However, it is not produced by bacterial strains commonly used for industrial biocatalysis or recombinant protein production, such as Escherichia coli, limiting our ability to exploit it as an enzymatic cofactor and produce it in high yield. Here we have utilized a genome-scale metabolic model of E. coli and constraint-based metabolic modelling of cofactor F420 biosynthesis to optimize F420 production in E. coli. This analysis identified phospho-enol pyruvate (PEP) as a limiting precursor for F420 biosynthesis, explaining carbon source-dependent differences in productivity. PEP availability was improved by using gluconeogenic carbon sources and overexpression of PEP synthase. By improving PEP availability, we were able to achieve a ~ 40-fold increase in the space–time yield of F420 compared with the widely used recombinant Mycobacterium smegmatis expression system. This study establishes E. coli as an industrial F420-production system and will allow the recombinant in vivo use of F420-dependent enzymes for biocatalysis and protein engineering applications.The deazaflavin cofactor F As a deazaflavin, it is structurally similar to flavin, but given its lower redox mid-point potential (− 360 mV for F420 cf. ~  − 230 mV for flavins) and obligate 2-electron transfer it functions analogously to NAD/NADP13. It has been suggested that cofactor F420-dependent enzymes have significant potential as biocatalysts for the reduction of enoates, imines and ketones, and potentially for other unexplored reactions and processes17. However, the lack of a cost-effective production system for cofactor F420 is a major deterrent in exploring F420-dependent reactions for biocatalytic applications. Development of a low-cost production system for F420 will be essential for advancing the application of F420-dependent enzymes. An ultimate goal of this study is to devise the development of a system for F420 production as an end-product and at scale.Cofactor F420 is produced via fermentation of organisms that naturally produce it, in particular several species of Mycobacteria18. However, Mycobacteria are not well suited for large scale-production of the cofactor because they are not generally recognised as safe (GRAS) organisms, tend to form dense aggregated “clumps”, and are slow growing19. Two 13-step chemical syntheses of F420 isomers have been reported, differing only in the peptide linkage between the two glutamate residues. Both reported extensive use of protecting groups and low overall yield21. While improved syntheses of the deazaflavin moiety FO have been reported  was reported and F420-dependent enzymes had substantially greater activity with FOP than FO, albeit still lower than with authentic F42023.For research applications, cofactor F420 biosynthesis pathway proposed by Bashiri et al.18 was further updated in this study. FO synthase (CofGH), one of the first steps in the F420 biosynthesis pathway, was found to be a radical SAM enzyme capable of generating two molecules of 5′-deoxyadenosine24. Therefore, FO synthase accepts two molecules of S-adenosyl-l-methionine as substrate in addition to tyrosine, producing two molecules of l-methionine, one molecule of ammonia, and two molecules of 5'-deoxyadenosine  and Methanosarcina mazei 18 18, which is comparable to yield obtained in wild type Mycobacterium smegmatis25. Thermodynamic analysis of cofactor F420 biosynthesis revealed that the overall pathway is energetically favourable, with the final steps being effectively irreversible. This suggests that yields attained could be improved upon through metabolic engineering15.Ideally, cofactor FParaburkholderia rhizoxinica prefers 3-phosphoglycerate (3PG)27 28 29 27 Fig. , Step B3)28 Fig. , Step B1420 biosynthesis in E. coli is heavily influenced by the carbon source. We used metabolic modelling to understand the underlying causes of carbon-source dependent differences in yield, which identified several potential bottlenecks. As a suitable genome-scale metabolic model was unavailable for production of F420 in E. coli, nor for any natural F420-producing organisms, we incorporated both the phospho-enol pyruvate-dependent and 3-phosphoglycerate-dependent F420 biosynthesis pathways into the iHK1487 genome-scale metabolic model for E. coli BL2131. The updated model (iEco-F420) was used to identify potential flux bottlenecks and to explore the theoretical limits of F420 production in this organism. Although the overall thermodynamics of the pathway are favourable, calculations revealed unfavourable energetics for the reaction catalysed by FbiD/CofC, which converts PEP and guanosine triphosphate into enolpyruvyl-diphospho-5′-guanosine15. Several strategies were explored to improve availability of PEP for this reaction. Through a combination of metabolic engineering and rational carbon source selection, we were able to improve the yield of cofactor F420 from 0.28 to 1.60 µmol/g DCW. The highest productivity observed with E. coli was a yield of 1.60 µmol/g DCW and culture time of 13 h ; this space–time yield is fourfold higher than is in recombinant M. smegmatis, for which the highest published yield of cofactor F420 achieved was 3.0 µmol/g DCW with culture time of 96 h 32.Herein, we report that cofactor F420, we tested acetate, fumarate, glucose, glycerol, pyruvate, and succinate as carbon sources, as these carbon sources enter central metabolism at different points, have varied uptake mechanisms and therefore distinct bioenergetic consequences for the cell33. Pyruvate and fumarate, (followed closely by acetate and succinate) supported the greatest F420 production per gram of dry cell weight  varied significantly by carbon source , glycerol was the most productive carbon source  to compare flux profiles. Figure 420 productivity profiles. FBA predicted assimilation of 72% of glucose, as the sole carbon source, via the phosphoenolpyruvate (PEP): phosphotransferase system (PTS); all enzymes involved in glycolysis were active. Given the defined criteria, FBA predicted no flux through PEP synthase (PPS) or PEP carboxykinase (PPCK) indicating tight control over the pool of PEP during in silico growth with glucose   Fig. A, leavin420 biosynthesis  was chosen to interpret flux variations with respect to PEP availability for cellular growth versus F420 production. Figure 420 requires more carbon to be diverted into the target product rather than biomass, up to the point where the growth of the host is so negatively affected that it becomes uneconomical. When biomass yield drops to 80% of its maximum, for example, the minimum and maximum fluxes through PEP hydratase are still both positive, meaning that essential cellular processes take priority. As a result, 2-PG needs to be metabolized to provide stoichiometric requirements of PEP. However, at 50% of maximum biomass yield, the minimum flux  for all reactions in the metabolic network, including the PEP-consuming reactions  of glucose, most of the PEP is still required for cellular activities rather than biosynthesis of F420. Nonetheless, glycerol remains a candidate carbon source for large-scale F420 production compared with glucose when maintaining high cell masses is essential because it allows for higher cellular mass yields while bypassing PTS-dependent PEP depletion. In the case of acetate, ATP-dependent acetate assimilation is the only route for producing acetyl-CoA, which is an essential precursor for the biosynthesis of most amino acids and fatty acids and therefore biomass yield drops significantly  is a glycolytic pathway intermediate present in E. coli at 10 times the concentration of PEP38. Moreover, in the context of the iEco-F420 model, PEP-dependent F420 biosynthesis requires an additional FMN-dependent reduction step (the FbiB-dependent conversion of dehydro-F420-0 into F420-0) indicating that additional carbon would need to be diverted into FMN biosynthesis39. Preliminary evidence suggests that 3PG-F420, unlike FO and FOP, is accepted as a cofactor by F420-dependent enzymes with similar kinetics to standard F42030. Given the relative abundance of 3PG, we investigated it as an alternative to PEP by substitution of M. smegmatis FbiD with that of P. rhizoxinica.As PEP is likely to be a flux-limiting metabolite, we explored the possibility of using an alternative metabolite in its place. Three different metabolites have been proposed to be incorporated in the sidechain of Fcf. 0.18 mM)38 and is predicted to provide relatively similar maximum theoretical F420 yields  was greater than 1.0, which resulted in significant improvement in F420 yield. We tested overexpression of PPS when grown on different carbon sources, as shown in Fig. 420 from 0.27 to 0.54 µmol/g DCW using glucose and from 0.53 to 0.80 µmol/g DCW using glycerol. When grown on pyruvate, an F420 yield of 1.60 µmol/g DCW was observed without the addition of IPTG. With the addition of IPTG, the yield of F420 yield decreased to 0.90 µmol/g DCW. The yield of F420 also decreased after PPS induction when grown on succinate or fumarate  by simulating over-expression of PPS or PPCK. The results, shown in Supplementary Fig. 420 biosynthesis as a result of over-expressing PPS or PPCK; however, the overall kinetics of the system and regulatory mechanisms for growth with different carbon sources would significantly influence F420 yields, in vivo. The experimental results 48. What influences the tail length of F420 is still not clear, although in vitro analysis of F420-0:g-glutamate ligases from different organisms has revealed that they typically produce F420 species with polyglutamate chain lengths consistent with F420 obtained from the native organisms49. The number of glutamate residues influences the cofactor affinity of some F420-dependent enzymes; for example, the F420-dependent oxidoreductases MSMEG_2027, MSMEG_0777 and MSMEG_3380 from M. smegmatis reportedly having a high affinity for long chain F420 rather than shorter-chain F42050. Similar effects are seen with polyglutamylated folates and folate mimics53. Interestingly, F420-n composition changes with different growth phases of Methanosarcina barkeri and M. mazei54. We therefore investigated the composition of F420 during different growth phases of E. coli. The composition of F420-n at various time points is shown in Fig. E. coli initially produced short chain F420-(1–4) in higher proportions, which shifted over time to predominantly longer chain F420-(5–8)  has been shown to produce predominantly longer F420 species (5–8) in stationary phase50.The final step in F420, with F420-(5–8) comprising > 90%. Glycerol has the next highest proportion of F420-(5–8) at > 80%, with glucose and acetate with the lowest levels of F420-(5–8) produces   > 70% , total energy production (in the form of ATP) is higher for biosynthesis of F420 with one glutamate than that for biosynthesis of a mixture of F420 molecules  vs. slower growth . We also examined whether alternative substrates for FbiD could be used to remove the reliance on PEP, showing that although 3PG is a viable substrate and is more abundant in E. coli, the use of the P. rhizoxinica FbiD did not result in higher 3PG-F420 yields (compared with F420), presumably because of the downstream enzymes, which are sourced from M. smegmatis, have a preference for F420-metabolites over 3PG-F420 metabolites. Replacing other enzymes in the pathway with those from P. rhizoxinica may be a worthwhile strategy to improve 3PG-F420 yield, as the theoretical yield of 3PG-F420 is similar to that of PEP-derived F420 and strategies have already been developed to improve 3PG availability by increasing glycolytic flux by knocking out the zwf gene involved in first step of pentose phosphate pathway55. Other strategies that we tested to increase F420 yield included increasing PEP production through over expression of PPS; this produced the highest productivity with pyruvate. Our results also indicate that F420 composition and concentration in E. coli is comparable to some of the best natural producers such as M. smegmatis. The F420 yield obtained with M. smegmatis is approximately 0.30 µmol/g DCW in wild type25 and 3.0 µmol/g DCW in engineered M. smegmatis32. However, cultivation of M. smegmatis in shake flasks takes up to 96 hours32, while engineered E. coli cultivation takes 16 h (this study). This results in space–time yield of 114 nmol/h/gDCW for the recombinant E. coli system, vs. 31 nmol/h/gDCW for the recombinant M. smegmatis system32. This increased yield is in addition to the many other advantages of using E. coli, including less expensive antibiotics (ampicillin vs. hydromycin), reduced safety risks and thus greater accessibility to the technology, and the fact that E. coli is a much more widely used strain for protein engineering and DNA modification. By systematically optimizing growth and production conditions in E. coli, we have created a system that should make production of F420 more economical at an industrial scale and the study of F420-depenent enzymes more accessible, improving on the previous attempts for F420 production through the use of more exotic and challenging bacterial species, such as M. smegmatis.This work establishes that intracellular PEP concentration is the key limiting metabolic bottleneck for heterologous FE. coli BL21 DE3 (New England Biolabs) was used for protein expression and DH5α (New England Biolabs) strain was used for plasmid propagation. LB media consisting of 10 g/L tryptone, 5 g/L yeast extract and 10 g/L of NaCl was used for the plasmid propagation and cloning, 15 g/L of agar was added to prepare LB agar plates. E. coli protein expression and F420 production studies were done in M9 minimal media consisting of 6.78 g/L Na2HPO4, 3 g/L KH2PO4, 1 g/L NH4Cl, 2.5 g/L NaCl, 100 × trace elements solution , 0.246 g/L MgSO4∙7H2O, 0.011 g/L CaCl2, and 5 g/L of glucose or 0.167 C-mol of different carbon sources with the final pH of 7.3. Chloramphenicol (25 µg/ml), kanamycin (50 µg/ml) and ampicillin (100 µg/ml) were added where appropriate.E. coli was cultivated in shake flasks at 30 °C with shaking at 200 rpm. The F420 biosynthesis genes in pSB1C3 plasmid were induced with 200 ng/ml of tetracycline. E. coli with pRSF duet plasmid was induced with IPTG (0.1 mM) for the expression of PPS and PEP carboxykinase (PPCK) enzymes. The biomass was measured using optical density at 600 nm.420 biosynthesis pathway operon was described in Bashiri et al.18. The pathway consists of CofD (Accession number: Q8PVT6), CofE (A0QTG1), CofC (A0QUZ4) and CofGH (NC_008596) genes under the control of the tetracycline-inducible promoter BBa_R004056 and the artificial terminator BBa_B100657. The F420 biosynthesis operon had been previously synthesized by GenScript and cloned in to pSB1C3 containing the constitutive tetracycline repressor cassette BBa_K145201 with EcoRI/XbaI and PstI/SpeI restriction enzymes, plasmid construction is explained in Bashiri et al.18. This construct, hereafter referred to as pF420, enables production of F420 to be induced by the addition of tetracycline. All the gene sequences except CofD were obtained from Mycobacterium smegmatis, CofD gene sequence was obtained from Methanosarcina mazei. All the genes were codon optimized for expression in E. coli BL21 DE3 strain. All F420 proteins were FLAG tagged and soluble expression of F420 pathway proteins was confirmed using western blot18. In order to produce 3PG-F420, the M. smegmatis CofC homologue was replaced by the homologue from Paraburkholderia rhizoxinica (E5ASS2). This F420 biosynthesis operon was synthesised (Biomatik) in two fragments (P_rhizo_3PG-F420-1 and P_rhizo_3PG-F420-2), P_rhizo_3PG_F420-1 was a 3.2 kb flanked with EcoRI and BamHI restriction sites in pUC57, P_rhizo_3PG-F420-2 was a 3.6 kb flanked with BamHI and PstI restrictions sites in vector pUC57. Three-way ligation was performed to ligate P_rhizo_3PG-F4201 (cut with EcoRI and BamHI), P_rhizo_3PG-F4202 (BamHI and PstI) and pF420 (cut with EcoRI and PstI). The ligation resulted in plasmid pF420-3PG (Supplementary Table The synthesis of the plasmid expressing the Fnica E5AS. This F4ppsA gene encoding PPS from E. coli (P23538) and pck gene encoding PPCK from E. coli (B5YTV3) were synthesized and cloned into pETCC258 plasmid flanked by NdeI and BamHI restriction sites (Twist Bioscience) to produce PPS-pETCC2 and PPCK-pETCC2 plasmids  or PPCK (PPCK-pRSF) were used to transform BL21 DE3 containing pF420 plasmid using electroporation. Transformed cells were selected on LB agar plates supplemented with chloramphenicol and kanamycin. Supplementary Table The 420 pathway proteins in pF420 had been confirmed previously using immunoblotting. The procedure is explained in detail by Bashiri et al.18. Soluble expression of PPS, PPCK and F420-3PG operon proteins was confirmed through SDS-PAGE gel  96-well plate spectrofluorometer (excitation at 420 nm and emission at 480 nm). Fluorescence correlated directly with F420 concentration in the cell lysate. F420 amount relative to the biomass concentration was obtained in fluorescence (Units)/Biomass (OD600). The correlation between Biomass (OD600) and dry cell weight (DCW) was 0.56 g/L DCW for OD600 of 1.0. There was a linear correlation between absorbance at 420 nm and fluorescence (420 nm excitation and 480 nm emission). The fluorescence values of the cell lysate were converted to absorbance, and the extinction coefficient of 41.4 mM−1/cm was used60 to convert fluorescence unit of cell lysate to mM of F420 in cell lysate. Using these parameters fluorescence (units)/Biomass (OD) was converted to µmol F420/g DCW.Production of F420 species based on the length of its glutamate tail is shown in S Supplementary Fig. 61. The supernatant was run on an Agilent 1200 series HPLC system equipped with an Agilent fluorescence detector and an Agilent Poroshell 120 EC-C18 2.1 × 50 mm 2.7 mm column. The system was run at a flow rate of 0.5 ml/min and the samples were excited at 420 nm and emission was detected at 480 nm. A linear gradient of two buffers were used: Buffer A, containing 20 mM ammonium phosphate, 10 mM tetrabutylammonium phosphate, pH 7.0. Buffer B, 100% acetonitrile. A gradient was run from 25 to 40% buffer B as follows: 0–1 min 25%, 1–10 min 25%–35%, 10–13 min 35%, 13–16 min 35–40%, 16–19 min 40%–25%.Analytical separation of Fm/z in negative mode in order to estimate PEP concentration in cell lysate.Intracellular PEP was measured using single ion monitoring method (SIM) in single quad (Agilent 6120). Two buffers were used, Buffer A containing 10 mM ammonium formate with pH adjusted to 4.0 and Buffer B containing 100% acetonitrile, isocratic flow with 25% buffer B was used with flow rate of 0.5 ml/min using Agilent Poroshell 120 EC-C18 2.1 × 50 mm 2.7 mm column. 10 µl of sample was injected and SIM method was used to detect mass of 167 E. coli31, containing 1487 genes, 2701 reactions, and 1164 metabolites, was used as the scaffold to integrate F420 biosynthesis pathway in this study. 360 reactions were found to be mass or charge imbalanced in the model, of which 338 reactions were demand or exchange reactions and one was biomass reaction, and therefore, were excluded from further corrections. Of the remaining 21 imbalanced reactions, 12 were involved in the lipopolysaccharide and cell envelope biosynthesis/recycling pathways, four were in alternate carbon metabolism pathway, three were in capsular polysaccharide biosynthesis/recycling pathway, and two were transporters . For that, either an F420 molecule with one glutamate residue or a stoichiometric combination of F420 molecules with varying glutamate tails can be set as the target to analyse their flux and biosynthesis profile. Furthermore, the iEco-F420 model contains F420-dependent formate dehydrogenase62, F420-dependent G6P dehydrogenase63, F420-NADP oxidoreductase64, F420-dependent oxidoreductase50, and F420-reducing hydrogenase65 allowing for the analysis of cofactor F420 recycling and regeneration within the metabolic network of engineered F420-producing E. coli.The bacterial F420 biosynthesis pathway along with correcting imbalances resulted in the iEco-F420 metabolic model of E. coli with 26 new metabolites and 43 new reactions. The updated metabolic model is available in Excel format in Tables −1. The M9 minimal medium composition was used to constrain the input of nutrients in the updated model. Independent simulations were run using glucose, glycerol, pyruvate, acetate, fumarate, or succinate as sole carbon sources (60 C-mol of carbon source). The uptake of C-source was fixed to 60 C-mol in all simulations to account for differences in number of carbon atoms in C-sources. The objective function was to maximize F420 production, while maintaining growth at 30% of its maximum to represent in vivo growth conditions. Maintenance ATP requirements were fixed at 5.17 mmol/g DCW and the minimum oxygen uptake was set to 18.5 mmol/g DCW/h. For simulating in vivo over-expression of PPS and PPCK experiments, flux through PPS or PPCK was fixed in each run by constraining their lower and upper bounds to a value between zero and 70 mmol/g DCW/h, whereas zero represents no over-expression. Only one carbohydrate transporter was allowed to be active in each of these simulation runs. The model was assembled in a format compatible for flux balance analysis66. FBA optimization problems were solved by GNU Linear Programming Kit (GLPK) (http://www.gnu.org/software/glpk/) solver in MATLAB using COBRA toolbox67. Flux variability analysis (FVA) was performed to obtain range of fluxes under optimal growth conditions as described previously68.Integrating the FSupplementary Information 1.Supplementary Information 2.


Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering. Mesoplasma florum is reported. Comparing the model‐driven prediction of a M. florum genome reduction scenario to a closely related minimal cell reveals key features of a minimal gene set.The first genome‐scale metabolic model for the near‐minimal bacterium  Using this mathematically structured knowledgebase, key phenotypic predictions such as gene essentiality, metabolic flux states, and growth medium requirements can be obtained from the genotype without the need for precise enzyme kinetic data  of the protein‐coding genes included in the metabolic reconstruction were orthologous to JCVI‐syn3.0, a slightly higher fraction than the number of orthologs present in the entire genome , yeast extract (YE), and heart infusion broth .While the genomic complexity of Mollicutes is remarkably low . This allowed a 64‐fold reduction in the concentrations of HS (to 0.313%) and YE (to 0.02%) required for significant growth  could be observed, which is desired for modeling purposes where the optimal conditions are assumed. Accounting for experimental variability and the number of data points available, the maximum sucrose uptake rate was set at −5.26 mmol/gDW/h and the combined lactate/acetate secretion rate at 8.69 mmol/gDW/h .Nevertheless, at low sucrose concentrations, initial sucrose and growth rate displayed a Monod‐like relation Monod, , with a −1 Fig  and S5A.−1 Fig . The ran−1 Fig . InteresM. florum’s dry mass, as well as multiple omics datasets  is a reaction of the stoichiometric matrix used to simulate an organism’s growth  was also added during Step 3. In the pathways leading to the production of the four most frequently identified metabolites in Step 3, 10 out of 15 genes had their essentiality prediction modified compared with Step 2  was wrongfully identified as essential compared with transposon mutagenesis data. The final iJL208 biomass composition is presented in Appendix Table Using the Step 3 of BOFdat, the correspondence between single‐gene essentiality prediction and genome‐wide transposon mutagenesis data ‐glucosidic linkage  to convert mannose‐6‐phosphate into fructose‐6‐phosphate, hereby entering glycolysis , the addition of both the promiscuous transport and digestion reactions was sufficient to provide a growth prediction on maltose and mannose. As reported previously, both glucose and mannose were detected in the M. florum polysaccharide layer  could also hydrolyze maltose. This hypothesis was supported by the very high structural similarity between the reconstructed Mfl499 and a sis Figs  and EV2Bosa Figs  and EV2Cet al, et al, M. florum were used as a reference for the validation of model flux states and gene essentiality predictions  reaction  was similar to the number of JCVI‐syn1.0 proteins deleted to generate JCVI‐syn3.0 and the number of proteins shared with M. florum (63).The smallest predicted rpmE, Mfl648). While functionally important, this protein was categorized as non‐essential in our dataset  chosen based on the expression data was exacerbated following sensitivity analysis (˜ 15:1). This shift in ratios can be explained by the upper flux limit applied to the NADH oxidase reaction, which utilizes oxygen to recycle NADH cofactor produced when generating acetate, whose addition was necessary to ensure a non‐linear relationship between oxygen uptake and growth rate   could be assessed by generating a model that includes the expression machinery (ME‐model) . The complete genome sequence of this strain is available in GenBank under RefSeq accession number NC_006055.1. Genome annotations were either based on RefSeq (NC_006055.1), PATRIC (Genome ID: 265311.5), or both depending on specific analysis context and needs. The transposon mutagenesis dataset was taken from Baby et al  was used to identify orthologous proteins between M. florum L1 (Genome ID: 265311.5) and the following strains: Mycoplasma gallisepticum str. F; Genome ID: 708616.3, M. pneumoniae M129; Genome ID: 272634.6, M. genitalium G37; Genome ID: 243273.27, and M. mycoides JCVI‐syn3.0; Genome ID: 2102.8. The parameters to identify orthologous proteins were the following: minimum positives of 0.2, minimum sequence coverage of 0.3, a minimum identity of 0.1, and a maximum E‐value of 1e5. In the case of pairwise proteome comparisons, both unidirectional and bidirectional best hits are used to define orthologous genes. Gene names were considered similar if they shared the same initial three characters in at least two species.The proteome comparison tool from PATRIC . HMMER was then used to determine structural domain coverage in the PDB and identified an initial set of 459 proteins with a match to known domains. The following parameters were applied to filter this initial set to determine proteins that were likely to provide reliable structures from homology modeling: E‐value < 1e‐4, domain sequence identity > 10%, and domain sequence similarity > 30%. The transmembrane proteins (20) were also discarded given the limited capability of I‐TASSER to produce a relevant model for such proteins  and the similarity between the template and modeled structure (TM‐score). The 361 structures with a “C‐score” higher than −1.5 and a “TM‐score” higher than 0.5 were defined reliable  using the DETECT v2  to ensure reaction conformity with a standard database and quality control. The reactions issued from the scaffold generated through the comparative approach were added first. Both RefSeq and PATRIC annotations, along with the identified EC numbers found with DETECT v2 and COFACTOR, were screened manually to identify metabolic candidates. Metabolic reactions associated with these genes were determined based on the information available in public databases  where every row or column represents a unique metabolite or reaction, respectively. The stoichiometry of each metabolite in a reaction is given as a coefficient in the matrix. If we assume a vector of metabolic fluxes v, the variation of metabolite concentration over time becomes the following:X is the vector of metabolites in the network. FBA assumes that the metabolic network will reach a steady state. In this case, the concentration of metabolites over time should be in equilibrium where the inputs are equal to the outputs so that:Flux‐balance analysis (FBA) is a mathematical approach to simulate cellular phenotype (Orth Z) allows the formulate an optimization problem on which constraints apply:a and b are the flux bounds on every reaction. This mathematical formulation can be solved using linear programming and allows finding the optimal solution of a given metabolic network at steady state.Defining a physiologically meaningful objective . 50 evolutions were performed for 200 generations each. The Matthews correlation coefficient (MCC) was used to score the biomass compositions. MCC can be calculated using the following confusion matrix:The M. florum CPS was added and its stoichiometric coefficient determined using the carbohydrates MWF (4.1%).The biomass compositions  with the highest were saved into a “Hall of Fame'' for each evolution. The frequency of apparition of metabolites within the individuals saved in the Hall of Fame was determined, and the ones appearing most frequently were added as part of the BOFdat Step 3. From the nine metabolites identified, seven were considered part of the metabolite pool category (molecular weight fraction [MWF] = 1.2%). The stoichiometric coefficients of every metabolite in this category were re‐computed using the BOFdat with this MWF and assuming that each metabolite is represented equally. Two metabolites belonged to the lipids category, so the same procedure was employed but using the lipids MWF (18.3%). Finally, the metabolite representing the M. florum preculture grown at 34°C in ATCC 1161 medium (1.75% (w/v) heart infusion broth, 4% (w/v) sucrose, 20% (v/v) HS, 1.35% (w/v) YE, 0.004% (w/v) phenol red, 200 U/ml penicillin G)  and either 1.75% (w/v) heart infusion broth (ATCC 1161 base), PBS 1X (PBS base), or CMRL‐1066 chemically defined medium , all supplemented with 0.004% (w/v) phenol red and 200 U/ml penicillin G. Medium bases were adjusted to a pH of ˜ 7.5 and transferred into a 96‐well microplate for growth assays. Half of wells were also supplemented with sucrose at a final concentration of 4% (w/v) for the ATCC 1161 base and 1% (w/v) for PBS and CMRL‐1066 medium bases. The inoculated microplate was incubated with shaking at 34°C in a Multiskan GO microplate reader (Thermo Scientific), and the optical density at 560 nm (OD560 nm) was measured every 10 min for 24 h. Changes in the absorbance of phenol red at 560 nm caused by the metabolic activity of M. florum (medium acidification) were previously shown to correlate with the number of CFU/ml  and compared between inoculated and non‐inoculated conditions, resulting in a growth index for each carbohydrate tested. All conditions were tested in technical triplicate. For comparison with the model’s predictions, the growth index measured for each carbohydrate was finally normalized to growth on sucrose.A 96‐well microplate was filled with CSY supplemented with either one of the following carbohydrates, at a final concentration of 1% (w/v): sucrose, trehalose, fructose, glucose, maltose, mannose, glycerol, sorbitol, lactose, galactose, ribose, arabinose, The formulated model with defined biomass composition and constraints was used for the prediction of carbohydrate utilization. An exchange reaction, the model equivalent of the medium composition was added for each carbohydrate tested experimentally. When tested, a lower bound of −10 was applied to the exchange reaction and the model was optimized for biomass production. The predicted growth rates for each carbohydrate were saved and normalized over sucrose to facilitate comparison with experimental results.M. florum growing in CMRL‐1066 base medium with variable HS and YE concentrations was measured using colorimetric assays as described previously  with variable initial concentration of sucrose, the doubling time was measured according to CFU counts of time‐course experiments. Briefly, a simple exponential growth model was fit to the mean CFU counts measured over time for each initial sucrose concentration (equation (5)):0A is the initial number of bacteria and r is the growth rate. In simple exponential growth, the relation between growth rate (r) and doubling time (d) is given by:The doubling time of isolated transposon insertion mutants as well as M. florum preculture grown at 34°C in ATCC 1161 medium was centrifuged for one min at 21,000 g and washed twice with PBS 1X. Washed cells were inoculated at an initial concentration of ˜ 1e5 CFU/ml into different CSY media containing variable concentrations of sucrose. Inoculated media were adjusted to a pH of ˜ 7.5 and transferred into a 96‐well microplate for growth experiments. Cultures were incubated with shaking at 34°C in a Multiskan GO microplate reader (Thermo Scientific), and growth was followed by measuring CFU counts every ˜ 90–120 min until late exponential phase. CFU were evaluated by spotting serial dilutions of the cultures on ATCC 1161 solid medium and counting colonies after an incubation of 24–48 h at 34°C. For modeling purposes, CFU/ml were converted to gDW/l (biomass) according to the previously determined M. florum dry weight , and 99.7% acetic acid (Fluka). Since lactate and acetate peaks were indiscernible, corresponding peak areas were combined, resulting in a combined lactate/acetate estimate. Following quantification, substrate‐specific (sucrose) uptake rate and fermentation product‐specific (lactate/acetate) secretion rates were calculated according to the following equation:qS is the substrate (or product)‐specific rate, ΔS is the variation of substrate concentration over time and t2X is the biomass concentration at the end of the time interval  were obtained from the exponential fits applied to biomass data of each condition (equation (5)). The maximum substrate and product‐specific rates are expected to reach a plateau as the initial concentration of substrate increases. We estimated that this plateau would be reached at the two highest initial sucrose concentrations tested in this study (0.05 and 0.1%). For modeling purposes, substrate‐ and product‐specific rates were therefore determined by computing the average of the possible rates obtained for these two initial sucrose concentrations.An exponential growth phase E. coli growing in minimal medium batch cultures . Similarly, the NGAM was identified by varying its value from 0 to 30 mmol/gDW/h, fixing the theoretical GAM and keeping the initial uptake and secretion rates. This allowed the identification of the NGAM value at which the predicted growth rate fits its experimental counterpart.Maintenance costs represent the amount of energy necessary to support the cell aside from the production of biomass components from the metabolic network. While GAM is the ATP hydrolysis reaction within the BOF, the NGAM is represented by the ATP maintenance reaction which also consumes ATP but is independent of biomass production. These rates were defined first as they are most impactful for growth rate prediction (Lachance −1). This value was found at 8.16 mmol/gDW/h. The acetate secretion rate was defined as the difference between the average experimentally determined value of 8.69 mmol/gDW/h for both products and the theoretical lactate secretion rate, corresponding to 0.53 mmol/gDW/h. Finally, the impact of oxygen uptake rate was assessed using all constraints and rates previously determined. The oxygen uptake rate varied between 0 and 30 mmol/gDW/h. The final uptake rate (4.81 mmol/gDW/h) was chosen to match the experimental growth rate value in CSY (0.44 h−1).The model sensitivity to uptake and secretion rates was then evaluated using the fixed theoretical maintenance costs. Using the initial oxygen uptake as well as lactate and acetate secretion rates, the sucrose uptake rate was varied between 3 and 15 mmol/gDW/h, which encompasses the experimentally measured uptake rate of 5.26 mmol/gDW/h . Sensitivity to lactate and acetate production was evaluated on specific ranges  with the determined sucrose uptake rate and initial oxygen uptake rate. The lactate secretion rate was determined as the minimum rate matching the experimental growth rate in CSY  was obtained by multiplying the MCC score for each threshold pair  by the number of genes expressed at these same thresholds :Transcriptomic and proteomic expression datasets were available for This score accounts for the correlation between each dataset while maximizing the number of expressed genes. The thresholds providing the optimal score were used for this study and were found at 23 proteins per cell (proteomics) and 168 FPKM (transcriptomics).et al, M. florum, interrupted genes were considered essential only if the transposons were strictly restricted to the terminal region of genes, defined as the last 20% of the gene length. Both Hutchison and colleagues , a version of FBA that allows the generation of a unique flux state prediction through minimization of enzyme usage (Lewis M. florum genome. The transcription units (relationship between gene and promoter locations) were obtained from the integrative characterization of M. florum  of the deletion to be performed. To identify the minimal gene set for M. florum, the optimization was performed iteratively 100 times. Deleted genes and promoters were encompassed between a deletion start and end site.The MinGenome algorithm  and JCVJ‐CL conceptualized the study, curated the data, involved in formal analysis and in funding acquisition, investigated the study, contributed to methodology and visualization of the study, developed software, validated the study, wrote the original draft, reviewed and edited the manuscript. DM conceptualized the study, curated the data, investigated the study, contributed to methodology and visualization of the study, validated the study, reviewed and edited the manuscript. JB investigated and validated the study. NM involved in formal analysis, curated the data, investigated the study, and contributed to methodology. CJL conceptualized and validated the study. ZAK supervised and validated the study. TFK investigated and curated the data. AMF provided resources and involved in funding acquisition. JMM conceptualized, supervised, and provided resources. BOP conceptualized and supervised the study, provided resources, and involved in funding acquisition. P‐ÉJ conceptualized and supervised the study, provided resources, involved in funding acquisition, and reviewed and edited the manuscript. SR supervised project administration, conceptualized and supervised the study, provided resources, involved in funding acquisition, and reviewed and edited the manuscript.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Code EV1Click here for additional data file.Code EV2Click here for additional data file.Source Data for Expanded ViewClick here for additional data file.Review Process FileClick here for additional data file.


Urinary tract infections (UTIs) are frequent in humans, affecting the upper and lower urinary tract. Present diagnosis relies on the positive culture of uropathogenic bacteria from urine and clinical markers of inflammation of the urinary tract. The bladder is constantly challenged by adverse environmental stimuli which influence urinary tract physiology, contributing to a dysbiotic environment. Simultaneously, pathogens are primed by environmental stressors such as antibiotics, favoring recurrent UTIs (rUTIs), resulting in chronic illness. Due to different confounders for UTI onset, a greater understanding of the fundamental environmental mechanisms and microbial ecology of the human urinary tract is required. Such advancements could promote the tandem translation of bench and computational studies for precision treatments and clinical management of UTIs. Therefore, there is an urgent need to understand the ecological interactions of the human urogenital microbial communities which precede rUTIs. This review aims to outline the mechanistic aspects of rUTI ecology underlying dysbiosis between both the human microbiome and host physiology which predisposes humans to rUTIs. By assessing the applications of next generation and systems level methods, we also recommend novel approaches to elucidate the systemic consequences of rUTIs which requires an integrated approach for successful treatment. To this end, we will provide an outlook towards the so-called ‘uncomplicated environment of UTIs’, a holistic and systems view that applies ecological principles to define patient-specific UTIs. This perspective illustrates the need to withdraw from traditional reductionist perspectives in infection biology and instead, a move towards a systems-view revolving around patient-specific pathophysiology during UTIs. Recent advances in DNA and RNA sequencing contradicts prior assumptions that human urine is sterile; instead it harbors a unique microbiome with ecological interactions in health and disease . As the Escherichia coli (UPEC) primarily causes UTIs and is isolated from approximately 80% of patients  and Bacteroidetes (Bacteroides), which are typically absent from their male counter parts  that are the dominant bacteria of the vagina are demonstrated to repel or suppress non-native pathogens in environments through the production of secondary metabolites and control of environmental pH  was found to be overexpressed in the database and acts as a key-driver for MDR phenotypes in E. coli   between Over time, recent evidence has uncovered that the infection route of both UTIs and rUTIs goes far beyond the fecal-perianal-urethra transmission route. UPEC specifically is an intermingling pathogen which switches between humans, animals, and the environment. This paradigm described as One Health requires the standardization of new studies to better identify sources of transmission by integrating an ecological and epidemiological framework that can be translated into clinical settings . For exaRecently, the first MDR E. coli classified as a superbug on US soil acquired the mobilized colistin resistance (MCR-1) gene from a commercial hog farm and manifested itself as a clinical UTI in a human patient . The MCRThese studies illustrate a rapidly evolving story of MDR pathogens in which the ever-increasing emergence of UTIs that are associated with the interactions of contaminated animals, presenting a significant threat to public health in both communities and clinical settings. However, the correlation between farm or companion animals should be interpreted with caution. Identifying similarities between human/animal isolates does not include the transmission of microbiota and the routes that are employed to cause disease in humans and therefore requires an integrated One Health approach to better understand the issue .Urine is the primary source of information for identifying UTIs in patients. Analytical chemistry techniques use urine to detect biomarkers, such as excreted metabolites from pathogens . SpecifiE. faecalis is able to grow in urine containing greater concentrations of glucose and has enhanced recurrent biofilm formation potential, primarily in diabetic patients   are required for Fe uptake and transport systems to incite UPEC virulence mechanisms  is necessary for most biological processes such as cellular respiration, DNA replication, and oxygen transport irulence . For E. irulence . This ocirulence . UPEC opchanisms . Sideropchanisms . Uptake chanisms . During chanisms . Bacterichanisms . Hyre ethe urine . This mohe urine . Consequhe urine . In conthe urine . Table in vivo or in vitro models. To understand the underlying environmental mechanisms which control host invasion, these methods are useful to develop novel treatment strategies for rUTIs. To achieve precision treatment options for UTIs, the application of next-generation sequencing, genome-scale metabolic modeling, and the development of novel microfluidic or stem cells technologies to mimic the bladder is needed based on patient specifics, rather than generalizations. These next-generation methodologies foreshadow a future for treating UTIs from an ecological and systemic perspective, rather than through reductionist approaches. These examples bolster the argument for a necessary adjustment towards clinical diagnosis of infections and progression of host-specific diseases.The following represents recent advances in next-generation models and methods to examine UTI pathomechanisms. These methods reach endpoints that cannot be typically reached through traditional E. coli are flexible microbes that thrive in various niches and environments by tuning gene expression. These modifications allow E. coli to rapidly adapt to stressful environments for successive colonization during environmental stressors, like antibiotic exposure, to incite host pathogenicity , there is a need to quantify microbial communities phenotypic reactions to host-specific perturbations and interpret how these changes impact human health or disease in real-time.Presently, the “Tolerome” describes transcriptome-level information detailing nditions . Understnditions . ComparaG. vaginalis upregulated CRISPR-Cas genes as a stress response to mitigate DNA strand breakage from metronidazole by inducing DNA repair mechanisms. Female patients colonized by G. vaginalis with this unique change in gene expression were shown to be non-responsive to antibiotic treatment. Overall the observations from this study cast doubt on previous assumptions regarding microbial communities and pathogenesis progression. Metatranscriptomics for assessing the functional roles of diverse microbial strains in various environmental conditions of humans is growing in the literature (in vitro or in vivo. Application of metranscriptomics for the bladder microbiome would clarify perspectives of not only the microbiota occupying the bladder, but also their community interactions in respect to UTI pathophysiology.Shotgun metatranscriptomics profiles the transcriptome of all present strains of a microbial community in an environment and their functions . While mterature . But to E. coli employs transcriptional or post-transcriptional mechanisms to regulate metabolic pathways for stress adaptation (Organisms adapt their metabolism to maintain homeostasis in ideal or adverse environments by selecting either rapid growth or resistance phenotypes . Specifiaptation . Identifaptation .E. coli strains have been defined and applied to predict or engineer various metabolic capabilities through reconstruction of constraint-based genome-scale models. Modeling metabolic systems has been constantly updated since the original metabolic network reconstruction of E. coli in 2000 (By assessing the remarkable flexibility and diversity of differentially expressed genes between different stressful conditions, we can decipher UPEC adaptation towards colonization and persistence within differing human hosts . Transcr in 2000 . Genome- in 2000 . ApplicaIn silico community metabolic models of microbiota provide valuable insights into the ecological interactions within a microbial consortia. In brief, metabolic models of microbial species are constructed based on the organism’s annotated genome sequence. Enzymes, for which predicted genes are assigned to reactions, which the enzymes can catalyze. By connecting the individual reactions to a metabolic network, in silico representations of the organism’s catalytic capabilities allow predictions of metabolic phenotypes, for instance environmental context-dependent growth rates, nutrient utilization, and metabolic by-products release (in silico modeling of the human gastrointestinal microbiota revealed specific metabolic processes, including fermentation and metabolic cross-feeding interactions that are altered in disease when compared to healthy controls ( release . Models controls . The ratcontrols .in silico models of community metabolism can expand clinical understanding of rUTI pathophysiology and foster the development of novel therapeutic strategies to target the urobiome specifically.In systems medicine, these models are applicable to generate hypotheses of factors that promote ecosystem colonization with a potential pathogen. Thereby providing potential treatment options, as well as prevention strategies against resistant pathogens. Thus with the growing appreciation of the role of the urinary tract microbiome in rUTIs, in vitro model reflective of human rUTIs that yields meaningful data, mimicking human conditions realistically is necessary. Identification of a relevant urine medium, characterization of the physical bladder environment, and understanding the patient-specific urobiome function is required. Standard artificial urine medium that was first detailed in 1961 for UTI modeling is based on the 17 most prevalent urine substances (For designing an bstances . Howeverbstances . Consequin vitro. Generally, preliminary animal models have provided the investigative foundation to understand human systems, but consistently fail to replicate and provide specific insights into the complexity of the human system, let alone the intricacy of the human microbiome over a lifetime. The nature of precision medicine requires that advances move away from the testing of in vivo models which do not reflect human health. There is a rapid drive to develop in vitro models which accurately mimic human systems.For instance, a flow chamber culture system was developed to model human bladder infection in real-time to observe the phenotypic changes. These dynamic UPEC changes demonstrated a switching between filamentous or rod-shape forms to facilitate secondary infections, which could not be observed in standard in vivo or static in vitro models (E. coli was allowed to flow through a microfluidic system, in comparison to static growth (Due to the advancing need to model and replicate human disease outside of human systems and away from animals in a translational way towards precision medicine, a plethora of methods have been developed to interpret complex human diseases o models . . In c growth . A key dc growth . Recentlc growth .This modc growth . These win vitro approaches to employ induced pluripotent stem cells from donors to differentiate stem cells into various cell types for creating organ-like structures. One patient-specific method surgically collects human urothelium from donors to create organoid cultures, relevant to a patient-specific bladder and urinary tract (Stem cells used to create human-like systems to model disease pathophysiology, towards drug discovery, and toxicity testing by developing cell lines relevant to humans for precision medicine . Advancery tract . More rery tract . Overallin silico metabolic modeling or systems biology methods, and culturing induced pluripotent stem cells with microfluidic systems which are representative of the host. Overall, the presentation of the ‘uncomplicated environment of UTIs’ perspective can become an essential framework to further assess the effects of UTIs and their treatments on entire patients, rather than only the urinary tract. Thus advancing systems biology applications and precision medicine practices towards understanding UPEC pathology and other urogenital colonizing pathogens is ultimately necessary to effectively treat patients. The broad aspect of systems medicine in tandem with omics-based analysis and in vitro modeling can pave the way towards a mechanistic understanding of evidence-based diagnosis for rUTIs. To date, there are no systems medicine works on UTIs. Systems medicine is evolving more rapidly as available data becomes available and is translated into patient-specific models of biological systems. In particular, UTIs are not only one of the most common types of human infections but it is also poorly understood as a complicated system. That is why presently, scientists, biomedical engineers, physicians, and bioinformaticians have the best chance together, for combinatorial efforts towards employing precision treatments for rUTIs while maximizing benefits to patients.This review challenges previous interpretations of UTI treatment and diagnosis to discuss UTIs as a host-centric disease, requiring a holistic approach. There is an overarching need to understand the uncomplicated UTIs as a dynamic system, complicated by host-associated environments and dysbiotic microbial communities. Integrating the ecological interactions between the host and microbial factors is necessary to progress rUTI diagnosis and therapy in the age of antimicrobial resistance. This integration includes observations of the female bladder microbiome in health and disease, host metabolic dysregulation, and dietary contaminants such as antibiotics or transition metals. This information can be processed through the combination of transcriptomics, JJ-S drafted the manuscript. TK, HR, SG, ML, MC, SW, JR, JM-J, and CK provided extensive input and materials into the support of this work. JJ-S and TK drafted and refined the figures. All authors contributed to the article and approved the submitted version.We acknowledge financial support from the Land Schleswig-Holstein within the funding program Open Access Publication Fund”.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.


Escherichia coli, Pseudomonas aeruginosa, and Pseudomonas putida. We combined growth dynamics and dynamic changes in intracellular metabolite levels with a coarse‐grained model that integrates fluxes, regulation, protein synthesis, and growth and uncovered fundamental limitations of the regulatory network: After nutrient shifts, metabolite concentrations collapse to their equilibrium, rendering the cell unable to sense which direction the flux is supposed to flow through the metabolic network. The cell can partially alleviate this by picking a preferred direction of regulation at the expense of increasing lag times in the opposite direction. Moreover, decreasing both lag times simultaneously comes at the cost of reduced growth rate or higher futile cycling between metabolic enzymes. These three trade‐offs can explain why microorganisms specialize for either glycolytic or gluconeogenic substrates and can help elucidate the complex growth patterns exhibited by different microbial species.Central carbon metabolism is highly conserved across microbial species, but can catalyze very different pathways depending on the organism and their ecological niche. Here, we study the dynamic reorganization of central metabolism after switches between the two major opposing pathway configurations of central carbon metabolism, glycolysis, and gluconeogenesis in  Microbes face a series of fundamental trade‐offs that limit their ability to optimize simultaneously for both glycolytic and gluconeogenic growth. The irreversible reactions are allosterically regulated by FBP (fructose 1‐6‐bisphosphate) and PEP (phosphoenolpyruvate), where “outward” facing reactions are activated (green arrows) and “inward” facing reactions are repressed (red arrow). (2) Biomass production requires precursors from glycolytic carbons, PEP and gluconeogenic carbons . (3) Glycolytic and gluconeogenic enzymes are regulated by Cra, which is in turn modulated by FBP.(A) Detailed metabolic reaction network and (B) minimal network of central carbon metabolism. Coarse‐graining was done by combining irreversible glycolytic (orange) and gluconeogenic reactions (blue), as well as metabolites. Influx can either occur from glycolytic carbon sources  or gluconeogenic carbon sources,  cycle carbon‐like acetate or malate). (1) Gatekeepers to the central section of glycolysis and gluconeogenesis are the two irreversible reactions αi, where cj is the concentration of the regulatory metabolite and cj∗ is a reference concentration. Reversible fluxes are modeled with simple mass action kinetics. (2) Biomass production is implemented in the model as single reaction that drains all three metabolites simultaneously at catalytic rate kcat,BM. (3) Enzyme expression depends linearly on FBP concentration cFBP. Growth rate: μ, steady‐state abundance: ϕi∗, steady‐state concentration cFBP∗ and xi & xj modulate the sensitivity of regulation to FBP. Glycolytic and gluconeogenic enzymes are produced as part of protein synthesis. Thus in the model, flux through metabolism automatically leads to synthesis of metabolic enzymes and biomass production, resulting in dilution of existing enzymes.Mathematical formulation of the model. Numbers correspond to features in panel (B). (1) Fluxes et al, The defining feature of the model is a coarse‐graining of the irreversible reactions . These metabolites regulate the activity and expression of the irreversible enzymes , the balance of forward and backward fluxes results in a fixed quadratic dependence of FBP and PEP in equilibrium,To understand the molecular cause of the asymmetric response and lag phases, we investigated the role of allosteric and transcriptional regulation in our simulation. During steady‐state growth, the differential regulation during glycolysis and gluconeogenesis is achieved by PEP and FBP, the metabolites that are “sandwiched” between the two irreversible reactions and connected by a series of reversible enzymes, coarse‐grained in our model into the “super‐eno” enzyme. First, we focused on regulation during exponential growth and wanted to investigate how the cell achieves differential regulation of glycolytic and gluconeogenic enzymes using the metabolites FBP and PEP. In equilibrium, forward and backward reactions would balance, i.e., The form of equation  is speciFBP≫PEP or FBP≪PEP, such that either glycolytic flux is bigger than gluconeogenic or vice versa . This is achieved by the irreversible reactions, which drain and supply metabolites to the “super‐eno”. Because of the positive feedback between enzyme activity and non‐equilibrium of the “super‐eno”, this regulation topology achieves differential regulation during glycolysis and gluconeogenesis. As we observed in the analysis of the glycolytic and gluconeogenic steady states . These fold changes are the result of both allosteric and transcriptional variations. We found that metabolism in the majority of randomly generated models is inefficient and dominated by futile cycling; only a minority of models were able to reduce futile cycling in glycolysis and gluconeogenesis. Remarkably, despite probing variations of all possible model parameters, including Michaelis Menten parameters of enzymes and the strengths of allosteric and transcriptional regulation, lag times could not be reduced at‐will by the cell. Instead, individual parameter sets with similar allosteric regulation (colors) are bound by a “Pareto frontier” (solid lines) between futile cycling in pre‐shift conditions and lag times. Points close to the “Pareto frontier” are Pareto‐optimal, meaning that any further decrease of either parameter must come at the expense of the other. Overall, stronger allosteric regulation shifted the Pareto frontier, but was not able to overcome it. Parameter combinations that led to low futile cycling in either glycolysis or gluconeogenesis showed long lag times in at least one condition  and grows on a wide variety of substrates.Taken together, the results of Figs P. aeruginosa grows fast on gluconeogenic substrates that are considered “poor” substrates for E. coli, but slow on glycolytic substrates that are considered “good” , K2HPO4·3H2O (17.7 g), KH2PO4 (4.7 g), MgSO4·7H2O (0.1 g), and NaCl (2.5 g) per liter. The medium was supplemented with 20 mM NH4Cl, as the nitrogen source, and either of the following carbon sources: 20 mM glucose‐6‐phosphate, 20 mM gluconate, 0.2% glucose, 20 mM succinate, 20 mM acetate, 20 mM citrate, 20 mM malate, or 20 mM fumarate.Strains used in this paper are wild‐type Growth was then carried out at 37°C in a water bath shaker at 200 rpm, in silicate glass tubes (Fisher Scientific) closed with plastic caps (Kim Kap). Cultures spent at least 10 doublings in exponential growth in pre‐shift medium. For growth shifts, cultured were transferred to a filter paper and washed twice with pre‐warmed post‐shift medium. Cells were resuspended from the filter paper in post‐shift medium and subsequently diluted to an OD of about 0.05.Each metabolite sample was filtered, and the filter was immediately plunged in 4 ml ice‐cold methanol (40%)+acetonitrile (40%)+water (20%) and kept in 50‐ml tube. Bacteria were washed off from the filter by pipetting, and the solution was transferred to 15‐ml tube. Original 50‐ml tube was further washed with 4 ml of ice‐cold methanol+acetonitrile+water mix and added to respective 15‐ml tube . Each sample was dried by Speed Vac, and dried extracts were sent for Mass spec analysis.et al, v/v) methanol in water; and B, 2‐propanol. Data were acquired in negative ionization mode using previously published MRM settings  system (Waters) with a Waters Acquity T3 column coupled to a Thermo TSQ Quantum Ultra triple quadrupole instrument (Thermo Fisher Scientific) as described previously (Buescher The integrated minimal model of metabolism and growth was implemented in MATLAB using the SimBiology toolbox and is described in detail in the Appendix.Severin Josef Schink: Conceptualization; Formal analysis; Investigation; Writing – original draft; Writing – review and editing. Dimitris Christodoulou: Conceptualization; Investigation; Methodology; Writing – review and editing. Avik Mukherjee: Conceptualization; Investigation; Methodology; Writing – review and editing. Edward Athaide: Investigation. Viktoria Brunner: Investigation; Methodology. Tobias Fuhrer: Resources; Software; Methodology; Writing – review and editing. Gary Andrew Bradshaw: Resources; Methodology. Uwe Sauer: Conceptualization; Resources; Funding acquisition; Methodology; Project administration; Writing – review and editing. Markus Basan: Conceptualization; Resources; Supervision; Investigation; Writing – original draft; Project administration; Writing – review and editing.CRediT author contributions listed above, the contributions in detail are SJS, DC, AM, TF, US, and MB contributed to the design of the project and writing the manuscript. SJS, DC, EA, and MB performed modeling. AM and MB performed growth experiments. VB, TF, and GAB performed metabolomic measurements.In addition to the AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.


Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology. Within the advancement of science and technology in recent years, the application of omics strategies has allowed a paradigm shift that provides a holistic perspective on biological studies. Among the available omics sciences, metabolomics has stood out , and is http://www.hmdb.ca, version 4.0, 2018) contains over 114,000 metabolite entries  with a wide dynamic concentration range, from high abundance (>1 mM) to relatively low abundance (<1 nM) . ,22. 8,22A hypothesis-generating metabolomics approach covers different strategies to provide sample classification : (i) fingerprinting; and (ii) footprinting analysis. The metabolic fingerprint refers to the global snapshot of the metabolites present inside the cells (intracellular metabolome or endometabolome), while the metabolic footprinting explores the (exo)metabolites excreted or consumed by an organism (extracellular metabolome or exometabolome), providing a cumulative picture of metabolism over time ,26,27. CA good experimental design and the choice of appropriate methods of samples and data processing are prerequisites for the success of any metabolomics study. For the present topic, the workflow commonly used in high-throughput untargeted metabolomic studies  will be The first stage of a metabolomics study consists of the right formulation of the biological question to be answered. This process is of utmost importance, since it will determine the experimental design to be followed, namely the type of approach (untargeted vs. targeted metabolomics), sample type, sample size, experimental conditions to be tested, frequency and timing for sample collection, sampling conditions, storage conditions, sample preparation strategies, and the analytical platforms to be used.The choice of samples for metabolomic studies depends essentially on the research question. Metabolomic experiments are typically carried out in complex matrices, such as cell culture samples (cell extracts and culture media), tissues, and biofluids  ,30. CellIn vitro metabolomic studies provide information on specific cell types under different conditions, which may be important for the development of drugs that target specific cell phenotypes . These swww.atcc.org) [A broad diversity of cellular models, including tumorigenic and nontumorigenic immortalized cell lines ,37,38, ptcc.org) . Immortatcc.org) ,31,43. Htcc.org) .Primary cells, on the other hand, have the ability to retain the morphological and functional characteristics of their tissue of origin, and constitute the closest model to the in vivo situation . NeverthStem cells are undifferentiated cells that have long-term capabilities for multipotent differentiation and self-renewal. They have the ability to replenish damaged somatic cells and maintain a self-renewal reservoir of progenitors that is crucial for the homeostasis in many tissues . These cMore recently, several novel cell culture technologies have become available . These new models have enormous physiological relevance, as they are also able to generate results closer to the in vivo situation, but they also increase the number of parameters that must be controlled to reduce the variability , which can be an obstacle in metabolomics .The analysis of tissue samples provides localized snapshots of metabolic activity, making it possible to study the origin of the metabolites, unlike what happens with biofluids that reflect changes in multiple organs ,47. In cThe application of metabolomics to the analysis of biofluids faces different challenges compared to that of cells and tissues. Urine and blood are the two most studied biofluids in metabolomics due to their ease of collection, richness in metabolites, for allowing the study of temporal changes, and for being ethically acceptable and cost-effective ,52,53. HUrine, in particular, offers advantages over other biofluids due to its noninvasive nature, being suitable for young children and individuals for whom venous access is problematic . In addiThe method of collection and preparation of samples can have a significant impact on the metabolomics data and conclusions derived from a study, since these preanalytical steps can be sources of variation . An inadThe first critical factor to consider is selecting an appropriate collection time, because a large fraction of the metabolism oscillates due to circadian rhythm, physical activity, and dietary status ,65. TwenTo ensure the stability of biofluids, some additives may be added to the collection tubes, namely sodium fluoride, sodium azide, heparin, citrate, or ethylene diamine tetra acetic acid (EDTA). Considering that these substances can affect the efficiency of extraction and derivatization processes and the ionization process during the mass spectrometry (MS) acquisition, thereby suppressing metabolite ionization and/or introducing artefact signals, its use should be consistent throughout the study and should be adequate to the analytical platform ,61,67. FRegarding tissues, considering their heterogeneity, sampling should always be done in the same organ region ,60,68. OAn additional procedure to consider during tissue sampling should be the cleaning of samples with saline solutions in order to avoid contamination with blood metabolites ,69. The Another crucial point for reducing variability and improving data quality is the quenching step. This procedure aims to interrupt cellular metabolism to prevent the turnover of metabolites such as adenosine triphosphate (ATP) or glucose-6-phosphate, and to obtain a precise picture of the metabolome at the time of sampling. For that, during collection, samples should be kept at the lowest temperature possible, and the metabolism must be stopped immediately after collection in order to avoid possible bias in analysis, and consequently, misleading results . Snap-frFollowing quenching, the next step is to extract the metabolites. In the case of cell models, according to the study aim, quenching and extraction can be combined or sequential ,73. The Regarding storage, whenever possible, samples should be aliquoted to avoid repeated freeze–thaw cycles that lead to a progressive change in the metabolic profile and loss of sample quality ,61. The Advances in analytical techniques in recent years have facilitated metabolomic studies, improving our capacity to obtain more data from biological samples. However, the complexity of the metabolome, due to the diversity of metabolites with differences in molecular weight, polarity, solubility, volatility, and concentrations, challenges the capabilities of any single analytical platform. Thus, the optimal and simultaneous extraction, detection, and quantification of all the metabolites is not possible with a single approach, and to overcome this problem, several analytical platforms can be used to expand metabolite coverage ,84. To d1H-NMR) due to its natural abundance in biological samples, although other nuclei , phosphorus (31P), or nitrogen (15N)) can be used to obtain complementary metabolic information [1H-NMR spectroscopy is the most commonly used method for high-throughput metabolomic studies due to its short acquisition time and because it provides a direct measure of metabolite concentration and information on their chemical structure. However, sometimes it is necessary to resort to longer experiments such as two-dimensional NMR (2D-NMR), which are useful to assist in the identification of metabolites and increase their specificity, since this technique allows the separation of overlapping spectral peaks [NMR is a spectroscopic technique that is based on the energy absorption and re-emission of atomic nuclei due to variations in an external magnetic field . In NMR-ormation . This teormation ,85,86. NDue to the evident superiorities of sensitivity (nM to pM range), selectivity, and a wide dynamic range over other techniques, MS has become an ideal platform for metabolomic applications . As receA wide range of instrumental variants are currently available for MS analysis. The separation of compounds in CE is carried out quickly and in a simple way based on their different migrating velocities in the electric field . AlthougIn contrast, LC and GC are currently the leading separation techniques used in untargeted metabolomics . SimplicHigh-resolution mass spectrometry (HRMS) is another variant increasingly used to produce metabolomics data. Due to its high mass resolution and mass measurement accuracy, HRMS considerably improves the metabolomic data quality, and is especially beneficial for metabolite identification in complex biological mixtures.At present, other exciting and novel MS-based metabolomics technologies, such as matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging and desorption electrospray ionization (DESI) mass spectrometry imaging, have been developed ,91. ThesMetabolomics analysis of biological samples results in an enormous amount of data that can be time-consuming and difficult to process and analyze manually. Therefore, it is necessary to use fast and accurate bioinformatics and statistical tools to deal with the large and complex raw data sets, and provide a workable and understandable format in order to extract meaningful biological information ,84. Eachm/z-retention time (RT) pair). It is important to note that instrumental  and biological factors  can affect metabolomics data set by inducing fluctuations in chemical shifts/retention times, intensity, and background noise [After data acquisition, a two-dimensional matrix of intensities is constructed, in which each row corresponds to observations/object , and each column represents a variable/metabolic feature quantification of the underlying metabolite . HoweverSubsequently, in order to reduce the unwanted systematic bias, so that only biologically relevant variations are present in the data and that all samples become comparable in terms of absolute intensities, a normalization step is usually performed . The selThe final preprocessing method is scaling, which considers differences in concentration levels so that changes in abundant metabolites do not dominate statistical models. Thus, scaling methods attempt to normalize the contribution of all variables to the model . Among sAfter the preprocessing steps, multivariate statistical methods are applied to the matrix to reduce the dimensionality of the data due to its ability to deal with several variables simultaneously in a single analysis . MVA is PCA, PLS-DA, and OPLS-DA express their variation into principal components (PCs) or latent variables (LVs), in which each sample is graphically (score plots) expressed as a point (score). Each PC or LV accounts for decreasing proportions of the total variance, and although the number of PCs/LVs can be quite large, generally the first two are sufficient to describe the observed trends . In turnn-fold cross-validation. Cross-validation consists of randomly dividing the dataset into n blocks of equal size and then training the model n times, while each time keeping out one of the folds as an internal validation set [2 and Q2 are used to provide information about the goodness of the fit  and the predictive capacity , respectively. Thus, an R2 around 1 indicates a perfect description of the data by the model, while a Q2 around 1 indicates perfect predictability. Generally, a Q2 > 0.4 is considered good, and in an ideal model, R2 and Q2 should be similar [When supervised techniques are used for data analysis, the conclusions must be carefully validated due to the risk of data overfitting; while the models (training models) can successfully discriminate groups, they may not be able to classify future samples . Thus, ttion set ,113. The similar ,113.Another way to validate the MVA results is by using an independent set of samples . If the same predictability appears in an independent study, the MVA models can be considered as reliable. In fact, this is the best way to assess the robustness and predictive ability of any model, but this approach is not always possible due to the low number of samples . As alteData analysis methods in metabolomics are mostly based on multivariate analysis, though univariate methods are also used to extract the statistical meaning of the variations observed according to a critical threshold . Althougnull hypothesis (H0) postulates a null difference between the mean (or median) of the areas or concentrations of the metabolites detected in the populations under study . The probability of null hypothesis rejection is calculated , and if it is below the threshold of probability  may occur, which is particularly undesirable in a metabolomics study [p-values must be corrected. Two possible ways used in metabolomics to deal with multiple testing problems are the Bonferroni and false discovery rate (FDR) corrections. Bonferroni’s correction is extremely conservative, and its use increases type II errors , which can result in the loss of many features of interest [In untargeted metabolomic studies, the number of parallel univariate tests depends on the number of cs study . To avoiinterest . In retuinterest .Translating variables into metabolite identities is one of the main bottlenecks in the untargeted metabolomics approach. Nevertheless, this is an essential prerequisite to integrate data from multiple studies (metabolomic studies or in conjunction with other omics data) and perform an adequate biological interpretation . In mostTaking into account that the identification process is very time-consuming, it is often carried out only after data analysis for the compounds that have undergone significant changes .After the discriminating metabolites are identified, the potentially affected metabolic pathways should be studied. Several databases are available for this purpose: Kyoto Encyclopedia of Genes and Genomes (KEGG) , MetaboAIn the past few years, metabolomics has become a highly versatile tool, creating a new era of opportunities for toxicological research. It has been considered an important area of research that can help to unveil several questions left unanswered by other omics and/or traditional approaches. This topic will focus on the most recent applications of metabolomics in toxicological research.tert-butyl hydroperoxide). Several metabolites have been identified and linked to each toxicity mechanism. Metabolites associated with glutathione and γ-glutamyl cycle were suggested as biomarkers of oxidative stress, and were found altered after exposure to all compounds. In turn, phospholipidosis was characterized by a possible inhibition of phospholipid degradation, while steatosis was related to the increase in triacylglyceride synthesis. Unique metabolomics fingerprints associated with the different mechanisms were used to develop a predictive model with a satisfactory predictive power (area under curve (AUC) of 0.97) for tracking and classifying hepatotoxicity based on the modes of action of the compounds.With increasing demands to reduce drug development time and costs, one of the most important research and development goals in the pharmaceutical industry is the selection of robust new drug candidates with few adverse effects. If a new drug can be screened for adverse toxicity before reaching clinical trials, companies can reduce costs that these trials entail . ThroughN-acetylglycoprotein, pyruvate, and uric acid.In addition to deciphering discriminant mechanism-specific metabolic signatures, it is also possible to identify biomarkers for drug-induced organ toxicity, with several matrices being useful in these kind of metabolomics studies. For example, Shi et al.  used NMRSeveral studies have also been performed in attempts to identify biomarkers of drug-induced nephrotoxicity. Hanna et al.  studied 1H-NMR, revealing that acetate and succinate could be useful as cardiotoxicity biomarkers. Additionally, the authors also showed that oleuropein, a phenolic antioxidant present in the olive trees with documented cardioprotective effects, restored the changes of metabolites to the normal levels. More recently, Li et al. [To a lesser extent, metabolomics has been used to study drug-induced cardiotoxicity and neurotoxicity. One of the first metabolomics experiment in the field of cardiotoxicity was carried out by Andreadou et al.  to studyi et al.  investigStudies on drug-induced neurotoxicity are still very limited, although in vitro/in vivo experiments have already demonstrated the benefits and utility of metabolomics to comprehensively detect and characterize neurotoxicity and discover new biomarkers ,135,136.Toxicometabolomics has grown far beyond its initial preclinical and clinical applications. A rising number of studies showed that metabolomics is a powerful tool for elucidating biochemical modes of action and toxicological effects of a wide range of toxic compounds in a variety of toxicology domains, including food safety ,138, envToxicological research has made significant advances since the advent of metabolomics. Over the last few years, a large amount of data has been generated using metabolomic approaches, resulting in the identification of several potential biomarkers associated with susceptibility, exposure, and/or effects of toxicants, and a better understanding of the metabolic responses of many biological systems has been achieved.Given the lack of standardized metabolomic protocols, some toxicometabolomic studies using similar toxic agents, species, and biological specimens resulted in disparate conclusions. To further increase the reproducibility and translatability of metabolomic studies, it is urgent to standardize the different steps of the metabolomic workflow, including the experimental design and statistical analysis, as these steps can considerably affect the obtained results. It should also be noted that the use of different models, which are frequently simplistic, as is the case of in vitro models, as well as the use of small cohorts, are potential sources of bias. Furthermore, despite the high potential of metabolomic approaches in hypothesis generation and biomarker discovery, the translation of these findings to the real world remains low. Preliminary metabolomic results must be validated, and for that, putative biomarkers identified through metabolomics studies must be accurately and precisely measured in larger groups of individuals. Special consideration should also be given to data stored in databases and biobanks, which must be significantly expanded and improved.The use of metabolite ratios to successfully compare datasets from different studies is another important recommendation to take into account in future studies, as the ratios between related metabolite pairs reduce overall noise and biological variability in the dataset. More importantly, metabolite ratios show the flux through a metabolic pathway when a metabolite pair is coupled by that pathway. However, measuring metabolic flux with stable isotope tracers is equally important, and has already been used successfully in metabolomics research. Tracers based on stable isotopes can reveal which parts of a metabolic network are in use.In conclusion, while there are still some bottlenecks in metabolomics, it is expected that with continuous optimization and improvement of research methods, the use of metabolomics to uncover previously unknown information will become more accurate and efficient, creating a powerful tool for improving human health and environmental safety.