacterial fuels (Cyanofuels),2 toward target end products.Global concerns targeting the reduction of greenhouse gas emissions and sustaining the supply of energy and chemicals have brought attention to the development of sustainable platforms to convert carbon dioxide to chemicals and fuels, in the form of solar-to-chemical (S2C) and solar-to-fuel (S2F) technologies.E. coli has resulted in the enhanced production of 2,3-butanediol , beside CO2 fixation, in cyanobacteria supplemented with glucose or xylose. Subsequently, metabolite profiling analysis was performed to assess the ratio of carbon assimilation from sugar and CO2 through the feeding of labeled [U-13C] glucose. Recently, the co-utilization of glucose and CO2 has been optimized to improve 2,3-BDO production and yield, by guiding the glucose flux into the central metabolism.zwf (encoding for a glucose-6-phosphate dehydrogenase) and gnd (encoding for a 6-phosphogluconate dehydrogenase) in the oxidative pentose phosphate pathway, prk (encoding for a phosphoribulokinase), and rbcLXS  subunit) led to the rewiring of glucose catabolism and the fixation of CO2, and increased the growth rate, glucose consumption, and 2,3-BDO production (1.1 g/L/d) in cyanobacteria. Photomixotrophic production using engineered cyanobacteria via the rewiring of metabolic pathways could be advantageous for high cell-density cultivation to increase the production yield, despite some concerns regarding the cost and the contamination of the sugar feedstock. In addition, D-lactic acid (2.17 g/L)To achieve the production of final products at a feasible scale, product yield and productivity must be considered under both light and dark conditions. The implementation of the sugar utilization pathway in cyanobacteria has successfully increased the product yield under either continuous or diurnal conditions.2 and sugars, and to assist in the metabolic engineering of cyanobacteria. A relatively small fraction of carbon fluxes from CO2 were directed to fatty acids and the terpenoid biosynthesis pathway.Synechococcus elongatus PCC 7942, a model cyanobacterium, harboring heterologous genes for acetone biosynthesis, did not produce any acetone from CO2 under light conditions.S. elongatus PCC 7942 through the rewiring of the phosphoketolase (PHK) pathway to the acetone biosynthesis pathway has allowed the production of photosynthetic acetone from CO2 .The cyanobacterial metabolism is complex and plastic. This is supported by genetic and biochemical evidence that demonstrates the presence of 2-oxoglutarate decarboxylase and succinate semialdehyde dehydrogenase activities in the tricarboxylic acid cycle (TCA),Metabolic plasticity is also associated with the metabolic capabilities under various environmental growth conditions ,2 has been addressed by focusing on the metabolic engineering of cyanobacteria. The rewiring of the metabolic pathways in cyanobacteria has allowed for the production of non-native chemicals, and facilitated carbon partitioning toward target chemicals using the S2C and S2F platforms. In addition, protein engineeringThe status of solar-to-chemical and solar-to-fuel platforms for the production of value-added chemicals from CO


GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering.Genome‐scale metabolic models ( For the past 20 years, a recurrent modeling approach for reaching this objective has been constraint‐based modeling    , which is equal to the intracellular concentration of the corresponding enzyme multiplied by the enzyme's turnover number . However, intricate relationships between enzymes and reactions are quite frequent and complicate the aforementioned constraint  has a basic constraint: The flux cannot exceed the reaction's maximum rate  and lower molecular weights (median = 41.7 kDa) than the other two groups; that is, they were faster in catalysis and smaller. Conversely, the intermediate and secondary metabolism enzymes were slower in catalysis  and larger (median molecular weight = 53.6 kDa). Finally, amino acid, fatty acid, and nucleotide primary metabolism enzymes had intermediate kcat values (median = 65.9 s−1) and molecular weights (median = 47.1 kDa). This is in agreement with previous observations  carbohydrate and energy primary metabolism; (ii) amino acid, fatty acid, and nucleotide primary metabolism; and (iii) intermediate and secondary metabolism see the  section.alue Fig B and molalue Fig C distribWe also compared biochemical properties across different types of enzymes, that is, complexes, isozymes, and promiscuous enzymes . We obseConnectivity metrics for Yeast7 and ecYeast7 were computed using the metabolite network Table , with an−1) was applied, and the model was allowed to freely allocate the enzymes within this overall constraint. This was performed using a module of GECKO that introduces a pseudo‐metabolite that acts as an enzyme pool , enzymes from the oxidative phosphorylation pathway (the most mass intensive pathway) are progressively replaced by increased abundance of glycolytic enzymes. This supports the view that energy synthesis in yeast is dominated by two strategies: metabolic efficiency at low specific growth rate and catalytic efficiency at high specific growth rate   so that the model would fit experimental data . For comparison, we calculated how much growth the purely metabolic model predicted under the same conditions; for this purpose, upper bounds were imposed on all uptake rates based on the results from the enzyme‐constrained model, given that unlimited uptake rate leads to unlimited growth in a purely metabolic model. It can be observed that Yeast7 still widely over‐predicted growth  and 12 different carbon sources, and compared the results with literature studies . We see that flux simulations from Yeast7 are quite similar among them, with more than 97% of the variability explained with only one component and clustering mainly depending on the specific growth rate Fig C. This i−1 aerobically in a minimal media, limited by glucose . For the other 311 enzymes in the model, the overall mass constraint previously mentioned was used, limiting the sum by 0.036 g gDW−1, which represents the remaining mass in the model according to PaxDB  flux values between both models, and only 3.7% of them are both significantly different and have an average difference higher than 0.1 mmol gDWh−1. By performing PCA to all flux samples, we observed that 37% of the difference between both model predictions is explained by the first two components . In particular, only 1.5% of the variable reactions in ecYeast7 have complete variability, that is, 1,000 mmol gDWh−1, as opposed to the original Yeast7, in which 25.3% of the variable reactions have this freedom. Out of all 4,972 fluxes in the original model , 3,177 had their variability reduced by including enzyme constraints, 1,757 remained the same, and 38 had minor increases  , [Ei] is the intracellular concentration of Ei, and kcatij is the turnover number of Ei catalyzing Rj. With GECKO, we impose this constraint on each reaction in the model  approach . This implies that there will be more than one non‐zero coefficient in some rows of the lower left submatrix of the stoichiometric matrix . Both GECKO and ecYeast7 are compatible with the COBRA toolbox  was chosen to avoid over‐constraining the model , some reactions’ reversibilities were modified for proper glycerol production, and the growth associated maintenance was set to 16 mmol gDWhPtotal (g gDW−1) multiplied by a fraction f, which represents the mass fraction of enzymes that are accounted for in the model according to PaxDB , which was absolutely quantified against the UPS2 protein mix and used as an internal standard in the proteome analysis.Absolute proteomic measurements were obtained from a recent study  were separated on an Ultimate 3000 RSLCnano system  using a C18 cartridge trap column in a backflush configuration and an in‐house packed  analytical 50 cm × 75 μm emitter column (New Objective). Following a LC separation, the peptides were eluted to a Q Exactive (Thermo Fisher Scientific) tandem mass spectrometer operating with a top‐10 strategy and a cycle time of 0.9 s. The raw data were identified and quantified with the MaxQuant 1.4.0.8 software package. The heavy spike‐in standard was quantified with the iBAQ option enabled using log fit  and σ = 46%. See the The measured total protein content was 0.448 g gDWNDI1 (YML120C) knockout in ecYeast7 was simulated by blocking the usage of the associated enzyme (P32340). Additionally, both cytosolic NADH dehydrogenases NDE1 (YMR145C–P40215) and NDE2 (YDL085W–Q07500) were blocked to simulate the limited capacity of the ethanol‐acetaldehyde shuttle in vivo . Additionally, two modifications were performed in ecYeast7 for the case of farnesene: the addition of the farnesene synthase from Malus domestica [Tippmann et al, kcat = 0.0553 s−1 according to the literature .The data used in this study and the developed method and model are all available online:http://www.ebi.ac.uk/pride/archive/projects/PXD005041; only reference conditions were used)Absolute proteomic data: PRIDE PXD005041, “Absolute quantification of yeast proteome” GECKO method: GitHub ecYeast7 model : GitHub (JN, BJS, and CZ conceived the project. BJS and CZ developed the computational method. P‐JL performed the protein quantifications. BJS performed the computational simulations and analysis of the results. AN, CZ, and EJK contributed to analysis of the results. BJS wrote the draft of the paper. All authors read, edited, and approved the final manuscript.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Review Process FileClick here for additional data file.


Rickettsia . While some aspects of host-dependent rickettsial metabolism have been characterized, the array of host-acquired metabolites and their cognate transporters remains unknown. This dearth of information has thwarted efforts to obtain an axenic Rickettsia culture, a major impediment to conventional genetic approaches. Using phylogenomics and computational pathway analysis, we reconstructed the Rickettsia metabolic and transport network, identifying 51 host-acquired metabolites (only 21 previously characterized) needed to compensate for degraded biosynthesis pathways. In the absence of glycolysis and the pentose phosphate pathway, cell envelope glycoconjugates are synthesized from three imported host sugars, with a range of additional host-acquired metabolites fueling the tricarboxylic acid cycle. Fatty acid and glycerophospholipid pathways also initiate from host precursors, and import of both isoprenes and terpenoids is required for the synthesis of ubiquinone and the lipid carrier of lipid I and O-antigen. Unlike metabolite-provisioning bacterial symbionts of arthropods, rickettsiae cannot synthesize B vitamins or most other cofactors, accentuating their parasitic nature. Six biosynthesis pathways contain holes (missing enzymes); similar patterns in taxonomically diverse bacteria suggest alternative enzymes that await discovery. A paucity of characterized and predicted transporters emphasizes the knowledge gap concerning how rickettsiae import host metabolites, some of which are large and not known to be transported by bacteria. Collectively, our reconstructed metabolic network offers clues to how rickettsiae hijack host metabolic pathways. This blueprint for growth determinants is an important step toward the design of axenic media to rescue rickettsiae from the eukaryotic cell.Reductive genome evolution has purged many metabolic pathways from obligate intracellular  Rickettsia, arthropod-borne parasites with the potential to cause serious human disease, the range of pilfered host metabolites is unknown. This information is critical for dissociating rickettsiae from eukaryotic cells to facilitate rickettsial genetic manipulation. In this study, we reconstructed the Rickettsia metabolic network and identified 51 host metabolites required to compensate patchwork Rickettsia biosynthesis pathways. Remarkably, some metabolites are not known to be transported by any bacteria, and overall, few cognate transporters were identified. Several pathways contain missing enzymes, yet similar pathways in unrelated bacteria indicate convergence and possible novel enzymes awaiting characterization. Our work illuminates the parasitic nature by which rickettsiae hijack host metabolism to counterbalance numerous disintegrated biosynthesis pathways that have arisen through evolution within the eukaryotic cell. This metabolic blueprint reveals what a Rickettsia axenic medium might entail.A hallmark of obligate intracellular bacteria is the tradeoff of metabolic genes for the ability to acquire host metabolites. For species of  Rickettsiales (Alphaproteobacteria) are obligate intracellular bacteria found in species across nearly every major lineage of Eukaryota   . Rickettmataceae , though tic cell .Rickettsiaceae  are unique among the members of the order Rickettsiales in lysing the host phagocytic vacuole and residing primarily in the host cytosol , members of the family Rickettsiaceae require the host cell for replication and cannot persist extracellularly , which exchanges host ATP for bacterial ADP without a change in the total adenylate pool  (S-adenosylmethionine (SAM), for which a novel transporter (EamA) was characterized  , 52 and cterized . Additiocterized . CollectCoxiella burnetii ; similar patterns across a range of taxonomically diverse taxa imply alternative metabolic strategies shared by divergent bacteria. The comprehensive metabolic blueprint developed here illuminates what a successful Rickettsia axenic medium might entail. Furthermore, our work elucidates the parasitic nature by which rickettsiae pilfer host metabolites to counterbalance the many biosynthesis pathways that have disintegrated through evolution within the eukaryotic cell.Although they possess limited metabolic activity, isolated rickettsiae are unable to grow extracellularly. Despite tremendous efforts in rickettsial genetic manipulation over the last 2 decades , the lacburnetii , 57. In Rickettsia genomes, with 74 genomes comprising the three well-established rickettsial lineages (n = 10), the typhus group (n = 15), and the spotted fever group (n = 49). Ten additional genomes are from basal lineages that do not form a monophyletic group. When considering only closed genomes (n = 54), Rickettsia genomes range in size from 1.5 Mb  to 1.1 Mb , with a core genome including 621 protein-encoding genes. Variation in accessory genomes is dominated by pseudogenization events, but for some species , it canN-acetyl-α-d-glucosamine (UDP-NAG), which is typically synthesized from glucosamine-1-P (GlcN-1-P) in bacteria by the bifunctional protein GlmU. The GlmU C-terminal acetyltransferase domain generates N-acetylglucosamine-1-P (NAG-1-P) from GlcN-1-P, while the N-terminal uridyltransferase domain converts NAG-1-P to UDP-NAG -lipid A acceptor and O-antigen polymerization  in sequential steps  to generate energy, as well as important substrates for other biosynthetic processes. The principal source of acetyl-CoA is pyruvate, generated by the breakdown of sugars via glycolysis—a pathway that rickettsiae do not possess. Rickettsiae must obtain pyruvate for a variety of reasons, including use in DAP biosynthesis (leading to PGN) and for the generation of phosphoenolpyruvate (PEP), a cofactor in the biosynthesis of both PGN and LPS . PyruvatRickettsia species . Like the PDC, this pathway requires CoA, a cofactor that rickettsiae must acquire from the host (discussed below), as well as acetate, which was previously shown to be imported by typhus group rickettsiae . Remarka-antigen , indicatRickettsia-generated acetyl-CoA, imported Gln and Glu also regulate the flow of acetyl-CoA into the TCA cycle  and predicted  host metabolites, as well as numerous host cofactors (see “Rickettsieae cannot synthesize B vitamins and most other cofactors” below) are used by rickettsiae to drive the TCA cycle, which in turn feeds the pathways for the generation of DAP and heme A for PGN and oxidative phosphorylation, respectively.In addition to host-acquired malate and CA cycle . The preCA cycle . Asp genCA cycle . Succinyase HemJ , are useRickettsia PCC substitutes for bacterial ACC in the generation of malonyl-CoA. Other lines of evidence support this proposition: (i) PCCs are included within a large family of diverse biotin-dependent carboxylases with highly variable substrate specificities .Bacterial fatty acid biosynthesis initiates with malonyl acyl carrier protein , which is formed from holo-ACP and malonyl-CoA . Rickettficities ; (ii) seficities , 78; andC mutant . FinallyC mutant , as they2-lipid A synthesis, and hexadecanoyl-ACP for glycerophospholipid synthesis  and acyl-ACPs (16C and 18C) for Kdoynthesis . For theynthesis , 52. Altl-serine , 81. Thein vitro  that are known to ligate surface molecules with phosphorylcholine, a process often associated with modulation of host immunity . This indicates that either these previous studies failed to completely eliminate host glycerophospholipids from isolated rickettsial membranes, or host cardiolipin and/or PC are incorporated into rickettsial membranes in trace amounts. Rickettsiae have been shown to hydrolyze PC during infection , and allin vitro . Rickettimmunity . It is cimmunity , intrigu8) pathway. It is also combined with IPP to generate geranyl diphosphate (GPP), which in turn combines with IPP to yield trans,trans-farnesyl diphosphate (FPP), the precursor of terpenoid backbone synthesis. FPP combined with eight IPP molecules generates di-trans,poly-cis-undecaprenyl diphosphate (UPP), which is subsequently dephosphorylated to yield di-trans,poly-cis-undecaprenyl phosphate, the lipid carrier for lipid I of PGN (see trans-octaprenyl diphosphate (ODP), a precursor of CoQ8 synthesis.Isoprenes are the general precursors of all terpenoids, a diverse family of compounds involved in a variety of cell functions, including electron transport and membrane synthesis. Bacteria employ the mevalonate or nonmevalonate (MEP/DOXP) pathway to synthesize isoprenes, namely, isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). DMAPP is the source of the dimethylallyl phosphate used in the first step of the ubiquinone  into 3-octaprenyl-PHBA, a reaction carried out by PHBA polyprenyltransferase (UbiA). Many bacteria synthesize PHBA from chorismate, a precursor of aromatic amino acid synthesis. Although they possess UbiA, rickettsiae lack the enzymes to either synthesize chorismate or convert it to PHBA, indicating that PHBA must be imported from the host   see . In E. c pathway . A diffe pathway , however pathway , the imp8 by rickettsiae, pathways for the synthesis of B vitamins and other cofactors are incomplete or entirely lacking. Our analysis indicates that at least 11 such molecules must be acquired directly from the host—and possibly more when derivatives are considered—on the basis of the pathways that do function in rickettsiae. Thiamine cannot be synthesized, and rickettsiae do not encode any enzymes that generate thiamine’s five phosphate derivatives; at a minimum, rickettsiae must import thiamine diphosphate (ThDP), as this cofactor is essential for the PDC and the oxoglutarate dehydrogenase complex of the TCA cycle (S-transferase (GstA), respectively—but not the capacity to synthesize the cofactors.In contrast to the conserved pathways for the synthesis of porphyrins, lipoate, and CoQCA cycle . No enzy+ + transhydrogenase providing NADH and NADPH. Rickettsiae acquire SAM directly from the host via the highly conserved EamA transporter (see R. prowazekii can import SAM (l-Met using the SAM synthase MetK (Rickettsia species (data not shown). Our observations are in line with the proposition that the acquisition of metabolite transport systems facilitates the degradation of apposite biosynthetic pathways in rickettsiae , comparative genomics originally indicated a degraded biosynthesis pathway in Rickettsia genomes  and suggested that folate derivatives must be imported from the host (Rickettsia endosymbiont of Ixodes pacificus (REIP) can synthesize the active form of the vitamin, tetrahydrofolate (THF), and provision it to its arthropod host. REIP is closely related to R. buchneri, a species found exclusively in Ixodes scapularis, the tick vector of Lyme disease  to 6-hydroxymethyl-7,8-dihydropteridin (HMDHP), a function mediated by Glu replacing or accompanying the usual Cys in the PTPS active site  PTPS enzyme QueD  actually contains the canonical PTPS active site . Third, we show that it is unlikely that rickettsiae are unable to synthesize the 4-aminobenzoate (pABA) that is incorporated into the pterin ring of HMDHP, as the enzymes that would normally generate pABA from chorismate  are not present, and chorismate itself is not synthesized and probably not needed by rickettsiae (discussed above for PHBA metabolism). Some genomes do encode “frC,” an atypical fol_rel_CADD domain-containing enzyme (TIGR04305)  and carrying out glutamylation of host-acquired THF (FolC). Thus, we predict that rickettsiae import host THF  to carry out the critical one-carbon transfer reactions involved in amino acid interconversion, pyrimidine synthesis, and translation.In the present study, we present several lines of evidence that argue against site see . Our anality see . Second,lity see ; surprislity see . It seemGR04305)  that mayGR04305) ; howeverales see . Fifth, udogenes . While w10.1128/mBio.00859-17.5FIG S5 Rickettsia species use DHN-P3 for queuosine biosynthesis and import host THF for one-carbon transfer reactions by folate. Download FIG S5, PDF file, 1.3 MB.Copyright © 2017 Driscoll et al.2017Driscoll et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00859-17.6FIG S6 Rickettsia queuosine biosynthesis pathway contains a hole for the reduction of epoxyqueuosine to queuosine. Download FIG S6, PDF file, 0.4 MB.The Copyright © 2017 Driscoll et al.2017Driscoll et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Rickettsia metabolic reconstruction identified six holes occurring in otherwise conserved pathways for the synthesis of DAP, CMP-Kdo, CDP-diacylglycerol (CDP-DG), terpenoid backbones, CoQ8, and queuosine  and diversity of bacterial genomes simply lacking DapC within the predominant DAP pathway likely indicate the existence of an alternative aminotransferase that awaits experimental characterization. Similarly, within the CMP-Kdo biosynthesis pathway, the lack of KdsC homologs across divergent bacteria indicates that these species may use a different, possibly nonspecific, phosphatase to convert 3-deoxy-d-manno-octulosonate 8-P to 3-deoxy-d-manno-octulosonate. In support of this, KdsC is not essential in E. coli  , a very recent report proposed reannotation of the various proteobacterial CoQ8 flavin monooxygenases  into two novel groups named UbiM and UbiL , the enzyme from Rhodospirillum rubrum was shown to complement ΔubiI and ΔubiH mutant E. coli strains, suggesting that the missing UbiI functionality may be carried out by UbiH in rickettsiae. Regarding UbiC (chorismate lyase), an alternative means to convert chorismate to PHBA was previously discovered for Xanthomonas species  of PHBA to fuel CoQ8 synthesis  in tRNAs with GUN anticodons or that another epoxyqueuosine reductase unrelated to QueG is found in rickettsial genomes. The latter scenario seems more plausible on the basis of two lines of evidence. First, QueG requires the cofactor cobalamin (vitamin B12) . Second, 45 of these genomes that lack QueG, as well as all rickettsial genomes, encode DUF208-containing proteins (data not shown), which have very recently been characterized as cobalamin-independent epoxyqueuosine reductases unrelated to QueG  in the min B12) , 111, a min B12) . Rickett to QueG . Genes eRickettsia-like pathway holes on the basis of their lifestyle  to explore whether reductive genome evolution as a consequence of an intracellular lifestyle might be a driving factor for gene decay within these pathways . Despite an extracellular lifestyle, this gammaproteobacterial species is a primary symbiont of the brown marmorated stink bug, where it resides primarily in the gastric cecal lumina , and its gene repertoire contains characteristics of both highly specialized obligate mutualists and facultative species , Orientia chuto (Candidatus Occidentia massiliensis” (O. tsutsugamushi genomes are laden with proliferative/degradative mobile genetic elements (Rickettsia species do (Rickettsia sister clade lacks idi and must therefore acquire both IPP and DMAPP (in addition to FPP) from the host to initiate the CoQ8 and terpenoid backbone synthesis pathways. All other Rickettsiales/Holosporales genomes likewise lack idi, though they do encode enzymes of the MEP/DOXP pathway for the synthesis of isoprenes, indicating that IPP-DMAPP interconversion is unique to Rickettsia species. Remarkably, phylogeny estimation suggests that rickettsiae likely acquired idi from non-Alphaproteobacteria sources . The strict conservation of Tgt in all genomes suggests that species lacking Que-encoding genes incorporate host-acquired queuine directly into tRNAs with GUN anticodons, similar to the eukaryotic salvage mechanism   were not identified in any other tsia see . Unexpecechanism . Phylogeporales) . The oriporales) ; thus, iin vivo or in vitro assays. Roughly 80% of these metabolites are synthesized by both vertebrates and arthropods, with the other metabolites essential for both the host and the microbe  of transporters that are highly conserved across Rickettsia genomes . While the substrates of these transporters are unknown, their importance in obligate intracellular living is evident, given their spread by lateral transfer across rickettsiae and unrelated obligate intracellular bacteria such as “Candidatus Amoebophilus asiaticus” and Cardinium endosymbionts of arthropods  or putative  imported metabolites. Both branched-chain amino acids and nsporter . As discnsporter , BioY, ithropods , 127.+  to the E. coli sodium/pantothenate symporter PanF; however, as discussed above, rickettsiae most likely import dephospho-CoA and not pantothenate, since they cannot metabolize the latter. Finally, we did not identify any transporters for other carbohydrates, isporenes, or terpenoids.The requirement for several large metabolites  implies that rickettsiae must have transport systems to import these molecules. We did not identify any of the energy-coupling factor transporters –130 typi–+ , there i+ . The RicE. coli . Given the possible shared ancestry of rickettsiae and the mitochondrial progenitor , a reasoE. coli , it is p8, queuosine, type II fatty acid synthesis). To this end, the Rickettsia metabolic network may approach the minimal metabolic unit for a bacterial parasite of the eukaryotic cytoplasm.Our metabolic reconstruction illustrates the few biosynthesis pathways that remain in rickettsiae. In most free-living and facultative intracellular bacteria, these pathways are interconnected primarily by glycolysis and the pentose phosphate pathway, two of the most conserved metabolic processes across life’s three domains . Rickett10.1128/mBio.00859-17.10FIG S10 Rickettsia metabolic pathways and metabolite transporters. Download FIG S10, XLSX file, 0.4 MB.Phylogenomics analysis of Copyright © 2017 Driscoll et al.2017Driscoll et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. prowazekii strain Madrid E  antifiliis , 146), a fluctus . AlthougRickettsia species associated with the silverleaf whitefly  and thechophila , both no females . The boopathogen . Our prekettsiae . It remaRickettsia species has been shown to provision its host(s) with metabolites; indeed, rickettsiae seem to be in competition with arthropod and vertebrate cells for at least 20 metabolites that are either essential or synthesized from essential nutrients   . Remarkaits host , this inlineages  have entRickettsiales were likely facultative intracellular species of protoeukaryotes, with a symbiosis established by one lineage that arguably facilitated eukaryogenesis  to import many large metabolites. Thus, future studies that characterize transporters in heterologous expression systems will continue to be invaluable tools in elucidating how rickettsiae pilfer host metabolites. We anticipate that careful evaluation of large-scale transcriptomic and proteomic data sets will reveal clues about how the host metabolism is hijacked throughout the course of rickettsial infection. Ultimately, establishing an axenic culture for rickettsiae will greatly facilitate genetic studies correlating transporters with imported substrates. Such an effort is conceptually appealing yet practically difficult. The present study represents an important contribution to that effort; it may be that differences between our estimated 51 essential metabolites and the complex media used in early metabolic assays  provideRickettsia genomes continually acquire genes throughout their evolution and that genes encoding metabolic enzymes, metabolite transporters, and stringent response regulators are often components of mobile genetic elements  from 86 sequenced genomes were used to estimate a genus-level Rickettsia phylogeny. Rickettsia genomes were either retrieved from the NCBI Assembly database (n = 82) or obtained via personal communication   were included as outgroups. The RAST v 2.0 server  see . Two non0 server  was usedOrthoMCL , at an iOrthoMCL  using deOrthoMCL . All modOrthoMCL , with thOrthoMCL . A phyloOrthoMCL  using a n = 86,029) from 56 sequenced genomes were used to estimate an order-level phylogeny of Rickettsiales/Holosporales, sampling the groups Holosporales (n = 9), Anaplasmataceae (n = 22), Midichloriaceae (n = 4), and Rickettsiaceae (n = 18), as well as three outgroup genomes . Genomes were retrieved from NCBI  were used to construct 3,576 orthologous gene families as described above. These families were subsequently assigned Kyoto Encyclopedia of Genes and Genomes (KEGG) K numbers by submitting all 109,191 individual protein sequences to the KEGG BlastKOALA service  our data set with orthologous sequences from E. coli. The taxonomic distribution of confirmed Rickettsia pathway holes was assessed by converting each pathway into a binary feature vector, where each position in the vector represents a pathway step containing either a 1 (gene present) or a 0 (gene absent). Pathway data for all KEGG genomes were subsequently analyzed (khole) for genomes that possess Rickettsia-like feature vectors.A pathway hole is simply defined here as a missing gene in an otherwise complete metabolic pathway. Pathways that are absent or highly fragmented are not considered. Candidate pathway holes were initially identified during inspection of the metabolic reconstruction results for Rickettsiales enzymes corresponding to Rickettsia pathway holes), individual protein phylogenies were estimated. To construct data sets for each protein, the rickettsial protein was used in blastp queries against several taxon-specific databases, i.e., (i) “Rickettsiales,” (ii) “Holosporales,” (iii) “Alphaproteobacteria ,” (iv) “Proteobacteria (minus Alphaproteobacteria),” (v) “Bacteria (minus Proteobacteria),” and (vi) “minus Bacteria.” The top 5 to 10 (query-dependent) subjects from each search resulting in significant (>40 bits) alignments were all compiled and aligned with MUSCLE v3.8.31 using default parameters. Protein phylogenies were estimated under maximum likelihood with RAxML v8.2.4 using a gamma model of rate heterogeneity and estimation of the proportion of invariant sites. Both the Le and Gascuel (LG) and blocks of amino acid substitution matrix (BLOSUM62) models of amino acid substitution were used, and branch support was assessed using 1,000 pseudoreplications.To determine the evolutionary history of specific genes , ion channels (n = 3), secondary transporters (n = 48), and unclassified transporters (n = 7). Several Rickettsia genomes were also evaluated on the Transporter Classification Database (TCDB)  . Transpo


Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from  Metabolomics profiling technologies are increasingly used for phenotyping of biological systems to understand the contribution of metabolism to complex phenotypes, including growth and diseases . This approach has allowed the elucidation of novel regulators of reaction rates.Assuming random fluctuations around a given steady state, metabolic correlations have been related to the Jacobian of the system of ODEs that describe the change in metabolite concentrations , here we propose a novel means to analyze metabolic profiles based on the concept of constrained maximal correlation coefficient. We use this approach to analyze and characterize the role of metabolites in a network that captures the reaction rate coupling. First, by using a paradigmatic model of the tricarboxylic acid (TCA) cycle, we investigate the effect from departures of the assumption of mass action on the identified reaction coupling and couplings of reactant complexes. We then show that Stoichiometric Correlation Analysis (SCA) can be employed to perform cross-species comparison of the TCA cycle and amino acid synthesis pathways. In addition, we demonstrate that the proposed approach can be used to mechanistically understand the agronomicaly important process of domestication, here, in the case of wheat as well as in tomato and strawberry.W and Z, introduced by Gebelein  relationships. The maximal correlation coefficient ρ, between a pair of random variables Gebelein  and alreGebelein  and RényGebelein , is defif of W and g of Z with finite variances, i.e., V(f(W)) > 0 and V(g(Z)) > 0. Maximal correlation then infers (non-linear) transformations of two random variables by maximizing their pairwise correlation .where the supremum is taken over all functions There exist efficient algorithms to compute maximal correlation for both discrete  with which metabolite i is involved as a substrate or product in the reaction j , N denotes the stoichiometric matrix with dimensions m × n, with m the number of metabolites and n the number of reactions, v denotes the reaction rate functions, x, the concentrations of the considered metabolites, k, the parameters on which the reaction rates depend, and t stands for time , are described by a non-linear function . The rate of reaction j with a substrate complex ij > 0 where αij is the stoichiometry with which Si enters the substrate complex of reaction j, under mass action kinetics is then expressed as kj denotes a rate constant. For instance, the rate of reaction with rate constant k1 in Figure k1x1x2, since S1 and S2 enter this reaction as substrates, each with stoichiometric coefficients of one; similarly, the rate of reaction with rate constant k3 is given by k3x4, since S4 enters the reaction as a substrate with a stoichiometric coefficient of one.Let To arrive at our approach termed Stoichiometric Correlation Analysis (SCA) we rely on the observation that metabolic networks, as part of inter-related cellular systems , operate toward providing robust functionality Kitano, . We tranDefinition 1: Two elementary reactions, p and q, have coupled rates under mass action kinetics if for any steady-state concentration of the participating components, gathered in x,r1 and r2, r1 and r3, as well as r3 and r2 have coupled rates  and g(Uq) are the logarithm of the rate of the reactions p and q under mass action kinetics, respectively. The presence of coupled rates in mass action for reactions p and q, after taking the logarithm, leads to stoichiometric correlation of value one for Up and Uq. This observation pinpoints the main principle on which SCA relies.If 2||. Therefore, stoichiometric correlation can be regarded as constrained maximal correlation, where the constraints pertain to the limited set of values that the entries of β and η are allowed to take following the stoichiometry of reactants. The transformation used in the constrained maximal correlation is explicitly non-linear, since the function f involves logarithms.If there exist multiple vectors β and η, yielding the same value of the stoichiometric correlation, we consider the one of smallest magnitude ||β+ηUp and Uq are disjoint sets of random variables denoting the data profiles of metabolites, the entries of β and η are positive. For instance, given several steady-state measurements for the components in network on Figure Up = {S1, S2} and Uq = {S4} is one with coefficients in β and η equal to one. Similar conclusions can be drawn for all components involved in the coupled reaction rates given in Figure I or (2) generating all subsets of at most two variables with different contribution due to stoichiometry, and determining the Pearson correlation coefficient only between the disjoint subsets. Since the number of available metabolic profiles from time-resolved studies usually does not exceed 100, the second alternative can be efficiently implemented with appropriate parallelization . Note that since n metabolites over c samples , we implemented SCA by determining: (1) the Pearson correlation r(log(xi), log(xj)), for all couples 1 ≤ i ≠ j ≤ n of metabolic profiles, (2) the values for a, b ∈ {1, 2, 3, 4} that maximize the Pearson correlation between a log(xi) + b log(xj) and xk for every triple of metabolic profiles, (3) the values for a, b, c, d ∈ {1, 2, 3, 4} that maximize the Pearson correlation between a log(xi)+b log(xj) and c log(xk)+d log(xl) for every quadruple of metabolic profiles. In addition, we determined the statistical significance for each of the maximum correlations. We used the R package Hmisc  and the biomass metabolite (biosyn) were removed, as a comparable reaction and metabolite were not present in the other two analyzed models. The modified Michaelis-Menten model contains therefore 11 metabolites and 10 reactions. All kinetic parameters remained unchanged. The mass action based models contain the TCA cycle of the E. coli model of Khodayari et al.  and a domesticated strawberry species (F. ananassa) . We note that every enzymatic reaction ∑i αijiS → ∑iα′ijiS  can be rewritten to include the action of an enzyme ∑i αijiS + E ⇄ SE ∑iα′ijiS + E (E denotes the enzyme and SE the substrate-enzyme complex), so that the elementary reaction can be still modeled with mass action kinetic. Therefore, SCA can also include the effect of enzyme action. However, while this approach provides a way to model Michaelis-Menten kinetic which accounts for enzyme saturation, it does not explicitly consider the Michaelis-Menten form for the reaction kinetic.From the derivation of our SCA, it follows that the findings based on the constrained correlation of metabolic data profiles reflect the apparent couplings of elementary reaction rates, assumed to obey mass action kinetic. In addition, the findings reflect the additional couplings which cannot be directly related to reaction rates but are direct consequence of them , we considered three models of the tricarboxylic acid (TCA) cycle. All three models include the same metabolites, and differ only with respect to whether or not they include the effect of enzyme action and if they use mass action kinetic or the more involved functional forms of the Michaelis-Menthen kinetic. All reactions are considered reversible, and they are split into irreversible reactions in the cases in which mass action kinetic was employed. We used the TCA cycle model embedded in the kinetic model of To conduct the comparative analysis, we simulated the models metabolite concentrations with physiologically relevant randomly chosen initial values. The simulation time ranged from 0 to 1,280 min and metabolite concentrations were obtained at 21 time points identical to those used in the study of Caldana et al.   (ppGpp) whose level is controlled by two enzymes, RelA and SpoT .To help answer this question, we analyzed the set of metabolites from the TCA cycle and the amino acid synthesis pathways from the two model organisms. We used these metabolites since ppGpp controls transcription and translation, which is ultimately reflected in the levels of amino acids. Moreover, a comparative analysis between the two organisms is only meaningful for the same set of metabolites. Altogether, we used the publicly available data profiles of three metabolites from the TCA cycle  as well as 16 amino acid measured over seven and five conditions in S can be defined as the number of stoichiometric correlations above a given threshold τ in which the metabolite S participated. Based on the derivation of SCA, a higher degree of coupling on the same set of metabolites then implies maintenance of more coupled reaction rates over a set of studied conditions in one organism in comparison to another. We considered the significant stoichiometric correlations  0.8, 0.85, 0.9, and 0.95, and compared them to classical Pearson correlations (Table The degree of coupling for metabolite ns Table . The quiUp| = |Uq| = 1). The reduction in the number of significant stoichiometric correlations for metabolite pairs can be explained by the monotonic transformation of metabolite profiles. We would like to emphasize that the result does not suggest that metabolites are linearly related, which would be contrast to what is expected from mechanistic understanding of metabolism.For the purpose of comparison, at all threshold values and in both species, we observed a decrease on the number of significant stoichiometric correlations for pairs of metabolites, compared to Pearson correlation  was domesticated from wild emmer (T. turgidum ssp. dicoccoides) around 12,000 years ago  subsequently originated from emmer, followed by the selection of durum wheat (T. turgidum ssp. turgidum convar. durum). Therefore, it has been suggested that the evolution of tetraploid wheats consists of at least two steps: primary domestication, from wild emmer to emmer, and secondary domestication, from emmer to durum wheat  within each population at four threshold values . These metabolites were selected based on their presence in every of the analyzed accessions to allow comparative analysis of the populations without the need of imputation as well as assumptions about the reasons for absence of detected metabolite. The number of stoichiometric correlations due to triples shared between emmer and wild emmer was the highest, followed by that between durum and wild emmer (at threshold of 0.8). At a threshold value of 0.85, both emmer and wild emmer had one overlapping triple with durum. However, at a threshold value of 0.8, durum wheat shared more stoichiometric correlations due to quadruples with wild emmer than emmer. At thresholds of 0.85 and 0.9, durum shared the same number of quadruples with emmer and wild emmer. In all cases, only stoichiometric correlations due to quadruples were shared between all three populations  emmer  to the group of the other five species, as their fruits drastically differ from those of M82  and F. ananassa  without direct domestication lineage between the two species. In contrast to our observations in wheat and tomato, the domesticated strawberry exhibits a higher number of stoichiometric correlations above all thresholds  controlled than that of A. thaliana. Therefore, while the enzymes underlying the stringent response are preserved in these two model organisms, their integration in the metabolic networks may have evolved different regulatory action. In addition, SCA can be used to investigate the differences between undomesticated and domesticated species, and to determine if the difference can be ascribed to alterations in metabolic couplings brought about by various regulatory mechanisms. Based on this idea, we demonstrate that stoichiometric correlations from metabolic profiles from natural variation in wild and domesticated species indicate that domestication is associated with loss of regulatory control. Therefore, our findings provide the basis for future flux-oriented studies toward mechanistic understanding of this important evolutionary process.Here we proposed a constrained extension to the concept of maximal correlation, based on the concept of reaction rate coupling in networks of metabolic reactions. The concept of reaction couplings forms the core of the stoichiometric correlation analysis. The constraints in the maximal correlation are due to the values which the linear combinations of log-transformed metabolic profiles are allowed to take. SCA facilitates the comparison of data sets on the same metabolites between two scenarios with the idea of comparing and contrasting the degree of coupling. By determining the stoichiometric correlations of metabolic profiles from the TCA cycle and amino acid synthesis, we showed that ZN conceived the project and wrote the article with contribution of all authors. KS performed most of the analysis and analyzed the data. RB provided data and helped interpret the findings related to domestication. NO designed parts of the algorithm and helped with its implementation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer WZ and handling Editor declared their shared affiliation.