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


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


Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.Marine cyanobacteria are promising microbes to capture and convert atmospheric CO Synechococcus sp. BDU 130192 isolated from salt pans in India, which shows fast growth and a higher polyglucan content compared to Synechococcus sp. PCC 7002 (a fast-growing and model marine cyanobacterium) in normal photoautotrophic condition. Its genome has been sequenced [Photosynthesis captures solar energy and converts atmospheric carbon dioxide into organic compounds . Cyanobaequenced .Synechocyctis [Synechococcus [Chlamydomonas [Chlorella [Nannochloropsis [Chlorella [Nannochloropsis [Chlamydomonas [The availability of its genome sequence makes it possible to identify the various genes present in the organism. A set of metabolic genes defines the metabolic capabilities of an organism. Genome-scale metabolic models (GSMMs) are large-scale stoichiometric models based on the annotated genome sequence that contain metabolic reactions present in the majority of pathways of the organism. These models allow qualitative predictions such as testing of gene essentiality and the metabolic responses to environmental perturbations, as well as quantitative predictions such as ratios of nutrient utilization, central carbon metabolism fluxes, cell growth and nutrient exchanges under different growth conditions . Furtherhocyctis ,19, Synehococcus ), microaydomonas ,23, Chlohlorella ,25 and Nloropsis ,27) haveloropsis , succiniloropsis  and ethaloropsis . The methlorella ,25, Nannloropsis  and Chlaydomonas ,23 have ydomonas . Previouydomonas  as well ydomonas .Synechococcus sp. BDU 130192 to that of Synechococcus sp. PCC 7002, a fast-growing model marine cyanobacterium. We also present a manually-curated genome-scale metabolic model of Synechococcus sp. BDU 130192, iSyn706. The GSMM was used to compare the intracellular flux distributions of the two marine cyanobacteria. Lastly, the model was employed to determine the biotechnological potential of the native cyanobacterium for various bioproducts and to determine engineering targets for the production of heterologous compounds.In this work, we compare the characteristics such as growth, biomass composition, cell-surface structure, oxygen evolution rates and glycogen productivity of the Indian marine cyanobacterium Synechococcus sp. BDU 130192 exhibits biomass accumulation similar to that of Synechococcus sp. PCC 7002  with air bubbling, PCC 7002 a, while PCC 7002 b.Synechococcus sp. BDU 130192 cells have a comparable size to that of Synechococcus sp. PCC 7002 cells  compared to the Synechococcus sp. PCC 7002 cells, though this was not quantified.02 cells . HoweverSynechococcus sp. BDU 130192 was 1.65 ± 0.07 mmol∙(gDCW∙h)−1 at 34 °C and for Synechococcus sp. PCC 7002 was 1.89 ± 0.06 mmol∙(gDCW∙h)−1 at 38 °C . There was a remarkable difference in the respiration activity, which was estimated for Synechococcus sp. BDU 130192 as 1.38 ± 0.03 mmol∙(gDCW∙h)−1 and for Synechococcus sp. PCC 7002 as 0.868 ± 0.05 mmol∙(gDCW∙h)−1 .Measuring the rate of oxygen evolution provides an indication of how well the photosystem II (PSII) is functioning and at what rate the electrons are being produced at PSII at a particular light intensity. We compared the oxygen-evolving activities of both the strains. The rate of photosynthetic oxygen evolution for Synechococcus sp. BDU 130192 and Synechococcus sp. PCC 7002 showed that the levels of PGMU 1 is approximately 4 fold higher, PGMU 2 is 3-fold, glgC is 4-fold, glgA1 is 77-fold and glgA2 is 10-fold higher in Synechococcus sp. BDU 130192 vs. in Synechococcus sp. PCC 7002 cells  that conducts the isomerization of G-6-P to G-1-P, glgC catalyses the ADP-glucose pyrophosphorylase, the enzyme that converts G-1-P to ADP-Glucose, and glgA encodes for glycogen synthase. The analysis of transcripts levels of glycogen-synthesis genes of 02 cells . Thus, tSynechococcus sp. BDU 130192 was 0.265 ± 0.002 g/l/OD while that of Synechococcus sp. PCC 7002 was 0.318 ± 0.022 g/l/OD . The measurement of biomass composition of Synechococcus sp. PCC 7002 and Synechococcus sp. BDU 130192 showed that Synechococcus sp. BDU 130192 has significantly high levels of polyglucans and DNA but reduced levels of other biomass components compared to Synechococcus sp. PCC 7002 −1, based on the experimentally measured value. This led to a calculated CO2 uptake rate of 1.92 mmol∙(gDCW∙h)−1, photon uptake rate of 25.45 mmol∙(gDCW∙h)−1, and nitrate uptake rate of 0.22 mmol∙(gDCW∙h)−1. The model simulations predicted an O2 release flux of 2.43 mmol∙(gDCW∙h)−1. Thus, the O2/CO2 ratio was predicted to be 1.26 compared to 1.05 with the iSyp708 model and 1.5 in the iJN678 model.For photoautotrophic simulations, the growth rate was fixed at the measured value of 0.051 hThe model simulation revealed that overall, 502 reactions are active under photoautotrophic condition. 133 out of the 148 reactions of fatty acid metabolism are active, making it the most active subsystem followed by the subsystem of amino acid metabolism with 92 out of the total 165 reactions as shown in A total of 450 reactions were predicted as essential for growth in photoautotrophic condition. A further analysis of these essential reactions revealed that the fatty acid metabolism pathway has the highest numbers of essential reactions followed by the amino acid metabolism pathway. iSyp708 model and found that 83 essential reactions involved in amino acid metabolism, 19 involved in carbohydrate metabolism, 52 in fatty acid metabolism, 36 in nucleotide metabolism and 10 reactions involved in vitamins metabolism were essential in that model. Overall, there are 277 out of 648 reactions (~42%) that are essential in the iSyp708 model against 450 reactions (~50%) in the iSyn706 model. We investigated the reason for less essential reactions in iSyp708 model and found that the model has lumped reactions while iSyn706 has reactions for each step of fatty acid synthesis, i.e., initiation, elongation and termination. Thus, all the reactions of these three steps are essential due to their roles in biomass precursor biosynthesis in iSyn706 model while fewer reactions (lumped) are essential in iSyp708 model. However, the fraction of fatty acid and pigment metabolism reactions that were essential in our model was similar to that in iSyp708 . Keeping the expanded form of fatty acid metabolism reactions in the model makes it more accurate and makes it possible to study the effect of different model parameters  on the fatty acid metabolism reactions.We also performed the reaction essentiality analysis for the 2 fixation by C3 cycle, utilising ATP and NADPH produced during the light reactions. The Calvin Benson Bassham (CBB) cycle is almost equally active in both the models. We compared the flux distributions of the central Carbon metabolism reactions in both iSyp708 and iSyn706 models  are slightly more in iSyp708 in comparison to iSyn706 model. Similarly, the NO3− intake in iSyn706 was about half of that in iSyp708, reflecting the reduced protein content in the former.A detailed analysis of the model simulated under photoautotrophic condition showed CO6 models  by minimiSyn706 in spite of the presence of the 2OGDC and SSADH shunt in simulated photoautotrophic conditions. This result is in agreement with a previous study [−1) through both of these reactions under photoautotrophic conditions. A fraction of the total uptake flux goes into the TCA cycle via AcCoA. The flux from 2-OG  is diverted to the GS-GOGAT (glutamine synthase-2-oxoglutarate amido transferase) cycle. The bacterium assimilates NO3−, converts it into NH4+ and finally into glutamine and glutamate via the GS-GOGAT cycle by the action of the enzymes glutamine synthase and glutamate 2-oxoglutarate amido transferase, respectively.A unique feature of cyanobacteria is their unusual TCA cycle . Insteadus study  which shiSyn706 to predict the theoretical yields (mol product produced/mol CO2 consumed) for four native and six non- native products under phototrophic condition  through a cascade of reactions which in turn gets converted through a series of reactions into carotenoids like beta-carotenoids. A very small amount of flux goes to pigments metabolism. Cyanobacteria are promising organisms to work as photosynthetic cellular factories ,44. Theyondition . The groSynechococcus sp. PCC 7002, but has higher polyglucan content under normal growth conditions. Identifying such a potential organism is a first step in subsequent process and genetic engineering strategies to improve the growth rates of photoautotrophic microbes. We have measured the macromolecular composition of biomass of this organism and also created a GSMM for this cyanobacterium to help in understanding its metabolic capabilities.Cyanobacteria are potential feedstocks for biotechnological processes. However, there is a need to investigate, identify and develop non-model strains that show unique characteristics. In this study, we report a strain isolated from Indian salt pans that shows growth comparable to Synechococcus sp. BDU 130192 under normal growth conditions are comparable to those observed in other strains after nitrogen/phosphorus deprivation. The elevated polyglucan level is associated with a lesser degree of reduction compared to that reported for Synechococcus sp. PCC 7002 [Synechococcus sp. BDU 130192 contains significantly lower amounts of proteins compared to Synechococcus sp. PCC 7002 while the growth rate is comparable. Whether the faster growth is because of efficient use of the proteome will require further studies. Similarly, currently it is not clear why Synechococcus sp. BDU 130192 cells showed comparable growth to Synechococcus sp. PCC 7002 cells even though the chlorophyll a levels in the former are about a third of those in the latter. It appears that the cells exhibit a nutrient-limiting behaviour  much earlier compared to Synechococcus sp. PCC 7002 cells and while still in growth phase.Generally, in bacteria, including many cyanobacteria, proteins are the biomass components present in the greatest amounts. Upon nitrogen starvation, there is a breakdown of proteins and the carbon is stored in the form of polyglucans. Interestingly, the glycogen and total carbohydrates observed in PCC 7002 . In agreSynechococcus sp. PCC 73109, a strain that, unlike Synechococcus sp. PCC 7002, is not auxotrophic to vitamin B12. It has been shown in Synechococcus sp. PCC 7002 that vitamin B12 is primarily used as a cofactor in methionine biosynthesis [Synechococcus sp. BDU 130192 genome has two isoforms of metE (reactions R04405 and R09365 in the model) and hence should not require vitamin B12 for growth. Our preliminary experiments indicate that Synechococcus sp. BDU 130192 grows well in medium lacking vitamin B12 (results not shown), which will result in reduced cost of medium for this organism.The strain was identified to be closely related to ynthesis . The metSynechococcus sp. BDU 130192 will have good osmotic and ionic stress tolerance. This includes higher carotenoids levels, a rougher surface suggestive of a thicker membrane and elevated levels of polyglucans. Genome-wise, 5% of the detected genes belong to the “stress metabolism” subsystem. It will be of interest to investigate the salt tolerance levels of this organism because an ability to grow at high salt concentrations may be advantageous when growing cultures in non-axenic conditions such as in open ponds. This stress-tolerance comes at a cost of higher maintenance ATP as suggested by the higher dark respiration rates of this strain. The elevated glycogen levels may be there to supply the excess ATP required during dark phase. However, for biotechnological applications where the cells may be grown in photo bioreactors under constant illumination, the excess glycogen stored would lead to greater glycogen productivity, as shown in this study.The strain was isolated from salt pans. There are several indicators that suggest that Synechococcus sp. PCC 7002, which may be oversimplification, but a common practice for cyanobacterial models [Synechococcus sp. BDU 130192 is lower than that of Synechococcus sp. PCC 7002 (due to its lower protein levels) and the NGAM is higher (as suggested by a higher oxygen consumption in dark). We investigated the effect of lower GAM (35 and 40 mmol∙(gDCW)−1) and higher NGAM −1). Our results suggest that mainly the photon intake and oxygen evolution change slightly, while the underlying flux distribution, which is a function of biomass equation and metabolic network, isn’t altered significantly. Yet, the inclusion of precise GAM and NGAM values for the particular strain will make the GSMM even more accurate. Currently, the oxygen evolution predicted by the model is different from the measured value with the model over-predicting the O2 production. The reason for this divergence is not clear, but may involve some errors in the biomass composition measurement [2 produced to CO2 intake, [−1, a value in-between the measured one and that predicted by the GSMM. Therefore, an error in the measurement of O2 evolution rate could not be entirely ruled out.The detailed, manually-curated genome-scale metabolic model will also help in understanding the metabolic capabilities for photoautotrophic production of biochemicals. Currently, the model employs GAM and NGAM values based on l models ,40. It isurement . The oxy intake,  utilizinSynechococcus sp. PCC 7002, earlier the gene locus SYNPCC7002_A2770 was shown to code for acetolactate synthase (E.C. 2.2.1.6) as well as 2-OGDC [Synechococcus sp. BDU 130192 genome. These results strongly suggest the presence of the 2-OGDC reaction in the metabolic network of Synechococcus sp. BDU 130192. However, because an exact locus cannot be assigned to the 2-OGDC gene yet, this reaction is designated as an orphan reaction. Under conditions of localized high O2 concentration, RuBisCO can catalyse the photorespiration reaction, i.e., oxidation of 3-PGA to generate 2-phosphoglycolate (2-PG). As such, a very small flux was predicted for the oxygenase reaction of RuBisCO. The 2-PG is further metabolized to glycolate via phosphoglycolate phosphatase (PGP) reaction present in the model. Cyanobacteria have three pathways for metabolism of glycolate, viz. the canonical C2 cycle that is common with algae and higher plants, the decarboxylation pathway and the glycerate pathway [Thermosynechococcus sp. though it is not clear whether HPR1 is expressed in Synechococcus sp. BDU 130192. Addition of the HPR1 reaction to the model made no difference to the flux distribution (not shown). Nonetheless, similar additions/ deletions could be incorporated in the model and tested as further evidence of their existence in this specie becomes available.Another area for improvement is further characterization and gene-assignment for the orphan reactions. Not surprisingly, some important central metabolic reactions are orphan in this model, as well as in other models. This includes reactions in the lipid and amino acid metabolism, as well as 2-oxoglutarate decarboxylase . In s 2-OGDC . Similar pathway . However pathway  have shoThe model was analysed through FBA, which simulates the flux distribution under conditions of some assumed optimality . The availability of a GSMM makes it possible to conduct other metabolic network analyses such as elementary flux mode (EFM) analysis which can comprehensively cover all possible metabolic states of a network.iSyp708).We also performed reaction essentiality analysis and interestingly, we identified that close to half of the total reactions in the model were essential. The number of essential reactions within each metabolic pathway agrees well with the number of reactions carrying flux (active reactions). The large fraction of essential reactions shows the relative non-redundancy of the metabolic network, typically associated with lower organisms. The number of reactions in the subsystem of fatty acid metabolism is higher in our model as every fatty acid synthesis reaction is included, unlike in some other models ,40 whereiSyp708. Manual identification of the genes to be added was done here due to a limited number of compounds tested. However, computational strain design methods are available [Synechococcus sp. BDU 130192, the flux going towards glycogen/ polyglucans can be easily channelled away from the storage compounds towards target product, increasing the potential yields. This would be especially useful when cells are grown in continuous illumination. Systems for genetic engineering of Synechococcus species are available. It is expected that the systems could be applied or adapted for this strain too, though this needs to be tested.We have identified the potential yields of some compounds using the reconstructed GSMM. The yields are similar to that got with vailable  and the Synechococcus species and provides its GSMM. The organism shows potential for development as a feedstock organism while the availability of its GSMM will help in systems-level analysis and metabolic engineering of this strain.Overall, this work presents the general properties of the biomass composition of a non-model Synechococcus sp. BDU 130192 was obtained from the National Facility for Marine Cyanobacteria (NFMC) at Bharathidasan University  and the Synechococcus sp. PCC 7002 was obtained from the Pasteur Culture Collection . The strains were inoculated in A+ medium  at 0.05 OD720 nm and grown in a Multi-cultivator . Synechococcus sp. BDU 130192 was cultured at 34 °C and continuously illuminated with cool white LED lights at 300 μmol∙m−2∙s−1, while the Synechococcus sp. PCC 7002 cells were cultured at 38 °C under 250 μmol∙m−2∙s−1 of illumination. The cultures were aerated at a rate of 0.5 mL per min using compressed air. Cell growth was observed at OD720 nm after every 24 h for 7 days.g and washed with 0.1 M sodium phosphate buffer (pH = 7.2) and fixed using in 3% glutaraldehyde for 2 h at 4 °C. The cells were then treated with 1% osmium tetraoxide, dehydrated in ethanol, dried with an air dryer, mounted on a specimen stub, coated with sputter coater gold coating unit  and imaged under SEM .The exponentially growing cells were harvested at 7000 + medium supplemented with 10 mM sodium bicarbonate to a final OD730 = 1. Cell suspensions were maintained with continuous stirring and light. For dark respiration rates, cells were added to an oxygen-saturated medium kept in dark and the oxygen consumed was measured. The dissolved oxygen in the medium was measured using the DO probe.Photosynthetic oxygen evolution and respiration rates were measured by using a dissolved oxygen (DO) probe . The cells were grown to mid exponential phase, harvested and resuspended in fresh A490) versus various glucose concentrations estimated using the same method.The total carbohydrate content of the biomass was estimated using the phenol sulfuric acid method . Five mgg for 10 min. The pellet was then washed with 100% ethanol. 100 µL of 40% KOH was added to the pellet, vortexed and incubated at 95 °C for an hour. Then, 200 µL of 100% ethanol was added to the solution after cooling and kept at −20 °C overnight to precipitate glycogen. The samples were centrifuged for 1 h at 13,000 g, supernatant was removed, and 40 µL of 2 N HCl was added to the pellet and incubation at 95 °C for 30 min. The sample was cooled to room temperature, and 40 µL of 2 N NaOH, 20 µL of 1 M phosphate buffer (pH = 7.0) and 40 µL of autoclaved Milli-Q water was added. The sample was vortexed thoroughly and glucose was then analysed via HPLC.For glycogen estimation, 1 mL of precooled methanol was added to 5 mg dried cells and vortexed . The susThe total lipid was extracted using the Bligh and Dryer method  and measg for 15 min. The supernatant was collected and diluted as required. The protein concentration in the supernatant was measured using the Bicinchoninic acid method [Total protein was extracted from 10 mg dried cells using 1 mL of 1N NaOH . The susd method  , phycoerythrin (PE), and allophycocyanin APC), respectively .The following equations were used to estimate the concentrations of PC, APC and PE in µg/mL:Synechocystis model [The relative proportions of the constituents of soluble pool, inorganic ions and the content of the peptidoglycan were adopted from the is model .® Green Supermix (Bio-Rad) and 20 ng of template cDNA in 10 µL reaction mixture. The PCR profile was: 3 min at 95 °C followed by 40 cycles of 30 s at 95 °C, 30 s at 62 °C and 30 s at 72 °C. Standard dilutions of the cDNA were used to check the relative efficiency and quality of primers. RT-qPCRs were performed with two biological replicates and technical triplicates of each cDNA sample in the iCycler iQ5 Real-Time PCR Detection System (Bio-Rad). The data obtained were analysed using the iQ5 Optical System Software v2.1 (Bio-Rad).Total RNA was extracted using a commercial kit . The cells were disrupted by crushing in pestle and motor in liquid nitrogen. The lysis and extraction steps were performed according to the manufacturer’s instructions. RNA quantity was estimated spectrophotometrically and the quality through gel electrophoresis. The RNA samples were treated with 1 U of RNase-free DNase (Thermo Scientific) according to manufacturer’s instructions. For cDNA synthesis, 2 µg of total RNA was transcribed with Revert Aid First Strand cDNA Synthesis kit (Thermo Scientific) in a final volume of 20 µL, following the manufacturer’s instructions. 20-fold standard dilutions of the cDNA were made and stored at −20 °C. The RT-qPCRs were performed on 96-well PCR plates covered with Optical Sealing Tape . Reactions were manually assembled and contained 0.25 µM of each primer, 5 µL of iQ™ SYBRpgmu1 and pgmu2), ADP-glucose-phosphorylase (glgC), glycogen synthase (glgA1 and glgA2) were compared in Synechococcus sp. BDU 130192 and Synechococcus sp. PCC 7002 by RT-PCR. Negative controls (no template cDNA) were included and a melting curve analysis was performed in all assays. Efficiency values were calculated and the Cq values  for each data set were exported to a Microsoft Office Excel file, the relative quantities of each sample were calculated using the gene-specific efficiency acquired from the dilutions series and normalized to the mean Cq value. Phosphoenolpyruvate carboxylase, a central enzyme in carbon concentrating mechanism, was used as the reference gene [The transcripts levels of the glycogen synthesis genes: phosphoglucomutase  numbers of metabolic genes, we prepared a list of all the metabolic enzymes present in the genome of the native cyanobacteria. Then, the draft model was reconstructed by extracting the reactions of all of the E.C. numbers and metabolic enzymes from KEGG . The draThe biomass equation was derived based on experimental measurements of the biomass composition. The stoichiometric coefficients in the biomass equation are the molar amounts of the individual components in 1 g of biomass. The amounts of total carbohydrates, glycogen content, lipids, proteins, DNA, RNA and pigments were normalized to the biomass (mg) to obtain the respective content in µmol/mg or mmol/g of dry cell weight. The measured biomass composition was used to generate the biomass equation as given in Synechococcus sp. BDU 130192. The genes having an E-value of ≤ 10−15 were included in the model along with their associated reactions. However, some reactions were included in the model without any significant similarities to complete the pathway for biosynthesis of biomass precursors.The gaps in the draft model were manually identified by performing optimisations for production of every biomass precursor  one by one. Gap(s) in the pathways was/were identified if the model was unable to synthesize a required biomass precursor. We then searched the previously-published cyanobacterial genome-scale metabolic models ,34 as weThe thermodynamic infeasible loops are identified by fixing the ATP-Synthase reaction flux to some positive value  while fixing all the exchange reactions to zero during optimization. If the optimization is still feasible then the reactions with non-zero fluxes are the infeasible loop reactions.These loops were analyzed on a case-by-case basis and rectified by correcting the directionality of the reactions based on KEGG, BioCyc, MetaCyc databases as well as previously-published GSMMs.Finally, the model was thoroughly checked for the presence of any thermodynamically infeasible cycles by testing for the production of ATP without any carbon source. Such infeasible cycles, if detected, were removed by correcting the directionality of some reactions in the cycle.Synechococcus sp. PCC 7002 models, [Microorganisms require two types of energies: (a) Growth-associated ATP maintenance (GAM), which accounts for the energy required in synthesis of precursors, polymerisation to form DNA and proteins and (b) Non growth-associated ATP maintenance (NGAM), which accounts for the energy required to maintain cellular structure and integrity. The GAM energy contribution is taken into account as a coefficient of ATP in the biomass equation while the NGAM contribution was accounted by adding a separate ATP hydrolysis reaction and giving it a flux equal to the NGAM. As the values of GAM and NGAM are not available for this strain, these values were adapted from that of  models,  for the  models,  for the  models, ,66.Flux balance analysis (FBA), a constraint-based approach that is used to calculate the internal fluxes distributions of metabolic models, was performed using the Cobra Toolbox  in MATLA2, photon and a few ions exchange fluxes were left free while the other carbon sources  were constrained to carry zero flux. The metabolic reactions essential for growth in photoautotrophic condition were identified by fixing the flux of each reaction at a time to zero and maximizing growth. The deletions which resulted in biomass flux values of ≤ 10−4 h−1 were defined as essential reactions. The active reactions are those reactions whose flux values are ≥ 10−8 mmol∙(gDCW∙h)−1.During the photoautotrophic simulations, COSynechococcus sp. BDU 130192 with Synechococcus sp. PCC 7002, a GSMM of Synechococcus sp. PCC 7002 (iSyp708) was used to simulate the flux distribution in Synechococcus sp. PCC 7002 without any modifications, while our model (iSyn706) was used to simulate the flux distribution in Synechococcus sp. BDU 130192. The growth rates of the two strains were set to those measured experimentally.When comparing the flux distribution of v is the reactions flux vector and c is the objective function. In this case, the objective function is exchange reactions for product formation. vobj and vc are the fluxes through the objective function and carbon source, respectively.The model was utilized to explore the capabilities of the organism to produce some native and non-native (heterologous) industrially-relevant compounds. For each of the products considered , the corresponding exchange reaction was set as the objective function to maximize its production while constraining the flux through the biomass reaction to be at least 80% of the wild-type photoautotrophic growth flux. Transport and exchange reactions were added to the model wherever required. For simulating synthesis of non-native products, the least number of reactions required for their synthesis were identified manually using the KEGG  databaset-test was conducted to identify statistical significance.Experimental data are presented as mean ± standard deviation (SD) of three biological triplicates. Student’s Synechococcus sp. BDU 130192 is an attractive candidate for feedstock applications and also for photoautotrophic production of biochemical. The high quality manually curated genome-scale metabolic model of this cyanobacterium yielded information on the metabolic response of this organism and will provide a useful basis for further investigation of its metabolism and to design metabolic engineering strategies.Our results show that


Based on previous developments in the literature, we characterized the resulting kinetic models by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. The two parameters include maximal growth rate (μmax) and harvest volume (Vh) . For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics using ultra high-resolution metabolomics datasets derived from Fourier transform ion cyclotron resonance mass spectrometry analyses, our method predicted how oxidative degradation is primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements (as shown by correlation coefficients of up to 0.61), and how biogeochemical reactions can vary in response to carbon and/or oxygen limitations. Lastly, we showed that incorporation of enzymatic regulations into substrate-explicit models is critical for more reasonable predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling—termed  Organic matter (OM) is a key determinant of global biogeochemistry and exerts far-reaching impacts on regional and global ecosystem health. Degradation of complex OM is primarily driven by microbial and enzymatic activities around environmental substrates . TherefoEarlier biogeochemical models provide a lumped description of microbial and enzymatic activities, as well as chemical entities . This rebottleneck in building predictive biogeochemical models because further elaboration of microbial and enzyme activities becomes difficult without expanded consideration of complex OM chemistry that influences the specific metabolic pathways employed by microorganisms.Enzyme-based or enzyme-explicit models (EXMs) are a complementary approach that collectively describes biogeochemical functions performed by a microbial community with a focus on extracellular enzymes, alleviating the difficulty in identifying functional traits of individual organisms or their groups . In contExtension of biogeochemical models to include detailed OM chemistry is critically important to pushing the boundaries of environmental science forward. For example, a present paradigm in environmental science views aerobic respiration rates as being primarily determined by kinetics . However, recent field and laboratory studies suggest that OM thermodynamics can be a main driver of aerobic respiration . Conventsubstrate-explicit modeling (SXM), as opposed to MXM and EXM. SXM features flexibility in input data types, enabling incorporation of an unlimited number of compounds detected from various state-of-the-art instrumentation including GC-MS, LC-MS/MS, HPLC-MS, NMR, Orbitrap MS, and Fourier transform ion cyclotron resonance (FTICR-MS). Our modeling framework therefore overcomes a central challenging in using such information—how to condense the massive amount of data produced by these technologies into variables that are both useful and computationally feasible in predicting biogeochemical dynamics and function.Toward filling this gap, we propose a new modeling concept drawn from thermodynamic theory that can explicitly account for the chemical composition of individual molecules in OM pools when generating biogeochemical rate estimates, therefore having the potential to significantly advance predictive capabilities of current-generation ecosystem models. Due to the ability of our approach to directly incorporate OM chemistry for every compound, we termed it By combining a suite of previously developed thermodynamic theories , we deveConsistent with the findings from Our development of SXMs from chemical formula of OC go through multiple-step procedures as illustrated in Stoichiometric equations provide quantitative relationships among the reactants and products involved in given reactions. We derived a stoichiometric equation for oxidative degradation of OC by accounting for catabolism  and anabolism  . Combinai denotes chemical species i, yiS (≥0) is the stoichiometric coefficient of Si, which is set to be negative for reactants and positive for products. For a defined array of Si’s, Eq. (1) can be conveniently represented as a column vector of stoichiometric coefficients:where Si’s) involved in OC oxidation reactions  and provided standard state Gibbs energy of formation of each compound  by coupling the catabolic and anabolic reactions based on the parameter λ, i.e.,A stoichiometric equation of metabolic reactions can be derived by accounting for the energetic coupling between catabolism and anabolism, i.e., the balance between the energy production through substrate degradation and the energy consumption for cell synthesis  see  and the whereyCat and yAn are column vectors of stoichiometric coefficients in catabolic and anabolic reactions, and ΔGr,Cat and ΔGr,Ana, respectively, denote Gibbs energies of catabolic and anabolic reactions, and ΔGr,Dis is dissipation energy. The parameter λ implies how many times the catabolic reaction needs to run to provide the energy required for the synthesis of a unit C-mole of biomass. Lower values of λ therefore represent higher thermodynamic favorability of metabolic reactions involving given OM. The value of λ is expected to increase under stress conditions where energy efficiency for growth can become low, e.g., due to increased energy dissipation.Here, Gr,Dis) because the formula in the dissipation method  is not extendable to complex OCs with more than six-carbon OCs, while most of the compounds profiled in our samples are more complex than those. The TEEM considers the energy provision of catabolic reaction to meet the energy spent in the following two steps of energy conversion in anabolism: (1) the carbon source to biomass building blocks , and (2) the conversion of biomass building blocks to biomass . The original formulation includes the energy conversion from the nitrogen source to ammonium, which is neglected in our case where ammonium is taken as the nitrogen source. The energy balance is then represented using the following equation:We used TEEM to calculate the value of λ and subsequently dissipation energy  or −1 . Following Gr,Syn = 200kJ/(molBiom), and η = 0.43; assumed the composition of biomass building block to be the same as Biom . Constant composition of biomass and building block means thatΔGr,Block is the same across samples. Specific biomass composition used in this work (CH1.8N0.2O0.5) has been widely used in the literature . With the value of λ calculated as such, one can now determine the value of ΔGr,Dis from Eq. (4) and the full stoichiometric equation of metabolic reaction from Eq. (3). In the following section, we describe how to calculate the Gibbs free energy changes in a general context.Determination of λ from Eq. (5) then requires the calculation of the Gibbs free energy ΔGr) can be calculated fromThe Gibbs free energy change of a reaction (ΔR is the gas constant [= 0.008314 kJ/(K ⋅ mol)], T is temperature in Kelvin, and Qr is the reaction quotient defined bywhere ai and yi denote the activity and stochiometric coefficient of the ith chemical species.where Qr = 0 and therefore ΔGr reduces to yiS), i.e.,For aqueous systems, Gibbs free energy of a reaction is often calculated for the standard state where substances take unit activities  of aqueous species  does not properly represent the state of biological cells, one can also define the biological standard state where pH is set to 7 , while acidophiles and alkaliphiles optimally grow at lower and higher pH than 7 . The usepiration .For all compounds considered in this work except OC, the values of ture see   can be represented byThermodynamic theory by max is the maximal specific growth rate, Vh is the volume that a microbe can access for harvesting energy from the environment (thus termed harvest volume), yOC,i and yO2,i are the stoichiometric coefficients of OC and O2 in the metabolic reaction associated with oxidative degradation of OCi, and |yOC,i| and |yO2,i| denote their absolution values. Through comparison with Michaelis–Menten (M–M) kinetics, where μ2 are limited. If only C or O2 is limited, Eq. (14) is reduced toThe above equation shows the case when both C and O2, and iin Eq. (14) or (15), i.e.,Consumption and production rates of other chemicals  can be combined with other complementary approaches. Integration of SXM with MXM requires explicit consideration of distinct microbial groups, which leads toj denotes the contribution of the jth microbial group to the production and consumption of metabolites, and μi,j is the growth rate of the jth microbial group on OCi, i.e.,where the subscript Note that this formulation accounts for the difference in growth rate among microbial groups. Production and consumption rates of metabolites are accordingly dependent on microbial groups, i.e.,For the integration of SXM with EXM, we introduce enzyme concentrations as additional variables. Dynamic mass balances in Eq. (17) remain the same, but we consider individual reactions to be catalyzed by distinct enzymes, i.e.,i) is composed of two parts: enzymatic regulation  Upper bank at unvegetated site (N1), 40–50 cm depth interval (sample N1-40-50) and (2) Upper bank at vegetated site (S1), 0–10 cm depth interval (sample S1-00-10). These two samples respectively represent the lowest and highest activity in aerobic respiration except an outlier (see section “Results: Comparison of Low-Activity and High-Activity Samples”).Gr,D), Gibbs free energy changes for catabolic reactions , and the parameter quantifying the energy coupling between catabolic and anabolic reactions (λ). The first two parameters  evaluate the thermodynamic character of OM with a focus on the energy generation . By contrast, the parameter λ quantifies the same based on the energy balance . Therefore, all of these three parameters measure thermodynamic inefficiency of OM , but at different levels, i.e., half/complete catabolic reactions for ΔGr,D and ΔGr,Cat, and entire metabolic reaction for λ. We examined their correlations with experimentally determined aerobic respiration at pH = 0 and pH = 7 , while it showed a negative correlation at pH = 7  as a respiration indicator is relatively limited.Together, these results identify the parameter λ as the most robust indicator of aerobic respiration, which shows consistent correlations across all conditions considered above. The results also indicate that the usefulness of the other thermodynamic parameters  both C&O2 limitation. For each, we differentiated the level of limitation to be severe vs. moderate.Comparison of predicted reaction rates with experimentally determined aerobic respiration provides a means to validate the model. The experiment of Raz reduction assay in Graham and colleagues , 2018 wamax = 1 in Eqs. (14) and (15) because this value does not affect the correlation with aerobic respiration, while other parameters and variables such as Vh, [OC], and [O2] are unknown. To implement different levels of substrate limitation, we set Vh[OC] and/or Vh[O2] to be 1 (moderate limitation) (rC ≡ (# of C) × rOC], O2 consumption rate (rO2), and inorganic carbon production rate   and 0.2 itation)  in the f2− or both C&O2-limited conditions . Similar patterns were observed for |rC|  and rO2| with aerobic respiration was consistently positive across all three different limiting conditions. As mentioned above, this result partially validates our model because Raz reduction to resorufin represents an estimate of the consumption rate of O2, rather than other chemicals  and the other from the high-activity zone (sample S1-00-10) . With outliers removed, these two samples represent the highest and lowest activity as shown in the correlations of aerobic respiration with λ and |rO2| .Gr,Cat, and ΔGr,D) indicated that respiration reactions of the OM pools at HA are thermodynamically more favorable than those at LA  at LA vs. HA under C-, O2-, and both C&O2-limited conditions. In the case that substrates were moderately limited  (2 were limited (as indicated by higher portions of faster |rO2| in the HA distribution)  , predictibution) ; however samples .severe substrate limitation   , predict] = 0.2) , while t limited . InteresHA/LA) showed the level of elevated oxidative respiration in the HA zone  that the magnitude of the ratio always maintained the following order: rO2| = respiration rate; μ = microbial growth rate; |rC| = carbon consumption rate; Ratios of predicted reaction rates between the two samples  SXM, and (2) a hybrid model that integrates SXM and EXM. The latter is a special case of a general IBM platform that hybridizes three complementary modeling platforms approaches: SXM-EXM-MXM. In hybridizing SXM with EXM, we used the cybernetic approach , 2018 toompounds , while tompounds . These rIn this work, we proposed a novel and flexible biogeochemical modeling concept, termed SXM. As a key feature, SXM uses thermodynamics to incorporate data from increasingly high-resolution metabolomics technologies into biogeochemical models by formulating OM-specific reaction kinetics for an unlimited number of organic compounds in a sample. The entire set of the resulting reaction kinetics is then represented by only two parameters . Our framework is a unique and scalable tool for modeling complex biogeochemical cycles at the ecosystem-scale, as no other approach can describe dynamic biogeochemical reaction networks composed of thousands of compounds with a small, computationally feasible set of parameters.The substrate-explicit model is built upon recent experimental studies that reveal a close relationship between OM thermodynamics and aerobic respiration . In consQr, we used thermodynamic functions corrected to the biological standard state  provided the more interpretable results than In this regard, it has been customary to use standard state Gibb energies  . While a2 and, both C&O2 limitation), aerobic respiration showed positive correlations with oxygen consumption rate when substrates are moderately limited. By contrast, in the case of severe C and O limitation, a positive correlation between oxygen consumption rate and aerobic respiration was obtained only for C-limited conditions. This matches field conditions for the study system in which OC concentrations are low and porewater within saturated sediments is consistently aerobic  [1/h] or [1/d] and harvest volume (Vh) [m3] or [L]. They are tunable parameters, which are quantitatively determinable via data fitting, e.g., when dynamic concentration profiles of substrates are available. Our model can guide experimental design to identify parameters and understand factors driving their variation across systems. In the present study using OM profiles from FTICR-MS, lack of quantitative information  on individual OM molecules is an intrinsic limitation. Consequently, we focused on evaluating carbon quality based on the normalized distribution of OM, but this gap can be filled for improving predictions, e.g., through the integration with other complementary metabolomics approaches that can provide quantitative data  data into biogeochemical modeling greatly facilitates other research programs in the field that collect OM chemistry datasets. The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS), for example, is a global research consortium that aims at understanding cross-scale dynamic interactions among hydrology, biogeochemistry, and microbiology in river corridors . As an iAs shown, accounting for enzymatic regulation in SXMs is critical for more reasonable simulations. Considering metabolic regulation can be best described by EXMs with direct association with specific enzymes, this results suggests that a synergistic integration of SXMs with other existing frameworks is an important future direction to fill the gaps that each approach has and to address advanced science questions that could not be addressed individually. We illustrate a conceptual master platform of integrative biogeochemical modeling (IBM) that accounts for the interaction among substrates, microbes, and enzymes at an unprecedented level of detail . We envihttps://github.com/hyunseobsong/lambda.All relevant data and numerical codes including KBase apps for computing thermodynamic parameters and reaction rates are available at: H-SS, JCS, EBG, VAG-C, and TDS conceptualized the study. H-SS developed the codes and performed the theoretical analyses. J-YL, XC, and JDM developed the SDK Apps for the implementation of the codes in KBase. J-YL and WCN contributed to the data processing. H-SS drafted out the manuscript, which was edited by JCS and EBG. All the authors contributed to the writing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.