Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define “aero-types”, a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of  Escherichia coli genotypes grown under different conditions, which leads to the discovery of a stratified distribution of phenotypes. The observed distribution is determined by distinct ATP generation strategies, defined as “aero-types”, associated with optimal proteome allocation modulated upon differential usage of the electron-transport-chain enzymes. This mechanistic approach offers us a genome-scale understanding of the fitness landscape, and a fundamental interpretation of bacterial physiology and evolution dynamics.Genome-scale models enable quantitative prediction of bacterial phenotypes and a fine-grained description of the underlying optimal proteome allocation. Thus, we can now analyze the phenotypic potential of a large number of  Sewall Wright’s fitness landscape  represenThese well-studied fitness landscape models, whether discrete or continuous, address evolutionary dynamics towards an optimal phenotype based on the rare beneficial mutations that arise historically or in the course of microbial evolution experiments . DirecteAn alternative approach to explore the fitness landscape and phenotypic distribution comes from the solution space of a genome-scale metabolic model (M-model) , 18. GenE. coli, in particular, explicitly incorporate the full reconstruction of transcription and translation pathways to allow for quantitative predictions of proteome allocation at the gene level [E. coli populations on a fitness landscape.Expansion of the M-models to include constraints on the cost of protein biosynthesis has been improving the accuracy of phenotypic predictions for different organisms under various environments –28. The ne level –31 and tne level . A more ne level ), enabliE. coli phenotypic growth data in the rate-yield plane and find consistent non-uniformity in the fitness distribution. Both computationally and experimentally determined phenotypes display multiple distinct phenotypic categories that distribute in stripes on the rate-yield plane and form a landscape with a “stratified” topology. We then show, by detailed analysis of metabolic fluxes and protein expression, that the stratified topography of this phenotypic fitness landscape can be fully described by the energy production strategy, which in turn is determined by a balance between proteome allocation cost and the metabolic efficiency of ATP production. Interestingly, we find that a simple quantity—the fraction of total ATP that is generated by the ATP synthase (fATPS)—is capable of outlining the stratification. Consequently, we define E. coli “aero-types” based on the multimodal distribution of fATPS modulated through the discrete usage of electron-transport-chain enzymes. An aero-type not only describes the cellular respiratory behavior, but also indicates the associated metabolic state and proteomic compositions. Finally, we discuss how the aero-type, as an effective fitness descriptor, can be used to address important biological questions such as the predictability of microbial evolution and the interpretation of the rate-yield tradeoff.Here, we assemble and analyze large amounts of Y) and substrate uptake rate (q), to span the phenotypic space for E. coli (n = 199) includes characterizations of different naturally occurring E. coli strains, evolved gene knock-out mutants, and growth under various nutrient conditions. It is immediately noticeable that both the high and low yield regions are densely populated, yet the regions in between (0.2 < Y < 0.3gDW/g) are almost empty  and thermal stability (ΔG) by a random but small amount  the  at 37°C  and 42°C at 37°C , and conrylation .fATPS, fPGK, fACKr, etc.) formed an eight-element vector that we used as the explanatory variables in a stepwise linear regression analysis. The results showed that six of the ATP production fractions could explain 89.5% of the variation in phenotypic distance  ATP production by the ATP synthase (ATPS4rpp), and reactions catalyzed by the polyphosphate kinase (PPKr and PPK2r) in oxidative phosphorylation; 2) reactions catalyzed by the phosphoglycerate kinase (PGK) and the pyruvate kinase (PYK) in the lower glycolysis pathway; 3) the reaction catalyzed by the acetate kinase (ACKr) in mixed acid fermentation; 4) the reaction catalyzed by the succinyl-CoA synthetase (SUCOAS) in the TCA cycle; and 5) the reaction catalyzed by the ribose-phosphate diphosphokinase (PRPPS) in nucleotide biosynthesis. These quantities  are located within the top two hyperbolic bands on the rate-yield plane  as a simple, yet effective, quantification for the cell’s metabolic state, and key determining factor for the stratified phenotypic distribution on the rate-yield plane.In summary, we introduced the fraction of total ATP produced by the ATP synthase . Second, the average number of subunits per enzyme increases  reactions . For exaatalysis . CompareThese results reveal an intricate balance between proteome complexity and the energy requirements for cell growth. As the energy demand increases, more and more enzyme complexes are necessary to achieve higher ATP yield. However, larger complexes also require significantly more metabolic resources for their biosynthesis. Thus, once activated, these enzyme complexes should be used as much as possible, inducing necessary rewiring of the metabolic network for optimal balance in proteome allocation, and shifting the ATP production strategy to the next discrete state.i to v to describe the five populated phenotypes represented by the five peak values of fATPS (from fATPS = 0 to fATPS ∼ 0.71) observed in the strain sampling simulations. Computationally, we compare aero-type with the P/O ratio, a commonly used parameter that describes the cellular respiratory behavior. We show that the P/O ratio outlines only the local stoichiometry of the oxidative phosphorylation pathways. Aero-type offers a more global description of cellular fitness by representing the metabolic and phenotypic state, and the proteome complexity associated with a specific energy production scheme  and two from the cytochrome oxidase (cyoB from the cytochrome bo oxidase and cydB from the cytochrome bd-I oxidase) for genetic manipulation  would most likely leave the cell in aero-type i and ii, with lower Y and higher qglc. The mutants depleted of cydB and/or nuoB are, in principle, still accessible to all aero-types. However, it is less likely for the ΔnuoB mutant to have higher Y and lower qglc, because the NADH dehydrogenase I is almost always activated for aero-type iv and v.We resorted to the characterization of genetic mutations that may trigger a switch in the aero-type. According to the comprehensive decomposition of the ETC enzyme usage shown in pulation . We woulndh, ΔnuoB, ΔcydB, and ΔcyoB) and double  knockout strains to test the predicted phenotypic effect experimentally  and a quinol monooxygenase gene, ygiN, were removed from the E. coli genome. This mutant strain was characterized by the lowest possible Y and highest qglc, corresponding to aero-type i (fATPS = 0).We constructed the single  to ii (purple).Next, we confirmed the correlation between aero-type and the proteomic state of the mutant strains using RNA-Seq analysis . Hierarcn groups . ConsistIn short, we design mutant strains where the major ETC enzymes are removed combinatorially to perturb the cell’s respiratory potential and ATP production strategy. We show that the phenotypic outcome, proteome re-allocation, and the phenotypic aero-type switch of these strains are consistent with the computational predictions.E. coli is able to thrive under a variety of environmental conditions, from highly oxic to completely anoxic, with its amazingly versatile pool of fifteen primary dehydrogenases and ten terminal reductases [As a facultative anaerobe, ductases . So far,cydAB, cyoABCD, and appBC) and a quinol monooxygenase gene (ygiN) were set to zero. This mutant strain was shown to produce a phenotype that was almost incapable of oxygen utilization and presented fermentative behavior under oxic condition [E. coli under anoxic conditions [To answer this question, we created an in silico strain where the expression of all terminal cytochrome oxidase genes  is a governing parameter describing the discretization; and 3) the discretization is rooted in a balance between the modularity of proteome composition and metabolic functions underlying optimal growth.In this study, we develop a systems biology definition of the phenotypic fitness landscape based on the solution space of the ME model . Simulatandscape , 40. We E. coli “aero-type” to summarize the complex relationships between genotype, metabolic state, proteome allocation, and phenotype. We reason that a switch in the aero-type may occur if differential usage of the ETC enzymes is imposed by genetic mutations or environmental stresses. To confirm the hypothesis, we experimentally construct the mutant strains where major ETC enzymes are removed combinatorially, and show that the measured aero-types of the mutants are consistent with computational prediction.The direct correlation between a cell’s energy production strategy and the phenotypic landscape topography inspires the definition of the fPGK, fPYK and fACKr, With the aero-type defined as a key phenotypic descriptor, it is worth pointing out that discretization of ATP production through other reactions  and genetic perturbations  on the fitness landscape can be rationally evaluated based on how the cell’s energy production is regulated. In principle, such a fitness landscape should be a general and effective framework with which to understand adaptation and evolution of different cell types in a variety of organisms  under diverse conditions.q (qglc), mmol/gDW/h) and the biomass yield (gDW/g) to denote the E. coli phenotypic space.Rate and yield are the most fundamental quantities used to describe bacterial ecology and physiology. The rate can be measured as growth rate, or moles/grams of substrate, ATP, or biomass production per unit time. Yield is usually measured by moles/grams of biomass or ATP per unit of substrate. Regardless of which definition of rate and yield to use, these two physiological parameters are tightly correlated with each other. However, the exact form of the relationship is context-dependent, which may vary according to different experimental procedures and conditions. Here, we aim to resolve the controversy and provide a unified explanation for the condition-dependent rate-yield correlation. Therefore, the particular definition should not affect our investigation and discussion. Without loss of generality, and to compare with the genome-scale model simulations using glucose as carbon source, we choose to use the substrate (glucose) uptake rate (q) and growth rate (μ) are collected from two main types of experimental measurements  the global physiological parameters, 2) the in vivo turnover rate of metabolic enzymes, and 3) the protein-specific thermodynamic parameters. The first two categories of parameters are common to all ME-models, and thus are set to the default values as first developed in O’Brien et al. . ProteinWe showed that the FoldME model improved the precision and scope of prediction for the optimal proteome composition over a wide variety of perturbations, including temperature, nutrient availability, and genetic mutations, and is therefore suitable for the study of phenotypic distribution presented in this paper.E. coli using in silico strains reconstructed to represent the diversity of naturally occurring strains. We assume that adaptation is achieved through gradual accumulation of large amounts of mutations that emerge independently, each with a random small effect on the affected genes. To simulate the genome-scale consequence of this long-term dynamic evolutionary process and estimate its fitness effect, we design a two-step process: 1) select genes for mutation according to the probability of observing a mutation in each gene, and 2) determine the molecular effect of the mutation on the corresponding gene.The purpose of our sampling method is to evaluate the phenotypic distribution of E. coli strains collected from 1) the PATRIC database [E. coli (AIEC) strains. We compiled protein sequences for the 1,566 protein-coding genes present in the FoldME model, and performed pairwise sequence alignments of the protein from each strain against its homologous sequence in E. coli K12 MG1655 [E. coli strains  the database , and 3) 2 MG1655 . We foun strains .keff) of the affected enzyme with an exponentially distributed random number between 0.5 and 2. In the same time, we perturbed the enzyme’s thermo-stability (free energy of unfolding ΔG) by a random amount between -2 to 2 kcal/mol to account for (de)stabilizing mutations with a small effect. The direction of change in the enzyme efficiency and stability was assumed to be opposite considering the pleiotropic effects of mutations [In the second step, we perturbed the catalytic efficiency and the thermo-stability of the enzymes selected for mutation in the first step. The beneficial and deleterious effects of mutations were known to distribute exponentially, with many small‑effect mutations and fewer large‑effect ones , 70. To utations , such thFinally, to introduce environmental stresses, we simulated 100 strain samples at each temperature from 25 to 46°C, resulting in a total of 2,200 simulations.keff or the stability of the enzymes selected for mutation; 3) used a different wild-type keff profile according to the recent machine learning study [keff of the mutated enzyme with larger scaling factors. None of these modifications in the sampling procedure changed our main conclusion regarding a stratified phenotypic landscape determined by the multimodal distribution of fATPS. As an example, we showed the result for sampling simulations in which keff was scaled between 0.1 to 10 fold, and 0.01 to 100 fold  fixed the number of mutated genes to 10%, 20%, or 30% of the total number of modeled genes, and selected mutations assuming uniform mutation fixation frequency for all genes; 2) perturbed only the ng study ; and 4) fATPS and the stratified structure of the phenotypic fitness landscape, we performed sampling simulations where fATPS was constrained to its five most likely values: 0, 0.37, 0.53, 0.64, and 0.71. The constraint was formulated as followed:Vreaction_name denoted the flux of the corresponding reaction and p was the value that fATPS was constrained to. For every fATPS value, 24 sampling simulations were performed at each temperature from 25 to 46°C. However, this strong constraint caused many incompatibilities with the sampled genotype, resulting in a final 2,237 feasible and optimal solutions reported in To confirm the relationship between the multimodal distribution of fATPS was a result of the bias induced by growth rate difference at different temperatures, we performed additional sampling simulations at one particular temperature. In the same time, it was desirable to cover as much on the rate-yield space as possible. Thus, we examined the accessible range of qglc and Y  at each temperature in the previously described 2,200 sampling simulations : 3 around the average growth rate at 30°C , one close to the upper limit for growth at 30°C (0.65), and two slightly lower than the lower bound . Simulation at higher fixed growth rate generated large number of infeasible solutions, thus were not included. The results confirmed that at each simulated growth rate, fATPS showed similar multiple Gaussian distribution that differed only in the relative weight of each Gaussian. Because of the same number of peaks and the mean values, we reported in Next, to maximize instances in every discrete fATPS value for each aero-type to be normally distributed. It followed that fATPS calculated from the sampling simulations should be fitted to a mixture of multiple Gaussian distributions, each representing one aero-type. The number of Gaussian distributions (peaks) should be chosen as the number of aero-types determined based on the distinguishable metabolic (fATPS = 0 (the fully fermentative phenotype) as one “peak”, and fitted the remaining data with four Gaussians using Matlab.We assumed the etabolic  and protetabolic . TherefofATPS distribution constantly showed five peaks, although the heights of the peaks varied. Peaks around 0.0, 0.37 and 0.53 were clearly present throughout all data sets, whereas peaks around 0.64 and 0.71 could be blurred under certain conditions. This final uncertainty likely came from unresolved proteome complexity of the highly respiratory phenotypes, which should not impair the validity of the fitting and the sampling process.To check whether our choice for the number of peaks was consistent, we compared distributions generated from many separate sets of sampling simulations, including those using different sampling strategies for sensitivity analysis. The E. coli electron transport chain contains two types of enzymes: a dehydrogenase that oxidizes an electron donor and a cytochrome oxidase that reduces the electron acceptor (bo oxidase (CyoABCD) and cytochrome bd-I oxidase (CydAB) for the cytochrome oxidase.The acceptor . To creanuoB contains the N2 4Fe-4S cluster, which may play a role in proton translocation activity of the enzyme [bd-I oxidase, although both subunits are required for binding of the heme b595 and heme d components of cytochrome bd-I, subunit II encoded by gene cydB binds a structural ubiquinone-8 cofactor that may have a role in the dimer assembly [cyo operon results in nonfunctional enzymes, yet we choose to disrupt cyoB because it encodes subunit I which is involved in proton translocation [Three of the chosen ETC enzymes are multi-protein complexes, and we aim to choose the gene that maximally disrupts the function of the whole enzyme. For NADH dehydrogenase I, all subunits are required for the assembly or stability of a functional enzyme . The sube enzyme . For cytassembly . Similarlocation .E. coli K-12 MG1655 (ATCC 700926) as the recipient strain. Keio collection strains were used as donor strain for the generation of gene knockout cassettes containing a kanamycin resistance marker [The four single-ETC-gene-knockout and four double-ETC-gene-knockout strains were constructed with the P1 phage transduction method , using Ee marker . Knock-oCharacterizations were performed fully aerated, at 37°C, in 15 mL working volume tubes containing M9 glucose medium, as described in LaCroix et al. . CultureSamples were filtered through a 0.22 micrometer filter  at the same time OD measurements were taken, and the filtrate was analyzed for glucose concentrations using a high-performance liquid chromatography system  with an Aminex HPX-87H column . Glucose uptake rates in exponential growth were determined by best-fit linear regression of glucose concentrations versus cell dry weights, multiplied by growth rates over the same sample range.The oxygen uptake rate of each aerobic culture was determined by measuring the rate of dissolved oxygen depletion in an enclosed respirometer chamber using YSI 5300A Biological Oxygen Monitor System that utilized Clark type polarographic oxygen probes .pgi-deficient E. coli strain, growth-improved clones along the ALE trajectory were isolated and grown in M9 minimal medium supplemented with 4g/L glucose. Cells were then harvested while in exponential growth and genomic DNA was extracted using a KingFisher Flex Purification system previously validated for the high throughput platform mentioned below [To determine the mutations emerged during adaptive laboratory evolution of the ed below . Shotguned below , in whicTotal RNA was sampled from duplicate cultures. All strains were grown in M9 minimal medium supplemented with 4g/L glucose. 3 mL of cell broth (taken at OD600 ∼ 0.6) was immediately added to 2 volumes Qiagen RNAprotect Bacteria Reagent (6 mL), vortexed for 5 seconds, incubated at room temperature for 5 min, and immediately centrifuged for 10 min at 17,500 RPMs. The supernatant was decanted, and the cell pellet was stored at -80°C. Cell pellets were thawed and incubated with Readylyse Lysozyme, SuperaseIn, Protease K, and 20% SDS for 20 minutes at 37°C. Total RNA was isolated and purified using the RNeasy Plus Mini Kit (Qiagen) columns following vendor procedures. An on-column DNase treatment was performed for 30 minutes at room temperature. RNA was quantified using a spectrophotometer  and quality was assessed by running RNA electrophoresis on the Agilent 2100 Bioanalyzer . The rRNA was removed using Illumina Ribo-Zero rRNA Removal Kit (Gram-Negative Bacteria). Stranded RNA-Seq Kit (Kapa Biosystems) was used following the manufacturer’s protocol to create sequencing libraries with an average insert length of around ∼300 bp. Libraries were sequenced on an Illumina HiSeq 4000 instrument.log2(TPM+ 1)), referred to as log-TPM, was taken for the downstream analysis. The log-TPM values of the two biological replicates were highly correlated (R2 > 0.97), expect for the ΔndhΔcydB mutant (R2 = 0.91). Uncertainty of the ΔndhΔcydB mutant might come from partial knockout for one of the replicates, which showed relatively high expression of the cydB gene. We considered the aero-type assignment and other quantifications for this mutant to be less reliable compared to others.Raw sequencing reads were obtained as described above, and mapped to the reference genome NC_000913.3 using Bowtie 2.3.4.3  with theμ,Y ,qglc,qac) and our aero-type definition in the sampling simulations. It turned out that only qac and Y  showed significant correlation. Then, we computed the Pearson correlation between log-TPM and experimentally measured phenotypic parameters, and selected genes that were highly correlated with qac and Y , but not with μ. This process resulted in a set of 391 genes, which were used for generating the clustering diagram shown in Principal component analysis (PCA) of the log-TPM showed that the first four components could explain 84% of the variations throughout the expression profile. The first principal component was highly correlated with exchange rates such as the glucose/oxygen uptake rate and the acetate production rate, and the second with growth rate. These components, although highly explanatory, were enriched in gene clusters  that were non-specific to the conditions and phenotypes that we were interested in. Alternatively, the fourth component, explaining 5.4% of the overall variation, was highly enriched in genes involved TCA cycle, anaerobic respiration and ETC activity. Consequently, we considered selecting genes most representative for aero-type for subsequent hierarchical clustering analysis. First, we calculated the Spearman correlation between five phenotypic parameters (S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S1 FigE. coli strains (top left), for WT or strains with gene knockout (top right), under different nutrient conditions (bottom left), and for different E. coli strains (bottom right).(A) Visualization of a compendium of 199 experimental measurements. Contours of the phenotype density are overlaid on top of the experimental data (gray circles). (B) Experimental phenotypic distribution visualized by whether measurements are taken for WT or evolved (TIF)Click here for additional data file.S2 FigE. coli strains (left) and frequency of mutations per gene in the 2,200 sampling simulations (right).Comparison between the distributions of the number of observed mutations per gene for the 1,765 naturally occurring (TIF)Click here for additional data file.S3 FigY) decreases along the μ-isocline (aero-type decreases from v to i), flux through TCA cycle also decreases. Similarly, fluxes through acetate overflow are negatively correlated with PC1, hence as Y decreases, flux through acetate overflow increases. The opposite sign of correlation between fluxes through the two branches of glycolysis pathways nicely captures the trend that the glycolysis flux slowly switches from oxPPP to EMP as aero-type decreases from v to i. PC2  are not correlated with the flux through ATP synthase, and are not discussed in further details for the purpose of this paper. The name of the reactions are standard reaction IDs available for search on the BIGG database (http://bigg.ucsd.edu/). oxPPP: oxidative pentose phosphate pathway; EMP: Embden–Meyerhof–Parnas pathway; ED: Entner-Doudoroff pathway.The figures illustrate the observed correlation between phenotypic state and the metabolic fluxes, yet they can be best understood with the definition of “aero-type” introduced in later sections. (A) The metabolic states of the five aero-types can be clearly separated by the first principal component (PC1), representing 61.1% of the data variations. PC2 further decomposes metabolic states into sub-types. (B) PC1 is the only component that’s correlated (Pearson correlation = 0.97) with the flux through ATP synthase . Therefore data variations contained in PC1 best represent the observed differences in metabolic states between different aero-types. For example, the fluxes through the TCA cycle are positively correlated with PC1, hence the flux through ATP synthase. This correlation indicates that as the biomass yield ((TIF)Click here for additional data file.S4 FigE. coli strains under glucose minimal medium are highlighted on the rate-yield space. Data includes two wild-type strains at 37°C , one wild-type strain at 42°C , 7 strains evolved at 37°C , and 10 strains evolved at 42°C . (B) Mass fraction of the representative pathway is calculated using all genes involved in the corresponding pathway. The Pearson correlation between the biomass yield and the mass fraction of each pathway is shown. Usage of three pathways that are related to aerobic respiration is significantly correlated with biomass yield . (C) Biomass yield plotted against the mass fraction of genes involved in oxidative phosphorylation. Each point corresponds to a strain, labeled as in (A). (D) Biomass yield plotted against the mass fraction of genes involved in TCA cycle.(A) Phenotypic data of 20 (TIF)Click here for additional data file.S5 FigE. coli. Short and full name for the metabolites are listed as follows. 13dpg: 3-Phospho-D-glyceroyl phosphate; 3pg: 3-Phospho-D-glycerate; actp: acetyl phosphate; pyr: pyruvate; pep: phosphoenolpyruvate; succoa: succinyl-CoA; coa: coenzyme-A; prpp: 5-phospho-alpha-D-ribose 1-diphosphate; r5p: alpha-D-ribose 5-phosphate. The fraction of total ATP produced by each reaction varies significantly. The range of variation in the sampling simulations is indicated in the “Fraction” column. (B) Fraction of variations in the rate-yield phenotypic space explained by the indicated ATP-production reactions. 89.5% of the variations in phenotypic distance can be explained by the first six ATP-producing reactions. Among them, oxidative phosphorylation reactions ATPS4rpp and PPKr contributed the most. (C) Comparison between the actual phenotypic distance on the rate-yield plane and that reconstructed from the six ATP-producing reactions. A simulated phenotype is determined by a four-element vector containing the glucose uptake rate, acetate production rate, growth rate and biomass yield. Other typical phenotypic measurements are highly correlated with one or more chosen quantities, and are thus not included in the calculation. Then phenotypic distance is calculated as the Euclidean distance of this vector with respect to that of the wild-type solution at 37°C. Predictors of the stepwise linear regression are taken as the fraction of ATP produced by each reaction listed in (A).(A) Detailed information for the ATP-producing reactions in (TIF)Click here for additional data file.S6 Figμ-isoclines, which are used in the subsequent analysis shown in panel B, are highlighted. Growth rates shown on the μ-isoclines are computed relative to the wild-type growth rate calculated at 37°C. (B) Along each μ-isocline, the calculated fraction of total cellular ATP produced by ATP synthase (fATPS) is linearly correlated with biomass yield  and glucose uptake rate , with a positive and negative slope, respectively. The intercepts of these linear fitting at the minimum and maximum values of fATPS  provide a way to estimate the feasible range of qglc and Y. The estimations at each growth rate can be connected to draw the boundary of the rate-yield plane (gray shaded area in panel A). The accessible range of the phenotypic space generated this way encompasses the majority of data points from both experiments and model simulations.(A) Fitness distribution of the 2,200 simulated strains on the rate-yield plane (same as shown in (TIF)Click here for additional data file.S7 FigfATPS (hence positively correlated with the aero-type as defined in the later (A) ATP yield through the ATP synthase is positively correlated with (TIF)Click here for additional data file.S8 FigfATPS values: 0, 0.37, 0.53, 0.64 and 0.71. (B) Once fATPS is determined in the energy production strategy, finer structures can be seen on the phenotypic landscape. The distributions of fPGK, fACKr, and fPYK at each fixed fATPS value also show distinct multimodal distributions. The pie charts show the allowable energy production strategy that represents over 95% of the solutions at each fixed fATPS, all fractions shown have standard deviations smaller than 0.02. (C) At fixed fATPS, biomass yield is negatively correlated with the ratio fACKr/fPGK. Because acetate is secreted through the ACKr flux and no biomass is made, increase in fACKr reduces biomass yield. (D) At fixed fATPS, the overall ATP yield is positively correlated with the ratio (fACKr + fPYK)/fPGK. This ratio reflects the relative efficiency of all ATP-producing reactions in terms of ATP production per unit of substrate. The more inefficient reaction PGK is used , the lower the overall ATP yield is. As the yield of oxidative phosphorylation is much higher (∼34 ATP per glucose), the overall ATP yield increases with fATPS at fixed (fACKr + fPYK)/fPGK ratio.(A) Topography of the fitness landscape reconstructed from constrained sampling simulations at 5 most likely (TIF)Click here for additional data file.S9 FigY and qglc calculated for the sampled strains at each temperature are denoted by a circle, then the range of accessible values indicated by horizontal and vertical lines going through the average. For T = 25°C, 30°C, 37°C and 40°C, the optimal wild-type phenotype (square) is shown for reference. For T = 25°C, 30°C, and 40°C, shift in the preferred aero-type is shown by the difference in fATPS distribution. Eight μ-isoclines are drawn, each labeled with the relative growth rate with respect to the simulated optimal WT growth rate at 37°C. Distribution of fATPS at 30°C best captures the features of the full distribution, thus we select this temperature for the down-stream analysis. (B) The fATPS distribution of the 368 sampling simulations performed at 30°C and selected growth rate. Fitting to a mixture of four Gaussian distributions shows consistency with the observed stratified distribution shown in fATPS value shows a similar multi-modal distribution as the maximum fold change in enzyme efficiency increases to 10 and 100 fold in the sampling simulation.(A) The phenoptypic variations at each temperature. On this plot, the average values of (TIF)Click here for additional data file.S10 Figth and 75th percentiles, and whiskers extend to 1.5 times the interquartile range. Sample size in each aero-type is the same as in fATPS and the P/O ratio are tentatively binned into five separate groups based on their multimodal distribution, and mapped to the optimal solutions shown in panel B. (D) Comparison of the experimental and simulated relative abundances of selected genes  with respect to the ATP synthase. Length of the bar and error bar represent the average ratio and standard deviation for each aero-type as defined in (A) Major protein complexes involved in quinone turnover in the sampling simulations. Formate dehydrogenase N and O catalyze the same reaction, hence are designated to the same complex (FDN/O) for simplicity. Box plot of the normalized expression for the indicated protein complexes shows differential usage of the ETC enzyme between different aero-types. To enable direct comparison, the calculated mass fraction of the enzyme complexes is normalized by the total mass fraction of all ribosomal proteins to remove bias coming from different growth rates. The central red line of the box plot shows the median, the bottom and top edges indicate the 25(TIF)Click here for additional data file.S11 FigndhΔcydB, ΔcydB mutants were chosen to represent the higher aero-types v/iv, and the ΔnuoBΔcyoB mutant was chosen to represent a lower aero-type ii. Growth data at 30°C and 37°C nicely recapitulates the expected trend such that ΔndhΔcydB and ΔcydB stay in the region for aero-type iv and ΔnuoBΔcyoB in the region for aero-type ii. At 42°C, all three strains generate a lower biomass due to the temperature stress. However, they maintain well separated on the rate-yield plane representing the aero-type constraints caused by the removal of the respective ETC genes. Thus, the presented data supports the notion that the differential usage of the ETC genes determines the phenotypic aero-type of a cell.The Δ(TIF)Click here for additional data file.S12 Figpgi-deficient strain. (D) Intermediate evolutionary states were chosen at the indicated stages and characterized on the rate-yield plane. Four distinct genotypes were identified along the adaptive trajectory, indicated by red, blue, yellow, and green circles, respectively. Error bars indicate standard deviation of the biological duplicates.(A) The schematic of the proposed hierarchical energy production strategy. Blue and red arrows correspond to the thermodynamic and respiration-fermentation tradeoff, respectively. (B) A coarse-grained representation of the fitness landscape on the rate-yield plane. Color gradient indicates the level of proteome complexity, where blue represents the simpler proteome and red is the more complex proteome. (C) An example adaptive trajectory during the evolution of a (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.


Viability and metabolic assays are commonly used as proxies to assess the overall metabolism of microorganisms. The variety of these assays combined with little information provided by some assay kits or online protocols often leads to mistakes or poor interpretation of the results. In addition, the use of some of these assays is restricted to simple systems (mostly pure cultures), and care must be taken in their application to environmental samples. In this review, the necessary data are compiled to understand the reactions or measurements performed in many of the assays commonly used in various aspects of microbiology. Also, their relationships to each other, as metabolism links many of these assays, resulting in correlations between measured values and parameters, are discussed. Finally, the limitations of these assays are discussed. Similarly, the consumption of carbon sources and production of by-products  are also valuable tools. As these assays all focus on metabolism, there are correlations between some of them, making comparison or validation possible.Metabolism can be defined as the sum of all reactions in a living organism aimed at maintenance, development, and reproduction (division for microbes). Consequently, measuring all reactions that contribute to metabolism in microbial cells is impossible with current tools even if metabolomics using NMR and mass spectrometry (MS) can provide very valuable snapshots of the metabolites present . In addiMetabolic assays have been used for a wide variety of applications. For example, in ecological studies, such assays can be used to evaluate the number of active bacteria in a sample . However, in pharmacological and biotechnological applications, these assays are less often focused on metabolic rates and are mostly used to assess viability to compare the effects of different products on microbial cultures. Similarly, yield and other important information can be gathered from some of these assays or their combinations.Nevertheless, depending on the intended application parameters, such as volume and sensitivity, the necessary equipment can be a significant factor in the choice of a metabolic assay. For example, metabolic assays with pure cultures can have very different requirements from those with environmental or food samples. Indeed, many of these assays have been devised for pure cultures and are applicable only to such cultures. Many have been modified for applicability to environmental studies, but the resulting limitations are often neglected. In addition, the cost and practicality can also vary a lot, thus influencing potential application. Therefore, it is important to take these parameters into account and review the different assays.Using all the techniques described below, it is crucial to discriminate between activities  and amounts . In many studies, this difference is neglected, which results in directly linking growth to metabolic activity. Many studies have measured metabolic activity using growth (often measured by a net increase in cell number) as a proxy. Although such approximation can be understood as high metabolic activity is required for growth, it has major flaws. For example, in batch liquid culture, when entering the stationary phase, the cell number still increases while the metabolic activity is often already decreasing. In this context, it is often worth coupling both measures and determining the activity per cell over the course of the culture. Ultimately, at the end of the experiment, usually at the late stationary phase, there is a very large number of cells, but the overall metabolic activity is very low. On the other extreme of the spectrum, it was recently shown that even with no net biomass increase, biofilm could have a very high metabolic activity as measured by calorimetry . This isHaloferax volcanii, Haloarcula marismortui, and Halobacterium sodomense have been shown to perform such reduction  will have minimal (if any) reduction activity. This can be used to determine the respective proportion of active and inactive cells in various samples by comparison to microscopy count, for example . Similarp-tolyl)tetrazolium chloride (CTC), but also iodonitrotetrazolium chloride (INT) and 2,3- bis(2-methoxy-4-nitro-5-sulfophenyl)-2h-tetrazolium-5-carboxanilide inner salt (XTT); In addition, there are other limitations to the use of tetrazolium-based dyes that must be taken into account, especially for environmental studies. Several tetrazolium salts . Again, it is important to compare rates with other matching rates, for example, formazan production rates with oxygen consumption rates (see example in +/NADH) and their phosphorylated derivatives (NADP+/NADPH) are important redox-active molecules used in many catabolic and anabolic reactions. Ratios of NAD+ to NADH and NADP+ to NADPH are therefore considered key indicators of the overall intracellular redox potential and metabolic state. Because tetrazolium salts are often used to assess the activity of NADH or NADPH utilizing enzymes  and electron mediator (redox intermediate)   (Kern etmediate) . The ass+ and NADPH, the assays rely on the conversion of glucose-6-phosphate (G6-P) to 6-phosphogluconolactone by glucose-6-phosphate dehydrogenase , where the simultaneous reduction of NADP+ to NADPH is coupled to the reduction of a tetrazolium salt  have to be accounted for (see below). When considering only ATP, the general assumption is that microorganisms generate ATP through catabolic reactions and subsequently use it for “housekeeping,” growth, and replication. As a result, ATP is believed to indicate the presence of viable microorganisms in samples. Alternatively, as the intracellular concentration of ATP per cell has been shown to vary with changing environmental and physiological conditions, it can be used as a proxy to indicate the metabolic activity of cells . Indeed, to 5 mM . For a w5 mM  to oxidize luciferin into oxyluciferin with concomitant use of ATP and production of AMP. The overall reaction  releases2O2). Finally, a peroxidase (EC: 1.11.1.7) catalyzes the redox-coupled reaction of H2O2 with 4-aminoantipyrine (4-AAP) and N-ethyl-N-(3-sulfopropyl)-m-anisidine (ESPA), leading to the formation of a purple dye that is measured at 540 or 570 nm  to produce glycerol-3-phosphate and ADP. The glycerol-3-phosphate is then further oxidized by glycerol phosphate oxidase (EC: 1.1.3.21), producing dihydroxyacetone phosphate (glycerone phosphate) and hydrogen peroxide  into a colored compound. In addition, for some applications (mostly with cultures), there is also no need to remove cell culture medium or wash cells before adding the reagent, which can be fully automated for high throughput. Still, serial measurements are needed to assess the flow of intracellular and extracellular ATP and get a complete image of metabolic processes over time. If sample processing needs to be performed at a later stage , it is recommended to snap-freeze the samples in liquid nitrogen or on dry ice. For this assay, ATP extraction remains a crucial step and different extraction methods were tested on microbial culture samples, showing strong variation in ATP recovery . Still, 2 consumption, although many studies have shown that the two are often correlated (E. coli) and has been reported to be prone to artifacts, including incomplete stain penetration or false staining of live cells as dead  hydrolysis is considered to be a simple and affordable method of estimating microbial activity in various samples, including soils, sludges, marine sediments, and cell cultures . In thisrrelated . Still, rrelated . Intracerrelated . Such mi as dead  in many  as dead . FACS an490 nm) lies between 67,000 and 79,000 cm–1⋅M–1  to the sample. After a determined incubation period, the reaction is stopped using rather large amounts of organic solvents [chloroform:methanol (2:1) or acetone]. In many environmental samples, the solvent mixture also serves as a fluorescein extractant by dissolving membranes . In cellcm–1⋅M–1 . Such a cm–1⋅M–1  and its 2/H2O, NO3–/NO2– and Fe3+/Fe2+, for example (+/NAD(P)H have been discussed previously in the text.Electron acceptors are key players in microbial metabolism, providing energy for many other processes. Indeed, many microorganisms obtain their energy from redox reactions. Among these redox reactions, aerobic respiration, anaerobic respiration, and oxidation of reduced inorganic compounds rely on redox couples such as O example . The conOxygen is among the most used proxies to assess the metabolic activity of various organisms. This is also true for microorganisms and microbial oxygen consumption rates, and their measurement could be the topic of a review by itself. As a result, many methods have been developed to assess oxygen consumption by heterotrophs or production by photosynthetic microbes  can be used to measure several gases in gas phase. TDLAS measures the absorption of laser light in the near- to mid-infrared wavelength. Detection of gases can be improved using techniques such as wavelength modulation spectroscopy (WMS) or frequency modulation spectroscopy (FMS) . It workin vials , 2018. Faled air  using ot3–) and nitrite (NO2–) can be measured by high-performance liquid chromatography (HPLC), ion chromatography (IC), or ion-specific electrodes, the Griess reaction ethylenediamine (NED or NEDA). Most variations of the Griess assay were reviewed in Like many assays, the Griess reaction has been modified, and the original sulfanilic acid and 1-naphthylamine were replaced by sulfanilamide and Finally, it must be noted that the low cost and simplicity of the Griess reaction have attracted the attention of a wide spectrum of microbiologists ranging from soil scientists to clinical microbiologists.–3 g⋅L–1 − Ksp = 1.0842⋅10–10 (25°C)], which is measured turbidimetrically  ion-sensitive electrodes are very practical and are often used because they are available in macro and micro format. With microelectrodes that are used in situ (or ex situ), measurements are taken directly and possibly on-site. They have been extensively used in sediment, microbial mat, and biofilm studies , or nanoparticle of pyrite (FeS2)] that might be present on cell surfaces or attached to biofilms/extracellular polymeric substance (EPS) matrix  matrix . In huma) matrix .Many metal ions can be biologically reduced (and less frequently oxidized), thus releasing sufficient amounts of energy to sustain growth processes. Among those metals, Fe, Mn, V, Cr, Cu, Mo, As, Hg, Se, Au, U, and Tc have been reported to be reduced by microorganisms . Among t2+ and Fe3+) can be assessed easily using assays relying on the reaction of a chromogene with iron II   to convert Fe3+ into Fe2+. Many commercial kits do not provide the composition or the type of assay present in the kit. However, it is possible to decipher them according to the measurement wavelength of the assay  . Many ofiologist . UsuallyCarbon sources are the key to microbial metabolism, providing carbon for biosynthetic processes but also for energy metabolism in many cases. Current technologies (such as Biolog EcoPlate) using well-plate readers allow rapid screening of several important carbon sources for their utilization and eventually link such data with metagenomics data obtained separately . HoweverThe variety of carbon sources that can be used by microbes is indeed matched by a high number of assays to assess them. Especially, there are many enzymatic assays matching the substrates to be assessed. There are also plenty of chemical assays that are useful for assessing the uses of various carbon sources.o-toluidine, anthrone, carbazole) that allows the development of a color measurable by spectrophotomeric means  are hydrolyzed using a strong acid and heat. The reaction mixture contains a developing reagent  to form a colored compound in the reaction solution. Finally, oxidized o-dianisidine reacts with sulfuric acid to form a more stable colored product (Sigma Aldrich). The other enzymatic assay used for glucose determination relies on hexokinase (EC: 2.7.1.1). Glucose is phosphorylated by hexokinase, and the resulting glucose-6-phosphate is then oxidized to 6-phospho-gluconolactone by glucose-6-phosphate dehydrogenase (EC: 1.1.1.49). The NADH produced results in increased absorbance at 340 nm. Of note, glucose-6-phosphate dehydrogenase usually uses NADP+ as a cofactor, but this enzyme is rather unspecific with regard to the cofactor (NADP+ or NAD+). Thus, in many commercial assays, NAD+ is used because of its lower cost and better stability (2O2)] to measure glucose. The approach is cheap, although calibration is required as those meters have been optimized for blood tests and might have an offset in other media or solutions . The incorporation rate of the selected compounds is assumed to be proportional to the metabolic activity and the viability of anabolically active microbes. As a result, many incorporation assays relying on different detection methodologies have evolved. In addition, many of these techniques are compatible with further use of microscopy and single-cell analysis as well as (meta)genomics and (meta)proteomics tools . Thymidine is incorporated into microbial DNA and thus provides a measure of DNA synthesis. Similarly, leucine is incorporated into proteins and acts as a proxy for protein synthesis. The combination of both allows the measurements of protein synthesis and DNA synthesis in a single experiment. This approach assumes that thymidine and/or leucine is taken up into the cells; however, some microbes, especially strict autotrophs and oligotrophs, lack the necessary transporters for the uptake of many organic molecules, including amino acids and nucleotides . PracticStable isotope probing (SIP) refers to the use of stable isotope-labeled substrates to probe the microbial utilization of these specific substrates to build their macromolecules . As a reAs an alternative to MS, Raman microspectroscopy can be used in combination with optical tweezers or single-cell ejection to sort and separate labeled from unlabeled cells with high accuracy. The incorporation of heavy isotopes in the cell macromolecules results in a shift of the Raman peaks toward lower wavenumbers compared with unlabeled cell spectra . That deSubstrate analog probing is a term introduced by Hatzenpichler  to diffeSimilarly to radiolabeled thymidine incorporation, 5-bromo-2′-deoxyuridine (BrdU) has been used in the same way. Indeed, BrdU is an analog of the DNA precursor thymidine. When it is added to a sample containing growing cells , the cells incorporate BrdU into their DNA instead of thymidine. The BrdU-containing DNA is then detected using an anti-BrdU antibody. Then, a secondary antibody specific for the anti-BrdU primary antibody is used to capture the DNA (with the help of paramagnetic beads), thus providing a new sample of reduced complexity that corresponds to cells effectively dividing , which cin situ hybridization (FISH), nanoscale secondary ion mass spectrometry (nanoSIMS), (meta)genomics, and (meta)proteomics  has garnered much interest because of its compatibility with many other approaches such as FACS, fluorescence oteomics . In BONCoteomics . Indeed,oteomics . After aoteomics  using thoteomics .Similarly, biotin tags allowing quantification, affinity purification, and further identification of protein using MS have been used as well . AlthougIn living organisms, the result of metabolic activity leading to maintenance, development, and reproduction also includes some heat release , 2013. Trxn) can be easily estimated  and measuring the additional enthalpy caused by the hydration of CO2 into HCO3– (–108.5 kJ⋅mol–1) .In addition, isothermal calorimetry is a quantitative tool, and for some types of metabolism and media, the enthalpy of the overall metabolic reaction (ΔHJ⋅mol–1) , thus he3–, for example) or fermentation (sensu stricto). Similarly, calorespirometric ratios (heat per O2 and heat per CO2) can give valuable information. Heat per O2 can help detect the presence of anaerobic processes, and heat per CO2 can provide hints on the type of carbon source used  , the consequence is that many of them provide data that can be correlated or combined . Indeed, lipids) . For heamol–1 e- . Therefotabolism . Still, tabolism . In addiThe following conservation equations can now be written:d, e, and f are negative) allows for determining the other ones. Moreover, calorimetry can provide an additional equation to solve this system. Indeed, using the enthalpy of reaction provides an additional equation that can further help solve the equation system. The enthalpy of reaction (ΔHrxn) measured by the calorimeter can also be calculated using enthalpy of formation (ΔHf) or of combustion (ΔHc); using enthalpy of combustion simplifies the equation even more  (from ΔHH4) from The enthalpies of formation and combustion of organic matter, and especially organic matter from microbial biomass, can be found in several review papers , thus maHowever, such an approach requires that the metabolism of the organism (or group of organisms) is already known and that the organisms have been characterized up to the point where the growth equation can be written in an accurate manner.Many of these assays have been used for a long time and have been improved and optimized using new chemicals and instruments. Still, as mentioned for glucose, many studies continue to improve or adapt (not to say “hack” or “exploit”) these methods and approaches. Among the possible promising approaches for many of these assays, the lab on a chip is very appealing. Microfluidic systems, piezoelectric pumps, and sub-millimeter size sensors make it possible to build an array of sensors that can monitor different compounds over time and thus provide real-time monitoring of metabolic rates. It is likely that, in the future, many of these assays (or replacements for some of them) will be combined on single disposable chips. Alternatively, for now, 3D printing and rapid prototyping methods also allow rapid development of microfluidic devices that can incorporate such new sensors. Finally, it must be noted that such chips are very close to becoming reality, as several authors have already presented multiple parameters using separate microprobes or microfluidic chips simultaneously measuring pH, OD, and dissolved oxygen, for example . Still, OB and MA-F drafted and wrote the manuscript. TW and GB critically reviewed the manuscript and improved it. All authors read and approved the submitted version.GB was employed by the company Alta-Uro AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The loglinear pattern of respiratory scaling has been studied for over a century, while an increasing number of non-loglinear patterns have been found in the plant kingdom. Several previous studies had attempted to reconcile conflicting patterns from the aspects of statistical approaches and developmental stages of the organisms. However, the underlying enzymatic mechanism was largely ignored. Here, we propose an enzyme-driven law of photosynthetic scaling and test it in typical crop seedlings under different water conditions. The results showed that the key enzyme activity, the relative photosynthetic assimilation and the relative growth rate were all constrained by the available water, and the relationship between these biological traits and the available water supported our predictions. The enzyme-driven law appears to be more suitable to explain the curvature of photosynthetic scaling than the well-established power law, since it provides insight into the biochemical origin of photosynthetic assimilation. The scaling model of the power law has been widely employed to describe net primary production scaling5 in log–log space. Some researchers have applied log-transformed power law to dark respiration rates in trees8 and shrubs9. A few authors have used photosynthetic rates as metabolic indicators in ocean and plants11, but the subjects are marine systems and saplings, respectively. However, the relationship between photosynthetic rate and biomass in crop plants has not been studied under the light of power law.Photosynthesis is the fundamental metabolism for most of world’s ecosystems13, and towards its resolution, a series of arguments about the power law and the ‘true’ value of scaling exponents have been suggested17. It has theoretically been shown that the scaling exponents should range between 0.31 and 1.00 in aquatic invertebrates and algae18. The models based on polynomial equation have been used to describe and quantify the double-log linear deviation in basic metabolic scaling20. However, there are still contentious in the origin of photosynthetic assimilation and the methods used to estimate parameters21.Photosynthetic assimilation and respiration are both important metabolic processes, and they reverse each other. However, metabolic scaling of basic respiration is a longstanding problem22. Transformations of logarithms are referred to as patterns of “non-loglinear allometry”23 or patterns of “nonlinear allometry”24, which can be represented by adding other terms based on power laws27 or energy dynamics28. Models can explain the variation in scaling exponents in terms of resource allocation (Dynamic Energy Budget Model)29, metabolic level30 and thermodynamics processes31. A sigmoidal model predicted by the plant adaptive strategies hypothesis in NPP scaling1. However, although photosynthesis and respiration are a series of biochemical reactions, chemical kinetics is rarely used to explain the challenges of metabolic scaling. We have developed an enzyme-driven law (EDL), which was successfully applied to quantify the curvature and exponent variations of respiration scaling in microbes32. For terrestrial plants, photosynthesis differs significantly from respiration in microbes. Thus, a modified law is required to describe the photosynthetic scaling by incorporating biochemical mechanisms.Many log–log relationships of metabolic scaling have been shown to be nonlinear and cannot be represented by a simple power function34) to explain the biochemical origin of curvature in photosynthetic scaling.In this study, a new enzyme-driven law for photosynthetic scaling was derived from the hypothesis and both the relative rates of photosynthetic assimilation and growth were constrained by the key enzymatic activities. The law was tested by the data of photosynthesis and growth under different water conditions in represented crop seedlings (rice and maize3) and maize (C4) with different parameters  and C4 (maize) plants under the experiment conditions. Key enzyme activities varied with available water in accordance with the Michaelis–Menten equation  were bigger than that in maize , which meant maize was more sensitive to available water; The water potential in rice was lower than that in maize when the key enzyme activity was zero  and growth (lnM)  Fig. . Meanwhi Km Eqs. , Fig. 3. Km Eqs. . The AIC Km Eqs. . While t11, in which the effect of body size on the metabolism had not been considered. However, the remarkable36 effects of body size on the metabolism35 could not be ignored. It is widely acceptable that both the metabolism and growth are a series of enzymatic process, which are constrained by the key enzymatic activities37, so it is reasonable to deduce the general law of metabolic scaling. The enzyme-driven law may be the origin of various metabolic scaling.The enzyme-driven law had been tested gradually from enzymatic Eq. , Fig. 1,27 or the energy dynamics28. The constraint factor of limited resource(s) have been considered in metabolic level boundary hypothesis, which was used to explain the variations of scaling exponent against metabolic level30. Moreover, the similar nonlinear pattern of metabolic scaling had also been found in teleost fish38. Meanwhile, a model of growth scaling was used to describe the growth of many diverse species proposed with parameter-less curve39. In addition, the polynomial equation of energy dynamics had been used to describe the nonlinear curve of active metabolic scaling during ontogeny in birds and mammals28. Instead, our equation  and rice (Oryza sativa L. Nipponbare) are representative species of the C4/C3 photosynthetic pathways34 and therefore were used in this study. Seedlings of two leaves were cultivated in black plastic buckets filled with Hoagland solution (pH 6.0) under a series of water potentials, which were adjusted to using polyethyleneglycol 6,000 (PEG-6000). Before the stage of two leaves, photosynthetic photon flux density (PPFD) and temperature were controlled in a stepwise diurnal sequence, with daily PPFD of 300 μmol m−2 s−1, and a photoperiod of 16 h. Temperature varied between 25 ℃/daily and 23 ℃/might.Maize  was used for the measurement of photosynthetic rate. Sample CO2, block temperature and photosynthetic active radiation (PAR) were set at 400 μmol mol−1, 36 ℃ and 1,200 μmol m−2 s−1 respectively. Dry weights of plants were obtained by oven drying at 105 °C for 30 min and then at 65 °C for 48 h.A portable open system , equipped with a CO42. 0.2 g of leaf was put into a precooled mortar, then 100 mmol/ L of precooled Tris–HCl buffer ; This sample was then centrifuged 4 ℃ for 20 min at 15,000 r/m. Then, we used supernatant fluid to measure the activity of RuBPCase and PEPCase with spectrophotometry43.The fluid enzymes were extracted by the modified li-ren’s method37, we hypothesized that the relative change in photosynthetic assimilation (dQ/Q) was constrained by the change in key enzymatic activity (dvq) at individual level, including the effects of body size40. Hence, the relationship between photosynthetic assimilation rate (Q) and the key enzymatic activities (v) was obtained:According to metabolism is a series of enzymatic processesv and substrate concentration S was assumed to abide by the Michaelis–Menten equation, therefore the relationship between Q and S can be described as follows:Qm is the maximum metabolic rate when S approaches saturation, Kq is a half-saturation constant , R represent resource. S = R − R0, lnQ0 = lnQ when S equals zero.The relationship between dvg)41, which included the effects of body size on the growth40. The substrate-dependent equation of logarithmic body mass can be obtained by the integration of Mm is the maximum body mass when S approaches saturation. lnM = lnM0 when S = 0, Km is the half-saturation constant.We could hypothesize that the relative change in growth


Pseudomonas aeruginosa is an opportunistic human pathogen that is well known for causing infections in the airways of people with cystic fibrosis. Although it is clear that P. aeruginosa is metabolically well adapted to life in the CF lung, little is currently known about how the organism metabolizes the nutrients available in the airways. In this work, we used a combination of gene expression and isotope tracer (“fluxomic”) analyses to find out exactly where the input carbon goes during growth on two CF-relevant carbon sources, acetate and glycerol (derived from the breakdown of lung surfactant). We found that carbon is routed (“fluxed”) through very different pathways during growth on these substrates and that this is accompanied by an unexpected remodeling of the cell’s electron transfer pathways. Having access to this “blueprint” is important because the metabolism of P. aeruginosa is increasingly being recognized as a target for the development of much-needed antimicrobial agents. Pseudomonas aeruginosa is an opportunistic human pathogen, particularly noted for causing infections in the lungs of people with cystic fibrosis (CF). Previous studies have shown that the gene expression profile of P. aeruginosa appears to converge toward a common metabolic program as the organism adapts to the CF airway environment. However, we still have only a limited understanding of how these transcriptional changes impact metabolic flux at the systems level. To address this, we analyzed the transcriptome, proteome, and fluxome of P. aeruginosa grown on glycerol or acetate. These carbon sources were chosen because they are the primary breakdown products of an airway surfactant, phosphatidylcholine, which is known to be a major carbon source for P. aeruginosa in CF airways. We show that the fluxes of carbon throughout central metabolism are radically different among carbon sources. For example, the newly recognized “EDEMP cycle”  plays an important role in supplying NADPH during growth on glycerol. In contrast, the EDEMP cycle is attenuated during growth on acetate, and instead, NADPH is primarily supplied by the reaction catalyzed by isocitrate dehydrogenase(s). Perhaps more importantly, our proteomic and transcriptomic analyses revealed a global remodeling of gene expression during growth on the different carbon sources, with unanticipated impacts on aerobic denitrification, electron transport chain architecture, and the redox economy of the cell. Collectively, these data highlight the remarkable metabolic plasticity of P. aeruginosa; that plasticity allows the organism to seamlessly segue between different carbon sources, maximizing the energetic yield from each. Pseudomonas aeruginosa is an opportunistic pathogen. This cosmopolitan microbe has become one of the most frequently detected causative agents of nosocomial infection  such as palmitate. The liberated glycerol is then metabolized through the action of enzymes encoded by the glp operon, whereas the FAs are iteratively degraded by β-oxidation to yield acetyl CoA (acetyl-coenzyme A). The acetyl moiety is then shuttled into the tricarboxylic acid (TCA) cycle and the glyoxylate shunt to generate energy and gluconeogenic precursors for biomass production, respectively. Other sources of short-chain fatty acids in CF airways have also been recently identified , coupled with reverse genetics, to systematically investigate the pathway(s) of carbon assimilation during growth on acetate and glycerol. Surprisingly, the presence of these different carbon sources not only leads to a “rewiring” of central metabolism, it also gives rise to profound changes in the levels of expression of pathogenicity-associated functions, including denitrification, redox balance mechanisms, and the electron transport chain (ETC). These data underline the striking metabolic flexibility of P. aeruginosa, which allows this organism to carry out efficient, real-time free energy conservation, a trait which is likely to aid its ability to proliferate under diverse environmental conditions.Despite the obvious importance of these substrates for pathophysiology, we still do not know how glycerol and acetate impact the metabolism, redox balance, and gene expression profile of P. aeruginosa during cultivation on acetate or glycerol as a single carbon source. This revealed that P. aeruginosa grew more slowly in glycerol (μmax growth rate = 0.37 ± 0.01 h−1) than in acetate (μmax = 0.80 ± 0.01 h−1), glucose (μmax = 0.88 ± 0.05 h−1), or succinate (μmax = 0.87 ± 0.05 h−1)  in baffled shake flasks containing MOPS (morpholinepropanesulfonic acid)-buffered media. This allowed us to elucidate specific impact of carbon source utilization on P. aeruginosa metabolism and physiology without the confounding factors of nutrient and oxygen limitation that accompany entry into the stationary phase.We first examined the growth characteristics of P. aeruginosa central carbon metabolism and also are thought to have distinct effects on redox metabolism  (see Transcriptome sequencing (RNA-Seq) analyses of quadruplicate biological replicates yielded quantification of the mRNA levels from 5,578 genes. After normalization and statistical analysis  to C, we −2) see  15)..P value 10.1128/mBio.02684-19.1FIG S1P. aeruginosa. (A) Principal-component analysis plot (PCAplot) of RNA-Seq data. (B) Scatterplot matrix of gene-level and isoform-level expression (csScatterMatrix). (C) Scatterplot comparing the FPKM values determined under the two conditions (csScatter). (D) Principal-component analysis of proteomic data. (E) Heat map and hierarchical clustering of proteomic data. (F) Pairwise comparisons of proteomic data. Download FIG S1, TIF file, 2.4 MB.Transcriptomic analysis (CummeRbund) and proteomic analysis (LIMMA Package) of MOPS-acetate-grown versus MOPS-glycerol-grown grown Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.8DATA SET S1Data Set S1, XLSX file, 1 MB.Summary of the electron transport chain-associated omic changes described in the main text. Full transcriptomic and proteomic data are provided in Table B and Table C, respectively. Download Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. aeruginosa to date. Following normalization and statistical analysis   transcriptome and proteome analysis carried out on analysis , we idenl to −2) . There wl to −2) .P value ≤ 0.01; log2 fold change greater than or equal to 2 or less than or equal to −2) as Voronoi tree maps. This provided a global overview of the proteomic consequences of growth in each carbon source. As shown in To enable a more detailed analysis of the ‘omic alterations between these conditions, we used the proteomaps Web service  to illusPseudomonas species are scarce. In addition, all P. aeruginosa metabolic flux analysis (MFA) studies reported to date have been carried out using glucose as a sole carbon source  using three separate tracers per carbon source   . The cal10.1128/mBio.02684-19.9DATA SET S213C fluxomics data of MOPS-acetate-grown and MOPS-glycerol-grown P. aeruginosa, including calculations for anabolic demand (A), OpenFLUX SimVector files (B), reaction network (C), goodness of fit (D), and metabolic fluxes (E). Download Data Set S2, XLSX file, 0.1 MB.Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the During growth on glycerol, there was strong induction of the glycerol uptake system at both the proteomic and transcriptomic levels . Glycero27–bis-phosphate , then to fructose 6-phosphate (F6P), and finally to glucose 6-phosphate (G6P) through the action of Fda, Fbp, and Pgi, respectively. This route is a necessary prerequisite for the generation of hexose sugars for cell wall synthesis and biomass production. The alternative fate of DHAP is catabolic. Here, G3P is converted to 1,3-bis-phosphoglycerate for glycolytic energy production  of the flux proceeding through the ED pathway-catalyzed reactions as follows: glucose → gluconate → 6-phosphogluconate → 2-keto-3-deoxy-6-phosphogluconate → G3P/pyruvate  through the action of triose phosphate isomerase (TpiA). DHAP and G3P are subsequently converted into fructose 1,6-oduction . Howevereactions . This “Epyruvate . Intrigupyruvate .P. aeruginosa was grown on glycerol, the fluxomics indicated that about 15% of the triose phosphates (G3P + DHAP) were diverted into the EDEMP cycle to form hexose phosphates (P. aeruginosa lacks phosphogluconate dehydrogenase activity (a major source of NADPH in many other species), so this apparently futile cycling reaction may be of major importance for growth. Indeed, more than two-thirds of the carbon in the malate pool was shuttled back to pyruvate through the action of malic enzyme, with less than one-third being converted to oxaloacetate via the malate dehydrogenase reaction. Overall, glycerol metabolism seems to be characterized by operation of the EDEMP cycle, net catabolism of G3P to pyruvate, and cycling of the latter through the pyruvate shunt.Consistent with the proteomic/transcriptomic data, when osphates . Hexose osphates . HoweverP. aeruginosa 3234 [PA3234]) , which are known to be essential for growth on acetate , 34, andPA3234]) . The TCAPA3234]) . Unlike one pool .in vivo. This presumably relieves the limitation on oxaloacetate usage in anabolism. At the steady state, our flux data show that around two-thirds of the carbon flowing through the branch point is fluxed through the isocitrate dehydrogenases.Carbon fluxes in acetate-grown cultures were very different from those observed in glycerol-grown cells. First, the net flux through the lower reactions of the ED pathway was in the gluconeogenic direction. Second, the flow of carbon through the EDEMP cycle was much lower than that observed in glycerol, and the flux terminated at the gluconeogenic endpoint, G6P. The absence of flux through the G6P dehydrogenase (Zwf) reaction following this point is significant, since this reaction is often thought of as a major source of NADPH for anabolism. Third, and compounding this, the extent of carbon cycling through the NADPH-producing pyruvate shunt was low. Fourth, most of the carbon for anabolism was derived from oxaloacetate rather than malate. Presumably, this may reflect the increased rate of conversion of malate to oxaloacetate by the acetate-induced malate:quinone oxidoreductase (MqoB). However, perhaps the most notable difference was that during steady-state growth on acetate, around one-third of the carbon reaching the TCA cycle-glyoxylate shunt branch point was redirected into the glyoxylate shunt.  The glyoxylate shunt serves to supply the cell with malate and thence  also the gluconeogenic precursor, oxaloacetate. In an elegant feedback loop, high levels of oxaloacetate (such as would accumulate if the NADPH supply for anabolism was limiting) stimulate the activity of one of the isocitrate dehydrogenase isozymes, IDH, thereby restoring flux through the TCA cycle . One outP. aeruginosa by providing sufficient reducing power (with respect to NADPH) to counteract host-mediated oxidative stress , the HQNO (N-oxo-2-heptyl-4-hydroxyquinoline)-resistant proton pump Nqr (encoded by nqrA to nqrF [PA2994 to PA2999]), and a single-subunit flavoenzyme which does not participate in ion translocation   . All thrPA4538]) .P. aeruginosa respiratory chain is its use of high-affinity terminal oxidases, even during aerobic growth. This unusual wiring of the P. aeruginosa ETC is thought to drive the formation of a microaerobic environment, a trait that may give the pathogen a fitness advantage during infection  pool or by responding to electron flux through the terminal oxidases. UQ acts as the electron donor for complex III, the quinol oxidases (Cyo), and the cyanide-insensitive terminal oxidase, CIO. Complex III transfers electrons to a c-type cytochrome, which then acts as the electron donor for the terminal cytochrome oxidases Cox, Cco1, and Cco2. Cco1 is constitutively expressed at high levels, whereas Cco2 is thought to support growth under conditions of low-oxygen tension (∼2% O2), as it is regulated by the anaerobic sensor Anr ..P. aerug+ or NADP+ by NADPH or NADH, respectively. The primary physiological role of Sth is thought to be in the NAD+-dependent reoxidation of NADPH  were increased ca. 3-fold during growth on acetate. Pyridine nucleotide transhydrogenases catalyze the reversible reduction of either NADof NADPH . Reoxidaof NADPH . Interesnditions .P. aeruginosa cultured in glycerol has a significantly lower growth rate than cells grown in acetate, glucose, or succinate (P. aeruginosa cells grown in MOPS (morpholinepropanesulfonic acid) media with various carbon sources. To do this, we introduced an enhanced green fluorescent protein (eGFP)-expressing plasmid (pMF230) to PAO1 and cultured the strain in MOPS medium containing different carbon sources  is dependent on growth rate.As noted earlier, uccinate . This louccinate , suggestuccinate . To inve sources . When ce10.1128/mBio.02684-19.2FIG S2+ ratios alongside CFU data from cells grown in MOPS. (A) Acetate. (B) Glycerol. (C) Glucose. (D) Succinate. NADPH/NADP+ ratios alongside CFU data from cells grown in MOPS. (E) Acetate. (F) Glycerol. (G) Glucose. (H) Succinate. Values represent total NADP(H) concentrations detected over multiple time points from cells grown in MOPS. (I) Acetate. (J) Glycerol. (K) Glucose. (L) Succinate. Values represent total NAD(H) concentrations detected over multiple time points from cells grown in MOPS. (M) Acetate. (N) Glycerol. (O) Glucose. (P) Succinate. Three biological replicates were analyzed per time point. Download FIG S2, TIF file, 1.9 MB.NADH/NADCopyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.3FIG S3P. aeruginosa PAO1 carrying chromosomal promoter. Data represent lux fusions for the promoter regions indicated above for cells grown on various carbon sources, including MOPS-acetate, MOPS-glucose, MOPS-glycerol, and MOPS-succinate. (A) aceA. (B) glcB. (C) cco1. (D) cco2. (E) cox. (F) cyo. (G) glpD. (H) nir. Values were normalized to OD600 (RLU/OD600). Three biological replicates were analyzed per sample. Data were analyzed in GraphPad Prism (V 6.01) using t test statistical analysis . Download FIG S3, TIF file, 1.8 MB.Luciferase detection in wild-type Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.10DATA SET S3Data Set S3, XLSX file, 0.1 MB.Correlation plot calculations to illustrate the log2 fold change differences in the transcriptomic, proteomic, and fluxomic data (A to C) and cell size data (D). Download Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 is limiting, P. aeruginosa can use nitrate as an alternative electron acceptor. This is made possible by the presence of nitrate reductase (NAR), which can accept electrons from the UQ pool, and nitrite reductase (NIR), which receives electrons via complex III and cytochrome c  and those encoding nitrous oxide (N2O) utilization (nos) are unlinked, whereas the genes encoding nitrite respiration (nir) and nitric oxide respiration (nor) are adjacent. In addition to the enzymes named above, the denitrification operons harbor genes for ancillary functions such as cofactor synthesis, transport, and protein maturation  sensor and contains a ferrous heme. Dnr protein expression was upregulated 2.8-fold during growth on acetate compared with glycerol  and energy (ATP) balances during growth on each carbon source . This re10.1128/mBio.02684-19.4FIG S43. Three biological replicates were analyzed per time point. (D) CFUs. (E) NAD(H) concentrations. (F) NADP(H) concentrations extracted from PAO1 wild-type (WT) and Δdnr cells grown in MOPS-acetate with or without 20 mM KNO3. Three biological replicates were analyzed per time point. Download FIG S4, TIF file, 2.7 MB.(A) CFUs. (B) NAD(H) concentrations. (C) NADP(H) concentrations. Data represent concentrations extracted from wild-type PAO1 grown in MOPS-acetate with or without 20 mM KNOCopyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the + ratio toward a more reduced state . To determine if aerobic denitrification might be used by P. aeruginosa to correct the reduced status of its redox pool, we therefore examined the impact of nitrate addition on the NADH/NAD+ ratio. As shown in + ratio during exponential growth. Absolute quantitation of the NADH and NAD+ levels  expression was abolished in the dnr mutant and also in an anr mutant (Anr controls the expression of dnr)  . Crucialfication , the dnrnd NADPH . We conc10.1128/mBio.02684-19.5FIG S5P. aeruginosa in MOPS-glucose, MOPS-succinate, or MOPS-acetate. NirS was not expressed in a Δanr or Δdnr mutant. Isocitrate dehydrogenases ICD and IDH were used as loading controls. (B) Expression of NirS during exponential growth of P. aeruginosa in MOPS-acetate or in MOPS-acetate plus sodium nitrate (20 mM). Sodium nitrate (20 mM) was capable of inducing NirS expression in the WT strain and in a ΔroxSR mutant but not in a Δanr or Δdnr mutant. Three biological replicates were analyzed per sample. The Δanr, Δdnr, and ΔroxR lanes are representative of the triplicate analyses. Download FIG S5, TIF file, 2.1 MB.Western blotting. (A) Expression of the Anr/Dnr-regulated denitrification enzyme NirS during exponential growth of Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, Basfia succiniciproducens, and P. putida in single carbon sources  during growth on acetate and increased cox expression during growth on glycerol. This suggests that different terminal oxidases are employed under different growth conditions, allowing metabolism to be optimally adjusted for energy generation  in the cell  strain defective in denitrification was unable to maintain optimal redox homeostasis. However, growth was not affected in the Δdnr mutant, perhaps suggesting that the organism has additional (possibly compensatory) mechanisms to deal with redox dysregulation . Anr dimerization is thought to be dependent on the formation of an oxygen-liable [4Fe-4S]2+ reaction, which is destabilized and disassociates in the presence of oxygen, abolishing Anr activity. However, there is accruing evidence indicating that Anr may be active even in the presence of oxygen. For example, Jackson et al. have shown that choline catabolism leads to aerobic expression of the Anr regulon, suggesting that Anr activity can be sustained in the presence of oxygen. Also, Anr overexpression in well-aerated cultures was previously shown to increase the levels of dnr transcript 29-fold  and cell length . A previously determined conversion factor of 0.42 g cell dry weight (CDW) per OD unit was used to calculate biomass-specific rates and yields from the obtained OD600 values  Oligonucleotide primers used in this study. (B) Bacterial strains and plasmids used in this study , 83, 103Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 600 of 0.5  and added to an equal volume of RNAlater. Samples were then sedimented in an Eppendorf 5810R centrifuge at 3,220 × g for 15 min (4°C), and the pellets were stored at –80°C. Total RNA was isolated as described previously (PAO1 was grown in 40 ml MOPS with acetate or glycerol as the sole carbon source (in quadruplicate) in baffled flasks (500 ml) at 37°C with shaking (250 rpm). An aliquot (5 ml) of culture was harvested from each sample at an ODeviously , followehttp://www.pseudomonas.com/) using TopHat v.2.0.3  ( v.2.0.3  and Bowt v.2.0.3  with an  v.2.0.3  with def v.2.0.3 . Gene exP. aeruginosa cells (OD600 = 0.5) (30 ml) were harvested under growth conditions identical to those reported in the transcriptomics section above. Pellets were resuspended in lysis buffer  with cOmplete Mini protease inhibitor cocktail (Roche). Following three rounds of sonication (3 × 10 s) on ice, supernatants were clarified by sedimentation  in an Eppendorf 5424R centrifuge. Aliquots (100 μg) of each sample were reduced with TCEP, alkylated with iodoacetamide, and labeled with tandem mass tags (TMTs). TMT labeling was performed according to the protocol specified by the manufacturer (Thermo Fisher).18 precolumn  from an Ultimate 3000 autosampler with 0.1% (vol/vol) formic acid for 3 min at a flow rate of 10 μl/min. Separation of peptides was performed by C18 reverse-phase chromatography at a flow rate of 300 nl/min using a Thermo Scientific reverse-phase nano EASY-spray column . Solvent A was water–0.1% formic acid, and solvent B was 80% (vol/vol) acetonitrile–20% water–0.1% formic acid. The linear gradient employed was 2% to 40% solvent B over 93 min .Dried fractions from the high-pH reverse-phase separations were resuspended in 30 μl of 0.1% (vol/vol) formic acid (14 combined fractions). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments were performed using a Dionex Ultimate 3000 rapid separation LC (RSLC) nano-ultraperformance liquid chromatography (nanoUPLC) system  and a Lumos Orbitrap mass spectrometer . Peptides were loaded onto a Thermo Scientific PepMap 100 Cm/z values representing eluting peptide ions were measured in an Orbitrap mass analyzer  and were scanned at between m/z 380 and 1,500 Da. Data-dependent MS/MS scans (top speed) were employed to automatically isolate and fragment precursor ions by collision-induced dissociation  analyzed in the linear ion trap. Singly charged ions and ions with unassigned charge states were excluded from selection for MS/MS, and a dynamic exclusion window of 70 s was employed. The top 10 most abundant fragment ions from each MS/MS event were then selected for a further stage of fragmentation by synchronous precursor selection (SPS) and MS/MS/MS (MS3) . The m/z values and relative abundances of each reporter ion and of all fragments  in each MS3 step were measured in the Orbitrap analyzer, which was set at a resolution of 60,000. This was performed in cycles of 10 MS3 events, after which the Lumos instrument reverted to scanning the m/z ratios of the intact peptide ions and the cycle continued.The eluted peptides were sprayed into the mass spectrometer by means of an EASY-Spray source (Thermo Fisher Scientific Inc.). All MS (MS3)  in the hPseudomonas aeruginosa data (common repository of adventitious proteins [cRAP] v1.0) .Proteome Discoverer v2.1 (Thermo Fisher Scientific) and Mascot (Matrix Science) v2.6 were used to process raw data files. Data were aligned with UniProt t test with variances moderated by the use of Limma’s empirical Bayes method. P values were adjusted for multiple testing by the Benjamini Hochberg method  in 96-well microplates (Greiner Bio-One) , covered with gas-permeable imaging seals . Luciferase expression was assessed every 30 min  for up to 24 h. Growth was assessed by taking OD readings at 600 nm simultaneously with the luminescence readings. Luciferase readings were expressed as relative luminometer units (RLU) normalized to OD600 in order to control for growth rate differences across the selected carbon sources.Luciferase and OD13C flux experiments, naturally labeled acetate and glycerol were replaced with three separate tracers per carbon source to maximize data set resolution and to accurately determine substrate uptake. Naturally labeled glycerol was replaced with glycerol (99%), [2-13C]glycerol (99%), and an equimolar mixture of [U-13C3] glycerol (99%)  and naturally labeled glycerol. Naturally labeled acetate was replaced with 99% [1-13C]sodium acetate or [2-13C]sodium acetate or a molar 1:1 mixture of [U-13C2] sodium acetate  and natural sodium acetate.Starter cultures were grown In LB medium. For the second and main cultures, PAO1 was grown in MOPS minimal media with 60 mM acetate or 40 mM glycerol as the sole carbon source (120 mM carbon). For P. aeruginosa PAO1  of the final sampled cell concentration to exclude potential interference of nonlabeled inoculum with subsequent calculations of flux  in an orbital shaker . These growth conditions were selected to ensure sufficient aeration during cultivation. The oxygen level was maintained above 80% of saturation under growth conditions identical to those used previously for osa PAO1 . In cult of flux .Mass isotopomer labeling analysis of proteinogenic amino acids, mass isotopomer labeling analysis of cell sugar monomers (glucose and glucosamine), and metabolic reaction network and flux calculations were carried out as described previously .−1) equipped with refractive index (RI) and UV detectors (210 nm) with 50 mM H2SO4 as the eluent , were quantified in filtered culture supernatants  using isocratic high-performance liquid chromatography   at 37°C with shaking at 250 rpm, using a culture volume of 150 ml in a 2-liter baffled Erlenmeyer flask. For each NAD(P)(H) extraction, 1.8 ml of culture was removed and immediately added to 7.5 ml ice-cold 100% methanol followed by centrifugation at 3,220 × g for 14 min at 4°C to obtain a cell pellet. The pellet was resuspended in 0.2 M HCl for NAD(P)+ or 0.2 M NaOH for NAD(P)H extraction, after which incubation was performed at 52.5°C for 10 min followed by incubation on ice for 5 min. HCl or NaOH was then neutralized by the dropwise addition of 0.1 M NaOH or 0.1 M HCl, respectively, while vortex mixing was performed at low speed. The mixture was then centrifuged for 5 min at 15,800 × g, and 135 μl of the supernatant was removed for immediate NAD(P)(H) measurement or storage at —80°C. Samples were stored for a maximum of 1 week before measurement.2O) was prepared. The reagent mix was incubated at 30°C and primed to injectors in a BMG Labtech FLUOstar Omega microplate reader. Aliquots (15 μl) of NAD(P)(H) extracts were added to individual wells of a 96-well microtiter plate, which was then incubated in the microplate reader at 30°C. Reagent master mix (80 μl) was added via a microplate reader injector (300 μl s−1) and vigorously mixed  followed by static incubation for 10 min. Immediately before measurement, a solution of alcohol dehydrogenase (1 mg ml−1 in 0.1 M bicine) was prepared for NAD(H) measurement or glucose 6-phosphate dehydrogenase (0.1 mg ml−1 in 0.1 M bicine) for NAD(P)(H) measurement and primed to a second injector. To start the reaction, each well was injected (300 μl s−1) with 5 μl of enzyme solution, followed by vigorous mixing . The absorbance at 570 nm was then recorded every 30 to 60 s for 20 min, with vigorous shaking  before each read. Slopes from plots representing absorbance over time were calculated for NAD(P)H and NAD(P)+ and were then used to calculate ratios.NAD(P)(H) concentrations were measured using an enzyme cycling assay in a 96-well microtiter plate  as described previously . ReagentAlongside each NAD(P)(H) extraction, an aliquot from the same culture was removed, serially diluted, and plated onto LB agar using a previously described single-plate serial dilution spotting method , and col600 of 0.5 in MOPS minimal medium supplemented with the indicated carbon sources. The samples were centrifuged at 3,220 × g for 10 min. Equal amounts of protein were resolved on a 12% SDS-polyacrylamide gel. The proteins were blotted onto a nitrocellulose membrane, which was blocked with 5% (wt/vol) dried skimmed milk in Tris-buffered saline (TBS) buffer. The membranes were probed with mouse anti-NirS  with a 100× 1.49-numerical-aperture (NA) oil lens objective (Olympus UAPON100XOTIRF) and a 488-nm laser  (Coherent Inc.). Samples were imaged using epi-illumination microscopy, and the images were relayed onto an Andor iXon Ultra 897 camera by a ×1.3-magnification Cairn Twincam image splitter (the second port of the image splitter was not used during these experiments). The resulting pixel width on the sample was measured to be 118 nm. For each field of interest, 100 frames were captured at 100-ms exposure time in a 256-pixelThe micrographs captured for this study exhibited typically one or two bacteria in a 30-mm-by-30-mm area. The raw data were segmented and filtered for analysis using a MATLAB script . The data were first segmented as follows. The MATLAB function adaptthresh was used to binarize the data using an adaptive threshold. This was deemed good enough for use as a segmentation step, since the bacteria were sparsely distributed over a dark background in a single frame. The MATLAB regionprops function was used to extract data representing the area, major axis length (length of the major axis of an ellipse fitted to the detected blob shape), eccentricity, and centroid of each segmented blob. A filtering step was implemented to include only those shapes detected with major axis lengths of between 1.7 and 4.5 μm (14 and 38 pixels), corresponding to the possible size ranges for the bacteria.E-MTAB-8374). The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE (PXD015615.The sequencing data were deposited at ArrayExpress (accession number he PRIDE  partner