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• W2911187837 abstract "•Amino acid biosynthesis enzymes do not normally operate at maximum capacity•Allosteric feedback inhibition ensures that enzymes are overabundant•Enzyme overabundance provides robustness against decreases in gene expression Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli. Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli. Regulation of microbial metabolism involves a wide range of mechanisms that act on different cellular layers and together control the abundance and activity of enzymes (Chubukov et al., 2014Chubukov V. Gerosa L. Kochanowski K. Sauer U. Coordination of microbial metabolism.Nat. Rev. Microbiol. 2014; 12: 327-340Crossref PubMed Scopus (319) Google Scholar). An example is end-product inhibition of amino acid biosynthesis in Escherichia coli, which can act on enzyme abundance through transcriptional regulatory cues and enzyme activities through allosteric feedback inhibition. However, since metabolic reaction rates are determined by both enzyme abundance and enzyme activity, it has been difficult to disentangle the specific roles of the two regulatory layers and to understand how they interact to control metabolism (Chubukov et al., 2013Chubukov V. Uhr M. Le Chat L. Kleijn R.J. Jules M. Link H. Aymerich S. Stelling J. Sauer U. Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis.Mol. Syst. Biol. 2013; 9: 709Crossref PubMed Scopus (121) Google Scholar, Daran-Lapujade et al., 2007Daran-Lapujade P. Rossell S. van Gulik W.M. Luttik M.A.H. de Groot M.J.L. Slijper M. Heck A.J.R. Daran J.-M. de Winde J.H. Westerhoff H.V. et al.The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels.Proc. Natl. Acad. Sci. USA. 2007; 104: 15753-15758Crossref PubMed Scopus (185) Google Scholar, ter Kuile and Westerhoff, 2001ter Kuile B.H. Westerhoff H.V. Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway.FEBS Lett. 2001; 500: 169-171Crossref PubMed Scopus (275) Google Scholar). Allosteric feedback inhibition of the committed step in biosynthetic pathways is thought to maintain homeostasis of end-products (Umbarger, 1956Umbarger H.E. Evidence for a negative-feedback mechanism in the biosynthesis of isoleucine.Science. 1956; 123: 848Crossref PubMed Scopus (206) Google Scholar), and 16 out of 20 amino acids in E. coli feedback inhibit enzymes of their own biosynthesis pathway (Reznik et al., 2017Reznik E. Christodoulou D. Goldford J.E. Briars E. Sauer U. Segrè D. Noor E. Genome-scale architecture of small molecule regulatory networks and the fundamental trade-off between regulation and enzymatic activity.Cell Rep. 2017; 20: 2666-2677Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar). The consequences of dysregulating these enzymes were mainly studied in vitro (Schomburg et al., 2013Schomburg I. Chang A. Placzek S. Söhngen C. Rother M. Lang M. Munaretto C. Ulas S. Stelzer M. Grote A. et al.BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA.Nucleic Acids Res. 2013; 41: D764-D772Crossref PubMed Scopus (320) Google Scholar) or in the context of biotechnological overproduction strains (Hirasawa and Shimizu, 2016Hirasawa T. Shimizu H. Recent advances in amino acid production by microbial cells.Curr. Opin. Biotechnol. 2016; 42: 133-146Crossref PubMed Scopus (73) Google Scholar). For the case of nucleotide biosynthesis in E. coli, a detailed in vivo study showed that removing allosteric feedback inhibition did not perturb nucleotide homeostasis (Reaves et al., 2013Reaves M.L. Young B.D. Hosios A.M. Xu Y.-F. Rabinowitz J.D. Pyrimidine homeostasis is accomplished by directed overflow metabolism.Nature. 2013; 500: 237-241Crossref PubMed Scopus (68) Google Scholar). In the absence of allosteric feedback inhibition, additional regulatory mechanisms accomplished proper control of the pathway by channeling the excess of nucleotides into degradation pathways (so-called directed overflow). Theoretical analyses, in contrast, suggest a key role of allosteric feedback inhibition in achieving end-product homeostasis (Hofmeyr and Cornish-Bowden, 2000Hofmeyr J.-H.S. Cornish-Bowden A. Regulating the cellular economy of supply and demand.FEBS Lett. 2000; 476: 47-51Crossref PubMed Scopus (166) Google Scholar), metabolic robustness (Grimbs et al., 2007Grimbs S. Selbig J. Bulik S. Holzhütter H.-G. Steuer R. The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks.Mol. Syst. Biol. 2007; 3: 146Crossref PubMed Scopus (86) Google Scholar), flux control (Kacser and Burns, 1973Kacser H. Burns J.A. The control of flux.Symp. Soc. Exp. Biol. 1973; 27: 65-104PubMed Google Scholar, Schuster and Heinrich, 1987Schuster S. Heinrich R. Time hierarchy in enzymatic reaction chains resulting from optimality principles.J. Theor. Biol. 1987; 129: 189-209Crossref PubMed Scopus (39) Google Scholar), and optimal growth (Goyal et al., 2010Goyal S. Yuan J. Chen T. Rabinowitz J.D. Wingreen N.S. Achieving optimal growth through product feedback inhibition in metabolism.PLoS Comput. Biol. 2010; 6: e1000802Crossref PubMed Scopus (36) Google Scholar). The abundance of enzymes in E. coli amino acid metabolism is mainly regulated at the level of transcription, either by transcriptional attenuation (Yanofsky, 1981Yanofsky C. Attenuation in the control of expression of bacterial operons.Nature. 1981; 289: 751-758Crossref PubMed Scopus (457) Google Scholar) or transcription factors (Cho et al., 2008Cho B.-K. Barrett C.L. Knight E.M. Park Y.S. Palsson B.O. Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli.Proc. Natl. Acad. Sci. USA. 2008; 105: 19462-19467Crossref PubMed Scopus (142) Google Scholar, Cho et al., 2012Cho B.-K. Federowicz S. Park Y.-S. Zengler K. Palsson B.Ø. Deciphering the transcriptional regulatory logic of amino acid metabolism.Nat. Chem. Biol. 2012; 8: 65Crossref Scopus (73) Google Scholar). For example, a set of four transcription factors (ArgR, TrpR, TyrR, and Lrp) control expression of 19 out of 20 amino acid pathways by sensing the availability of amino acids via allosteric binding (Cho et al., 2012Cho B.-K. Federowicz S. Park Y.-S. Zengler K. Palsson B.Ø. Deciphering the transcriptional regulatory logic of amino acid metabolism.Nat. Chem. Biol. 2012; 8: 65Crossref Scopus (73) Google Scholar). This regulation ensures that enzymes in amino acid pathways are only made when they are needed (Schmidt et al., 2016Schmidt A. Kochanowski K. Vedelaar S. Ahrné E. Volkmer B. Callipo L. Knoops K. Bauer M. Aebersold R. Heinemann M. The quantitative and condition-dependent Escherichia coli proteome.Nat. Biotechnol. 2016; 34: 104-110Crossref PubMed Scopus (409) Google Scholar, Zaslaver et al., 2004Zaslaver A. Mayo A.E. Rosenberg R. Bashkin P. Sberro H. Tsalyuk M. Surette M.G. Alon U. Just-in-time transcription program in metabolic pathways.Nat. Genet. 2004; 36: 486-491Crossref PubMed Scopus (362) Google Scholar). As a consequence of such need-based enzyme level regulation, one would expect that enzyme levels are not higher than absolutely needed for amino acid biosynthesis. However, recent data suggest that cells express the majority of enzymes at higher levels than necessary to fulfill biosynthetic demands, and that such enzyme overabundance provides a benefit in changing environments (Davidi and Milo, 2017Davidi D. Milo R. Lessons on enzyme kinetics from quantitative proteomics.Curr. Opin. Biotechnol. 2017; 46: 81-89Crossref PubMed Scopus (41) Google Scholar, O’Brien et al., 2016O’Brien E.J. Utrilla J. Palsson B.O. Quantification and classification of E. coli proteome utilization and unused protein costs across environments.PLoS Comput. Biol. 2016; 12: e1004998Crossref PubMed Scopus (71) Google Scholar). For example, enzyme overabundance enables a quick activation of the pentose phosphate pathway upon stresses (Christodoulou et al., 2018Christodoulou D. Link H. Fuhrer T. Kochanowski K. Gerosa L. Sauer U. Reserve flux capacity in the pentose phosphate pathway enables Escherichia coli’s rapid response to oxidative stress.Cell Syst. 2018; 6: 569-578.e7Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar), and similar benefits were attributed to overabundant ribosomes (Mori et al., 2017Mori M. Schink S. Erickson D.W. Gerland U. Hwa T. Quantifying the benefit of a proteome reserve in fluctuating environments.Nat. Commun. 2017; 8: 1225Crossref PubMed Scopus (56) Google Scholar) and coenzymes (Hartl et al., 2017Hartl J. Kiefer P. Meyer F. Vorholt J.A. Longevity of major coenzymes allows minimal de novo synthesis in microorganisms.Nat. Microbiol. 2017; 2: 17073Crossref PubMed Scopus (25) Google Scholar). Here, we constructed seven E. coli mutants, each with a different feedback-dysregulated amino acid biosynthesis pathway (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline), and measured their proteins, metabolites, fluxes, and growth. In all seven feedback-dysregulated pathways, the concentration of amino acid end products increased, and in five pathways, we measured lower enzyme levels. Despite the lower enzyme levels, biosynthetic flux was not limited, indicating that these enzymes are not operating at maximal capacity in wild-type cells. By combining theoretical and experimental analysis, we showed that this enzyme overabundance provides a robustness benefit against genetic perturbations in the arginine, tryptophan, and histidine pathways. To explore the function of allosteric feedback inhibition in the arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline biosynthesis pathways, we first created a panel of seven allosterically dysregulated E. coli mutants (Figure 1A; Table S1). Using a scarless CRISPR method (Reisch and Prather, 2015Reisch C.R. Prather K.L.J. The no-SCAR (Scarless Cas9 Assisted Recombineering) system for genome editing in Escherichia coli.Sci. Rep. 2015; 5: 15096Crossref PubMed Scopus (142) Google Scholar), we introduced point mutations into genes encoding the allosteric enzyme that catalyzes the committed reaction in each pathway (argA, hisG, trpE, leuA, ilvA, thrA, and proB). These mutations have been shown previously to abolish the allosteric interaction while not affecting enzyme activity, thereby allowing us to study regulation of the pathway in the absence of allosteric feedback (Caligiuri and Bauerle, 1991Caligiuri M.G. Bauerle R. Subunit communication in the anthranilate synthase complex from Salmonella typhimurium.Science. 1991; 252: 1845-1848Crossref PubMed Scopus (34) Google Scholar, Csonka et al., 1988Csonka L.N. Gelvin S.B. Goodner B.W. Orser C.S. Siemieniak D. Slightom J.L. Nucleotide sequence of a mutation in the proB gene of Escherichia coli that confers proline overproduction and enhanced tolerance to osmotic stress.Gene. 1988; 64: 199-205Crossref PubMed Scopus (51) Google Scholar, Doroshenko et al., 2013Doroshenko V.G. Lobanov A.O. Fedorina E.A. The directed modification of Escherichia coli MG1655 to obtain histidine-producing mutants.Appl. Biochem. Microbiol. 2013; 49: 130-135Crossref Scopus (5) Google Scholar, Gusyatiner et al., 2005Gusyatiner, M.M., Ivanovskaya, L.V., Kozlov, Y.I., Lunts, M.G., and Voroshilova, E.B. (2005). DNA coding for mutant isopropylmalate synthase, l-leucine-producing microorganism and method for producing l-leucine. US patent application publication 6,403,342 B1, filed July 10, 2000, and published June 11, 2002.Google Scholar, LaRossa et al., 1987LaRossa R.A. Van Dyk T.K.V. Smulski D.R. Toxic accumulation of alpha-ketobutyrate caused by inhibition of the branched-chain amino acid biosynthetic enzyme acetolactate synthase in Salmonella typhimurium.J. Bacteriol. 1987; 169: 1372-1378Crossref PubMed Scopus (108) Google Scholar, Lee et al., 2003Lee J.H. Lee D.E. Lee B.U. Kim H.S. Global analyses of transcriptomes and proteomes of a parent strain and an L-threonine-overproducing mutant strain.J. Bacteriol. 2003; 185: 5442-5451Crossref PubMed Scopus (83) Google Scholar, Rajagopal et al., 1998Rajagopal B.S. DePonte J. Tuchman M. Malamy M.H. Use of inducible feedback-resistant N-acetylglutamate synthetase (argA) genes for enhanced arginine biosynthesis by genetically engineered Escherichia coli K-12 strains.Appl. Environ. Microbiol. 1998; 64: 1805-1811PubMed Google Scholar). For N-acetylglutamate synthase (ArgA), we confirmed with in vitro assays that the mutation does not affect enzymatic activity and abolishes inhibition by arginine (Figure S1). To analyze the metabolism of the mutants we quantified intracellular metabolites during exponential growth on glucose by liquid chromatography-tandem mass spectrometry (LC-MS/MS) (Guder et al., 2017Guder J.C. Schramm T. Sander T. Link H. Time-optimized isotope ratio LC-MS/MS for high-throughput quantification of primary metabolites.Anal. Chem. 2017; 89: 1624-1631Crossref PubMed Scopus (35) Google Scholar). Stronger metabolic changes were restricted to amino acid biosynthesis, with specific increases between 2- and 16-fold of only the amino acid products of the dysregulated pathways (Figure 1B). Despite these changes within the dysregulated pathways, the remaining amino acid concentrations as well as the global metabolite profile remained relatively stable (Figures 1B and S2). Thus, dysregulating allosteric enzymes in E. coli amino acid biosynthesis elevated the intracellular concentration of the corresponding amino acid product. With the exception of proline biosynthesis, all of the dysregulated pathways are additionally controlled at the layer of enzyme abundance via either transcription factors or transcriptional attenuation. To probe if elevated amino acid concentrations in our mutants affected enzyme levels in the corresponding pathways we measured their proteomes (Figure 2A). The data covered relative abundances of 173 out of the 204 enzymes annotated to amino acid metabolism in the latest E. coli metabolic model (Monk et al., 2017Monk J.M. Lloyd C.J. Brunk E. Mih N. Sastry A. King Z. Takeuchi R. Nomura W. Zhang Z. Mori H. et al.iML1515, a KnowledgeBase that computes Escherichia coli traits.Nat. Biotechnol. 2017; 35: 904-908Crossref PubMed Scopus (226) Google Scholar). Enzyme expression was indeed lower in five of the seven dysregulated pathways (argA∗, trpE∗, hisG∗, leuA∗, and thrA∗), indicating that the elevated amino acid concentrations caused a compensatory downregulation of their associated pathway (Figure 2A). Enzyme levels did not change in the proB∗ and ilvA∗ mutants, which is expected because proline biosynthesis lacks enzyme level regulation and isoleucine biosynthesis is subject to a second allosteric feedback that was not removed (Figures 1A and 2A). The leuA∗ mutant showed more global changes in enzyme levels compared to the other mutants. The high leucine concentration in this strain likely activates the leucine responsive transcription factor Lrp, which acts on many genes in amino acid metabolism (Cho et al., 2008Cho B.-K. Barrett C.L. Knight E.M. Park Y.S. Palsson B.O. Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli.Proc. Natl. Acad. Sci. USA. 2008; 105: 19462-19467Crossref PubMed Scopus (142) Google Scholar). In the argA∗ mutant we observed an expected accompanying decrease in histidine biosynthesis enzymes, which are additional targets of the transcription factor ArgR (Gama-Castro et al., 2016Gama-Castro S. Salgado H. Santos-Zavaleta A. Ledezma-Tejeida D. Muñiz-Rascado L. García-Sotelo J.S. Alquicira-Hernández K. Martínez-Flores I. Pannier L. Castro-Mondragón J.A. et al.RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.Nucleic Acids Res. 2016; 44: D133-D143Crossref PubMed Scopus (315) Google Scholar). Apart from the compensatory downregulation of biosynthetic enzymes, enzymes in dedicated amino acid degradation pathways were upregulated in three mutants (AstC in the arginine mutant, TnaA in the tryptophan mutant, and PutA in the proline mutant, Figure 2A). This likely constitutes an additional compensatory mechanism similar to the directed overflow reported for nucleotides (Reaves et al., 2013Reaves M.L. Young B.D. Hosios A.M. Xu Y.-F. Rabinowitz J.D. Pyrimidine homeostasis is accomplished by directed overflow metabolism.Nature. 2013; 500: 237-241Crossref PubMed Scopus (68) Google Scholar). To obtain additional evidence for lower enzyme levels in the dysregulated pathways, we used GFP-promoter fusions and measured fluorescence in single cells (Figure 2B). GFP expression from an ArgR-regulated promoter was indeed ∼3-fold lower in the argA∗ mutant compared to the wild-type. Similarly, a TrpR-regulated promoter was ∼3-fold stronger repressed in the trpE∗ mutant. The cell-to-cell variation in GFP content was similar in wild-type cells and the mutants, thus indicating that all cells in the population of allosteric feedback mutants have lower enzyme levels in the dysregulated pathway. A GFP reporter with the thrL leader peptide was only 17% repressed in the thrA∗ mutant compared to the wild-type, which is consistent with the small decrease of enzyme levels in the dysregulated threonine pathway (Figures 2A and 2B). We also fused GFP to the hisL and leuL leader peptides, but they did not report repression by amino acids even when they were added to the medium (Figure S3). Probably, transcriptional attenuation by hisL and leuL requires the genomic context and cannot function on plasmids. In summary, proteome data revealed a lower expression of enzymes for five of the seven dysregulated pathways (argA∗, trpE∗, hisG∗, leuA∗, and thrA∗). GFP-promoter fusions confirm this enzyme level regulation at the single-cell level and indicate that downregulation of enzymes in the argA∗, trpE∗, and thrA∗ mutants occurs at the transcriptional layer. Next, we wondered if lower expression of enzymes limits the biosynthetic capacity of the mutants. First, we tested steady-state growth on glucose minimal medium and seven other carbon sources (Figure S4). All mutants showed wild-type like growth, except the leuA∗ mutant, which grew on average 10% slower than the wild-type. To test if lower enzyme levels affect biosynthetic capacity in dynamics shifts, we starved cells for carbon and measured growth resumption on glucose minimal medium (Figure 3A). During the initial phase of growth resumption all mutants had the same growth rate as the wild-type. Only the leuA∗, ilvA∗, and thrA∗ mutants reached lower growth rates than the wild-type during the subsequent 4 hr. The three strains had also lower ODs after 20 hr starvation. Similarly, nutritional up- and downshifts between glucose and galactose had only a tangible effect on the growth of the leuA∗, ilvA∗, and thrA∗ mutants during the downshift (Figure S5). The three strains with the highest reduction in enzyme levels (argA∗, trpE∗, and hisG∗) grew like the wild-type in all tested conditions, indicating that biosynthetic capacity is not limited by lower enzyme levels. The advantage of lower protein costs in these pathways was either too subtle to be detected by growth assays or counterbalanced by negative effects of feedback dysregulation. To directly probe biosynthetic capacity, we traced intracellular fluxes of amino acids with 15N labeling experiments (Figure 3B). Labeling of arginine, tryptophan, and proline was similar in the respective mutant and the wild-type, whereas histidine, (iso)-leucine, and threonine labeled slower in the mutants. However, it is important to consider that labeling rates depend on fluxes and absolute pool sizes of amino acids. Because amino acid pools were higher in the mutants, we used a method for quantitative analysis of the labeling profiles to estimate fluxes (Yuan et al., 2008Yuan J. Bennett B.D. Rabinowitz J.D. Kinetic flux profiling for quantitation of cellular metabolic fluxes.Nat. Protoc. 2008; 3: 1328-1340Crossref PubMed Scopus (199) Google Scholar). To account for unknown labeling profiles of upstream nitrogen precursors, we calculated fluxes for a wide range of precursor labeling rates in the literature (Yuan et al., 2006Yuan J. Fowler W.U. Kimball E. Lu W. Rabinowitz J.D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli.Nat. Chem. Biol. 2006; 2: 529-530Crossref PubMed Scopus (105) Google Scholar). The flux estimates show that none of the mutants had lower flux through the dysregulated pathways than the wild-type (Figure 3B). In most cases, biosynthetic flux was even higher, indicating that downregulation of enzyme levels could not fully compensate the loss of allosteric feedback inhibition in some of the mutants. This might be the reason for the growth phenotype of the leuA∗, ilvA∗, and thrA∗ mutants in dynamic growth experiments (Figure 3A). In conclusion, the feedback-dysregulated mutants showed the same or higher flux through the dysregulated amino acid pathways than wild-type cells, although in five mutants (argA∗, trpE∗, hisG∗, leuA∗, and thrA∗) enzyme levels in the dysregulated pathway were lower. Especially, the argA∗, trpE∗, and hisG∗ mutants, which had ∼2-fold lower enzyme levels in the dysregulated pathways compared to the wild-type, showed 1- to 2-fold higher fluxes and normal growth. This indicates that these enzymes are not operating at maximal capacity in wild-type E. coli during growth on glucose. We then hypothesized that this enzyme overabundance emerges from allosteric feedback inhibition by maintaining a low concentration of end-products, which in turn increases production of enzymes (e.g., by de-repression of transcription). Next, we explored this interplay between control of enzyme activity and enzyme abundance and its relevance for cellular metabolism. To obtain a better mechanistic understanding of the interplay between allosteric feedback inhibition and enzyme level regulation, we developed a kinetic model of metabolism and enzyme expression (Figure 4A). Briefly, the model includes two enzymes, e1 and e2, and two metabolites, m1 and m2, in a two-step pathway. The end-product m2 represents an amino acid, which is consumed in the last reaction for protein synthesis and growth. The end-product m2 feedback inhibits the expression of both enzymes as well as the activity of the first enzyme. The first reaction and the expression of both enzymes follow simple inhibition kinetics, whereas the second reaction follows Michaelis-Menten kinetics (Figure 4A). As such, this model is a simplified representation of an amino acid biosynthesis pathway that is controlled at two layers (Figure 1A). As a starting point for the model analysis, we fixed the flux in the pathway to the amino acid requirement given by the growth rate of E. coli on glucose. We randomly sampled seven model parameters (maximal rates and binding constants) 5,000 times from physiologically meaningful ranges based on literature values (Davidi and Milo, 2017Davidi D. Milo R. Lessons on enzyme kinetics from quantitative proteomics.Curr. Opin. Biotechnol. 2017; 46: 81-89Crossref PubMed Scopus (41) Google Scholar, Li et al., 2014Li G.-W. Burkhardt D. Gross C. Weissman J.S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources.Cell. 2014; 157: 624-635Abstract Full Text Full Text PDF PubMed Scopus (783) Google Scholar, Milo et al., 2010Milo R. Jorgensen P. Moran U. Weber G. Springer M. BioNumbers—the database of key numbers in molecular and cell biology.Nucleic Acids Res. 2010; 38: D750-D753Crossref PubMed Scopus (582) Google Scholar). For each of the thus derived 5,000 parameter sets, we calculated concentrations of e1, e2, m1, and m2, for a model including feedback on enzyme activity and enzyme abundance (complete model, gray in Figure 4B), and also for a model including only feedback on enzyme abundance (single feedback model, blue in Figure 4B). The simulated concentrations of e1, e2, m1, and m2 matched qualitatively the measured protein and metabolite data: the two enzymes decreased in the single feedback model (Figure 2A), whereas the end-product m2 increased (Figure 1B). Also, the simulated concentration of the intermediate m1 matched the measured increase of intermediates in amino acid pathways (Figure S6). Thus, a simple model confirms our hypothesis that allosteric feedback inhibition enforces enzyme overabundance. In theory, other types of enzyme inhibition could cause a similar increase in enzyme expression. To test this, we replaced the allosteric feedback in the model with competitive product inhibition of the second reaction (Figure S7). However, removing competitive product inhibition was compensated by lower substrate concentrations (m1) and not by lower enzyme levels. This model result indicates that enzyme overabundance does not emerge from all types of enzyme inhibition. Next, we set out to investigate the function that emerges from the interplay between feedback on enzyme activity and enzyme abundance. While low enzyme levels are obviously advantageous due to lowering protein cost, high enzyme levels could provide a cellular benefit by improving robustness against perturbations in enzyme expression. To test this with the model, we made use of a numerical parameter continuation method to quantify robustness (Lee et al., 2014Lee Y. Lafontaine Rivera J.G. Liao J.C. Ensemble modeling for robustness analysis in engineering non-native metabolic pathways.Metab. Eng. 2014; 25: 63-71Crossref PubMed Scopus (69) Google Scholar). This method iteratively decreases a model parameter until instabilities occur in the model. Robustness can then be defined as the percentage change of this parameter that is tolerated. Using this method, we calculated robustness against perturbations of the maximal expression rate of the second enzyme (β2,max) in the complete model with 5,000 randomly sampled parameter sets (Figure 4C). Changing β2,max reflects genetic or environmental perturbations of gene expression that can lead to a bottleneck in the pathway. Consistent with our expectations, models with high enzyme levels showed increased robustness, while models with lower enzyme levels were more sensitive to perturbations of enzyme expression (Figure 4C). However, robustness was not proportional to the enzyme level: a relatively small increase of enzyme levels already conferred a large robustness benefit. Very high enzyme levels, in comparison, did not increase robustness substantially over more subtle changes in enzyme abundance. Our model thus reveals a tradeoff between protein costs and robustness, which can be solved by sensitively balancing enzyme levels. The optimal balance of enzyme levels occurs in models occupying the middle of the tradeoff frontier: those models with equally strong feedback on enzyme activity and enzyme abundance (indicated by similar inhibition constants Ki, black dots in Figure 4C). We then wondered if amino acid biosynthesis in E. coli operates in the middle of the tradeoff frontier, meaning that both feedbacks are simultaneously active. In particular, enzyme levels in the argA∗, trpE∗, and hisG∗ mutants demonstrated that wild-type E. coli does not operate with minimal enzyme levels in these pathways (blue dots in Figure 4C). To test if enzymes in these pathways are maximally expressed (orange dots in Figure 4C), we removed their transcriptional regulation, which functions by different mechanisms: a transcription factor (arginine), transcriptional attenuation (histidine), or both (tryptophan). In the arginine and tryptophan pathway, we deleted the respective transcription factor (ΔargR and ΔtrpR), and in histidine biosynthesis we removed the leader peptide hisL. Removing transcriptional regulation of all three p" @default.
• W2911187837 created "2019-01-25" @default.
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• W2911187837 date "2019-01-01" @default.
• W2911187837 modified "2023-10-11" @default.
• W2911187837 title "Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance" @default.
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