Escherichia coli MG1655:: tetA grown in the presence of ½ MIC (14 mg/L) of OTC, and for comparison WT MG1655 strain grown with 1//2 MIC of OTC (0.25 mg/L OTC). 1646 genes changed expression significantly (FDR > 0.05) in the resistant strain, the majority of which (1246) were also regulated in WT strain. Genes involved in purine synthesis and ribosome structure and function were top-enriched among up-regulated genes, and anaerobic respiration, nitrate metabolism and aromatic amino acid biosynthesis genes among down-regulated genes. Blocking of the purine-synthesis- did not affect resistance phenotypes (MIC and growth rate with OTC), while blocking of protein synthesis using low concentrations of chloramphenicol or gentamicin, lowered MIC towards OTC. Metabolic-modeling, using a novel model for MG1655 and continuous weighing factor that reflected the degree of up or down regulation of genes encoding a reaction, identified 102 metabolic reactions with significant change in flux in MG1655:: tetA when grown in the presence of OTC compared to growth without OTC. These pathways could not have been predicted by simply analyzing functions of the up and down regulated genes, and thus this work has provided a novel method for identification of reactions which are essential in the adaptation to growth in the presence of antimicrobials.We characterized the global transcriptome of  The prevalence of resistance to this class has steadily increased in clinical isolates from both humans3 and animals4, roughly by 0.5% per year from 1950 to 2001 in isolates from humans5. While the use of tetracycline in human medicine has gradually decreased, the drug class remains among the most used in livestock production worldwide6.Tetracycline drugs are bacteriostatic antimicrobials which bind reversibly to the bacterial ribosome and interfere with protein translation7, which selectively pumps tetracycline from the cytosol to the periplasm in exchange for a proton8. In E. coli, TetA and TetB are the most frequently observed tetracycline efflux pumps9. We have shown that a TetA-producing E. coli strain shows prolonged lag phase proportionally to the concentration of tetracycline in the growth medium, and that this concurs with increased expression of tetA10. This suggests that expression of the resistance mechanism or incorporation of the efflux pump into the cell-membrane constitute a burden to the bacterium. Similar observations of cellular adaptation in antimicrobial resistant bacteria grown in the presence of the drug to which they are resistant have been described for Extended Spectrum Beta-Lactamase producing (ESBL) E. coli12, methicillin resistant Staphylococcus aureus12 and colistin-resistant Klebsiella pneumoniae13, and interfering with these adaptive responses in certain cases re-sensitize the bacteria to the drugs13. It is therefore highly relevant to increase our understanding of the way that resistant bacteria adapt to growth in the presence of therapeutic concentrations of antimicrobials.The majority of the more than 40 genes encoding tetracycline resistance encode membrane-associated efflux proteinset al.11, a high number of genes encoding metabolic enzymes were shown to be significantly regulated. Understanding the importance of this for the metabolism is not straight forward, since the metabolic network contains a high number of redundant, alternative pathways15. Metabolic modeling has been a central tool for solving this problem16, but traditionally genome scale metabolic models (GSMs) only describe the network of enzyme-catalyzed and spontaneous reactions proceeding in the organism18, and not adaptation to changing environments19. Recently, however, several approaches to incorporate omic-data into modeling have been published (see for example20).In the study of ESBL adaptation to cephalosporin treatment by Møller tetA encoding E. coli strain to treatment with OTC. We show that despite being highly resistant to oxytetracycline, adaptation to growth in the presence of the drug involves a high number of genes, and using GSM with incorporation of transcriptome data, we identify metabolic adaptations that could not have been identified simply from looking at the list of significantly regulated genes.The aim of this study was to improve the understanding of the metabolic adaptations in an OTC-resistant, E. coli MG1655, where tetA, together with its natural regulator, tetR, had been cloned into the chromosome. RNA-sequencing was used to identify significantly up and down regulated genes, when the strain was grown in MH-2-broth with 14 mg/L OTC in comparison to growth without OTC. The chosen OTC concentration corresponds to ½ MIC of the strain to OTC, and is above the peak serum concentration that is obtained in humans following treatment21.In order to study the adaptation to OTC treatment, we selected a variant of 2FC) > 2.0 alternatively Abs(Log2FC) > 1.5 were used, 149 and 333 genes were up-regulated and 119 and 211 genes were down regulated. The changes were generally moderate in up-regulated genes with a maximum of Log2FC = 3.94 (14.58 fold). The most down regulated genes showed up to Log2FC = −7.46 (73.78 fold) change in expression  < 0.05, was demonstrated for 1646 genes, showing that either the presence of the drug in the cell cytosol or the production and incorporation of an increasing number of TetA-pumps caused dramatic changes in cell homeostasis. Of the significantly regulated genes, 946 genes were up-regulated and 700 were down-regulated . A total of 400 genes were only regulated in the resistant strain, suggesting that this was the number of genes associated with the TetA expression  > 1.5. None of the genes reported to belong to these systems were significantly regulated. pspE of the phage shock proteins was boarder line down-regulated (log2FC −1.44), however, this gene is believed to encode a thiosulphate sulfotransferase, and even though it is named phage shock protein E, is not considered a stress associated protein27. Together, the patterns of gene regulation did not indicate severe membrane stress due to incorporation of TetA pumps.es Table . This ge2FC) > 2.0 to focus on the most highly regulated genes. MG1655::tetA was found to up-regulate biosynthesis processes related to ribonucleoside and ribonucleotide production, in particular purine biosynthesis, and molecular processes and cellular components related to ribosome function  were up-regulated between Log2FC 3.62 and Log2FC 1.59. To analyze the significance of this up-regulation, single and double mutants of purN and purT were constructed. These genes encode the two redundant, phosphoribosylglycinamide transformylases acting at step three in the purine biosynthesis28. Blockage of this step results in accumulation of glycinamide ribonucleotide (GAR) and depletion of all down-stream products, and is therefore considered essential for the purine synthesis28. MIC of OTC was 28–32 mg/L for MG1655::tetA, MG1655::tetA∆purN and MG1655::tetA∆purT, while it increased to 64 mg/L for MG1655::tetA∆purN∆purT  were significantly regulated by treatment with ½ MIC of OTC in MG1655::tetA as well as in WT MG1655 .In total, 14 purine synthesis genes  was not significantly changed compared to MIC of the WT (28 mg/L) .Fifty-one ribosomal and translational genes were significantly up-regulated with LogtyrA and pheA, which both catalyzes the first step in the formation of the amino acids from chorismate. Though several optimizations were tested, it was impossible to construct this mutant, and we concluded that tyrA and/or pheA are essential for growth in LB media which we used for construction of the mutants. It should be noted, however, that others have succeeded in constructing combined tyrA/phe mutants in E. coli33. Instead, we knocked out aroB and aroC, which are the last enzyme in the Shimikate pathway before it branches to the aromatic amino acid biosynthesis pathways. As shown in Fig. tetAΔaroB only) the mutant grew at comparable growth rate as the wild type strain both with and without ½ MIC of OTC. MIC towards OTC did not differ significantly between MG1655::tetA and the two mutants .Genes involved in aromatic amino acid biosynthesis, as mentioned above, were highly down-regulated. In common, these amino acids are synthesized with chorismate as substrate. Chorismate is the end product of the Shikimate pathway. In order to analyse the importance of down-regulation of the amino acid biosynthesis genes, we first tried to knock-out ts Table . As expetetA (49.5% of up regulated genes), and 319 genes were down regulated (44.3% of down regulated genes). This suggested that changes in the metabolism were a major component in the adaptation to growth in the presence of high concentrations of OTC. In order to investigate the metabolic significance of this we used a modeling approach. We built a genome scale model of E. coli K-12 MG1655 from the ecocyc database. It had a total of 1335 reactions encoded by 880 unique genes, 1335 internal and 64 external metabolites. The model was curated to be fully balanced to all atomic species, including protons and was demonstrated to have no violations of conservation of energy or mass. The model was able to produce all biomass components of growing E. coli individually and in combination in proportions corresponding to published experimental data34 (data not shown). Generating this biomass composition, using FBA to minimize total flux in the network and assuming growth on M9 media, the model utilized 331 of the 1351 reactions in the model.In total 468 genes encoding metabolic enzymes were significantly up-regulated in MG1655::2 excretion systems, all pointing to a substantial increased energy demand resulting from OTC exposure. Other regulated reactions were primarily associated with central carbon metabolism in addition to reactions involved with amino acid and nucleotide metabolism. These reactions were then extracted to form a regulated sub-model for further analysis.We used the change in the expression of a metabolic gene to calculate a weighting factor for the reaction(s) associated with that gene, and when these weighting factors were applied, Flux Balance Analysis showed that 59 reaction fluxes increased significantly and 43 decreased. Inspection of this set of reactions showed a 50% increase in the flux of the proton-translocating ATP synthase reaction and concomitant increases in reactions of the electron transport chain (ETC), Oxygen and glucose uptake systems and COet al.35, the flux distributions in both the weighted and un-weighted sets could be accounted for by 8 of these elementary modes as shown in Table The sub-model formed from the 102 up and down regulated reactions contained a total of 50 metabolites that were internal to the sub-model, and 75 metabolites that were exchanged with the rest of the model (i.e. those reactions whose computed fluxes did not vary in response to the weighting associated with OTC exposure). It proved infeasible to calculate elementary modes of the sub model, presumably because of the large number of exchange reactions. However, as described in the methods section, it was possible to further reduce this sub-model to a network of 33 reactions and 50 metabolites of which 20 were exchanged with the whole model. This reduced sub-model had a total 94 elementary modes, and using the method described by Schuster The reactions used by the elementary modes comprised mainly of the Entner-Doudoroff (E-D) pathway, the oxidative pentose phosphate pathway (OPPP) and oxidative phosphorylation (OP). In addition to these reactions, the network also utilized fructose-6-phosphate aldolase, PEP-glycerone phosphotransferase and glutamate synthase  approach to characterize adaptive changes in pan-resistant K. pneumoniae13. Together, the data has shown that even for resistant bacteria, fighting antibiotics is not just a matter of expressing the resistance mechanisms, but involves regulation of genes that are not part of the resistance mechanism, per se. The work by Brochmann et al.12 also showed that there are strains differences in this response, and the results of the current study should be viewed with some caution, as only one strain was analyzed.Global expression analysis was performed to characterize adaptation to growth in the presence of OTC in an OTC resistant E. coli, belonging to the Extra Intestinal Pathogenic E. coli group APEC and grown in the presence of 32 mg/L tetracycline, was published36. Our study confirms the observation from that study of a general up regulation of ribosome associated genes and the purine metabolism and the down regulation of anaerobic respiration and nitrate reduction, when grown with tetracycline. Biosynthesis of aromatic amino acids was not down regulated in that study, confirming strain specific differences.OTC treatment of the resistant strain caused major changes in gene regulation, and genes belonging to 44 different biological functions were found to be significantly regulated. The majority of the significantly regulated genes were also significantly regulated when WT MG1655 was treated with ½ MIC of OTC, showing that the main part of the response was not directly related to expression of the TetA pump. In parallel to our study, another transcriptome study of a tetracycline resistant 10. In the current study, we did not observe indications of membrane stress. It has proved technically impossible to determine the number of TetA pumps in E. coli by proteomics10, and thus it is difficult to estimate whether there is a fixed upper number of TetA pumps that can be incorporated into the membrane, thus avoiding membrane stress.TetA strains grown in the presence of high concentrations of OTC show a significant increase in lag phase, and this has been proposed to indicate stress due to incorporation of TetA pumps in the membranede novo synthesis is an important part of the adaptive response to OTC treatment. Similar up-regulation of purine synthesis was observed in ESBL E. coli in the presence of cefotaxime11, and was also shown in the recent expression study with the APEC strain36. Despite this, an increase in MIC in the presence of OTC was observed when purine synthesis was blocked. Purines can be obtained in other ways, either by uptake from the medium or by purine salvage pathways38. In the current study, important genes in the purine salvage pathway were up-regulated in the TetA strains as well as in the WT MG1655 in the presence of ½ MIC of OTC, and it is likely that this system can fully compensate for lack of purine biosynthesis under these conditions. In minimal medium, lack of the purine synthesis pathway was costlier to the strain judged from growth responses, and it may be that the effect of blocking purine biosynthesis in vivo (in the patient) is more pronounced than the growth study in MH-2 media suggests.We tested whether significantly regulated pathways were essential for expression of the resistant phenotype. The purine synthesis was highly up-regulated indicating that purine tetA towards OTC. The MIC towards tetracycline decreased below the epidemiology cut-off for OTC (≤8 mg/L39) when low concentrations of the ribosome inhibitors chloramphenicol or gentamicin were present. MIC in the WT MG1655 strain did not decrease in the same way. This suggests that the mechanism was related to operating the efflux pump in the MG1655::tetA strain, putting high metabolic demands (presumably energy) on the resistant strain, and that this is then overloaded by the challenge from the second antibiotic. However, an additive effect of OTC and chloramphenicol on E. coli has previously been observed by Ciak and Hahn40, and confirmed by Garrett and Brown41 based on studies of antibiotic sensitive E. coli strains, and it may also be that the synergistic effect is too small to create a significant effect on MIC at the low OTC concentrations tolerated by the WT strain. To our knowledge, the promising effect of combining OTC and chloramphenicol with the goal of reducing OTC resistance in a highly resistant strain has not been reported previously. The combination of tetracycline and chloramphenicol has previously been used to treat against Streptococcus viridans infections, however, it only resulted in a partial treatment effect42. Furthermore, the two antibiotics showed no synergy when tested on several strains of Staphylococcus aureus42. Gentamicin, like OTC, targets the 30S ribosomal subunit, but with a different mode of action compared to OTC43. This could be the reason for the observed synergy. Combining gentamicin and doxycycline has been used as an effective combination for treatment of human brucellosis44.Protein synthesis was shown to be essential for the response of MG1655::aroB encodes the 3-dehydroquinate synthase, which is the second step in the synthesis of chorismate, while aroC encodes the chorismate synthase, which is the last step in the Shikimate pathway. After this step, chorismate is used in the biosynthesis of the aromatic amino acids46. All the enzymes of the Shikimate pathway are attractive targets for the development of anti-metabolites and antimicrobials, as the pathway is not present in mammals48. 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase, encoded by aroA, has been studied extensively and is the target by the active ingredient of the commercially available broad-spectrum herbicide RoundUp™ 49. In the current study, no effect on growth with OTC was seen from knocking out genes of the Shikimate pathway, which is in accordance with the observation that biosynthesis was downregulated. For tryptophan, we saw indication that the down regulation of biosynthesis was counter-acted by tryptophan uptake systems being highly upregulated.Biosynthesis genes for aromatic amino acids were significantly down-regulated. To investigate the importance of this, and the Shikimate pathway in general, we eliminated two essential enzymes in the Shikimate pathway. E. coli MG1655 genome scale metabolic model based on information available in the publicly available and standardized biochemical database, BioCyc. There is already an existing E. coli model, EcoCyc–18.0–GEM50, constructed from the EcoCyc database. There are important differences between these two models; EcoCyc–18.0–GEM is a larger model with 2286 reactions and 1453 metabolites. Most of these extra reactions were removed from the model presented here, owing to more stringent criteria for reaction inclusion in terms of metabolic relevance and atomic balance. Furthermore, a small number of reactions in our model had no equivalent in EcoCyc–18.0–GEM. These reactions were manually defined in the module of extra reactions (see methods section). The EcoCyc–18.0–GEM model has a greater number of un-conserved metabolites (1170), than our model. This includes compounds consisting of carbon, nitrogen, phosphate and sulfur. The model presented here had no un-conserved metabolites.The largest group of regulated genes was metabolic enzymes. In order to investigate the metabolic network on a global scale, we used a novel approach where we incorporated the transcriptome date into global metabolic modeling. We first build an et al.51. Different approaches have been used. Covert et al. used Boolean logic to evaluate whether sufficient expression had occurred for a given reaction to proceed in order to further constrain the FBA problem52. This may not be fully accurate, as many regulatory mechanisms cannot be described with Boolean logic19. Other methods, e.g. GIMME53 and iMAT55, are based on systematic removal of reactions from the model based on low transcriptomic levels. A more realistic approach was recently introduced be Yang et al.20, who simulated growth with incorporation of data on protein abundances. The advantage of the weighting system used in the current study was that it favours reactions that are associated with high levels of gene expression and select against those with low expression levels, and thus the method (in contrast to other approaches55) does not depend on setting arbitrary threshold values of expression that lead to the inclusion or exclusion of a given reaction. One problem with the integration of transcriptomic data into metabolic models consists of multi-gene and multi-reactions situations19 and that there is not necessarily a straight correlation between gene and protein expression.Methods for the integration of transcriptomic data with genome scale metabolic models have been described previously. The aim with all the integration methods is to obtain improved flux predictions using transcriptomic data, and they are based upon the assumption that the expression rate of a given gene, obtained from the experimental data, is correlated with the flux through the reaction catalysed by the enzyme encoded by the gene. This assumption is not necessarily correct as illustrated by Yang E. coli MG1655::tetA grows in the presence of high concentrations of OCT. This demand is being met by changes in flux in both the Entner-Doudoroff (E-D) and oxidative pentose pathways. However, these flux rearrangements do not follow a conventional view of the functioning of the E-D pathway; although the first steps, R3-R5 increase in flux, the subsequent steps, R6-R9, decrease. This is compensated for by additional consumption of GAP, by increased activity in the OPPP, whose fluxes also increase. The result of these changes is an increased flux in reactions of the electron transport chain (ETC) and oxidative phosphorylation (OP) utilising the increased production of reducing equivalents.The modelling analysis resulted in 102 reactions whose flux changed in response to the added weighting. This sub-network was reduced to a more tractable size of 25 reactions. The calculated flux changes are consistent with an increased demand for ATP when 57 and whose metabolic role is as yet not clearly understood.It is also of note that in addition to reactions that might be expected to be associated with increased metabolic stress, the network also contains fructose-6-phosphate aldolase (R19) and dihydroxyacetone kinase (R11). Both of the enzymes responsible for these reactions have been relatively recently discoveredpur-genes, only one, phosphoribosyl pyrophosphate synthetase (prs) showed an increase in flux in the model analysis, and this response was common to both the WT and the oxytetracycling resistant MG1655::tetA strain. The fact that increases in expression were not reflected in changes in calculated flux are not surprising for two reasons: Firstly, even if an increase in transcript level does lead to an increase in enzyme activity, the flux catalysed by that enzyme is a function of the activity of all enzymes in the network and not that of the enzyme alone; a major motivation in the design of the model analysis was to reflect this interdependency. Secondly these reactions are ultimately contributing to nucleotide and amino acid components of the biomass, and as the proportion of these was fixed, this places additional constraints on the values that these reaction fluxes can take.Of the reactions associated with the E. coli MG1655::tetA. This suggests that pumping out of OCT using the tetracycline specific pump TetA requires highly adaptive responses, and this may render the bacterium vulnerable in other parts of the cell than those targeted by the OTC. Indeed, we identified combinations of antibiotics (chloramphenicol and gentamycin), where even sub-therapeutic concentrations of the other drug were shown to increase the OTC susceptibility. The study investigated a novel approach to characterize the metabolic adaptation in antimicrobial resistant bacteria. It demonstrated that metabolic modeling with incorporated transcription data identified adaptive reactions that were not obvious from just looking at up and down regulated genes. On the contrary, the pathways which were identified using metabolic modeling were generally not controlled by enzymes encoded from genes which were among the most highly regulated genes. We suggest that this approach is generally applicable to studies of adaptation to antimicrobial treatment in antimicrobial resistant bacteria.In summary, the results of the study showed that growth in the presence of ½ MIC of OTC resulted in profound changes in gene expression in the OTC resistant 58. It has tetA gene and its repressor gene, tetR, cloned into the pseudo gene ybeM. Strains were maintained in DifcoTM Lysogeny broth (LB), Lennox  and on LB agar plates  supplemented with OTC (10 mg/L)  when appropriate.The strains used in this study are listed in Table 59, resulting in exchange of the gene of interest with chloramphenicol or kanamycin gene cassettes. During strain construction, the medium was supplemented with kanamycin (30 mg/L) or chloramphenicol (10 mg/L)  when appropriate. Primer sequences used for Lambda Red mediated mutagenesis and PCR verifications are listed in Table Site-specific mutants were produced by Lambda Red recombination11.The MICs for OTC, chloramphenicol , and gentamicin , were determined using the broth-micro-dilution method in the range from 0 to 512 mg/L by 2-fold dilutions increase following the CLSI guidelines, When necessary, more detailed determination with more concentration steps were included, as previous described11. The panel was incubated aerobically at 37 °C for 18–22 hours, without shaking. The fractional inhibitory concentration (FIC) index was defined as; ∑FIC = FIC A + FIC B, where FIC A is the MIC of drug A in the combination divided by the MIC of drug A alone, and FIC B is the MIC of drug B in the combination divided by the MIC of drug B alone60. The antibiotic combinations were interpreted as synergetic when the ∑FIC ≤ 0.5, indifferent when the ∑FIC > 0.5–4, and antagonistic when the ∑FIC > 432.Checkerboard assays were performed in Mueller-Hinton II (MH-2)  broth with two different antibiotic combinations: OTC and chloramphenicol, and OTC and gentamicin, as previously described61). Growth was at 37 °C and with shaking (225 rpm). The flasks were inoculated with pre-cultures grown for 2 hours at 37 °C and 225 rpm to a final cell density of 105 CFU/mL, as determined by use of Sensititre™ Nephelometer (Thermo Scientific™) with a McFarland 0.5 standard. The cultures were monitored by measuring optical density until samples reached the logarithmic phase (OD600nm = 0.5–0.6), where aliquots were taken for RNA extraction. Biological duplicates were performed.In order to obtain RNA for expression analysis, the strains were grown in 250 mL flasks containing 100 mL of MH-2 broth either without OTC or with OTC corresponding to ½ MIC of the OTC-resistant strain (14 mg/L) or ½ MIC of the wild type strain MG1655  representing ½ MIC of the corresponding strain when appropriate. Strains were also grown in M9 minimal media . Lag phase of growth curves was defined as the time necessary to reach an OD600 of 0.1. Statistical comparison of lag phase was performed by one-way ANOVA using GraphPad Prism. Growth characterization experiments were performed with three biological replicates and with three technical repeats for each of these.Growth for characterization of growth-responses to treatment were performed in Mueller-Hinton II (MH-2) on a BioScreen C11. Briefly, 3 mL cell samples were mixed with RNAlater . Total RNA was extracted from two independently grown samples using a FastPrep system  and an RNeasy Mini kit , and quantity and quality of RNA was determined using a NanoDrop 1000 spectrophotometer . After DNase treatment , rRNAs (23S and 16S) were removed by subtractive hybridization using the MICROBExpress kit . Removal of rRNAs was confirmed with an Agilent 2100 Bioanalyzer .RNA extraction for RNA seq was performed essentially as previously describedgapA and nusG were used as reference genes. RT-qPCR was performed twice by two separate biological samples and the results were caiculated by the 2−∆∆CT method as described by Pfallf62. Primer sequences can be found in the Table Purified RNA was reverse-transcribed into cDNA using the High Capacity cDNA Reverse Transcription Kit . RT-qPCR was performed using FastStart Essential DNA Green Master . The library for RNA-Seq was prepared using the TruSeq RNA Sample Preparation kit , following the manufacturer’s instructions. The samples were validated after each step using an Agilent 2100 Bioanalyzer , and the final concentration of mRNA was measured using a Qubit 2.0 Fluorometer . The libraries were sequenced using the Illumina HiSeq. 2000 platform with a single-end protocol, multiplexing and read lengths of 100 nt.E. coli MG1655 genome (RefSeq Accession No. NC_000913) as previously described11. Briefly, hits of reads to annotated genes were counted and all rRNA read counts were removed to obtain a proper normalization. The data was normalized using the conditional quantile normalization method, while also taking into account difference in replicate library sizes and gene GC content using edgeR bioconductor software package for R63. The differential expression of each annotated mRNA transcript was calculated using EdgeR and significantly regulated genes were selected based on the false discovery rate (FDR) (Benjamini–Hochberg multiple testing correction)64 using a threshold of 0.05. The resulting datasets from analysis of MG1655::tetA grown with and without 14 mg/L OTC and MG1655 growin with 0.25 mg/L OTC are included as supplementary material, Tables 65. EcoCyC database66 was used to further analyse the functions of differentially expressed genes.The analysis was made on libraries consisting of 2.5–3 million unique reads with a length of 100 bp. A total of 4482 genes were considered in the analysis. Reads were trimmed for adaptor sequences using the CLC Genomics Workbench . The trimmed reads were mapped onto the E. coli K-12 MG1655 genome scale model was constructed in a modular way using already described techniques68. The final model consisted of (1) A top-level module which contain information about the other model modules and its prime function was to import other modules; (2) A module containing all the automatically generated reactions (see below); (3) A module containing all transport reactions importing nutrients and exporting biomass components and metabolic by-products; (4) A module containing the electron transport chain, and (5) A module containing additional and modified reactions.The E. coli biomass components from a rich medium (Mueller Hinton broth 2 (MH-2)), consisting of glucose, amino acids 70, phosphate and O2.The final GSM describes a metabolic network which can generate all Details on model construction can be seen in Supplementary Material, Table The weighting factor wr, for a given reaction, r, was calculated as:Defining the linear program:i is the weighting factor described above and subject to three constrains: The first constraint specified stead-state, the second ensured that irreversible reactions carry positive flux and the third constraint represents the export of biomass components. The problem was solved twice, with and without wi, and the solutions compared to identify up or down regulated reactions. This set of reaction was then used to construct a regulated sub-model.Stating to minimize flux, where wa priori be guaranteed to represent a balanced network as a result of constant fluxes of metabolites exchanged between the regulated and non-regulated sub-networks. However, these could be identified by constructing a stoichiometry matrix, N’ constructed from the reactions in the regulated sub-network. We could then calculate:The set of reactions identified above could not r represents the stoichiometry matrix of the regulated sub-network with the additional transporters included, and vr the corresponding flux vector. In this way we generated not only fully a balanced sub-model, but also a corresponding steady-state flux vector.The non-zero elements of s′ corresponded to unbalanced metabolites, and hence required additional transport reactions to be added to N′. Their values corresponded to the negative flux to be carried by these transporters in order to generate a regulated sub-network, such that:targ to the minimum (absolute) flux in vobs, and minimise the total absolute flux in the system subject to the constraint that no flux in the solution can take an absolute value greater than that in vobs, and that the sign of the reaction flux in the solution is the same as that in the observed. We can now proceed as shown in Fig. The sub-models proved hard to analyse, presumably due to large numbers of additional transporters included, and correlation trees with standard algorithms for flux balance analysis were inconclusive. To solve this problem, we generated a new algorithm, taking advantage of the fact that our analysis had created a valid steady-state flux vector, and based on the assumption that any Linear Programming solution in which only a single reaction is fixed will represent an elementary mode (EM). The novel algorithm also generated a set of fluxes corresponding to reactions in the newly determined EM, so as well as identifying the EMs we could assign fluxes to them. The calculations were as follows: Given an initial (observed) steady-state flux vector, vobs, define an LP problem:Supplementary Dataset 1.


Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for  Streptococcus, Enterococcus, and Aerococcus is composed of a wide variety of bacteria important for the food industry (The mitis group of streptococci (MGS) consists of 20 different species, all gram positive cocci arranged as pairs or in chains . MGS is industry .Streptococcus oralis is the most common  of metabolism have proven to be valuable in investigating the metabolism in a single bacterial strain . GEMs haLactococcus, Lactobacillus, and Streptococcus for the food industry, GEMs have been created and published . The genome sequence was annotated using the RAST server on default settings . For gapfilling, pathways were visualized using Escher  version 0.13.4 .S. agalactiae and other Streptococcus species (version: 16-May-09) provided by the manufacturer with the following exception: Due to a high degree of background noise in the negative control well, phenotype microarrays 3 was tested using only 1/10 of the carbon sources reported.Biolog experiments were performed using Biolog’s phenotype microarrays . All experiments were carried out according to the phenotype microarray protocol for The Biolog results were analyzed with the “OPM” package in R, and statistical analysis was done with the “Dunnett-type comparison: one-against-all” function . All welS. oralis we used the macromolecular composition of a Lactobacillus plantarum previously published  (Streptococcus sanguinis (Strain SK36)   and Strein SK36) . OrtholoS. pneumoniae strain D39  as a carbon source. We sequentially opened and closed the exchange for all nitrogens sources tested. If the opening resulted in an increase in Biomass yield, it was considered a positive result.All simulations on gene essentiality, amino acid auxotrophy and carbon and nitrogen sources were done using Flux Balance Analysis (FBA) while optimizing for the Biomass reaction. For all metabolites present in the media, the upper and lower bounds were set to -1000 and 1000 mmol/g/h, respectively. When doing Biolog validations the exchange reactions for the carbon- and nitrogen sources tested were also set to -1000 and 1000 mmol/g/h for lower and upper bounds, respectively. -8 as a growth/no growth cutoff.For all FBA simulations we used a value of 10S. pneumoniae strain D39 . We used the same media conditions to simulate growth of iCJ415  categorized as discordant.There are 61 genes where there are discordant results. In 36 of those genes, iCJ415 shows essentiality while the essentiality datasets show non-essentiality. An explanation is that not much In the remaining 25 genes where there are discordant results, iCJ415 shows genes to be non-essential, while the essentiality data shows the genes to be essential. This can be explained in part by the use of rich medium in both gene essentiality datasets. Both of these studies use a broth-based, non-defined media, where it is not possible to get the exact composition of the different metabolites. When we simulate gene deletions in rich media we open all exchange reactions in the model, which corresponds to an experimental setup where all metabolites are present in the growth media. But if a metabolite is absent from the experimental growth media, a gene could be deemed essential, however the gene would not be essential if the metabolite was present.The two gene essentiality studies use either Todd Hewitt or BHI media for the experiments . Both ofWhen using glucose as a sole carbon source, seven “discordant” genes become essential; six of those are part of the carbon metabolism. These genes are becoming essential due to the lack of essential metabolite synthesis when only glucose is used as a carbon source, e.g. teichoic acid cannot be produced when the gene SK141_RS03865 is knocked out due to the inability of the model to synthesize ribulose 5-phosphate. When retesting all genes that were “concordant” in the full media, only one of the 169 genes were essential when testing with glucose as the sole carbon source. Furthermore, only 10 gene deletions had a lower growth rate when glucose was the only carbon source, and no deletion gave more than a 20% reduction in growth rate.S. oralis. Further, S. oralis has one of the smallest genomes in the MGS, having a genome size of 1.9 mio basepairs  were not able to promote growth in iCJ415. Four of them had transport reactions in iCJ415 but were still not able to use the metabolites as carbon sources . The remaining four metabolites were not present in the model and we could not find any information on the transport or metabolism in the genome annotation or in the literature .Knowledge about the differences in carbon source utilization within the MGS is limited. The published data is mainly about eumoniae . TherefoS. oralis and S. pneumoniae resides in two different ecological niches, different nutritional sources are available. While the oral cavity contains multiple different carbon sources, partly degraded by oral enzymes, pharynx has a low content of readily available carbon sources S. pneumoniae R6 and S. oralis SK141 this is reflected by the content of transport systems specific for breakdown products of human tissue. S. pneumoniae R6 contains a transporter for hyaluronate, which is not present in S. oralis SK141, and together with lyases this transporter can make part of the human extracellular matrix available as carbon sources . S. pneumoniae mutants deficient of these lyases are attenuated in their ability to colonization and infection making them important virulence factors These metabolic associated genes are increasingly considered crucial to the pathogenesis since they determine in which ecological niche an organism can grow, both during asymptomatic carriage and in disease . TherefoS. pneumoniae and S. pyogenes GEMs previously published all contains several carbohydrate transporters, but the hyaluronate transporter was not included in any of them. The GEM for S. pneumoniae R6, iDS372, contains transporters for nine carbon sources also present in iCJ415. While iDS371 can grow on six of these carbon sources, iCJ415 can grow on 8. Neither of the models can use glycerol as a carbon despite having a transporter. In contrast to iCJ415, iDS372 cannot grow on either stachyose or raffinose, which is due to lack of a raffinose degrading reaction (Stachyose is degraded to raffinose). Raffinose utilization by S. pneumoniae has been shown to be an independent predictor for disease spectrum The S. oralis SK141 was able to use some nitrogen sources which could be mapped to iCJ415 , we were not able to distinguish single nitrogen capabilities in iCJ415 with the used in silico media, revealing that there are still knowledge gaps in iCJ415.Though, Biolog data showed that S. oralis is able to grow on multiple carbon sources, but the transport and metabolism of some of these metabolites are unknown or only poorly understood.iCJ415 reflects that S. pneumoniae D39. We opened the exchange reactions for all the amino acids in iCJ415 and all the metabolites present in the media used by Härtel et al. To test amino acid auxotrophy we consecutively closed each of the amino acid exchange reactions in iCJ415 . To simulate the impact of the carbon catabolite repressor on the genes in the model, fluxes in reactions catalyzed by repressed genes were set to a maximum flux defined by the flux in the unconstrained media, see materials and methods.For validating Biomass yield and byproduct formation of iCJ415 we used previously published experimental growth data from niae D39 . The catS. pneumoniae D39. The experimental data was obtained when grown in glucose, galactose, N-acetyl-D-glucosamine, and mannose. When compared with iDS372, iCJ415 has a lower growth rate than iDS372 except when N-acetyl-D-glucosamine is used as a carbon source. Especially when using glucose as a carbon source, the growth rate is only 0.44 h-1 in iCJ415, with growth rates in iDS372 and in the experimental of 0.85 and 0.82 h-1 respectively. When grown on glucose the 0% activity of pfl causes severe growth retardation in iCJ415, but only a small increase to 1% pfl activity causes an increase in growth rate to 0.76 h-1. The reason for decrease in growth rate is due to lack of formate production when the pfl gene is knocked out. Formate is used for the synthesis of folates in the reaction “FTHLFI.” This reveals differences in the synthesis of folates, which in part can be explained by a folate transporter in iDS372. A folate transporter has earlier been identified in Streptococcus mutans, but we could not find any evidence for the folate transporter being present in the annotation of S. oralis or S. pneumoniae and it has been postulated earlier that such a transporter is not present in S. pneumoniaeS. oralis.Interestingly, iCJ415, like iDS372, has a much higher rate of acetate production, than the experimental data, when N-acetyl-D-glucosamine is used as a carbon source. The high acetate production I iCJ415 is explained by a production of acetate in the breakdown of N-acetyl-D-glucosamine 6-phosphate. This anomaly reveals a knowledge gap in the metabolism of N-acetyl-glucosamine in iCJ415 was tested using the Memote web application with all exchanges open. iCJ415 scores >99% on all consistency scores except “unbounded flux in default medium” where it scores 95% .S. oralis, the first GEM of a bacterium that is a major constituent of the human oral microbiome and an IE causing bacterium. We used an annotated genome together with strain, species or genus specific data obtained from the literature. To get information in carbon source utilization in S. oralis SK141 we used the Biolog phenotypic microarray.The aim of this study was to construct a GEM of a S. oralis are able to grow on at least 28 carbon sources, including commonly known carbohydrates as glucose and galactose, but also rarely encountered ones like lyxose. iCJ415 were able to grow on 21 of the 28 carbon sources tested. In the remaining seven carbon sources we did not have enough information to simulate growth on these carbon sources.When comparing gene essentiality between iCJ415 and published experimental data, we found concordance in 71–76% of the genes tested. When testing carbon sources and amino acid auxotrophies we found concordance with experimental data in 82% and 85% of metabolites tested, respectively. Especially the carbon source utilization data obtained in this study are valuable. It is, beside the genomic data, the only strain specific data used. Furthermore, carbon source utilization capacities are increasingly being considered as possible virulence factors in bacteria . We founiCJ415 should be used as a starting point for exploring the complicated metabolic interactions between different strains of the same species and between different species in the human mouth. Further it can be used to compare metabolic capabilities between non-pathogenic and known pathogenic strains, finding metabolic traits important for virulence.iCJ415 was tested using memote and scored >99% on all consistency scores except “unbounded flux in default medium” where it scores 95%.All datasets generated for this study are included in the article/BP, XN, JC, and CJ conceived the project. XF, CN, JM, and CJ performed the metabolic network reconstruction. CN and CJ performed the model simulations. CJ prepared the draft manuscript. All authors have read, commented and approved the final manuscript.This work was supported by the Region Zealand Foundation for Health Research, The Knud Højgård Foundation (Grant no: 18-02-0767), and the Ovesen Foundation. None of the funders had any influence on the study or the conclusions.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.


Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an ‘overflow’ metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as ‘reverse diauxie’. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence including its potentially, mutualistic interactions with microorganisms found commonly in the environment and in medical infections. Pseudomonas aeruginosa is an opportunistic pathogen commonly isolated from diabetic ulcers, burn wounds, and battlefield injuries, as well as from the lungs of patients with cystic fibrosis (CF)3. Its presence is correlated with high patient morbidity and mortality6. P. aeruginosa is found in ~ 80% of chronic, diabetic ulcers which cost the US medical system $20–50 billion per year to treat6. P. aeruginosa virulence and persistence mechanisms are enabled across strains by a pangenome possessing approximately 5,200 core genes7. Maintaining the large genome and implementing the myriad of virulence strategies necessitates effective strategies for nutrient acquisition and nutrient allocation to metabolic pathways. While foundational to its global distribution and virulence, the basis of the P. aeruginosa central metabolism is poorly understood6.8. The best studied examples of CCR are from Escherichia coli and Bacillus subtilis10. The metabolic designs of these model organisms, which prefer glucose over other substrates, form the basis of most textbook CCR examples11. The CCR strategy represented by E. coli and B. subtilis is referred to here as ‘classic carbon catabolite repression’ (cCCR) to distinguish it from the broader CCR term. P. aeruginosa does not display a cCCR phenotype. Instead, this competitive microorganism, as evidenced by its global distribution which is arguably broader than E. coli14, has substrate preferences that are almost opposite of E. coli. P. aeruginosa utilizes a CCR strategy termed ‘reverse diauxie’ or reverse CCR (rCCR) which is defined by a hierarchy of preferred carbon sources that is nearly reverse that of cCCR preferences15. P. aeruginosa can readily catabolize glucose although it is not a preferred substrate, instead this bacterium preferentially catabolizes less energetic, nonfermentable substrates like succinate. The contrarian hierarchy of preferred carbon sources is proposed to be central to the versatility of P. aeruginosa. The ecological basis of rCCR is an open question with few published theories17. A quantitative understanding of rCCR lags cCCR. This is a critical knowledge gap that contributes to degradation of patient quality of life and costs society tens of billions of dollars per year5.Global regulatory systems select preferred carbon sources from pools of substrates in a process known as carbon catabolite control (CCC) or carbon catabolite repression (CCR)18. Instead, life is constrained by the availability of resources such as reduced carbon or nitrogen sources18. Phenotypic plasticity can permit microorganisms to acclimate to resource scarcity21. In silico systems biology approaches have investigated resource investments  into different metabolic pathways via the enzyme synthesis requirements. The in silico methodologies, often referred to as resource allocation analysis or metabolic tradeoff theory, are powerful tools for predicting and interpreting phenotypes and have been applied extensively to cCCR microorganisms E. coli and B. subtilis30. For example, in silico and in vitro studies of E. coli quantified acclimation to carbon, nitrogen, or iron limitation along a metabolic tradeoff surface by optimizing the functional return on the limiting nutrient, at the expense of substrates found in excess31. This strategy resulted in ‘overflow metabolisms’ with the secretion of byproducts like acetate and lactate; overflow metabolisms are also known as the Warburg or Crabtree effect in eukaryotes32. Resource allocation analysis has not been applied to rCCR organisms. Given the large genomic potential and phenotypic plasticity of P. aeruginosa, these approaches hold potential for decoding the metabolic organization of this problematic bacterium.Natural environments do not permit unconstrained microbial growthP. aeruginosa rCCR was tested using a combination of physiological studies, exometabolomics, proteomics, and systems biology. The preference for substrates was measured and phenotypic characteristics, like the general lack of an overflow metabolism, were quantified. Proteomics measured a constitutive core metabolism centered on respiration and a dynamic set of enzymatic pathways that catabolized specific substrates, directing intermediates toward the core metabolism. The experimental data was analyzed with a genome-scale, metabolic model of P. aeruginosa and flux balance analysis (FBA) to identify ecological theories that predicted the observed phenotypes. P. aeruginosa did not optimize substrate preference based on standard systems biology assumptions such as the maximization of growth rate, as is commonly applied to cCCR phenotypes. Instead, P. aeruginosa metabolism was organized around a multidimensional, resource utilization strategy with constitutive expression of a respiration-based, core metabolism and substrate preferences that were based on minimizing the nutrient investment required to completely oxidize the substrate. Understanding a molecular-level basis of substrate preference, energy metabolism, and cell growth is foundational to controlling virulence mechanisms in P. aeruginosa including consortial interactions.Here, the ecological basis of Pseudomonas aeruginosa strain 215 (Pa 215) is a medical isolate from a chronic wound34. Pa 215 was grown in chemically-defined, glucose containing, CSP G medium , acetate (A), and succinate (S) Fig.  and lact35; these metrics were sensitive to experimental variability during the initial growth phase where small fluctuations in substrate concentration, based likely on analytical techniques, influenced predictions. All amino acid data, including bin designations, time metrics, and fitting parameters, can be found in supplementary material The order of amino acid catabolism was assessed for all five CSP medium formulations by binning the amino acids into three categories based on their time of exhaustion Table . BinningP. aeruginosa can use ammonium as the sole nitrogen source36. CSP G medium formulations were modified with the addition of 2 g/L ammonium chloride to test the effect of nitrogen form. The rCCR phenotype was not changed by the presence of ammonium. The cultures consumed the amino acids as preferred substrates followed by lactate and then glucose  of whole-cell lysates collected mid-first, exponential growth phase (4 h), early-second, exponential growth phase (7 h), and late-second, exponential growth phase (11 h). The proteomics data were analyzed with focus on central metabolism proteins associated with catabolizing the available substrates and with producing cellular energy.Proteomic data were collected from CSP G and CSP GL grown cultures. Proteomic data are more predictive of cell function than transcriptomic or genomic data alone because they represent an actual allocation of resources into relatively stable, macromolecular poolsEnzymes from the tricarboxylic acid (TCA) cycle and associated auxiliary enzymes had largely, constitutive abundances regardless of the medium formulation and the growth phase Fig. . All TCA39. Abundance of ED phosphogluconate dehydratase (Edd) increased while glucose was being metabolized, after the exhaustion of top tier amino acids. Acetate kinase (AckA) abundance increased for the CSP GL culture at the exhaustion of lactate and glucose. AckA quickly metabolized the small amount of acetate (< 4 mM) secreted at the exhaustion of lactate and glucose.Enzymes associated with the processing of specific substrates did change in abundance based on presence and concentration of substrates, contrary to most TCA cycle enzymes Fig. . Aspartaftp://massive.ucsd.edu/MSV000085590/.Proteomic analysis measured additional proteins that displayed changes in expression during exponential growth and stationary phases. Data can be found at P. aeruginosa to thrive in environmental and medical niches. Computational systems biology was used to test hypotheses regarding what fitness properties were being optimized in the Pa 215 cultures, with predictions compared to experimental data. In silico analyses used flux balance analysis (FBA) of a published, genome-scale, metabolic model of P. aeruginosa updated here with genome-supported, amino acid catabolism reactions41  or cellular growth which was quantified as carbon moles (Cmol) of biomass.In silico testing of ecological strategies was applied first to amino acid utilization order and included all experimentally measured amino acids except for aromatic and sulfur containing amino acids due to their specialty chemistries. The experimental amino acid utilization order did not correlate with the amino acid frequency in genome open reading frames Fig. a indicat42. The criteria included (1) maximizing biomass or energy production rates based on electron donor, (2) maximizing biomass or energy production rates based on electron acceptor (O2), or (3) minimizing nutrient investment into the proteome required for either biomass or energy synthesis. The results of these simulations are presented in the next two sections and a summary of the results and analyses can be found in supplementary material The first round of in silico analyses considered six separate, single dimension, optimization criteria which were informed by previous studies that examined numerous optimization criteria45. The experimental amino acid utilization order, as quantified across five media formulations  did not predict amino acid utilization order, as the correlation was r2 = 0.17  also had high biomass yields (Cmol biomass (mol amino acid)−1); the two yields correlated with an r2 value of 0.99 (supplementary data Amino acids with high cellular energy yields (mol ATP (mol amino acid)P. aeruginosa. Therefore, two previously developed resource investment proxies were applied to estimate relative, proteome investment into central metabolism50. The flux minimization proxy assumes the total network flux is proportional to the enzymatic resources required to synthesize the necessary proteome51. Another proxy for protein investment minimizes the number of enzyme catalyzed reactions  identifying the smallest proteome required to realize an in silico phenotype19. These hypotheses assumed central metabolism enzymes could be approximated as having the same molecular weight with the same amino acid distribution.Computational analysis was used to identify phenotypes that minimized resource investment into catabolic pathways. Explicit investment models were not possible in non-model organism, Both proxies for resource investment, when applied as the single optimization criterion, predicted overflow metabolisms for most amino acids   at 37 °C with shaking (150 rpm), mixed with 3 mL of 20% glycerol, and stored at − 80 °C.All experiments used Clostridium, Staphylococcus, Pseudomonas (CSP) medium in culture tubes. CSP is a chemically defined medium developed to support the growth of P. aeruginosa, Staphylococcus aureus, and Clostridium perfringens as monocultures or consortia34  at 37 °C for 12 h, five colonies were picked to inoculate 10 mL of 600 of 0.5. 1 mL of each culture was then added to 49 mL of fresh CSP medium in 250 mL baffled flasks and an OD600 of 0.010. The baffled flasks were capped with gas permeable foam lids and incubated at 37 °C with shaking at 150 rpm. Sampling occurred about every hour during the first 12 h and less frequently afterwards.A total of three culturing tubes containing 10 mL of CSP were each inoculated with about five colonies from the TSA plates, incubated at 37 °C with shaking at 150 rpm (tubes were placed at a 45° angle in the shaker to increase mixing) and grown until the cultures reach an OD600, pH, amino acid, and carbon metabolite measurements. An aliquot of 1.5 mL of culture was collected at each sampling, cells were separated from the supernatant using centrifugation at 7000 rpm for 10 min (Eppendorf 5415D microcentrifuge). Supernatants were then filtered using 0.22 µm syringe filters prior to being stored at − 20 °C.Samples were drawn from each flask for OD600 measurement. OD600 readings were blanked with fresh CSP and samples were diluted, if necessary, to keep OD600 measurements ≤ 0.30.At each sampling, a volume of culture was collected for OD2SO4 was run at a flow rate of 0.6 mL/min for 25 min/injection. A volume of 200 µL of sample was added to an HPLC vial with 200 µL of an internal standard of 1 g/L fucose dissolved in 10 mM H2SO4. Each sample was injected twice for a total of two technical replicates for each of the three biological replicates for each time point. HPLC analysis of culture supernatants were compared to NMR analysis  as a verification of metabolite identities and to ensure no major metabolites were being missed34. Supplementary material HPLC analysis of select carbon metabolites including glucose and organic acids was performed with an Agilent 1200 series HPLC equipped with a refractive index detector (RID) and an Aminex HPX-87H ion exclusion column, 300 mm × 7.8 mm. A mobile phase of 5 mM H98.HPLC analysis of amino acids was performed with an Agilent 1100 series equipped with a diode array detector (DAD) and a ZORBAX Eclipse XDB-C18 column, 4.6 mm ID × 250 mm (5 µm) 80 Å. This setup was used with the Agilent protocol for HPLC analysis of amino acids600 and grams of cell dry weight (g CDW) per liter was constructed. 5 mL aliquots of P. aeruginosa culture harvested in mid-exponential growth phase diluted to a range of densities were dried at 80 °C for 24 h in aluminum drying pans and weighed. Correlation equation was: (g CDW/L) = (OD600)*2.23−1.A correlation curve between ODP. aeruginosa cultures (n = 3) were collected via centrifugation at 3600×g and washed three times with phosphate buffered solution to remove residual media. Cells were resuspended in 1.25 mL radioimmunoprecipitation assay buffer consisting of 12.5 μL of protease inhibitor  to prevent enzymatic degradation upon cell lysis, 0.1 mg/ml lysozyme to solubilize the cell peptidoglycan layer, and 5 mM dithiothreitol (DTT) to cleave protein disulfide bonds. Cells were lysed mechanically in a beadbeater  at 4800 oscillations/min with the remainder of the vial filled with 0.1 mm diameter zirconia/silica beads for a total time of 2.5 min (five cycles at a duration of 30 s each with chilling in an ice water bath between cycles).99, in which proteins were reduced with DTT, alkylated with iodoacetamide to prevent disulfide bond reformation, then enzymatically digested overnight into peptides with trypsin at 1:50 enzyme:substrate (w:w) at 37 °C. The resulting peptides were desalted using a C18 column , dried in a centrifugal evaporator, then resuspended in 5% acetonitrile with 0.1% formic acid to a concentration of 0.2 μg/μL.Protein concentrations were determined by protein assay kit . 15 µg of proteins were taken from all samples and transferred to centrifugal filter units . The samples were then processed following the filter aided sample preparation method6 ions and injection times of 1–200 ms. Full scan mass spectra from m/z 400 to 2000 at 30,000 mass resolution were collected in data-dependent acquisition mode. Ten precursor ions were selected from each full mass scan for analysis by tandem mass spectrometry .200 ng samples of peptides were separated in a HPLC system  and a C18 column  using a 60 min mobile phase gradient ranging from 5 to 85% organic (0.1% formic acid in water to 0.1% formic acid in acetonitrile) at 250 nL/min flow rate for a total run time of 75 min. Following LC separation, peptides were ionized by nanoelectrospray with a spray voltage of 1.90 kV and 275 °C capillary temperature, then analyzed in a high resolution Orbitrap mass spectrometer  with automatic gain control set at 10100 with main search parameters of 4.5 ppm peptide tolerance, 20 ppm MS/MS match tolerance, 10 ppm MS/MS de novo tolerance, seven minimum peptide length, carbamidomethyl as fixed modification, 0.01 FDR, oxidation and acetylation variable modification, and enabled search for contaminants. Protein abundances were further data processed and normalized with log transformation (base 2) for data visualization and statistical analyses  using the Perseus software (v.1.5.4.0)101. The Search Tool for Retrieval of Interacting Genes (STRING) database (v. 10.5)102 was used for protein–protein interactions amongst the statistically significant proteins, set at medium confidence of 0.4 and protein annotation .Raw mass spectra data files were processed for protein identification using the MaxQuant software (v. 1.5.3.30)P. aeruginosa (iMO1086)41 was analyzed using flux balance analysis (FBA) via the COBRA Toolbox (https://opencobra.github.io/cobratoolbox/stable/cite.html) in MATLAB using the Gurobi optimization program (http://www.gurobi.com)  or O2 (20 mmol/g/h) uptake rates, respectively, for each of the examined carbon sources and maximizing the production of biomass or cellular energy (i.e. quantified as the number of ATP bonds hydrolyzed). Enzyme limitation was modeled by minimizing the number of participating reactions for specified substrate uptake rates while producing biomass or cellular energy. Suboptimal solutions between the minimal total flux and the maximum product yield (biomass or cellular energy) or the minimum proteome and the maximum product yield (biomass or cellular energy) were identified by minimizing an aggregate objective function. The aggregate objective function was the sum of either total flux or total proteome and a weighted flux through the substrate transport reaction of interest. Optimization between proteome investment and product yield was achieved by changing the weight of the flux through the carbon or oxygen transport reaction of interest. The algorithms can be found in supplementary material A genome-scale, stoichiometric model of Supplementary Tables.


Corynebacterium glutamicum is a promising target to rise productivities of growth coupled product formation. As a prerequisite, detailed knowledge about the tight regulation network is necessary for identifying promising metabolic engineering goals. Here, we present comprehensive metabolic and transcriptional analysis of C. glutamicum ATCC 13032 growing under glucose limited chemostat conditions with μ = 0.2, 0.3, and 0.4 h–1. Intermediates of central metabolism mostly showed rising pool sizes with increasing growth. 13C-metabolic flux analysis (13C-MFA) underlined the fundamental role of central metabolism for the supply of precursors, redox, and energy equivalents. Global, growth-associated, concerted transcriptional patterns were not detected giving rise to the conclusion that glycolysis, pentose-phosphate pathway, and citric acid cycle are predominately metabolically controlled under glucose-limiting chemostat conditions. However, evidence is found that transcriptional regulation takes control over glycolysis once glucose-rich growth conditions are installed.Increasing the growth rate of the industrial host  Corynebacterium glutamicum, discovered in 1957 as a soil bacterium, has gained prominent acceptance in industry for the production of amino acids, organic acids, and even proteins  C. glutamicum shows only moderate growth in synthetic media in classical batch cultivations  which was confirmed by two different groups later . Under the tested glucose-limited growth conditions, C. glutamicum’s central carbon metabolism are likely more affected by concentrations of local substrates and allosteric metabolite effectors than by the concentration of enzymes. Recently, another systems biology study was performed in traditional non-substrate limited batch cultivations to investigate determinants of global regulation orchestrating the growth of C. glutamicum . Cells from a glycerol stock working cell bank were spread on 2 × tryptone-yeast extract (2 × TY) medium  medium  agar pla) medium  supplemeC. glutamicum were conducted in a 3 L steel bioreactor  equipped with process control . Cooling and heating elements and a temperature sensor  ensured constant cultivation temperature of 30°C. Mixing was realized with a six blade Rushton turbine. A pressure probe  and an automatic exhaust valve were installed at the head of the reactor to regulate pressure (1.5 bar). For aeration, compressed air was channeled through a mass flow controller  and filtered aseptically  before entering the bioreactor. Dissolved oxygen was monitored by an amperometric pO2 electrode  and pH with a conventional pH probe . Molar fractions of oxygen and carbon dioxide in the exhaust gas were measured by a sensor .Continuous chemostat processes with D, h–1) equaling the growth rate , the advanced controller scheme described in –1 K2HPO4/KH2HPO4 buffer (pH 7) for 20 min at 121°C. Prior removal of the buffer, the drain of the reactor was sterilized in situ with pure steam for 10 min.To enable continuous processes in chemostat mode at fixed dilution rates (D-glucose L–1 was pumped from a stirred sterile medium feed reservoir (later employed for continuous mode) into the sterile bioreactor until a volume of 1.4 L was reached. The required amount of C. glutamicum biomass from pre-culture shaking flaks  was harvested and resuspended in 100 mL 9 g NaCl L–1 solution yielding an inoculation biomass density of 0.3 gCDW L–1 and a cultivation volume of 1.5 L. During batch and continuous cultivation, pH was controlled at 7.4 by automatically adding 25% (v/v) NH4OH solution. In batch mode, pO2 was maintained above 30% by 50 rpm stepwise increase of the impeller speed  and analogous rise of aeration rate by 0.15 L min–1 . Antifoam agent  was manually added when necessary. After about 7.5 h of exponential growth, the sharp increase of the pO2 signal indicated glucose depletion.All chemostat processes started with an initial batch cultivation. Therefore, sterile CGXII minimal medium  suppleme–1, respectively. During the course of one continuous experiment, dilution rates of D = 0.2, 0.3, and 0.4 h–1 were sequentially installed. Constant antifoam concentrations were achieved by increasing related feed rates proportionally . Sampling of each growth rate was performed after at least five residence times (RTs), i.e., 25, 16.67, 12.5 h, respectively. Accordingly, the achievement of a metabolic steady state of the culture was ensured (600) and cell dry weight (CDW) measurements], providing cell-free filtrates for extracellular metabolite analyses, and determining total inorganic carbon (TIC) and organic carbon (TOC) as described in the 13C-Labeling Dynamics”) and absolutely quantified by isotope dilution mass spectrometry (IDMS) with a constant addition of (U-13C)-labeled C. glutamicum extracts (see 13C-Labeled C. glutamicum extracts for IDMS”). Apart from transcriptome samples, conventional and metabolome samples were withdrawn 3× in 1.5 h intervals at each growth rate. Continuous chemostat processes at D = 0.2, 0.3, and 0.4 h–1 were conducted in biological triplicates following the described scheme.Thereupon, the continuous mode was initiated by starting the control regime , adding  ensured . Samplin13C-carbon labeling experiment (CLE) in chemostat mode (D = 0.4 h–1) was initiated with a batch cultivation as described in section “Chemostat Processes at μ = 0.2, 0.3, 0.4 h–1,” but aiming for a working volume of 1.2 L instead of 1.5 L. Therefore, 1.1 L sterile CGXII minimal medium  until glucose was depleted from the medium. The continuous mode was started by feeding fresh supplemented CGXII medium with a dilution rate of 0.4 h–1 while keeping the working volume constant (1.2 L) and constantly feeding 200 μL h–1 antifoam agent to prevent excessive foaming. After 12.5 h equaling five RTs, online measurements of O2 and CO2 in the exhaust gas indicated a metabolic steady state of the culture. Conventional sampling was performed as described in 12C)-D-glucose was switched to labeled CGXII feed containing 67% (U-13C)-D-glucose and 33% (1-13C)-D-glucose according to the previously performed tracer design (see section “A priori Tracer Design”). After four RTs (10 h), samples for intracellular isotopologue metabolome analyses were withdrawn (see 13C-Isotopic Labeling Dynamics”) as a stationary isotopic enrichment in C. glutamicum’s intracellular metabolites was expected at this time. Isotopic distributions of 13C-labeled and non-labeled intracellular pools of analog samples were quantified by HILIC-based QTOF-HRMS analysis (see 13C-Labeling Dynamics”). For technical replicates of this measurement, intracellular metabolome samples were withdrawn 30, 20, and 10 min before reaching four RTs, and 10 min afterward.Unlimited batch growth prevailed for 7.45 h , discarding the supernatant and immediately quenching the biomass pellet in liquid nitrogen. Extraction and isolation of RNA from the samples, whole transcriptome sequencing, read mapping, and raw read count calculation was performed as described in E-MTAB-9371. Transcript data analysis was performed using R version 3.5.0. Raw read counts were normalized using the Relative Log Expression function of the DESeq2 . The models were composed according to the workflow described in 2). Carbon atom transitions were formulated for each reaction. For reactions with C-symmetric reactants (in the TCA cycle and lysine synthesis), scrambling reactions were introduced and for each variant equal fluxes assumed. The metabolic model used for flux fitting comprises 45 balanced intracellular and 4 non-balanced extracellular metabolite pools, connected through 87 metabolic reactions . The model has 27 degrees of freedom (7 net and 20 exchange fluxes), whereas the network model used for tracer design was slightly simpler . Full model specifications are provided in the The metabolic network model of D = 0.4 h–1, an experimental design study was performed using the high-performance simulation framework 13CFLUX2 (13C-tracer quantification study (12C)-, (U-13C)-, (1-13C)-, -, (6-13C)-, and -D-glucose]. The model used for the design as well as the assumed measured fragments and rates including their corresponding expected errors are detailed in the To determine an informative, yet cost efficient tracer mixture for the CLE at 13CFLUX2 . To thison study , and sixD = 0.4 h–1, extracellular biomass-specific rates  were estimated using an ODE-based bioprocess model adapted from 13C-metabolic flux analysis (13C-MFA). The bioprocess model was implemented in Matlab . Monte Carlo simulations were performed with custom Matlab scripts.For the CLE at 13C-MFA, raw MIDs were corrected for natural isotope abundance using the ICT-toolbox . In total 170 measurements (164 labeling data for 28 metabolites and 6 biomass-specific extracellular rates) were used to determine 55 model parameters .After measurements became available, metabolic fluxes were estimated by minimizing the variance-weighted sum of squared residual (SSR) between the simulated quantities and the measurements, i.e., the extracellular biomass specific rates  and mass-toolbox . Correctin vivo flux distribution. Statistical analysis was performed according to the method described in Flux estimation was performed using 13CFLUX2 using the optimizer LINCOA  and the D (in h–1) equals the exponential growth rate μ (in h–1) of the culture:According to the continuous mass balance (cf. Eq. 1 in Metabolic steady states were ensured by employing the controller scheme described in qGLC (in g gCDW–1 h–1) is:According to the continuous glucose mass balance  is the glucose concentration of the feed medium and cX (in g L–1) is the biomass concentration of the culture. Residual glucose in cell free biosuspension samples was below the detection limit and was therefore neglected in the calculation.where X,GLCY) was determined by:Furthermore, the biomass-glucose yield  were calculated by dividing volumetric oxygen consumption or carbon dioxide emission rates by the biomass concentration determined at set dilution rate (cf. Eq. 9 in Biomass-specific respiratory rates (D-glucose L–1. After about 7.5 h batch phase [μmax = (0.44 ± 0.01) h–1] the continuous mode was started with a dilution rate D = 0.2 h–1 (process time = 0), which was increased to 0.3 h–1 after ca. 28 h and to D = 0.4 h–1 after ca. 49 h. Three independent samples for OD, CDW, filtrate TC/TIC, metabolome, and transcriptome were withdrawn after five RTs within 1.5 h-intervals to monitor the respective metabolic steady-states at D = 0.2, 0.3, and 0.4 h–1.Three biological replicates A–C,  were conGLC,feedc = 12.47 ± 0.35 g L–1). Biomass levels elevated from 5.87 ± 0.13 to 6.43 ± 0.18 g L–1 with rising D. By analogy, glucose-to-biomass yields rose from 0.47 ± 0.01 to 0.52 ± 0.00 g gCDW–1  remained at 1. All carbon balances closed well with 99 ± 1, 98 ± 2, and 96 ± 3% for the growth rates μ = 0.2, 0.3, and 0.4 h–1, respectively (data not shown). No residual glucose or C. glutamicum typical by-products such as acetate or pyruvate (mS = (0.44 ± 0.04) mmol D-glucose gCDW–1 h–1 and the growth coupled real glucose-to-biomass yield 1/YXGLC,real = 9.73 ± 0.08 mmol gCDW–1 ] and 250 μmol gCDW–1 [L-glutamate (GLU)]. Very good reproducibility of pool sizes was found in biological replicates with low standard errors between 2 and 8%. Few exceptions are the cumulated citrate/isocitrate (CIT/ICIT) and L-proline (PRO) pools revealing standard errors of 13 and 25% at μ = 0.2 h–1. Noteworthy, most pool sizes changed proportionally with growth assigning μ = 0.2 h–1 either minimum or maximum contents , phosphoenolpyruvate (PEP)] decreasing about 40% in pool size , whereas sedoheptulose 7-phosphate (S7P) revealed a steady rise.To be precise, metabolites of upper glycolysis  linearly increased 5- to 11-fold from μ = 0.2 to 0.4 hool size . In PPP,In the TCA cycle , remaini–1. Noteworthy, the low growth rate served as reference. In total, 547 DEGs were found grouping in 257 with rising and 290 with decreasing DEGs  at μ = 0.2, 0.3, and 0.4 hing DEGs . By trenpgi was identified as DEG mirroring that expression levels of all other genes persisted despite growth rise from 0.2 to 0.4 h–1. In case of pgi falling expression levels with increasing growth were detected.Differentially expressed genes assigned to genes coding for central metabolism reactions are considered in aceB, mdh, pck, pqo, sucC, and sucD, respectively. In particular, sucC and sucD showed strongest log fold changes (logFCs) with logFC = −1.5 whereas the majority of other DEGs reduction was −0.5 ≤ LFCmax ≤ −1. For comparison, upregulation of ackA, pta, gltA, and lpdA was less pronounced revealing 0.4 ≤ LFCmax ≤ 0.9. Those genes somehow encode the pathway from acetate to citrate.By analogy, of the 28 genes presented in C. glutamicum - and 67% (U-13C)-D-glucose, see section “A priori Tracer Design”]. To further investigate the intracellular flux distribution at μ = 0.4 hcX = 6.09 ± 0.03 g L–1, pO2 = 67%, yO2 = 0.18 mol mol–1, yCO2 = 0.03 mol mol–1) as evidenced by four fold sampling revealing low standard deviations , cX values proportionally increased to 6.20 ± 0.11 g L–1. However, microbial kinetics remained constant compared to the non-labeled state , following the procedure described in section “External Rate Estimation.” For the best fit obtained, measured and simulated data agreed very well (From these rates and the (corrected) MIDs, extracellular and intracellular fluxes were estimated with the ery well ,4, as invGLC = 4,086 μmol gCDW–1 h–1 for the specific growth rate μ = 0.41 ± 0.02 h–1. Two-thirds (66.4 ± 7.6%) of the incoming glucose flux were metabolized via the EMP pathway through G6P isomerase (reaction pgi), whereas one-third (31.6 ± 7.6%) of the glucose was metabolized via the PPP through G6P dehydrogenase and phosphogluconate dehydrogenase (gnd) to regenerate nicotinamide adenine dinucleotide phosphate (NADPH) and accomplish P5P-precursor production for nucleotides and amino acids. A considerable portion of the input flux, namely 25.1 ± 1.2%, was utilized for acetyl-CoA precursors, e.g., the synthesis of fatty acids and to satisfy the demand for biomass. A relatively high proportion (58.3 ± 4.2%) of the incoming glucose was channeled into the TCA cycle via citrate synthase (gltA) to generate ATP via the respiratory chain and to satisfy the demand of amino acids of the ASP and GLU branches, whereas the contribution of the PCA flux to the TCA cycle via the β-keto-adipate pathway was negligibly small (vPCA = 0.3 ± 0.0%). Best fit results suggest that the anaplerotic net flux mainly goes through pyc, which channels 31.3% of the glucose uptake flux into the TCA cycle via oxaloacetate (OAA). In C. glutamicum, three reactions in central carbon metabolism, namely, isocitrate dehydrogenase (icd), gnd, and malic enzyme (mez) contribute to regeneration of NADP+ to NADPH  and icd  contribute in a roughly 1:2 proportion to NADPH generation. Because the TCA cycle contributes 1 NADPH molecule per CIT molecule and the oxidative PPP contributes 2 NADPH molecules per G6P molecule, flux estimations suggest that NADPH demands were equally met by icd (48% of NADPH) and the lumped reactions of the oxidative PPP (52% of NADPH), recognizing that the contribution of mez is negligible. From the flux map in CDW–1  and glucose uptake rates  conducted under batch conditions  mmol D-glucose gCDW–1 h–1 and the growth-coupled maintenance coefficient CDW mmol–1 which indicates about 9 and 1% standard deviation, respectively. Surprisingly, the mS value exceeds measurements of C. glutamicum WT ATCC 17965 in chemostat tests.–1 that represents growth conditions typically observed under non-limited batch conditions  as employed in this study. Our findings agree with the previous study because only gene pgi showed minor reduction of transcript levels of genes encoding enzymes in glycolysis and in PPP. Consequently, increasing glycolytic fluxes should be enabled by elevated intracellular pool concentrations fairly assuming Michaelis–Menten type kinetics for said enzymes. Because extracellular glucose concentrations were limiting under chemostat operation, the majority of glucose consumption is expected to happen via the PTS. PEP is dephosphorylated stoichiometrically to pyruvate (not measured) activating glucose to G6P. Accordingly, increasing PEP consumption coincides with raising glucose consumption rates and growth. Observing falling PEP pools anticipate limited PEP regeneration with increasing growth rate. In general, glycolytic metabolite dynamics agree with independent studies of Pool sizes of upper glycolysis and PPP rise with the growth rate  whereas intracellular concentrations of 2,3PG and PEP show opposite trends see . NotablygltA expression; icd amplification  and phosphate acetyltransferase (encoded by pta) showed amplifying trends (A primary observation is that changes of gene expression levels are more pronounced under batch conditions  than in g trends  and are aceA (isocitrate lyase), pqo (pyruvate:quinone oxidoreductase), and sucCD (succinate CoA ligase) revealed opposite expression trends in chemostat runs compared to the expectations as members of the growth modulon derived from batch observations. Even more striking, the entire glycolysis was assigned to the growth modulon anticipating increasing transcripts with rising growth. However, transcriptome  can be found below: MG designed the study, carried out the bioreactor experiments, analyzed the metabolic datasets, and drafted and corrected the manuscript. TH designed the study, analyzed the transcriptomic data sets and, drafted and corrected the manuscript. AT and AF conceptualized and performed metabolic analysis and corrected the manuscript. TB and JK measured the transcript samples and corrected the manuscript. MC and KN designed the flux study, performed the MFA, and drafted and corrected the manuscript together with WW. RT conceptualized the whole study and drafted and corrected the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.