Escherichia coli Δ321AM by precisely balancing glycolysis and N-acetylglucosamine production, resulting in a 4.54-fold increase in titer. GCE-CGBBE is further expanded to non-genome-recoded Bacillus subtilis to balance growth and N-acetylneuraminic acid bioproduction by titrating essential gene expression, yielding a 2.34-fold increase in titer. Moreover, the development of ncAA-dependent essential gene expression regulation shows efficient biocontainment of engineered B. subtilis to avoid unintended proliferation in nature.Metabolic engineering facilitates chemical biosynthesis by rewiring cellular resources to produce target compounds. However, an imbalance between cell growth and bioproduction often reduces production efficiency. Genetic code expansion (GCE)-based orthogonal translation systems incorporating non-canonical amino acids (ncAAs) into proteins by reassigning non-canonical codons to ncAAs qualify for balancing cellular metabolism. Here, GCE-based cell growth and biosynthesis balance engineering (GCE-CGBBE) is developed, which is based on titrating expression of cell growth and metabolic flux determinant genes by constructing ncAA-dependent expression patterns. We demonstrate GCE-CGBBE in genome-recoded  E. coli and N-acetylneuraminic acid in B. subtilis.An imbalance between cell growth and bioproduction of engineered microbes often reduces production efficiency. Here, the authors report genetic code expansion-based cell growth and biosynthesis balance engineering to achieve high levels production of N-acetylglucosamine in  High-level bioproduction of 1,3-propanediol and 1,4-butanediol demonstrates the success of bio-based chemical production7. However, metabolic fluxes in target chemical biosynthetic pathways need to be shifted from cellular endogenous metabolism for cell growth to bioproduction, which conflicts with a cell’s need for ensuring survival3. When redirection of target metabolic pathways is insufficient, abundant substrate is mainly used for cell growth, which is often accompanied by by-product synthesis, resulting in low production yield and titer. In contrast, excessive shunting affects cell growth, and subsequently reduces productivity  formation13. Moreover, parallel metabolic pathway engineering could be limited by precursor availability depending on multiple endogenous pathways, owing to an increased complexity of pathway designs, and the metabolic burden of introducing multiple substrate utilization pathways24. Designing a generic and robust cell growth and biosynthesis balancing strategy is challenging.Efforts have been made to solve these issues through various metabolic engineering strategies, such as de-coupling cell growth with biosynthesis based on genetic circuits, co-coupling cell growth with biosynthesis by coupling production to overall energy and biomass formation, and parallel metabolic pathway engineering via separating bioproduction from cell growth by implementing parallel dual carbon sources pathways25. More than 200 ncAAs can be site-specifically incorporated into proteins, thereby expanding the genetic code of microorganisms, which lays the foundation for expanding functional diversity of proteins and the construction of orthogonal protein translation systems  is least frequently used in bacteria, and it has only one corresponding release factor; therefore, it is usually used in orthogonal tRNA design to minimize direct impacts on the expression levels of non-target genes. Recent progress in genome engineering and writing has enabled the construction of genetically recoded Escherichia coli, including E. coli with all amber stop codons replaced by ochre stop codons (TAA), or with a 61-codon genome (serine codons (TCG and TCA) and amber stop codons were replaced by their synonyms AGC, AGT, and TAA, respectively), which provides a favorable chassis cell for applying GCE-based orthogonal translation systems by reassigning free codons to ncAAs31. The expression of genes with amber stop codon insertions requires ncAAs, and their expression levels are presumed ncAA-dependent. At the same time, tunable translation control tools using site-specific ncAA incorporation have been studied34. Therefore, by inserting amber stop codons into genes that are important for controlling cell growth, such as genes involved in central carbon metabolic pathways, should be regulated by ncAA abundances. Further titration of ncAAs to determine optimum concentrations to precisely control cell growth rates can potentially be used for balancing cell growth and bioproduction -based orthogonal protein translation system introduces orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs into a host to incorporate non-canonical amino acids (ncAAs) rather than the 20 proteinogenic amino acids into proteins by reassigning non-canonical codons to ncAAsems Fig. 25–29. Thion Fig. .E. coli and non-genome-recoded Bacillus subtilis, two of the best-studied Gram-negative and Gram-positive bacteria, respectively. We demonstrate GCE-CGBBE in genome-recoded E. coli by precisely balancing glycolysis and N-acetylglucosamine (GlcNAc) production. We further expand GCE-CGBBE to non-genome-recoded B. subtilis to improving N-acetylneuraminic acid (NeuAc) production. In addition, the development of ncAA-dependent essential gene expression regulation shows efficient biocontainment of engineered B. subtilis.In this study, a GCE-based cell growth and biosynthesis balancing engineering (GCE-CGBBE) strategy for efficient bioproduction is developed, which is based on titrating expression of cell growth and metabolic flux determinant genes by constructing ncAA-dependent expression patterns Fig. . A transE. coli35. Therefore, the widely used pEVOL-based ncAA/ p-acetyl-L-phenylalanine (pAcF) incorporation system was selected, which is specific for translating amber stop codons and have only limited activity for other 20 proteinogenic amino acids35. In our pAcF incorporation system, we selected the E. coli ∆321AM strain, which has been genetically recoded by (1) exchanging all 321 amber stop codons in the genome with ochre stop codons for translation termination; (2) knockout of peptide chain release factor RF1 to avoid competition between RF1 and the orthogonal tRNA for efficient translation; (3) recovering almost the same cell growth rate as wild type E. coli via adaptive evolution36. Therefore, E. coli Δ321AM was suitable for specifically using the amber stop codon for incorporating ncAAs.The orthogonal translation system for ncAA incorporation is composed of mutant aaRS/tRNA pairs for translating non-canonical codons, which has been well established in tac promoter. By replacing one or two original codons from second to seventh position with amber stop codons (six mutants with a single amber stop codon replacement (2TAG to 7TAG), and five mutants with double adjacent amber stop codons replacement (23TAG to 67TAG)), it was found that the pEVOL-pAcF system can enhance gene expression by 6.48-fold and 7.65-fold in E. coli Δ321AM when the second (2TAG) and fourth (4TAG) codon of the GFP coding sequence are replaced, respectively. Furthermore, we selected the gene of 2TAG GFP to test the regulation range of gene expression by adding different concentrations of pAcF (0.001–5 mM). With increasing pAcF concentration, the fluorescence intensity increased logarithmically, achieving a 9.01-fold enhancement of gene expression  under the control of the Pion Fig. . In addiE. coli is provided with glucose in excess, high growth rates cause an overflow of the glycolytic pathway, accompanied by acetate synthesis and ATP production37. Although ATP produced in the acetate synthesis pathway may be beneficial for intracellular thermodynamically unfavorable reactions, thereby improving cell growth, acetate accumulation seriously impairs cell growth and substrate to target product conversion , and by strengthening the GlcNAc biosynthetic pathway through integrated expression and promoter optimization of three pathway genes, i.e., glmS, GNA1, and yqaB, which originated from E. coli K-12 glmS mutant 2123-72 with a moderately strong promoter (P566), Saccharomyces cerevisiae S288C GNA1 with a strong promoter (Ptac), and E. coli K-12 yqaB with a strong promoter (Ptac), respectively  of the pfkA coding sequence in the genome of the engineered strain. At the same time, in order to increase transcription and subsequently reduce the working concentration of pAcF, we replaced the promoter of pfkA with strong P224 and Ptac promoters, yielding GNAMp224-pEVOL and GNAMptac-pEVOL strains, respectively. Therefore, pfkA expression could be regulated by titrating pAcF in the medium, further fine-tuning the carbon flux between glycolytic and GlcNAc biosynthetic pathways.In order to verify the above hypothesis, the E. coli without balanced growth and bioproduction, both cell growth and GlcNAc biosynthesis were greatly improved with decreased acetate. The GNAMptac-pEVOL strain grown with 0.01 mM pAcF resulted in a 2.06-fold increase in maximal OD600 (25.21), 83.75% decreased acetate levels (5.54 vs. 0.90 g/L), and a 4.54-fold increased GlcNAc titer (12.77 g/L) with 0.48 gGlcNAc/gglucose, reaching 88.52% of the theoretical pathway yield  incorporation tool for B. subtilis, pBUA, was first constructed and tested. We selected an aaRS/tRNA mutant pair derived from Methanococcus jannaschii that can incorporate OMeY into amber stop codons25. The coding gene of the mutant aaRS/tRNA pair for OMeY incorporation was inserted into pHT01 plasmid for expression. Accordingly, successful expression of genes with amber stop codon insertion should be observed in the presence of OMeY. Therefore, efficiency of the OMeY incorporation tool (pBUA) was tested by comparing fluorescence intensities of engineered B. subtilis harboring a GFP gene with or without amber stop codon substitution. The higher the percentage of GFP gene expression with TAG substitution compared to that without TAG substitution, the more efficient the OMeY incorporation tool. The first version of pBUA tools (pBUA-P1 to pBUA-P5) was constructed based on the pHT01 plasmid by integrating aaRS and mutant tRNA gene copies under the control of P43 or one of five original B. subtilis tRNA promoters (P1 to P5), respectively  based on pBUA-P1 by random A, T, G, and C insertions 10 bp upstream of the tRNA coding sequence, 20 bp upstream of the tRNA coding sequence, and 15 bp between the −35 and −10 regions of promoter P1 via degenerate primer design and PCR. After screening 6,000,000 cells using flow cytometry, and further characterization of 1000 strains obtained from each library, the most efficient pBUA mutant in all libraries was finally obtained , and five mutants with double adjacent amber stop codon replacements (23TAG to 67TAG)), it was found that pEBUA-P224 can activate up to 77.75-fold gene expression when the second and third codons of the GFP coding sequence were replaced (23TAG), while GFP expression was difficult to detect in the absence of OMeY, demonstrating that the corresponding tRNA was charged exclusively with the reassigned amino acid by an aaRS that did not interact with any other tRNA. In parallel, when the third codon was replaced with TAG, the expression level of GFP could be maximally rescued, reaching 58.50% of GFP without TAG codon insertion. Furthermore, we selected the gene of 3TAG GFP and tested the regulation range of gene expression by adding different concentrations of OMeY (0.001–3 mM). Fluorescence intensity increased with increasing OMeY concentration, achieving 24.82-fold activation of gene expression , two-component response regulator  and phosphatidate cytidylyltransferase (phospholipids biosynthesis), respectively. Precise regulation of the expression levels of these three genes can theoretically slow cell division and further control cell growth rate. To test the effect of OMeY on growth regulation, the growth curves of three engineered and control strains were first determined at different concentrations of OMeY, and specific growth rates were calculated  in 24-well plate . Moreover, NeuAc production of NABd8-SpP566 on cell reached 0.155 g/L/OD600, which was 4.43-fold of NAB-SpP566  is one of the major concerns for the biosafety of metabolic engineering research and industrial applications, which may pose potential threats to the survival of environmental microorganisms, plant growth, human and animal health, and may cause gene leakage by horizontal gene transferlth Fig. 47. TheseB. subtilis cell morphology was normal and whether the cell wall was damaged, cellular morphology was examined. In the presence of OMeY, the cell morphology was essentially the same as that of the control strain, NAB, except that NABd8 cells were longer than the control strain. In the absence of OMeY, the cell wall was ruptured, accompanied by content leakage , the first ever developed for B. subtilis, expanded ncAA-incorporated protein production to B. subtilis, which will potentially prove useful due to its strong protein secretion ability48. Given that the GCE-CGBBE strategy is pathway-independent and inactive without ncAAs that do not naturally occur in natural growth environments, it should hold generic and robust features for other important biochemical production in engineered bacteria.Fine-tuning cell growth and biosynthesis are important for efficient bioproduction. To precisely balance these processes, a GCE-based orthogonal protein translation system was introduced into genetically recoded 49.Because only trace amounts of ncAAs need to be added to balance cell growth and bioproduction, GCE-CGBBE should be economically feasible. We calculated the costs of two selected ncAAs for large-scale fermentation. For pAcF, it would cost ~$14 per ton of fermentation broth at an optimal initial concentration of 0.01 mM. In parallel, for OMeY, it would cost ~$27 per ton of fermentation broth with an optimal initial concentration of 0.1 mM  and Gram-positive (B. subtilis) bacteria, yielding 4.54-fold and 2.34-fold increased titers, respectively. In addition, the developed ncAA-dependent essential gene expression regulation shows efficient biocontainment of engineered B. subtilis, which can prevent its unintended proliferation during metabolic research and industrial application. Because the GCE-CGBBE strategy is pathway-independent and does not interfere with ncAAs that do not naturally occur in any medium or growth environment, it should hold generic and robust features for additional important biochemical production in engineered bacteria.Overall, the GCE-CGBBE strategy was developed as a generic approach for balancing bacterial growth and chemical biosynthesis for efficient bioproduction. Proof-of-concept applications for metabolic engineering revealed increased GlcNAc and NeuAc production in the best-studied Gram-negative  was constructed by fusion PCR . Then the homologous recombination cassette was transformed into a strain harboring pBUA-P224 and the bacterial suspension was plated on a plate containing 1 mM OMeY and 100 mg/L spectinomycin. After 16–24 h plate incubation, all colonies were further screened with OMeY-free LB medium, in which OMeY-dependent B. subtilis could not grow successfully. And if there are no colonies on the plate or no positive transformants are obtained after three experiments, it is considered that the essential genes cannot be used to construct OMeY-dependent strains.Construction of OMeY-dependent All plasmids and strains used in this study are shown in Supplementary Data E. coli, TMM 2SO4, 6 g/L urea, 0.025 g/L CaCl2·H2O, 0.1 mg/L CoCl2·H2O, 0.1 mg/L CuSO4·5H2O, 5 mg/L FeSO4·7H2O, 0.33 mg/L MnSO4·H2O, 3.8 mg/L ZnSO4·7H2O, pH 7.0, adjusted using NH4OH. To produce NeuAc by fermentation in B. subtilis, BFM 2SO4, 12.5 g/L K2HPO4·4H2O, 2.5 g/L KH2PO4, 3 g/L MgSO4·7H2O, and 60 g/L glucose, pH 7.0, adjusted using NH4OH.For cell culture, Luria–Bertani (LB) medium  was used unless noted otherwise. To produce GlcNAc by fermentation in 600 reached 1. Subsequently, 10 μL of the culture were similarly inoculated into 96-well black plates (Corning 3603) containing 190 μL fresh LB medium, cultured at 37 °C with shaking at 750 rpm, and fluorescence intensities and cell growth curves were determined.Cell culture using 96-well plates: first, 96-well deep-well plate with 300 μL fresh LB medium per well, which were then cultured for 10 h at 37 °C with shaking at 750 rpm. Next, 10 µL of the culture were inoculated in a 96-well microtiter plate containing 190 µL fresh LB medium with different concentrations of ncAA (pAcF or OMeY) and incubated at 37 °C with shaking at 750 rpm until ODB. subtilis at different OMeY concentrations, a 96-well plate was used to reduce the effect of the sampling process on cell growth. OMeY-dependent B. subtilis were first cultured in fresh LB medium supplemented with 1 mM OMeY for 10 h at 37 °C and 220 rpm. Before inoculation with different concentrations of OMeY, the bacterial suspension was washed to reduce residual OMeY inside and outside the cells. Then the bacterial suspension was first centrifuged at 5000 × g for 3 min and then resuspended in fresh OMeY-free LB medium. After performing this step twice, the bacterial suspension was incubated for 1 h at 37 °C with shaking at 220 rpm. Next, the bacterial suspension was centrifuged again at 5000 × g for 3 min, and resuspended in fresh LB medium. Other operations and detection methods were the same as those used for culturing cells in a 96-well plate.For determining the growth curves of OMeY-dependent E. coli, the colonies on the plates were first picked into 3 mL fresh LB medium and cultured for 9 h at 37 °C and 220 rpm. Then, 15 μL of bacterial suspension was inoculated into a 24-well deep-well plate containing 1.5 mL of TMM medium per well, and fermentation was performed at 37 °C and 220 rpm for 33 h. For OMeY-dependent B. subtilis, the colonies on the plates were first picked into 1.5 mL fresh BFM medium with 1 mM OMeY. Before inoculation, the bacterial suspension was washed to remove additional OMeY; the culture was centrifuged at 5000 × g for 3 min and resuspended in 3 mL fresh BFM medium, which was repeated twice. Thereafter, 15 μL of bacterial suspension was inoculated into a 24-well deep-well plate containing 1.5 mL of fermentation medium per well, and fermentation was performed at 37 °C and 220 rpm for 60 h. A breathable sterile film was placed on the 24-well plate to ensure sufficient oxygen supply. At 30 h of fermentation, 1.5 mL of the culture was concentrated to ~1.3 mL, and at 60 h of fermentation, 1.5 mL of the culture was concentrated to ~1.1 mL.Fermentation experiments performed in 24-well deep-well plates: For 4)2SO4, 13 g/L K2HPO4·4H2O, 3 g/L KH2PO4, 3.5 g/L MgSO4·7H2O, 2 g/L NaCl, 12 g/L urea, and 30 g/L initial glucose. First, colonies were picked from the plates and inoculated into baffled 500-mL shake flasks containing 50 ml of BFM medium, which was then cultured at 37 °C with shaking at 220 rpm until OD600 reached 15. Next, 100 mL of the seed was inoculated into a 3-L fermenter  with an initial 1.8 L of fermentation medium. Keep pH at 7.1 using 15% NH3 and maintain glucose concentration within 10–30 g/L by feeding with 700 g/L glucose throughout the fermentation period. The temperature was maintained at 37 °C, the aeration rate was maintained at 1.5 v.v.m. and the agitation speed was maintained at 800 rpm. Sampling every 2 h after inoculation to determine OD600, NeuAc titer, by-product acetoin titer and glucose concentration.Fed-batch fermentation in 3-L fermenter: The fermentation medium consists of 24 g/L yeast extract, 24 g/L tryptone, 8 g/L 40. The SIFT algorithm can predict amino acid substitutions that have little effect on protein function by using sequence homology. Of all the tolerable residue sites, we selected those sites closest to the translation start codon (ATG) to facilitate genome editing. However, if the distance between all predicted tolerable residue sites and the translation start codon was longer than 45 bases, and the active site of the protein was not at the N-terminus, the amber stop codon was inserted next to the translation start codon, which is the simplest method to prevent the production of truncated proteins45. The ten selected essential genes in the genome underwent P43 promoter (strong) substitution and TAG codon incorporation, respectively, by homologous recombination. Finally, three OMeY-dependent strains  were successfully constructed and sequenced: NABd4, NABd8, and NABd9  at excitation and emission wavelengths of 488 and 523 nm, respectively. Gen5 CHS 2.06 was used to collect the biomass and fluorescence intensity data. While measuring the fluorescence intensities (FLS) and OD600 (ODS) of the samples, fluorescence intensity (FLB) and OD600 (ODB) of the blank medium were measured for fluorescence intensity correction. The final fluorescence intensity per unit biomass (FL/OD600) was calculated as (FLS-FLB)/(ODS-ODB).Cell mass was determined by measuring OD2SO4 with a flow rate of 0.5 mL/min at 40 °C. Agilent OpenLAB Control Panel was used to collect the HPLC data.High-performance liquid chromatography  with an ultraviolet absorption detector (210 nm) was used to detect the concentrations of GlcNAc, NeuAc, and acetate in each sample. An Aminex HPX-87H column  was utilized, and the mobile phase was 10 mM HThe glucose concentration during fermentation was measured using a glucose-lactate analyzer . Microsoft Excel 2019 16.37 was used to analyze the biomass, yield and fluorescence intensity data, and carry out statistical calculation. Fluorescent-activated cell sorting (FACS) technology was used to screen single cells with high fluorescence intensities. Cell analyzer  with FlowJo_V10  software was used for cell sorting. For each random mutation library, 6,000,000 transformants were determined and screened to obtain ~1000 potential target transformants.63. The development of the kinetic model was mainly based on four kinetics, including the growth kinetic model of acetic acid-producing E. coli developed by Luli et al., Monod equation describing nutrient limitation, acetate synthesis and specific growth rate equations determined by Basan et al., and Michaelis–Menten equation describing the biosynthesis pathway of GlcNAc65. This model mainly captures three characteristics of engineered E. coli in fermentation experiments: (1) addition of pAcF can change the specific growth rate by regulating the flux of the glycolytic pathway; (2) accumulation of acetate can inhibit both cell growth and biosynthesis of target metabolites and acetate; and (3) cell growth is also limited by the concentration of nutrients  in the medium. Besides, concentrations of metabolites and all parameters in the model are expressed in arbitrary units (a.u.) which is sufficient to qualitatively simulate the dynamic characteristics of metabolites and biomass. Based on these points, model construction and parameter selection were described as stated in the following sections.MATLAB R2019a was used for kinetic modeling of cell growth, as well as for target metabolite (GlcNAc) and by-product (acetate) synthesisvEMP) per unit biomass per unit time was calculated as a function of the pAcF concentration (xpacf):xpacf ranged from 0.1 to 10, and vEMP ranged from 0.3 to 1 according to the experimental results of Hollinshead et al. regarding the culture test of pfkA-deficient E. coli66. Therefore, the parameters in the kinetic model were calculated as A = 0.152 and B = 0.65. According to the experimental results of Basan et al., as the glycolytic pathway flux rate (vEMP) gradually increases, the specific growth rate (μ) subsequently increases. Further, as the glycolytic pathway flux rate (vEMP) continues to increase, ‘overflow metabolism’ activates the acetate synthesis pathway, and μ increases linearly with the increase of acetate synthesis rate (vace)65. Therefore, the promotion of the glycolytic pathway flux rate (vEMP) to μ is divided into two phases, including non-acetate-producing (vEMP = 0.3–0.6) and acetate-producing (vEMP = 0.6–1) phases. The μ of the intersection of the two phases  is taken as the rate of growth in the absence of all limitations. Therefore, in the model described, ‘IEMP’ is used as a coefficient to calculate the effect of glycolytic pathway flux rate on cell growth rate. During the non-acetate-producing phase, IEMP (0.3–1) increases linearly with increasing vEMP. During the acetate-producing stage, the synthesis rate of acetate  increases linearly with increasing vEMP, while IEMP  increases linearly with increasing vace67. Therefore, the relationship between IEMP and xpacf could be modeled by the following equation with parameters calculated as C = 7/3, D = −0.4, E = 1.5, and F = 0.1.First, according to the experimental results regarding the regulation of pAcF concentration on gene expression, the flux rate of glycolytic pathway (vace) could be modeled by the following equation: G = 0.6, H = −0.36.The acetate biosynthesis rate  under conditions of sufficient carbon source (glucose) in fermentation experiment. Therefore, we also set the coefficient ‘IN’ to calculate the effects of nutrients on cell growth rate according to Monod equation:xN is the residual nitrogen source in the fermentation system, xN0 is the initial nitrogen source, and KS is the half-saturation constant  of Monod equation  has the following dynamics:After calculating the three parameters, Km values in the metabolic pathway were equal to 1, and all maximal reaction rates (vmax) were 0.24, in order to match the synthesis rate of acetate. At the same time, the biosynthesis rate (vGNA) of GlcNAc was multiplied by Iace to inhibit accumulation. Therefore, the GlcNAc concentration (xGNA) has the following dynamics:vmax of YqaB per unit biomass per unit time (vGNAmax) was 0.24, and Km of YqaB (Km6) was 1. The initial parameters of the model are initial nitrogen source xN0 = 50 and initial biomass xcell0 = 0.1.In addition, Michaelis–Menten equation was used for qualitative simulation of GlcNAc biosynthesis. All 46. Briefly, the OMeY-dependent strain was first cultured in 50 mL of fresh LB medium containing 1 mM OMeY for 10 h at 37 °C and 220 rpm. Cells were then washed by centrifugation and resuspended in fresh LB medium. After the cells were washed twice, OD600 was measured. Three technical replicates were performed for each strain. For each sample, ~1010 cells  were plated onto OMeY-free, solid LB plates. Next, the numbers of escapees growing on the plates were counted after 1, 5, and 15 days of culture. At the same time, the bacterial suspension was diluted and plated on an LB plate with 1 mM OMeY to count the total number of cells. Escape frequencies for each OMeY-dependent strain were calculated as mean of escapees per colony forming unit (c.f.u.), plated on three OMeY-free LB solid plates. Standard error of the mean (SEM) was calculated as described by Mandell et al.46.Escape frequency was determined according to methods already reported in previous studiesFurther information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Classical evidence and newer evidence indicating the antiquity of the acetyl CoA pathway are summarized here. The acetyl CoA pathway requires approximately 10 enzymes, roughly as many organic cofactors, and more than 500 kDa of combined subunit molecular mass to catalyze the conversion of H2 and CO2 to formate, acetate, and pyruvate in acetogens and methanogens. However, a single hydrothermal vent alloy, awaruite (Ni3Fe), can convert H2 and CO2 to formate, acetate, and pyruvate under mild hydrothermal conditions on its own. The chemical reactions of H2 and CO2 to pyruvate thus have a natural tendency to occur without enzymes, given suitable inorganic catalysts. This suggests that the evolution of the enzymatic acetyl CoA pathway was preceded by—and patterned along—a route of naturally occurring exergonic reactions catalyzed by transition metal minerals that could activate H2 and CO2 by chemisorption. The principle of forward (autotrophic) pathway evolution from preexisting non-enzymatic reactions is generalized to the concept of patterned evolution of pathways. In acetogens, exergonic reduction of CO2 by H2 generates acyl phosphates by highly reactive carbonyl groups undergoing attack by inert inorganic phosphate. In that ancient reaction of biochemical energy conservation, the energy behind formation of the acyl phosphate bond resides in the carbonyl, not in phosphate. The antiquity of the acetyl CoA pathway is usually seen in light of CO2 fixation; its role in primordial energy coupling via acyl phosphates and substrate-level phosphorylation is emphasized here.For decades, microbiologists have viewed the acetyl CoA pathway and organisms that use it for H It is part of our human condition to want to know about the past, where things come from and ultimately how life began. Indeed, most human cultures have an origins narrative of some sort. Scientists are also a form of human culture, in the broad sense, and as such scientists also have origins narratives. However, just like the origins narratives of different cultures tend to differ, so do the origins narratives of different groups of scientists. Mathematicians tend to prefer stochastic or probabilistic models; physicists tend to prefer complicated models that gravitate toward problems of self-organization, whereas chemists tend to prefer models that focus on the synthesis and polymerization of RNA bases. Biologists, on the other hand, tend to find deficiencies with all such models, probably because biologists recognize that life is a very complicated action involving all of the above and more. Life is a set of chemical reactions that are set in motion by energy metabolism. Given a source of electrons, energy metabolism, carbon metabolism, and sufficient nutrients, life reacts to generate cells that produce more cells until one of the educts becomes limiting. Cells deposit protein as the main substance, RNA as peptide-condensing agents, and DNA as memory; they self-organize, and they generate populations as side products of energy metabolism, the main chemical reaction that runs all of the above. The self-organization property of cells is not obvious. 2 are ancient. The idea that the first cells on earth were anaerobes and met their carbon needs from CO2 alone without the help of chlorophyll goes back 110 years. In 1902, Haeckel expressed the view that the first step in the origin of life  was the formation of an inorganic formative fluid (“anorganische Bildungs-Flüssigkeit”) containing the essential components, namely, carbonic acid, ammonia, and binary salts  (Bilden Plasma unter Reduction”), which today we would call autotrophy. Famous for his classifications, Haeckel placed these first organisms at the top of his system in the class Probiontes, represented by the first order Archibiontes, which contained only hypothetical types named Primordia vitae hypothetica!  , p. 361.thetica! . HaeckelFähigkeit, Eiweiße und Kohlenhydrate (letzteres ohne Vermittlung des Chlorophylls) aus unorganischen Stoffen zu bilden.]; and (6) resilience against alkaline solutions, concentrated salt solutions, sulfur compounds, and diverse toxins ; in the fourth, the water had cooled to less than 50°C ; p. 359.e toxins ; p. 359.The idea that the first forms of life might have been phenotypically simple and chemically robust, arising in and inhabiting geochemically active environments, is intuitive conjecture. A similar conjecture shared by many biologists is that clues about the origin and early history of life are preserved in the biology of cells themselves and that some lineages of modern cells might be physiologically unchanged relative to the first life forms, which had to have been anaerobes, as 2 with electrons from H2, acetogens, and methanogens have what they need for growth.What were ancient life forms doing from the standpoint of energy metabolism? Electromagnetic radiation from the sun was long assumed to be energy source for energy at origins: 2 are not dependent on solar radiation. Following the lead of 2 and CO2 at hydrothermal vents ca. four billion years ago and have preserved to this day the founding physiology of the first free-living cells in the bacteria and archaeal lineages (arkhaios primitive) is an interesting ancient name for an ancient grade, the first free-living cells, which could have been acetogens and methanogens, based on their physiology , the more ancient they appeared.How far back can we trace the idea that acetogens might be living fossils from the origin of life? 2 was used by the Calvin cycle”  it is linear rather than cyclic, (ii) it is the only exergonic CO2 fixing pathway, (iii) it is the only CO2 fixing pathway that occurs in archaea and bacteria ? One has to think things through in full, whereby the details can harbor demons. Three problems stand out.The idea that acetogenesis and methanogenesis are ancient  is appea2–CO2 couple to simultaneously support carbon assimilation and ATP synthesis via substrate-level phosphorylation. Their mechanisms of ion gradient formation entail flavin-based electron bifurcation   use chemiosmotic coupling, and the rotor–stator ATP synthase is not only structurally conserved across bacteria and archaea  . The met  is the circumstance that acetyl CoA pathway has two segments: one of which is conserved across acetogens and methanogens; the other is not. The two segments are CO-dependent acetyl CoA synthesis at carbon monoxide dehydrogenase/acetyl CoA synthase (CODH/ACS)  and meths is not . Using ecarbonyl . The metcarbonyl , and thecarbonyl . To me, carbonyl , until r2), robust geochemical (prebiotic) solutions to the three problems outlined in the foregoing section—electron bifurcation, ion gradient formation, and methyl synthesis—are required. In 2-dependent CO2 reductase  or pyruvate (carbon metabolism).If acetogenesis and the acetyl CoA pathway are genuinely ancient H, reduced ferredoxin, or F420  using only one very simple and naturally occurring iron nickel compound, awaruite , as the catalyst  cofactor, to an organic substrate, methenyl H4MPT, generating methylene H4MPT , can substitute for the entire enzymatic pathway , the solution is that serpentinization generates alkaline and H2-rich hydrothermal fluid. The midpoint potential of the low potential ferredoxins in acetogens and methanogens is on the order of −500 mV , temperatures (100°C), and pH (8–10) used in the H2-dependent CO2-reducing reactions reported by 2 to organics or ferredoxin reduction if suitable catalysts are provided. The reducing power of serpentinizing systems in the Earth can, in principle, functionally substitute for electron bifurcation in cells by providing conditions sufficiently reducing to generate reduced ferredoxin  or greigite (Fe3S4), methyl synthesis from H2 and CO2 under hydrothermal conditions is facile  gradient, between the emerging effluent of the serpentinizing system and the ocean bottom water at the vent ocean interface of roughly three to four orders of magnitude. The polarity of the gradient is the same as that in modern cells: more alkaline on the inside than on the outside, generating a proton motive force from outside to in For ion gradient formation, the solution is that the serpentinization process generates magnesium hydroxides from magnesium silicates  with the2, H2S, and iron ions as sources of energy, in addition to a statement (p. 265) that will ring true to those interested in acetogens and methanogens: “I find it possibly of relevance that hydrogen activation, which would be involved here, is mediated by one of the more primitive catalysts, the recently discovered ferredoxin.”Although serpentinizing systems solve several problems in early physiological evolution, they present another: How, in terms of energetics, could genes and proteins (protein synthesis is ATP and GTP dependent) have evolved before a universal mechanism of ATP synthesis, ion gradient harnessing via a rotor–stator ATP synthase, which is a protein encoded by genes, had come to be? This question touches many facets of the origins problem, because it concerns the relationship between nucleic acids as molecular memory, peptides as catalysts, and the coupling of environmental energy to the polymerization reactions that generate both classes of biopolymers from their monomers. This harkens to the genetics-first versus metabolism-first discussion, which is widely thought to stem from the clash in the origins literature of the 1990s between Wächtershäuser’s ideas about pyrite-based metabolism contra efforts by proponents of an RNA world to quash them. As with most debates, the debate is older, as summarized yet again by Living cells are approximately 80–90% water by fresh weight. Nonetheless, a common criticism of hydrothermal systems as sites for biochemical origins is that they are full of water. This criticism typically comes from the genetics first camp and is based on the argument that, in aqueous solution, peptide bonds and the phosphoester bonds linking nucleotides will hydrolyze, leading to an inference that systems containing genetic material could not have evolved in a permanently aqueous environment . Rather G0′), hydrolysis of these high-energy phosphate bonds in metabolism releases on the order of −60 to −30 kJ/mol (Escherichia coli cell synthesizes roughly of 30 billion ATP (30 pg) or approximately 30 times its bodyweight (1 pg) per cell division  as the first chemical energy currency, coupled with his notion that SLP is more ancient than the ion gradient phosphorylation, which led to the idea that the entry of high-energy phosphate bonds into primitive metabolism came from high-energy phosphate bonds in phosphorus minerals in the environment. Although Lipmann’s idea of obtaining metabolic energy from pyrophosphate or polyphosphate minerals in the environment has a long tradition of acceptance in origins literature  resides in carbon, not in phosphorus.2 reduction. SLP powered by CO2 reduction appears to be restricted to the acetyl CoA pathway. In that sense, 2 reduction) and 2 reduction and energy metabolism? It well could be. Does that energy coupling work without enzymes? Almost.In autotrophic metabolism, acetyl phosphate can be synthesized for SLP during the process of CO3+ , or in enzymes and cofactors that transfer methyl groups, as shown in Findings from various disciplines tend to home in on the acetyl CoA pathway when it comes to origins. Investigations into ancient metabolism from the standpoint of modern metabolic networks are uncovering clues that converge on the acetyl CoA pathway , 2019. A2 reduction to acyl or amino acyl phosphate synthesis would enable a great many biologically relevant reactions, such as peptide synthesis. One thinks of small molecule chemical networks of the kind that Kauffman had in mind  and gradus , or stepping backward. The concept of retrograde evolution of pathways traces to an article by The term “retrograde” comes from From tips to root means backward steps in evolution along the pathway relative to the biosynthetic direction, hence retrograde, although Horowitz did not use that word. Horowitz required the pathway evolving species to be heterotrophic for the compound in question, or in modern terms auxotrophic for all pathway products, taken across all pathways. Note that Horowitz’s model starts with organisms, species that already are alive, such that the retrograde model describes a process of inward biochemical pathway growth in a world where genes and organisms already exist in an organic soup having all intermediates and end products of a modern metabolic map in ample supply. A related concept is that of 2. They differ from heterotrophic theories in that they assume that the organic molecules from which life arose were synthesized from CO2 and that the evolution of biochemical pathways to complex organics (amino acids and bases) thus recapitulates a vector of biochemical evolution that starts from CO2 and moves outward toward the tips, or products, of metabolism. In that regard, the main products that we see in metabolism today  were not selected from a soup; rather, they were synthesized in a sequence of reactions such that they were the endpoints, not the starting points of biochemical evolution. In other words, heterotrophic origin theories operate via consumption of preformed products, whereas autotrophic origin theories operate via synthesis of products from CO2. In contrast to 2. That is true for autotrophic theories of Autotrophic theories assume that the first organisms were autotrophs that obtained carbon from CO2, hence that biochemical synthesis evolved from C1 compounds to C2 compounds to C3 and larger, such that the origin of metabolic networks was a process of growth from simpler to more complex . Genes require the existence of the code; this article is not about the origin of the code. Once genes had arisen , it is eminently reasonable to posit that the first genes to arise and evolve, in general, were those that anchored the genetic code in place, namely, aminoacyl tRNA synthetases . In termed bases , which aed bases .2 fixation , and on to enzymatic (gene encoded) catalysts, entails the very broad premise that the reactions of central metabolism leading to products (amino acids and bases) tend to take place naturally. Catalysts merely accelerate chemical reactions that tend to take place anyway, or the catalysts can alter the immediate products in the case kinetically controlled reactions. In that sense, the evolution of pathways under such a set of premises for autotrophic origins is prepatterned , or simply patterned by the natural reactions of carbon. Some readers will ask why not use the word palimpsestic instead of patterned. Palimpsestic, in addition to lacking all prosody, emphasizes the process of overbuilding or overwriting a prior state. Patterned, and more specifically vorgezeichnet, places the emphasis on the process of putting the original pattern, the ancestral state, in place. Patterned evolution of pathways emphasizes the process of generating the original pattern, namely, the natural reactions of organic compounds.This sequence of pathway evolution, namely, a sequence of COThus, the concept of patterned evolution of pathways is the autotrophic counterpart of retrograde pathway evolution inherent to heterotrophic theories. Patterned pathway evolution has it that the reactions that comprise biochemical pathways were etched into the space of all possible chemical reactions according to kinetic and thermodynamic constraints, with environmentally available and novel synthesized catalysts bearing upon the relative rates of competing reactions. As pathways evolved forward, the spontaneous chemical reactions of preceding products determined the vector of evolutionary progression. The connections between products of different pathways, sometimes connecting pathway intermediates to generate new routes and products (widespread in cofactor biosynthesis) as one moves distal to the core, emerge as a natural result of patterned pathway evolution, as does the noteworthy thermodynamic stability of the main pathway end products, amino acids, and bases. Patterned evolution of pathways would readily explain why so many reactions in metabolism work well without enzymes .2 and CO2 for energy metabolism generally requires a syntrophic partner that can scavenge the H2 so that the H2-producing reaction is exergonic , pyruvate synthesis adds 71 more carbon atoms, oxaloacetate synthesis adds 22 more carbon atoms, and 2-oxoglutarate synthesis adds nine more , E. coli (on the order of 28 g per mol ATP synthesized) is approximately three times the measured value (approximately 10 g per mol ATP synthesized). Escherichia coli cells synthesize approximately three times more ATP than they require for biomass synthesis, similar to the situation in A three-fold excess of energy relative to protein cell mass seems odd at first sight, but in 2 into acetyl CoA, pyruvate, oxaloacetate, and 2-oxoglutarate via the acetyl CoA pathway and incomplete reverse citric acid cycle. This conserved core provides the carbon backbones for the synthesis of amino acids, whereby amino acids  plus CO2 and C1 intermediates of the acetyl CoA pathway provide in turn the carbon backbones and nitrogen for the synthesis of purines and pyrimidines . Theories based in polymerization of formaldehyde, cyanide, activated methane, or acetate do not intersect at all with central metabolism of real cells, whereas theories based in the sequential condensation of CO2 do—seamlessly  from carbonyls (acetyl), whereas the other five pathways incorporate CO2 as carboxyls that have to be reduced to carbonyls at the cost of energy input. As with almost all forms of SLP, SLP in the acetogen pathway entails the nucleophilic attack of a carbonyl carbon in a thioester by an otherwise inert inorganic phosphate ion to generate an acyl phosphate that can phosphorylate ADP  that lack cytochromes growing on H2 and CO2 likely reflects an ancestral state of microbial physiology from which the evolution of more complex pathways involving fermentations and cytochrome-dependent electron transport chains emerged.Autotrophic theories have a long tradition. Hydrogen-dependent acetogens figure centrally in modern autotrophic theory because the backbone of their metabolism, the acetyl CoA pathway, provides both carbon and energy from the Hlate ADP . The eneWM wrote the manuscript and prepared the figures.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


By assessing physiologically-accessible parameter space, we observed a wide range of dynamic behavior and examined the different limiting cases. Graphical solutions for this dynamic system can be viewed simultaneously and resolved in real time via buttons on the graphical user interface (GUI). Metabolic bottlenecks in the system can be accurately predicted by varying the respective rate constants. The GUI serves as a diagnosis toolkit to troubleshoot genetic circuits design constraints and as an interactive workflow of using this arabinose based genetic switch to dynamically control carbon flux, which may provide a valuable computational toolbox for metabolic engineers and synthetic biologists to simulate and understand complex genetic-metabolic system.Modeling synthetic gene circuits to implement dynamic flux balancing is crucial in teaching and exploring metabolic engineering strategies to repartition metabolic precursors and construct efficient microbial cell factories. Microbial fitness and production rates are often complex phenotypes that are governed by highly non-linear, multivariable functions which are intrinsically linked through carbon metabolism. The solution of such dynamic system can be difficult for synthetic biologists to visualize or conceptualize. Recently, researchers  have implemented an arabinose based genetic switch to dynamically partition the central carbon flux between cell growth and product formation. The autonomous switch allowed dynamic shift from arabinose-associated cell growth to acetate-associated product (wax ester) formation. This system clearly demonstrates the effectiveness of using a genetic switch to decouple cell growth from product formation in a one-pot bioreactor to minimize operational cost. Coupled with Michaelis-Menten kinetics, and Luedeking-Piret equations, we were able to reconstruct and analyze this metabolic switch  Microbes use a wide range of substrates with different energetic and redox states. These substrates may be assimilated to form biomass or final products. Examples of such substrates for microbial production include methane , acetateMany simple regulatory genetic switches have been reconstructed and studied, for instance the genetic toggle switch , and theAcinetobacter baylyi) grown on a simple carbon source (acetate), a team at Tampere University of Technology, Finland, was able to demonstrate dynamic control of a critical catabolic enzyme, such that the cells would automatically switch from cell growth to product formation, when the inducer arabinose was depleted  …One of the important feature of this metabolic switch is to use arabinose as the signaling molecule to tune the cell growth rate, so that the substrate (acetate) consumption is diverted to product formation that leads to significant improvement in the pathway yield. By altering the initial concentration of arabinose , the autd for each of the appropriate cellular components. In the case for cell growth, this term should be explained as ‘death rate’, while proteins have their own degradation rate, and the metabolites do not degrade or have a dilution term. Since the expression of GDH (glucose dehydrogenase) is coupled with biomass, we observed a similar pattern of GDH expression as compared to cell biomass . We next investigated the system dynamics by varying expression levels of key proteins araC. The araC transcriptional regulator represses the expression of aceA under the control of pBAD, and the metabolic product of aceA (succinate) controls the glyoxylate shunt flux that determines cell growth rate. Experimentally, this kind of genetic manipulation could be achieved by changing gene copy number, promoter strength or As expected, the level of GDH is strongly associated with the amount of biomass due to its constitutive expression . AraC expression , indicatpression . The corTo explore the parameter space of the model, a GUI was developed so that this model could be analyzed more systematically. This made it significantly easier to tune the various parameters and determine whole system dynamic behavior. We discovered that some variables are order of magnitudes more sensitive than others, indicating that variables ranging relevantly from 0.01 to 1 may lead to similar dynamics as those parameters ranging from 1 to 100. Considering the fact that manual curation of the parameter space is very difficult and time consuming, this GUI may easily help us to find a biologically relevant window of initial parameters for all variables. With the scope for each variable included and a slider bar to change that variable in the GUI interface, we may explore the rich dynamic patterns of this dynamic system. The GUI makes the demonstration and interpretation of this model more intuitive, and explanatory than a traditional static graphical view.The GUI is built using three separate instances of a new figure, with a listener and a callback function. The GUI figure on top represent solutions , the botin vivo work is the tight regulation of critical catabolism through isocitrate lyase via the pBAD promoter, and linking that to the orthogonal arabinose inducer molecule arabinose. This tight regulation dictates the level of cell growth permitted and also governs when these cells stop growing and start producing the final product (wax ester). This elegant system was designed successfully to decouple cell growth and product formation, both of which are associated with carbon utilization. We reconstructed this genetic system in silica and demonstrate the effectiveness of this system to dynamically redistribute carbon flux. By working in Acinetobacter baylyi, there are two distinct advantages. They chose a host organism which natively relies on a shortened citric acid cycle, the glyoxylate shunt pathway, instead of the full TCA cycle. They also chose an organism whose native glucose dehydrogenase could not glycolytically degrade arabinose but would instead oxidize it into other inductively inactive pentoses. By intuitively choosing a host, and a feedstock, they effectively minimized the cloning work needed to be done to demonstrate success. They chose a product which very likely follows growth and non-growth associated production patterns. They chose a tight genetic switch, as well as a key enzyme required for growth as an output signal. In creating such a unique system, they have created an ideal case, which demonstrates a genetic toggle switch being implemented to control central carbon metabolism, effectively also controlling the carbon flux between growth and wax ester formation. Although this user interface might be helpful for explaining individual variables contributions toward system behavior, it is not perfect and is limited by initial conditions and the range of the variables set forth in the code. Each slider bar is a manually set range, and if someone decides to go outside of that range, this would require code manipulation. Another flaw is that in many of these complex nonlinear systems, imaginary numbers may give rise to unrealistic solutions which will not update in the graph correctly when the callback function is prompted to update the real number counterparts. This is because it would have to change the plot type, not just line values, in order to display complex answers. Some solutions also take a while to converge and cause the interface to lag before updating. The easiest way to troubleshoot what happened is to monitor the MATLAB command window, which will display an error if an imaginary number causes a bug or will loop in the solver indefinitely if it is not converging on a solution given that variable set. In both cases, re-running the code from the beginning will reestablish the GUI on the screen, in working order. This highlights another problem, that GUI is locked onto a local solution rather than the optimal solutions. One can move around within this solution set, but some variable region may lead to a highly stiff region or an imaginary solution set. Under this scenario, the GUI will not continue to function, and one could not probe variable values beyond that, unless they manually updated the code to change the GUI starting conditions and the range of the slider bars. The advantages of this extended type of graphical analysis include interactivity and a decreased level of involvement for manual input for small changes. Instead of changing one number in the code and replotting the graph, it is very easy to change, observe, and learn from the system by manipulating a slider bar on the user-interface. The disadvantage is primarily that the slider bars are range bound and stiff and unrealistic solutions cause the system to lag and need restarting.The phase plane analysis demonstrates two separate regimes of wax ester production pattern, for example, the initial linear regime carries the growth associated production term, but the verticality of these configurations represents the non-growth associated production of wax ester which is independent of cell growth. This simulation mathematically and qualitatively matches the results of the work reported by the researchers in Finland who found a four times increase in specific wax ester productivity by controlling carbon flux . The keyFuture work for this strain, or some congruent genetic configurations, could prove to be quite valuable. One might consider another model where arabinose is glycolytically consumable to the cell, a dual substrate model, where arabinose can be fed to the cell, and induce cell growth on acetate. Once arabinose was gone, along with all the glycolytic catabolites from supporting growth, one would hypothesize the phenomena observed in this paper would take over. Another interesting case could be growing this strain in CSTR conditions. One might think of alternatively pulse-feeding substrate and arabinose for highest yields. This is only one application in one host organism, whereas this is hypothetically possible in any microbial workhorse genetically tractable enough to knockout the greater TCA cycle in favor of the glyoxylate shunt. Researchers may have the flexibility to use a different inducer/actuator module with strain specific orthogonality, and other synthetic biology-based logic gates, genetically-encoded biosensors A. baylyi represents a potentially valuable discovery in terms of maximizing carbon conversion and product yield from substrates, and minimizing bioburden from gene overexpression or host cell protein associated with certain hosts. The tight regulation of isocitrate lyase by the arabinose inducible pBAD promoter offers an ideal control scheme to tightly improve cell productivity though central carbon metabolism. The simplicity of the proposed models is sufficient to describe genetic circuits dynamics, yet extendable to understand the dynamic carbon flux balancing in various organisms. This system may hold incredible promise to facilitate learning and engaging students with these complex, often abstract, and intertwined ideas of microbial growth, genetic circuits, enzyme kinetics, and coupled differential equations.By simulating this arabinose-based genetic switch, we demonstrate our model could effectively recapitulate the dynamics of the metabolic shift from cell growth to product formation, solely dependent on the exogenously added inexpensive carbons (arabinose). We also demonstrate that a graphical representation of this model can be immensely helpful in analyzing and understanding the dynamic behavior of gene circuits. This model system in The author declares no conflicts of interests.


Totally, 8, 16, 21 and 19 significant metabolites were primarily changed in the P6, P10, P14 and P18 cells compared with the P3 cells, respectively. These metabolites were mainly involved in 14 significantly altered metabolic pathways. Furthermore, we observed taurine retarded oxidative damage resulting from senescence. In the case of energy deficiency, HUVECs metabolized neutral amino acids to replenish energy, thus increased glutamine, aspartate and asparagine at the early stages of cellular senescence but decreased them at the later stages. Our results indicate that cellular replicative senescence is closely associated with promoted oxidative stress, impaired energy metabolism and blocked protein synthesis. This work may provide mechanistic understanding of the progression of cellular senescence.Cellular senescence is a physiological process reacting to stimuli, in which cells enter a state of irreversible growth arrest in response to adverse consequences associated with metabolic disorders. Molecular mechanisms underlying the progression of cellular senescence remain unclear. Here, we established a replicative senescence model of human umbilical vein endothelial cells (HUVEC) from passage 3 (P3) to 18 (P18), and performed biochemical characterizations and NMR-based metabolomic analyses. The cellular senescence degree advanced as the cells were sequentially passaged  Aging is characterized by a progressive loss of physiological integrity, leading to impaired functions and increased vulnerability to death . Numerouin vitro cell aging models have been widely used in aging-related studies  with water suppression. The following experimental parameters were used: spectral sweep width, 20 ppm; total relaxation recovery delay (RD), 4 s; a short delay t1, 4 μs; the mixing time (τm), 10 ms; acquisition time per scan (Acq), 1.88 s; data points, 64 K; number of the scans, 128.Lyophilized powder of each aqueous extract sample was then dissolved in 550 μl of NMR buffer containing 50 mM Khttp://www.hmdb.ca/), and relevant published references  > 0.06, the absolute value of p(corr)[1] > 0.75, and VIP ≥ 1.00.p < 0.005.We used relative integrals of the significant metabolites identified from the OPLA-DA models to quantitatively analyze metabolite levels in the five groups of HUVEC cells (n = 8 for each group). For metabolites related to highly overlapping peaks, non-overlapping peaks were selected to accurately calculate spectral integrals. Averaged metabolite integrals were expressed as Mean ± Std. Error for each group. Variations were statistically calculated by utilizing one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test with the Bonferroni correction using the SPSS software . A total of 10 independent hypotheses were tested for multiple comparisons among the five groups of HUVECs. The statistical significance level used for each hypothesis separately is 1/10 times what it would be if only one hypothesis were tested. Thus, we identified differential metabolites with statistical significances of p) scores > 8 and pathway impact values > 0.03 were identified to be significantly altered metabolic pathways.We conducted metabolic pathway analysis to identify significantly altered metabolic pathways during the progression of cellular replicative senescence base on the significant metabolites identified from the OPLS-DA analysis, using the module of Pathway Analysis provided by the MetaboAnalyst 4.0 webserver. The following parameters were selected for the pathway topological analysis, including the ‘Homo sapiens (human)’ library, the default ‘Global Test’ and ‘Relative Betweenness Centrality’. Metabolic pathways with -ln(Supplementary FiguresSupplementary Tables


A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds.The developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates.With this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts. Natural products provide an immense source of bioactive small molecules of medical and agricultural importance –3. The bOne promising avenue towards exploration of the bioactive potential of these silent BGCs is heterologous expression in host strains that are engineered to achieve maximal production of the encoded natural products , 10. WitActinobacteria [Genome-scale metabolic models (GEMs) can predict the consequence of genetic modifications  and are bacteria , a phylubacteria . Previoubacteria –24 or prbacteria . One reabacteria . In thisWe have chosen to focus on non-ribosomal peptide synthetases (NRPSs) and two types of polyketide synthases (PKSs), namely type 1 PKSs and trans-AT PKSs. These BGC classes are of particular interest because of their vast abundance , 27 and cis) modular PKSs by having a larger module diversity and deviations from canonical rules, but they are also recognized by freestanding AT domains that perform the chain elongation [Similar to NRPSs, chain elongating modules of PKSs rely on three functional domains: an acyltransferase (AT) domain that recognizes a specific extender unit and attaches it to the acyl carrier (ACP) domain. The third domain, ketosynthase (KS) catalyzes the Claissen condensation required to extend the polyketide chain. A standard PKS load module contains only the AT and ACP domain, and a TE or TR domain is required for the release of the polyketide chain by the final PKS module. PKS modules can also feature the reducing domains ketoreductase (KR), dehydratase (DH) and enoylreductase (ER), and different combinations of functional domains yield a large variety of molecular transformations, in particular for the trans-AT PKSs . These tongation . The divin silico predictions that are biologically relevant. After assembling and evaluating the accuracy of the new pipeline presented in this work, we demonstrate its value towards high-throughput assessment of BGCs by reconstructing the metabolic pathways for 943 of the BGCs currently in MIBiG [Streptomyces coelicolor, a model organism among the Actinobacteria and a popular heterologous BGC expression host [We acknowledge that experimental analyses of the final and intermediate products, as well as enzyme activity assays, are required to fully unravel the details of the metabolic pathways associated with a BGC. However, for the chosen classes of BGCs , we hypothesize that the information acquired from genome mining is sufficient to make in MIBiG . Furtherion host , 38.We have developed the Biosynthetic Gene cluster Metabolic pathway Construction (BiGMeC) pipeline that leverages antiSMASH results to create the metabolic pathway corresponding to a PKS or NRPS biosynthetic gene cluster Fig. a. The piThe hallmarks of PKS- and NRPS-genes are adjacent functional domains that in total make up one or several modules that initiate, extend or cleave off the polyketide or peptide product, respectively , 32, 33.We first assessed the accuracy of the BiGMeC pipeline by comparing its predictions with experimentally characterized and manually curated metabolic pathways. To this end, we compared the substrates, cofactors, and reaction products of each step of the metabolic pathway associated with eight well-characterized BGCs Fig. . These BA large number of the incorrect predictions derive from wrong assignments of inactive KR domains by antiSMASH . Across S. coelicolor (Sco-GEM) [To investigate how much the errors in the constructed metabolic pathways affect model predictions, we introduced both the literature-based and the BiGMeC pathway reconstructions into the consensus GEM of Sco-GEM)  and compThe anticipated use of the developed pipeline towards strain engineering of expression hosts underscores the need to elucidate if model-based strain designs using BiGMeC-constructed pathways deviate from results using pathways reconstructed according to literature. To this end, we predicted optimal single-reaction knockout mutants that should increase the production rate of the associated product Fig. c. Note tTo demonstrate the power of BiGMeC in high-throughput assessment of BGCs, we employed the pipeline on 1883 of the 1923 BGCs in the MIBiG database (version 2.0) . For 40 in silico heterologous expression experiments  initiative [The BiGMeC pipeline is, to our knowledge, the first tool for automatic metabolic pathway reconstruction specifically targeting PKS and NRPS BGCs. Although the reconstructed pathways are not able to capture the entire diversity seen in the biosynthesis of NRPSs and PKSs , 32, theitiative . This wihttps://github.com/AlmaasLab/BiGMeC. BiGMeC runs from a command-line interface and takes an annotated NRPS or PKS BGC in the format of a region-specific GenBank file as produced by antiSMASH 5.1 [S. coelicolor GEM [https://github.com/SysBioChalmers/Sco-GEM.We developed BiGMeC to translate information about PKS and NRPS BGCs to detailed outlines of the metabolic reactions governing the production of the associated secondary metabolites. The BiGMeC software and all other associated scripts are implemented in Python 3 and publicly available at MASH 5.1 . It leveolor GEM . This moThe BiGMeC pipeline first parses information about the location and annotation of the genes and modules as annotated by antiSMASH from the GenBank file Fig. . If avaiRare extender units appear in both PKS and NRPS biosynthesis. The synthesis of rare extender units is usually carried out by genes in the BGC , and we The pipeline also handles a number of deviations from the canonical rules, for example the deactivation of the KS domain often seen in modules containing O-methyltransferases . Furthertty acid , 63. It tty acid .Streptomyces lohi [Streptomyces hygroscopicus [Bacillus velezensis FZB42 [Serratia plymuthica [Streptomyces albus [Sorangium cellulosum [Anabaena sp. 90 [Pseudomonas costantinii [real reaction, i.e. the associated reaction as described in the literature. When clearly defined in the literature, tailoring reactions were included, but we focused on the synthesis of the core peptide/polyketide. The very complex tailoring of leupyrrin [To evaluate how well biosynthetic pathways can be constructed solely based on antiSMASH data we compared BiGMeC-constructed pathways with literature-based reconstructions for 8 different BGCs, covering different species and classes of BGCs  we could only obtain the antiSMASH result for 1883 of the entries. For all BGCs at least annotated to either type 1 PKS, trans-AT PKS or NRPS we used the BiGMeC pipeline to reconstruct the corresponding metabolic pathway. We predicted optimal knockout strategies for each of successfully constructed pathway using the same procedure as described for the 8 BGCs used to evaluate the BiGMeC pipeline.To demonstrate the value and efficiency enabled by BiGMeC we applied this pipeline to all relevant BGCs from the MIBiG database . To get Additional file 1. Details on tailoring reactions and synthesis of the rare extender unit methoxymalonyl-ACP, as well as a description of the analysis used to develop the heuristics that indicate the presence of these reactions from smCOG annotations.Additional file 2. Detailed comparison of 8 BGCs for evaluation the accuracy of the BiGMeC pipeline.


Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context. Synthetic biologists, metabolomics researchers, and microbiome researchers increasingly demand information regarding organism-scale metabolic networks. Conversely, the convergence of genome sequencing with computational methods for metabolic network reconstruction have yielded organism-scale metabolic network information for thousands of sequenced organisms. These networks are large, complex, and highly interconnected, and are therefore challenging for scientists to comprehend. Visualizing metabolic networks accelerates scientific understanding and analysis, and enables scientists to perform numerous network-based analysis operations, such as comparing the networks of one or more organisms, and interpreting omics datasets.We present a rich set of bioinformatics tools that are available within the Pathway Tools (PTools) software for generating and manipulating organism-scale metabolic network diagrams. The simplest versions of these metabolic charts depict the full metabolic network of a unicellular organism. The charts have also recently been extended to depict the different subnetworks that are active in the different cell types of multi-cellular organisms. These charts have also been extended to depict the pan metabolic network computed from a pan-genome and to illustrate the metabolic networks of an organism community. Pathway Tools’ metabolic charts are also used to visualize comparisons of genome-scale metabolic networks and serve as tools for interpreting high-throughput datasets, including transcriptomics data, metabolomics data, and metabolic fluxes computed by metabolic models. These tools have been evolving for several years and have recently been re-engineered for faster operation with higher quality graphics within modern web browsers.The remainder of the Introduction summarizes our past work on PTools metabolic charts including their graphical organization and generation. That past work lays the foundation for the recent enhancements presented in the Results section. The new implementation is JavaScript-based for speed and improved graphics quality. It provides fast semantic zooming whose most detailed level identifies metabolites, genes, enzymes, and pathways. The new diagram gives animated views of omics data with a dynamically controlled color scale.BioCyc.org [PTools ,2 is an oCyc.org .PTools metabolic charts have evolved through multiple generations of improvements ,10,11,12Escherichia coli is at Reference [Each PTools metabolic chart, also known as a cellular overview diagram, is organized according to the cellular architecture of the organism (that architecture is described within the PGDB), depicting the cytosol, extracellular space, and plasma membrane. For Gram-negative bacteria, an additional outer membrane and periplasm are included (see eference ). WithinTransporters and other membrane proteins are drawn on the appropriate membrane, and reactions or proteins located in the periplasm or extracellular space are drawn between or outside the membranes. Lines in the chart depict biochemical reactions, and nodes depict metabolites. Nodes are shape coded, for example, to distinguish amino acids from carbohydrates from tRNAs.BioCyc.org via the command Metabolism→Generate Metabolic Map Poster.Connections between pathways are not shown to keep the diagram from becoming unduly tangled, except insofar as pathways are combined into superpathways that are defined in the PGDB. Using the desktop PTools, the user can interactively add selected connections between metabolites to explore the connectivity of the network. A PDF version of the chart for any organism, which can be printed as a poster, is available at The input required to construct a PTools metabolic chart is the metabolic network of one or more organisms described as one or more PGDBs. Each PGDB can capture the genome, proteome, metabolome, reactome, and metabolic pathways of an organism. PGDBs in turn are created by the PathoLogic module of PTools, which computes a metabolic reconstruction from a user-supplied annotated genome. All metabolic charts in this article were generated from PGDBs obtained from BioCyc. BioCyc PGDBs were generated from GenBank annotations with varying degrees of subsequent manual curation.Most BioCyc metabolic charts are organism-specific in the sense that they include only the pathways, reactions, enzymes, genes, and metabolites present in a single organism’s PGDB. When the chart is regenerated, it automatically includes the latest information from the organism’s PGDB, such as reactions or pathways that curators have deleted or added using the PTools editors .PTools creation of a new diagram follows the following procedure.Query the PGDB to determine the cellular architecture of the organism, meaning what membranes and cellular spaces are presentQuery the PGDB to determine the sets of reactions, proteins, and pathways present in different regions of the diagram, such as the sets of biosynthetic, catabolic, and energy-related pathwaysLay out each individual pathway diagram using PTools pathway layout algorithms Run a layout algorithm that arranges the pathways within each pathway block (such as Amino Acid Biosynthesis)Run a layout algorithm that arranges the pathway blocks relative to one another within the biosynthesis, catabolism, and energy-metabolism regionsLay out the grids of individual reactions in the cytosol, periplasm (if applicable), and extracellular spaceArrange the transporters in the cellular membrane(s)We recently re-engineered the web version of PTools charts to overcome several limitations of the previous implementation. The previous implementation used sets of tiled images at different zoom levels that were generated on the server side. The diagram was slow to zoom  because the tiled images were slow for the web browser to download from the server. Also problematic was the existence of discrete zoom levels for the diagram—the user could not obtain a zoom level in between those pre-specified levels.BioCyc.org.In the new implementation see  the grapTo achieve smooth semantic zooming see , four diTooltips provide further details when the user mouses over a metabolite or a reaction, such as the reaction equation, EC number, enzyme name(s), and containing pathway(s). E. coli metabolic chart highlighting all genes directly regulated by FNR, a transcriptional regulator that mediates the transition from aerobic to anaerobic growth.Search operations are available in the right-sidebar menu to locate genes, metabolites, reactions and pathways of interest within a chart. For example, the user can search for reactions by name, identifier, substring, EC number, enzyme name, evidence code, and by cellular location of the enzyme. In addition, users can search by regulation , or by curation level . The Species Comparison operation highlights all reactions shared or not shared with one or more other selected organisms. Because BioCyc charts are so information-dense, visually picking out all highlighted entities can be difficult. In the web implementation, we offer two solutions. Sliders at the top of the page enable the user to decrease the opacity of non-highlighted portions of the diagram and/or increase the width of highlighted reaction edges relative to non-highlighted edges. Either individually or in combination, these simple adjustments make the highlighted entities stand out visually. Alternatively, the user can request a list of all entities highlighted in a particular operation and then select one to identify with a distinct marker icon.A particular metabolite may appear in multiple different reactions, and a particular reaction may appear in multiple pathways within a chart. In the desktop implementation, right-clicking on a compound node or reaction edge brings up a menu of operations, including navigating to the detail page for that compound, reaction or pathway either in the main display or in a pop-up window, and highlighting the compound, reaction, pathway, or all reactions of the compound everywhere they appear within the diagram. One option in the compound menu is to draw connections from that compound node to other instances of that compound elsewhere in the chart. A control panel enables the user to selectively hide or show subsets of possible connections.A new visualization, currently available only in the downloadable PTools software running in desktop mode, depicts the metabolic chart customized for the metabolic reactions and pathways active in specific cell types. When a PGDB captures a representation of multiple different cell types, each protein can be annotated with the set of cell types in which it has been observed. We have defined six cell types in the HumanCyc database: endothelial cell, hepatocyte, leukocyte, oocyte, sperm, and type B pancreatic cell. These cell types were selected because protein data for them is available and could be imported from the Bgee database of gene expression patterns . When viClostridioides difficile, Escherichia coli, Helicobacter pylori, Listeria monocytogenes, Mycobacterium tuberculosis, Pseudomonas putida, Salmonella enterica, Shigella flexneri, and Vibrio cholerae.Although most metabolic charts include pathways, reactions and enzymes for a single organism, BioCyc also includes several pan-genome PGDBs. These databases contain the union of all genes, enzymes, reactions, and pathways for all strains of a particular species for which annotated genomes are present in BioCyc. Thus, the metabolic chart for a pan-genome PGDB illustrates the full range of metabolic and transport functionality across members of the species. Pan-genome databases are currently available in BioCyc for nine medically important and well-studied species: A pan-genome PGDB is constructed as follows. A new, empty PGDB for a species is created. We select a so-called “lead PGDB” from the set of available strain-specific PGDBs. It is usually the earliest sequenced, most experimentally characterised, and best annotated strain. All of the lead organism’s replicons, genes, proteins, reactions, and pathways are imported into new the pan-genome PGDB. Thereafter, all remaining strain PGDBs from the chosen set are visited, and each protein-coding gene in that PGDB is checked for orthology with the genes in the pan-genome PGDB. If an ortholog is found, the software records the existence of the ortholog in the gene of the pan-genome PGDB. If no ortholog is found, then the gene is imported from the strain PGDB, along with its proteins and any reactions and pathways associated with that gene that are not yet in the pan-genome PGDB.Pseudomonas putida pan-genome, with both core and unique genes highlighted.Two additional search options are available for pan-genome metabolic charts. Under the web Highlight Gene(s) menu are commands to highlight either pan-genome core genes or pan-genome unique genes. Core genes are those for which orthologs exist in all strains of the species. Thus, this highlight command illustrates the metabolic and transport functionality that is a fundamental characteristic of the species. Unique genes are those that only exist in a single strain. Note that some reactions will be highlighted both as core and unique if they have multiple isozymes, one of which is part of the core genome and another of which is unique to one strain. A new visualization, currently only available in the downloadable PTools software running in desktop mode, is the community overview diagram, which combines the metabolic charts for multiple selected organisms, along with optionally one or more exchanged metabolites, into a single display see . The metThe user must specify the set of exchange metabolites to be included, if any. If particular metabolites are known to be exchanged, they can be specified explicitly, along with which organisms in the community import and export those metabolites. Alternatively, the user can request that the software suggests possible exchange metabolites. Suggestions are based on the presence of influx or efflux transporters for a metabolite, and/or the identification of metabolites that are consumed but not produced in a dead-end metabolite analysis.Most of the same search and highlight operations that are available for BioCyc single-organism metabolic charts have been extended to also support searching and highlighting across all the organisms that make up a community overview. Many of these highlight operations give the user the choice of applying them to all of the organisms in the community or to just one or a subset of them.Combining metabolic charts for multiple organisms into a single diagram facilitates new species-comparison operations. We have added four new highlighting operations: reactions present in all organisms, pathways present in all organisms, reactions unique to a single organism, and pathways unique to a single organism. A pathway is considered present in an organism so long as some variant of it is present. For example, one organism might have a different variant of an arginine degradation pathway than some other organism—for the purposes of this comparison, these are not considered unique pathways.A PTools metabolic chart can be used as a vehicle for metabolism-based interpretation of omics data, such as gene expression or metabolomics data. In this mode of use the chart color codes reactions and metabolites according to omics data values provided in an input file, and enables the user to see which pathways and reactions are more or less active under some set of experimental conditions. The tool automatically maps the omics data range to a user-selected color scale. A data series that consists of multiple time points or conditions can be viewed as an animation. In the web implementation, the control panel to the right of Users can input omics data to this tool in several ways. Data can be uploaded from a tab-delimited file, from a SmartTable, or imported directly from GEO  curated When transcriptomics or proteomics data are displayed, mousing over a reaction (or mousing over a metabolite when metabolomics data are shown) in a chart and clicking the Omics button in the resulting tooltip window generates an “omics-popup” window that graphs the data for the relevant objects over the entire data series. The user can choose whether this data is shown as a bar graph, an X-Y plot, or a heatmap. Omics data can be overlaid onto a community overview diagram, just as for a single-organism diagram. Users specify which of the organisms in the community have available omics datasets and supply the data for each organism in a separate file.N most highly perturbed pathways based on a pathway-perturbation score calculated from the omics data; (3) on a user-specified set of pathways (a Pathway Collage); (4) on a genome-scale regulatory network diagram called the regulatory overview [This tool is part of a full suite of omics analysis and visualization tools available in PTools. In addition to displaying omics data on metabolic charts, data can be viewed (1) on individual pathway diagrams; (2) on a table of the overview ; and (5)PTools supports a variety of other analysis tools, many of which are tightly integrated with metabolic charts, making visualizing analysis results from multiple biological perspectives easy. Tools integrated with metabolic charts include the following:SmartTables  enable uE. coli). The steady state reaction fluxes computed by a model can be painted onto the metabolic chart for that organism, to visualize the activity levels of different portions of the metabolic network under the modeled conditions.MetaFlux  enables A dead-end metabolite analysis tool generates lists of metabolites that are either consumed but not produced, or produced but not consumed by any reaction in an organism’s metabolic network. These metabolites represent either true cellular inputs and outputs, or gaps in the metabolic network corresponding to missing information. The metabolite lists generated by such an analysis can be highlighted on the metabolic chart.A chokepoint analysis tool identifies reactions that are potential drug targets because they represent chokepoints in the metabolic network, such that eliminating them would eliminate the only path to or from a particular metabolite. These reactions can be highlighted on a metabolic chart.The Pathway Collage Viewer  enables The Omics Dashboard  is a novIn this section we compare PTools metabolic charts with those from several other tools: KEGG Mapper , KEGG AtPTools metabolic charts have been developed over many years and have grown from a manually drawn diagram for a single organism to automatically formatted diagrams for more than 18,000 organisms. The charts depict which pathways are active in different cell types and can be used to analyze pan-genomes and organism communities. PTools charts are viewable online and printable as posters. Their myriad search operations enable users to rapidly locate enzymes, pathways, and metabolites of interest, and to compare the metabolic networks of different organisms. PTools metabolic charts are tightly integrated with other modules of PTools including quantitative metabolic models, SmartTables, computation of dead-end metabolites and metabolic choke points, and with Pathway Collages and the Omics Dashboard. A new JavaScript-based implementation of BioCyc charts provides higher-quality graphics and enables real-time semantic zooming of the diagrams and animated overlay of omics data with dynamic control of the color scale.Project name: Pathway Tools.https://brg.ai.sri.com/ptools/.Project home page: Operating systems: Linux, MacOS and Windows.Programming languages: Common Lisp and JavaScript.https://BioCyc.org/download.shtml.License: Pathway Tools is free to academic users, and is available for a fee to commercial users. To obtain the software, see