Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5–200 μmol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry.Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the composition of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of  Diatoms are unicellular photosynthetic eukaryotes derived from a secondary endosymbiotic event when a heterotrophic eukaryote engulfed a red algal cell and acquired a plastid . They ap2 reduction by the Calvin-Benson-Bassham cycle -[KRH]-[LM]>” and “S-S-L>” [et al. [The protein localization pipeline developed by Levering, et al.  was upda TargetP  was repl TargetP , a plastnalP 4.1 , TargetPnalP 4.1 , HECTAR nalP 4.1 , MitopronalP 4.1 , ASAFindnalP 4.1 , predictnalP 4.1 , and scanalP 4.1 . Predict“S-S-L>”  and the “S-S-L>” ,48 and t [et al.  because Synechococcus elongatus model iJB785 [T. pseudonana. The pigment weight and composition [a/c binding proteins (FCPs) to chlorophyll a in the reaction centers. Photon absorption was constrained for each wavelength using the absorption spectrum of T. pseudonana cells acclimated to different light levels [et al. [iJB785. Photodamage of the D1 subunit was included as a component of the PSII reaction [Using the l iJB785  as a guiposition ,51 of ceposition ,53 to cat levels –56 and t [et al. . We alsoreaction ,57, and reaction  and as Adx) over time (dt) is equal to the stoichiometric matrix (S) describing a reaction network times a vector of fluxes (v), given thatFlux balance analysis (FBA) simulates the flow of metabolites through a network of reactions using the mass balance equation,v to be solved while maximizing an objective function within lower and upper bounds .Intracellular metabolites are assumed to be at steady state, which allows We used parsimonious Flux Balance Analysis (pFBA) to generate single solutions for each simulation. pFBA optimizes the model objective and then minimizes the total sum of flux to obtain a single solution. The second objective imitates a possible cellular objective of minimizing protein biosynthesis. The default GLPK solver in COBRApy  was usediTps1432 in three chemostats maintained at three different light levels  using photosynthetic production measurements from Fisher & Halsey [3, 0.050 mM PO4, 0.106 mM Si(OH)4, 28.8 mM SO4), the chemostat cell concentration, the gram dry weight per cell calculated from biomass composition, and the chemostat dilution rate. Cultures were continuously bubbled to avoid carbon limitation; we assumed the ambient CO2 levels to be 390 ppm pCO2 and calculated the resulting concentrations of CO2 and HCO3 in the media [T. pseudonana for CO2 and HCO3 [Ps and α are parameters of the hyperbolic tangent)GPP) curve generated by Fisher and Halsey , and 2 gDW-1) [T. pseudonana [Photon absorption flux (PFA) was constrained for each 20 nm in the PAR spectral range using the method published in Broddrick, et al. ,PFA=LBλ2 gDW-1) –56. The eudonana  was usedeudonana  were use18O2 signals in the light and the dark and includes all light-driven respiration reactions (the reduction of oxygen to water). We constrained the sum of all respiration reactions with a symbolic expression in which the lower bound is equal to the measured light dependent respiration values. Mitochondrial maintenance respiration is defined as the difference between gross carbon production and net oxygen production (converted to C units) and includes all organic carbon driven respiration reactions. We constrained the sum of all CO2 producing dehydrogenase reactions in the mitochondrial TCA cycle with a symbolic expression in which the lower and upper bounds are equal to the 95% confidence intervals of calculated mitochondrial maintenance respiration values. A Jupyter notebook of this analysis can be viewed at https://github.com/hmvantol/diatom-FBA-notebook.For these simulations, sink reactions for chrysolaminarin and glyceraldehyde-3-phosphate were added to the model to simulate the respiration of a transient carbon pool. Utilization of these pools was calculated as the difference between gross and net carbon production . MeasureT. pseudonana from mid-exponential under NO3-limitation to mid-stationary phase under N-starvation using biomass composition and PSII photochemistry measurements from Liefer et al. [-2 s-1 over a 12h: 12h light: dark cycle at 18°C in filtered seawater amended with half the concentration of all f/2 nutrients except for NO3, which was reduced by ~15 fold to 60 μM. At mid-exponential phase, the cells were diluted into fresh media with no added NO3 [et al. [3, 0 μM Si(OH)4, and 1.86 μM PO4. This assumption fit well with most nutrient measurements made 1 day after transfer to the new media and with estimates of nutrient concentrations in surface seawater at Cape Tormentine (Canada) from the World Ocean Atlas 4, ~2 μM PO4).A dynamic FBA simulation was set up with the solver tolerance set to 1e-8 to simulate the progression in a batch culture of r et al. ,26. Priodded NO3 ,26 and m [et al. . NutrienTo simulate the ten-day experiment, the growth period was divided into eighty 3-hour intervals. Steady-state was assumed for each time interval and pFBA was used to solve for the growth rate and reaction flux distributions every 3 hours. Units were converted from mmol to μmol prior to the pFBA step to avoid numerical precision issues associated with small fluxes. Each time point of the simulation involved four major steps: (1) calculate the biomass objective function, (2) calculate the uptake rates for each metabolite or nutrient and set photosynthetic constraints, (3) solve the pFBA problem, (4) update biomass composition and the environment.bof) used for each time interval was determined by calculating the relative difference between the simulated biomass composition at the current time point (t1) and the next biomass composition measurement (tm) provided by J. Liefer.i is translated from pg/cell (bioi(tm)′) into g/L (bioi(tm)) using cell abundance measurements (c(t2)), and typically f = 1. During the dark period, pigment biosynthesis was not included in the biomass objective function since T. pseudonana lacks a light-independent protochlorophyllide oxidoreductase [f to the fold-change values measured by Jallet, et al. in P. tricornutum grown across a 12: 12-h light: dark cycle [bioi components are < 0, the objective function was switched to the ATP maintenance reaction (ATPM). At each time point, the upper bound of ATPM was re-calculated for changes in mg chl a/gDW using the rNGAM equation [a, and total carotenoid) were also measured over the course of the experiment [The biomass objective function  biomass (bio(t)) per time period Δt, whichever is closer to zero.Using the method described by Chiu et al.  and publ [et al. , we deteT. pseudonana for CO2, HCO3 [3 [4 [4 [Vmax was set to 0.2 and Km was set to 0.05 for all other uptake reactions, with the exception of H2O and H+ which were assumed to diffuse freely and Vmax was set to 1000. The biomass-dependent lower bound assumes that iTps1432 exists in a well-mixed environment where it can sense nutrient-availability and adjust its uptake rate accordingly [Nutrient uptake rates were calculated using Michaelis-Menten parameters from the literature on O2, HCO3 , NO3 [70O3 [3 [4 , and PO4[3 [4 [4  (S3 Dataordingly .a) measured on days 0, 1, 3, 7, and 10 of the experiment  by balancing the equation for the oxidation of new biomass to NO3 or NH4 with the chempy package [2, HCO3, and CO2 was re-equilibrated with the atmosphere so that concentrations remained constant (well-mixed). We included a constraint for photorespiration where the oxygenase flux of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) ranges from 0.001–0.025 the carboxylase flux.PSII flux was calculated from fast repetition rate fluorometry (FRRf) parameters and culture data  ratios. Photon absorption flux was constrained for each time point as before, assuming a light absorption spectrum for cells acclimated to medium light [For each time point, constraints were converted from mmol mg chl um light  and the a, chitin, polyphosphate, chrysolaminarin, protein amino acids, free amino acids, RNA, and triacyglycerides by including sink reactions for each of these components. The availability of each component was calculated as follows if Δbioj is < 0.We allowed for the mobilization of the biomass components chlorophyll a is not characterized in diatoms, but chlorophyll a degradation was required to approximate the mg chl a gDW-1 factor. A demand reaction for chlorophyll a was included in the simulation and its lower bound was set to -1*Δbioj.The degradation pathway for chlorophyll et al. [bio(t2) for the next time interval Δt using the exponential growth equation,vDM_biomass_c. Next we calculated the fraction of new biomass attributed to each component using the biomass objective function. To calculate the concentration of each metabolite xj in the next time interval Δt,vj is the flux of each metabolite, and bio(t1) is the biomass density or sum of all components at the current time point. Scripts for this simulation can be obtained from https://github.com/hmvantol/diatom-dFBA.We adapted the method described by Chiu et al.  to calcuT. pseudonana CCMP 1335 (iTps1432) was generated using as a framework the Phaeodactylum tricornutum (CCAP 1055/1) models iLB1027_lipid [iLB1034 [T. pseudonana (iThaps987) designed to explore production of industrially useful compounds [iLB1027_lipid and iLB1034 and their relevance to iTps1432 are listed in iLB1027_lipid has more reactions than iLB1034 due to the inclusion of detailed lipid metabolism. Additional elements incorporated into the T. pseudonana iTps1432 model include changes in the subcellular localization of reactions based on protein targeting sequences, improvements in modeled light absorption, and the inclusion of known metabolic differences between the diatoms. Several blocked reactions were resolved in iTps1432, and the number of dead-end metabolites in the network was reduced when compared to the P. tricornutum models. The T. pseudonana model contains 6,073 reactions that represent a network of 2,789 metabolites and 1,432 genes, approximately 10–12% of the T. pseudonana genome  were accurately localized in the final model, 9 of which were manually curated  [iTps1432 to reflect known differences in lipid composition [We also extended the e (DHPS) ,81. Lipiposition ,67. We iposition . If geneposition . Followiposition , all newiTps1432 was inclusion of a mechanistic model of photon absorption and electron transfer by fucoxanthin chlorophyll a/c binding proteins (FCPs). We included reactions that describe energy transfer efficiency from excited pigments to chlorophyll a in the photosystems, and pigment de-excitation reactions to dissipate excess energy as heat or fluorescence , triacylglycerides, chitin, chrysolaminarin, osmolytes, a silica frustule, polyphosphate, and a soluble pool of vitamins and cofactors that together define the total biomass of T. pseudonana under a particular environmental condition. Biomass reactions were developed for T. pseudonana cells acclimated to three different light levels . Bulk biomass composition  and chlorophyll a concentration was measured in cells grown in chemostats maintained at 18°C under 5, 60 and 200 μmol photons m-2 s-1 [-2 s-1 [-2 s-1 [T. pseudonana [-2 s-1) or a power law relationship under N-limiting conditions (200 μmol photons m-2 s-1) [A critical step in creating genome-scale metabolic models is development of accurate biomass production reactions in which precursor metabolites are converted into the cellular components that comprise the millimolar contribution to 1 g dry weight (gDW) of cell mass under specific growth conditions . In iTps m-2 s-1 . Pigment [-2 s-1  and from [-2 s-1 . The remeudonana  and was m-2 s-1) . Calcula-2 s-1 [The biomass composition also impacts the modeled photon absorption rate. Photon absorption integrated over the Photosynthetically Active Radiation  spectrum in 20 nm units was calculated from whole cell absorption spectra for cells acclimated to 25, 40–60, and 250 μmol photons m-2 s-1 –56, as w-2 s-1 ,53 and pT. pseudonana under different conditions and thus potential growth and acclimation strategies. The resulting total cell dry weights . Similarly, the silica frustule was the greatest component of biomass at the lowest light intensity  were most abundant at 200 μmol photons m-2 s-1 ..T. pseudm-2 s-1  using our biomass objective functions and production measurements  from chemostats maintained at the same light levels [2 = 0.94) between ATP utilization and growth rate allowed us to estimate the non-growth associated maintenance (rNGAM = -27 ± 52 SE) costs as the y-intercept and the growth associated maintenance (rGAM = 3809 ± 1221 SE) costs as the slope  was calculated for each chemostat using the compensation light level (Ic), which is the light intensity at which photosynthesis is equal to respiration and the growth rate is zero [achl a is the chlorophyll a specific absorption coefficient (m2 mg Chl a-1), r is the ratio of chlorophyll a to gram dry weight per cell (mg Chl a-1 gDW-1), ϕm is the quantum efficiency of photosynthesis (mol O2 mol photon-1), and e is the amount of ATP generated per oxygen. The parameter Ik was estimated by fitting Chalker equation 1 [NPP) curve generated by Fisher and Halsey  for each chemostat .The theoretical upper bound of non-growth associated maintenance . The maximal total electron flow through a cell acclimated to medium light was estimated by setting the level of light exposure to ~2000 μmol photons m-2 s-1 (saturating). PSII, oxygen exchange, and bicarbonate exchange reactions were constrained by the 95% confidence intervals of gross and net photosynthesis and carbon uptake, respectively, measured for cells acclimated to 60 μmol photons m-2 s-1 and exposed to 1952 μmol photons m-2 s-1 [2 molecule evolved [ϕm) of 0.056 mol O2 per photon [Cyclic electron flow (CEF) and respiration both consume the reduced ferredoxin and downstream equivalents generated via linear electron flow (LEF), driving ATP generation and increased flux through PSII  and thusl et al.  proposedl et al.  light re m-2 s-1 . The rat evolved , using ar photon  (S3 Datab6/f: CBFC2). The pFBA simulation calculated 0.38 mmol gDW-1 h-1 as an upper bound of CEF and we used this value in subsequent simulations. CEF is relatively insensitive to short-term changes in light intensity and appears similar in a variety of diatom species [-2 s-1), CEF is an insignificant fractional component of total electron flow (TEF) in T. pseudonana; at the lower light intensity of ~100 μmol photon m-2 s-1, CEF corresponds to about half of TEF  is the sum of electron flux through PSI and PSII  minus the linear flow from PSII through to PSI (represented by cytochrome  species . At the iTps1432 at three different light levels [c oxidase); nitrogen assimilation ; sulfate assimilation ; carbon assimilation and biosynthesis of reduced metabolites  [T. pseudonana cell division is not synchronized [The allocation of photosynthetic energy to biomass production and respiration was quantified using pFBA with t levels , constraabolites . Some pate (G3P) . Additiohronized ). The sihronized .-2 s-1, nitrate reduction is a proportionally more important component of biomass production than at other light levels because of the high protein content of cells acclimated to low light .First, we noticed that photorespiration was not a part of the original pFBA prediction and that it could be an important sink for reductants in photosynthetic organisms. Photorespiratory flux, or the specificity of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) for COpression vcvo=VcKsfloggii  because t 100 μM . The con measure . We therm of 65 μM [2 concentrations are likely higher than estimated because diatoms are not carbon-limited, and O2 concentrations are thought to be lower  Thus, these simulations may overestimate the significance of photorespiration.Photorespiration was constrained with the following symbolic expression, oxidase . For a Kof 65 μM , pyrenoiIn diatoms, a major component of redox balance is the flow of reducing equivalents from the plastid to the mitochondria , based oT. pseudonana and how it may change with acclimation to different light intensities, so the value of this constraint (NADHOR_m/PSI_u = 0.0015) was chosen for exploratory purposes. Flow of reductants from the plastid activates the mitochondrial alternative oxidase reaction in iTps1432  is the closest condition examined in the previous simulation. Biomass composition and macro-nutrient concentrations were determined experimentally at 0, 1, 3, 7, and 10 days after the start of the experiment. Measurements of biomass C, N, and P were made more frequently at 0, 1, 2, 3, 5, 7, and 10 days after the start of the experiment. Liefer, et al. [The previous simulations highlighted the importance of nitrate reduction as an electron sink under steady-state conditions. We next explored the impact of nitrogen limitation and starvation on reductant flow using dynamic Flux Balance Analysis (dFBA) to simulate growth in batch culture where cells are not in steady-state. Throughout the simulation, we tracked simulated nutrient and excreted metabolite concentrations in the media as well as molecular and elemental biomass composition. The simulations relied on experimental data ,26 derivT. pseudonana uses up protein and accumulates carbohydrates and lipids; there is also accumulation of DNA as cells stop actively dividing, a decrease in RNA and pigments per cell, and accumulation of residual P likely corresponding to polyphosphate storage  . As nitrstorage , Fig 4a)T. pseudoposition . Small dposition . Neverth3 (and NO2) in the experimental media were depleted sometime between 1 and 3 days after the initiation of the experiment; low levels of background NH4 in the media were depleted after 1 day. Simulated NO3 was completely depleted from the media shortly after 1 day of growth whereas simulated NH4 was released into the media concurrent with the NO3 depletion and was subsequently taken up and re-excreted into the media between the different target days. The simulated PO4 concentration in the media matched the observed gradual decrease over time 4 concentrations were simultaneously drawn down with the NO3 during the first day and then plateaued at ~10 μM. The simulated drawdown of Si(OH)4 instead plateaued at ~25 μM shortly after 1 day in culture. This difference likely reflects errors in the weight of the cell frustule used in the biomass objective function. This value was not experimentally measured and so the value used for the biomass objective functions was extrapolated from the difference between dry weight measurements and the sum of other biomass components  to see how this assumption impacts metabolite secretion . Conversely, formate production is highest when respiration is high . Similar3 depletion . APS reduction to SO3 consumes a reduced thioredoxin, SO3 reductase is plastid localized and consumes 6 reduced ferredoxin to produce H2S. SO4 reduction to sulfide becomes more prevalent immediately after NO3 is depleted and results in DMSP excretion  and use of chemostat production measurements to constrain flux for different light intensities ; so we may be underestimating its importance at these light levels due to the poorly constrained values of growth-associated ATP maintenance, and given the contribution of CEF to energy generation.Photosynthetic organisms have evolved a variety of mechanisms to deal with the primary challenge of photosynthesis: how to capture light energy while evading potential damage. Over longer time periods, diatoms adjust their pigment composition to deal with incoming light energy, but short-term fluctuations in light intensity with inadequate sinks for electrons could theoretically cause a plastid to become over-reduced and damaged. Energy dissipation as heat or fluorescence  is one potential mechanism, summarized in periment . Our FBAperiment . The absperiment . We used [et al. , CEF makc oxidase is activated by the respiration of transient organic carbon pools by the mitochondrial TCA cycle. Some of the transient organic carbon that was calculated to be available (as the difference between gross and net carbon production measurements) is directly utilized, as evidenced by the decreased use of the Calvin cycle at 200 μmol photon m-2 s-1  may impiTps1432 reduces ATP demand by limiting flux through the TCA cycle and instead secretes ethanol and formate  or dimethyl sulfide (DMS) [Most of the metabolites secreted by n source , and bacn source . Many ban source . Alphaprde (DMS) . Differede (DMS) .Thalassiosira pseudonana CCMP 1335 was isolated from Moriches Bay, New York, in 1958, and the whole genome was sequenced in 2004 [T. pseudonana has been studied from a systems-wide perspective using transcriptomics, proteomics, and metabolomics –125Thalaing work , incorpoT. pseudonana under steady-state light limited conditions and found that cyclic electron flow plays an important role in generating energy and balancing redox under low-light conditions, while the role of energetic coupling between plastids and mitochondria is minimal. We also found that nitrate reduction plays a critical role in dissipating reductants in the plastid under nutrient-replete conditions, while sulfate reduction replaces nitrate assimilation during non-steady-state simulations of nitrate-limitation and N-starvation. The character and quantity of metabolites secreted during our simulations depends on the conditions of redox balance, the relationship between photosynthesis and respiration, and biomass re-modeling.We simulated the metabolism of iTps1432 incorporates a large body of experimental and genetic knowledge. We caution, however, that our predictions also rely on a few network reactions for which there is little or no experimental or genetic evidence. Many enzymes are promiscuous, catalyzing a wider variety of substrates than represented here, and errors in annotation are possible. iTps1432 should be considered a preliminary version of a metabolic network that will improve with further curation and expansion as research on this diatom continues. iTps1432 could be extended in a variety of directions in the future. Reactions describing complex formation for metal- and cofactor-requiring proteins could be added to the model to better describe vitamin and trace metal utilization, as trace metal limitation is an important nutrient condition in the ocean [iTps1432, could allow us to integrate molecular data with models of ocean biogeochemistry in the future.he ocean  and vitahe ocean ,127. AddS1 Fig(PDF)Click here for additional data file.S2 Fig(a) Fermentative metabolism, (b) Chitin synthesis and hydrolysis.(SVG)Click here for additional data file.S3 Figx error bars represent the 95% confidence interval of measured dilution rates, and the y error bars follow from the 95% confidence intervals of bulk biomass measurements .The (PDF)Click here for additional data file.S4 Figa /gDW, and (P) comparison of simulated elemental composition versus measured composition over the course of the experiment .The best and worst  estimates of biomass composition were marked for each biomass component. (A) Bulk biomass components, (B) RNA nucleotides, (C) Protein amino acids, (D) Free amino acids, (E) EPS sugars, (F) Pigment molecules, (G) sulfolipids, (H) phosphtidylcholines, (I) phosphatidylethanolamines, (J) phosphatidylglycerols, (K) diacylglycerides, (L) monogalactosyldiacylglycerides, (M) digalactosyldiacylglycerides, (N) triacylglycerides, (O) mg chl (ZIP)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Dataset(XLS)Click here for additional data file.S2 Dataset(XLSX)Click here for additional data file.S3 Dataset(XLSX)Click here for additional data file.S4 Dataset(XLSX)Click here for additional data file.S1 Note(DOCX)Click here for additional data file.S1 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S2 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S3 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S4 File(DOCX)Click here for additional data file.


Using a metabolic flux analysis with a 1-compartment mathematical model of brain metabolism, we report that compared to hippocampus, hypothalamus shows higher levels of aerobic glycolysis associated with a marked gamma-aminobutyric acid-ergic (GABAergic) and astrocytic metabolic dependence. In addition, our analysis suggests a higher rate of ATP production in hypothalamus that is accompanied by an excess of cytosolic nicotinamide adenine dinucleotide (NADH) production that does not fuel mitochondria via the malate-aspartate shuttle (MAS). In conclusion, our results reveal significant metabolic differences, which might be attributable to respective cell populations or functional features of both structures.Glucose is a major energy fuel for the brain, however, less is known about specificities of its metabolism in distinct cerebral areas. Here we examined the regional differences in glucose utilization between the hypothalamus and hippocampus using in vivo indirect  The brain is well known to be a particularly energy-demanding organ , however13C) MRS allows the investigation of metabolism with cell-specific information through dynamic assessment of metabolic fluxes in vivo upon administration of a 13C-labeled substrate [1H-[13C]-MRS) with 13C-labeled substrates such as glucose  [13C6]glucose ([U-13C6]Glc) Glc)  or [U-133C6]Glc) . Because1H-[13C]-MRS upon infusion of Glc. By combining an updated mathematical model of brain metabolism that includes GABAergic contribution and cerebral metabolic rate of glucose (CMRGlc), we have been able to assess simultaneously glycolytic and mitochondrial contributions non-invasively. We report that hypothalamus has higher rates of ATP synthesis and undergoes higher levels of aerobic glycolysis than hippocampus, possibly related to its glucose sensing properties. Better characterizing and understanding region-specific metabolic properties will help further our understanding on how metabolic diseases or stress may affect cerebral substructures differently.In this study, we have investigated for in vivo differences in the energy metabolic function of hippocampus and hypothalamus in mice using 13C-coupled 1H resonances such as GluC4, GlnC4, GlxC3, GlxC2, GABAC2 and GABAC3 were all clearly observed after ~1 h of Glc infusion , Tau  and Asp  were found to be higher, while Lac  was lower in hippocampus as compared to hypothalamus. Antioxidant Asc level was higher  in hippocampus, while GSH was comparable (n.s.). Importantly, neurotransmitter metabolites Glu and Gln were not found to be different (n.s.), while GABA levels were lower  in hippocampus. The membrane-degradation metabolite GPC was lower in hippocampus , while no difference in membrane-precursor PCho was seen (n.s.). Finally, the glia-specific myo-Ins metabolite was lower in hippocampus .The neurochemical profile was quantified from the non-edited spectra, as described in the methods, and revealed important metabolic differences between hypothalamus and hippocampus . Energy-2 = 0.973; hippocampus, R2 = 0.994) and is shown in Glc; hypo: 0.47 ± 0.06 vs. hippo: 0.29 ± 0.02 μmol/g/min; p < 0.0001) was found to be higher in hypothalamus than hippocampus, while tricarboxylic acid (TCA) cycle  and neurotransmission flux were comparable . Accordingly, the relative higher glycolytic activity of hypothalamus led to an apparent increase in lactate efflux  and a decreased influx  from blood lactate. Although total mitochondrial oxidative metabolism  appeared to be lower in hippocampus (−16%), the transmitochondrial flux , reflecting malate-aspartate shuttle (MAS) flux, and glial AcCoA dilution flux , associated with ketogenic substrate uptake and glial dilution, were both increased in this structure as compared to hypothalamus. Nevertheless, this did not appear to be directly related to a difference in glial metabolism as the pyruvate carboxylase flux was low in both regions . As expected from the higher GABAergic neuron population of hypothalamus, the GABA-Glu cycle  was found higher in this structure, but not the GABA dilution flux .The fitting of metabolite enrichment time courses with a 1-compartment model of glucose metabolism  led to c1H-[13C]-MRS upon infusion of Glc. By comparing both regions during a similar experimental protocol and using a simple model of brain metabolism that considers GABAergic contribution with minimal assumptions, we have been able to estimate the cerebral metabolic rate of glucose without addition of 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) measurement. We report that under our experimental conditions, the hypothalamus shows a higher rate of glycolytic activity, as compared to the hippocampus that appears to rely more on oxidative metabolism , that corresponds to the labeling exchange between two distinct GABA pools, provides a more accurate means of fitting the GABA labeling curves -MRS study that compares neuroenergetics of hippocampus and hypothalamus in vivo. The distinct neurochemical concentration characteristics . Although the transmitochondrial flux was comparable ) was found to be slightly higher in hypothalamus compared to hippocampus here, we observed higher mitochondria-related metabolites  and transmitochondrial flux (Vx) in the hippocampus, suggesting an important mitochondrial dependence for energy metabolism. Little is known about region-specificities in mitochondrial function, and only mitochondrial mass has been compared between hippocampus and hypothalamus, suggesting higher content in the latter -MRS protocol does not allow the quantification of ATP synthesis rate, however, by assuming a similar ATP yield for both structures  -MRS at 14.1T upon infusion of Glc enables detection of region-specific differences in mitochondrial and glycolytic metabolism in vivo. Comparison of the hippocampus and hypothalamus metabolism under similar experimental and analytical conditions allowed to identify a higher level of aerobic glycolysis in the hypothalamus, which might be associated to its particular glucose sensing properties and/or specific cellular population densities.Several factors are likely to influence brain metabolic fluxes, such as differences in the protocol  and analysis . However, our approach aimed at minimizing these effects by comparing both structures under similar experimental and analytical conditions. Nevertheless, anesthetics, and in particular isoflurane are known to affect neuronal electrical and metabolic activity, and the resulting metabolic state observed, even though studied in similar conditions, might be influenced by the respective neuronal/glial populations. For instance, we cannot exclude that the high level of glycolytic activity in hypothalamus is merely a consequence of a higher impact of isoflurane on its metabolic/electric activity. Nevertheless, this potential “differential response” to isoflurane between brain structures might by itself provide important metabolic functional information. We conclude that n = 8) or the hypothalamus (n = 6) after a 10h-long fasting period (glycemia at 7 ± 1 mM). First, a femoral vein was cannulated for infusion of a 20% glucose solution of Glc . This solution was first administered as a bolus , and secondly at a constant infusion rate  during the rest of the experiment Glc infusion leads to pyruvate C3 labeling from glycolysis in the brain (CMRGlc) and enters mitochondrial TCA cycle (VTCA), leading to aspartate and glutamate labeling through the transmitochondrial flux (Vx). In parallel, the absence of pyruvate C2 labeling, specific to the use of Glc, dilutes the C3 position of oxaloacetate (OAA), through the action of glia-specific pyruvate carboxylase (VPC). Consequently, anaplerotic balance is maintained with an efflux of extra glutamine from glial cells (Veff). After neurotransmitter release, glutamate is recycled in astrocytes by conversion into glutamine and recycled back into neurons for replenishing glutamate (VNT). In inhibitory neurons, GABA is synthesized from glutamate and recycled through the TCA cycle (VGABA). Dilution of GABA occurs in inhibitory neurons through the exchange between two metabolite pools attributed to the effect of both GAD isoforms (Vexi). Brain lactate is in exchange with plasma lactate through an inward flux (Vdilin) and outward flux (Vdilout), while AcCoA labeling is influenced by plasma acetate and other dilution processes (Vdilg). A schematic description of the model is presented on 13C-labeling curves, i.e., GlcC6, LacC3, GluC4, GlnC4, GlxC3 , GlxC2 , GABAC2 and GABAC3, were fitted in the proposed model with MATLAB  using a standard built-in ordinary differential equation solver with a modified Levenberg-Marquardt nonlinear weighed regression method. GlxC2 labeling curves were corrected to account for their high correlated quantification with GlcC6, as described in Lizarbe et al. [13C metabolite resonance were used for weighing in the regression cost function. Monte-Carlo simulations  were used to evaluate the fluxes precision and providing a probability distribution [A single-compartment model of brain glucose metabolism was adapted from Lizarbe et al.  and modie et al. . Glucoseribution .t-test, with a significance threshold set at p < 0.05 and no correction for multiple comparison. Group comparisons between metabolite fluxes of the two regions was done using a permutation test analysis with 2000 random permutations, followed by individual unpaired two-tailed Student’s t-test, as previously described [Differences in metabolic profile of both regions were assessed using unpaired Student’s escribed . Statist


Saccharomyces cerevisiae, based on the System Dynamics approach, a general process-based model for two prokaryotic species of biotechnological interest, such as Escherichia coli and Bacillus subtilis, is proposed. The model is based on the main assumption that glycolytic intermediates act as central catabolic hub regulating the shift between respiratory and fermentative pathways. Furthermore, the description of a mixed fermentation with secondary by-products, characteristic of bacterial metabolism, is explicitly considered. The model also represents the inhibitory effect on growth and metabolism of self-produced toxic compounds relevant in assessing the late phases of high-cell density culture. Model simulations reproduced data from experiments reported in the literature with different strains of non-recombinant and recombinant E. coli and B. subtilis cultured in both batch and fed-batch reactors. The proposed model, based on simple biological assumptions, is able to describe the main dynamics of two microbial species of relevant biotechnological interest. It demonstrates that a reductionist System Dynamics approach to formulate simplified macro-kinetic models can provide a robust representation of cell growth and accumulation in the medium of fermentation by-products.The metabolic shift between respiration and fermentation at high glucose concentration is a widespread phenomenon in microbial world, and it is relevant for the biotechnological exploitation of microbial cell factories, affecting the achievement of high-cell-densities in bioreactors. Starting from a model already developed for the yeast  Glucose is the main carbon and energy source for microbial metabolism. Glucose uptake supplies the glycolytic process producing different intermediates, with pyruvate representing a central catabolic hub, followed by the respiratory or fermentative pathway, depending on oxygen availability.Respiration is able to maximize ATP production and consequently biomass yield. However, despite the fully aerobic conditions, in several microbial species when glucose concentration is high, the respiratory metabolism is replaced by a fermentative one, which produces partially oxidized products .S. cerevisiae, others are Crabtree-negative (S. cerevisiae GAL4) in Komagataella phaffii results in a switch of the Crabtree phenotype from negative to positive with an increase in specific glucose uptake , glycolysis intermediates from glucose-6-phosphate to pyruvate (P), acetate produced by fermentation (A), cellular components produced by either fermentation or respiration (CM), reserve compounds (R), growth-associated inhibitory by-products (I), and dead cells (D).The resulting model is composed of a set of 7 ordinary differential equations representing glucose in the growth medium (G) is provided according to the feeding conditions of the bioreactor described for the simulated experiment. Glucose is assimilated by microbial cells, and then converted into the different intermediate products of glycolysis, from glucose-6-phosphate to pyruvate (P). These are used for the construction of new cellular material (CM), either through respiration or fermentation. In the case of fermentation, acetate (A) is the main end-product, which can also be used as carbon source for the respiratory pathway when glucose is limiting.Glucose (P) in the regulation of the metabolic shift between respiration/fermentation and, in general, cell metabolism. Therefore, high levels of P are assumed to be responsible for (i) the activation of aerobic fermentation due to overflow metabolism, (ii) the repression of respiration (“glucose effect”), (iii) the accumulation of reserve materials (R), and (iv) the induction of mortality with accumulation of dead cells (D)  .Moreover, considering that acetate is not the only end-product of bacterial fermentations even in aerobic conditions , in the I), as already described in The model also considers growth-associated inhibitory by-products  with a variable order solver (ode15s). The model calibration was performed by minimizing the sum of the squared errors (SSE)1, n2, n3 are the number of samples per observed outputs, CMi, G_i, A_i, are the values of the ith measured outputs and ith outputs predicted by the model. The minimization was performed by using the fminsearch MATLAB routine which implements a Nelder–Mead simplex algorithm  perturbation on the model outputs; Xj(P) represents the simulation values of the state variables Microbial mass, Glucose and Acetate without any parameter perturbation; max(Xj(p))−min(Xj(p)) is the standardization factor referring to the values of the baseline simulation; k is the number of parameters.where, E. coli among the most used in biotechnological applications, namely W3110 . The possible metabolic shift occurred according to the value of the specific growth rate of the population, determined by the specific feeding rate (SFR) applied to the bioreactor during the fed-batch phase .E. coli W3110, performed by MAX) of 0.31 h–1, presents the typical exponential growth behavior of an E. coli population growing on glucose and displaying a fermentative metabolism with acetate production. When glucose is completely depleted in the medium, fermentative metabolism is replaced by a respiratory one, with a short period of acetate consumption by respiration observed during the batch phase. Then, an exponential increasing feeding regime is activated (after 13 h from the beginning of the experiment) at a Specific Feeding Rate (SFR) value of 0.22 h–1, equal to the population specific growth rate, μ. This value is high enough (75% μMAX) to switch back the population to a fermentative metabolism with accumulation of acetate up to 0.3 g l–1. Three hours later (16 h from the beginning of the experiment), a constant feeding is applied to the bioreactor beginning at a SFR value of 0.11h–1, so that the bacterial cells are allowed to display a respiratory metabolism with no accumulation of acetate. During this phase, however, three glucose pulses are performed inducing temporary acetate production , starting at 0.11 h–1. In the first phase of exponential feeding, the simulated microbial population follows the observed growth corresponding to the feeding regime, while in the second phase (constant feeding) the model properly describes the declining growth rate due to self-produced inhibitory compounds. In turn, the reduced growth rate compared to the glucose feeding induces the metabolic switch reactivating acetate production. In the second simulated experiment  are below the threshold value for acetate production reported for the strain  at the end of the simulated experiment, and it is characterized by a continuous, although very low, production of acetate. vG and Gη) affected all strains (E. coli W3110 appeared to be sensitive to the fermentation process (vF and FAη); E. coli TG1 showed higher responsiveness to the secretion rate of inhibitory compounds (ρ) and the respiration parameters (vR and RPη); B. subtilis showed to specifically respond to the metabolic shift between respiration and fermentation (b1) and the efficiency of biomass production by fermentation (FPη).The last simulation presented in  strains , reflectEscherichia coli strains, as well as of Bacillus subtilis growing both in batch and fed-batch cultures on glucose as carbon as energy source. The highly significant agreement between experimental data and simulations obtained for different microbial species and strains demonstrates how the process-based System Dynamics approach, already followed in the case of the yeast Saccharomyces cerevisiae . The obtainment of the desired products  by the recombinant strains was achieved by shifting the temperature to 42°C or by addition of 0.5 mM IPTG after 22 h fed-batch phase. In both cases, induction of the products did not affect the dynamics of growth, in terms of glucose consumption and acetate formation, as shown by the simulations.This essential assumption characterizing the presented model proved indispensable to predict the occurrence of the metabolic shift between respiration and fermentation in two species of prevalent biotechnological interest such as tal data . TherefoB. subtilis (–1), a limited production of acetate was observed during all the culture run, showing a less clear-cut metabolic shift for this microbial species. This is also reflected by the high sensitivity of this strain to the parameter related to the metabolic switch (b1). A further assumption of the proposed model is related to glucose transporters which have been considered as constant within each microbial strain. Clearly, this is a strong simplification since it is known that transporters can be modulated according to glucose availability in the media and future specific studies could address this point more in depth.Noteworthy, in the case of subtilis , even thS. cerevisiae model presented by Moreover, in the previous S. cerevisiae in aerobic fed-batch cultures and later extended to describe the fermentation dynamics of E. coli by E. coli. Our proposed model has a similar level of simplification compared to the abovementioned works, although it was designed to be more general in order to reproduce different microbial species and strains rather than being applied to a single one. Moreover, our model is the only one considering the inhibitory effect on growth and metabolism of self-produced toxic compounds different from fermentation products which is relevant to reproduce the late phases of high cell-density cultures when this phenomenon becomes significant in limiting the growth rate. The phenomenon of self-toxicity regulating cell proliferation is evident only in prolonged fed-batch cultures, and it was clearly pointed out in the case of both wild-type and auxotrophic yeast strains cultured in fed-batch reactors   . For thiE. coli, show a detailed description of the metabolic pathways . On this point, Following a completely different approach, metabolic flux analysis models specifically focused on the central carbon metabolism of ys e.g., . RecentlInstead, our modeling procedure directly aims at the identification of a minimal number of processes sufficient to simulate the emergent properties of a complex system (top-down approach) and it is based on logical reasoning on existing knowledge of the system. Then, in this work only those variables relevant for the growth of microbial populations on glucose and their metabolic shift were mathematically described, whereas the many secondary pathways, also directly or indirectly affecting the selected variables, were not taken into account. In summary, our approach is based on the principle of parsimony and has the advantage of keeping the mathematical formulation simple and robust with a reduced number of parameters.S. cerevisiae 90032-q, https://doi.org/10.1016/0168-1656(94)00143-z, https://doi.org/10.1016/0168-1656(94)90215-1, https://doi.org/10.1007/s00449-004-0391-z, and https://doi.org/10.1021/bp034348k.Publicly available datasets were analyzed in this study. This data can be found here: SM, FC, CV, and FG coordinated the model design and its implementation in System Dynamics language. FC, FG, AO, CV, and EP performed the numerical simulations and model calibration. The manuscript was written by FC, EA, and SM. 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.


Cyanobacteria, given their ability to produce various secondary metabolites utilizing solar energy and carbon dioxide, are a potential platform for sustainable production of biochemicals. Until now, conventional metabolic engineering approaches have been applied to various cyanobacterial species for enhanced production of industrially valued compounds, including secondary metabolites and non-natural biochemicals. However, the shortage of understanding of cyanobacterial metabolic and regulatory networks for atmospheric carbon fixation to biochemical production and the lack of available engineering tools limit the potential of cyanobacteria for industrial applications. Recently, to overcome the limitations, synthetic biology tools and systems biology approaches such as genome-scale modeling based on diverse omics data have been applied to cyanobacteria. This review covers the synthetic and systems biology approaches for advanced metabolic engineering of cyanobacteria. A. AAnabaetivities . In addiadiation .PKS and NRPS are representatives of enzymes responsible for the biosynthesis of secondary metabolites in various organisms. Enzymes of these classes consists of at least three essential modular domains that facilitate chain elongation and modification . First, M. producens, and lyngbyabellin from M. bouillonii  enzymes [M. viridis, is a serine protease inhibitor, and patellamide A produced by Prochloron didemni has moderate cytotoxicity [RiPP is a class of secondary metabolites that includes, as its name depicts, ribosomally synthesized and post-translationally modified peptides. Post-translational modifications include leader peptide hydrolysis, cyclization, and disulfide bond formation. RiPP BGC generally consists of a short precursor peptide with an  enzymes ,88. The  enzymes . For exatoxicity .M. producens and Lyngbya semiplena, respectively  pathway to biosynthesize diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) from CO2. DMAPP is converted to farnesyl diphosphate (FPP), a substrate for squalene biosynthesis, by FPP synthase (ispA). Heterogenous genes, including 1-deoxy-d-xylulose-5-phosphate synthase (dxs), isopentenyl diphosphate isomerase (idi), and ispA were introduced into the S. elongatus PCC 7942 genome by homologous recombination to increase the intracellular concentration of FPP. Next, a squalene synthase (SQS) was also introduced by homologous recombination, resulting in a maximum of 5.0 mg/L/OD730nm squalene production [730nm by constructing a fusion protein of SQS with cpcB1, which encodes the β-subunit of phycocyanin and is highly expressed under the strong endogenous cpcB1 promoter [S. elongatus PCC 7942 strain to repress two essential genes, acnB and cpcB2 encoding aconitase and phycocyanin β-subunit, respectively, resulting in an improved squalene production [Genetic manipulation tools explained above have been applied in cyanobacteria to increase the production of secondary metabolites . In receutumnale ,102. HowPCC 7942 ,104,105.oduction . The titPhormidium autumnale [S. elongatus PCC 7942 [S. elongatus PCC 7942 has the methylerythritol phosphate (MEP) pathway to biosynthesize diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) from CO2. DMAPP is converted to farnesyl diphosphate (FPP), a substrate for squalene biosynthesis, by FPP synthase (ispA). Heterogenous genes, including 1-deoxy-d-xylulose-5-phosphate synthase (dxs), isopentenyl diphosphate isomerase (idi), and ispA were introduced into the S. elongatus PCC 7942 genome by homologous recombination to increase the intracellular concentration of FPP. Next, a squalene synthase (SQS) was also introduced by homologous recombination, resulting in a maximum of 5.0 mg/L/OD730nm squalene production [730nm by constructing a fusion protein of SQS with cpcB1, which encodes the β-subunit of phycocyanin and is highly expressed under the strong endogenous cpcB1 promoter [S. elongatus PCC 7942 strain to repress two essential genes, acnB and cpcB2 encoding aconitase and phycocyanin β-subunit, respectively, resulting in an improved squalene production [Genetic manipulation tools explained above have been applied in cyanobacteria to increase the production of secondary metabolites . In receutumnale ,102. HowPCC 7942 ,104,105.oduction . The titSynechocystis sp. PCC 6803 [crtW) and β-carotene hydroxylase (crtZ) were integrated for astaxanthin production. The promoter combinations with diverse strength were tested for expressions of crtW and crtZ, because their relative expression level is known to be critical to produce astaxanthin in E. coli [crtZ expression and a pea promoter PsbA for crtW expression, which showed two-fold higher activity than Pcpc560, resulting in improved production of astaxanthin. Further, based on liquid chromatography-mass spectrometry (LC-MS) metabolomics data, fructose-1,6-/sedoheptulose 1,7-bisphosphate (FBP/SBPase), which is involved in the Calvin–Benson–Bassham cycle, was found as an additional engineering target and overexpressed with episomal expression vector. Then, heterologous dxs and ispA gene was introduced into Synechocystis sp. PCC 6803 genome, and the engineered strain was finally able to produce astaxanthin of 29.6 mg/g dry cell weight, the highest level in the currently known engineered strain.Besides terpenes, a xanthophyll carotenoid called astaxanthin has been gaining significant attention in the healthcare field owing to the anti-inflammatory and antioxidant properties elucidated in human cells . AstaxanPCC 6803 . First,  E. coli . HoweverS. elongatus PCC 7942 as a host for producing 2,3-butanediol, a biochemical building block for plasticizers, liquid fuel additives, and industrial solvents [galP), glucose-6-phosphate dehydrogenase (zwf), and 6-phosphogluconate dehydrogenase (gnd) into neutral sites, and phosphoribulokinase (prk) and RuBisCO subunits (rbcLXS) into the cp12 site, resulting in a maximum production of 12.6 g/L of 2,3-butanediol. In addition, in Synechocystis sp. PCC 6803, metabolic engineering was performed to enhance the biofuel production [pdc)-inserted ethanol-producing Synechocystis sp. PCC 6803 strain, pyruvate dehydrogenase complex subunit (odhB) was repressed by CRISPRi. Repression of citrate synthase (gltA) in the phosphoketolase (xfpk) and acetoacetyl-CoA synthase (nphT7)-inserted n-butanol-producing strain increased N-butanol production.Along with the naturally produced cyanobacterial secondary metabolites, cyanobacteria also serve as an attractive platform for diverse heterologous biochemical production. For instance, cyanobacterial genome engineering was performed to use solvents . In thisoduction . To impr2. On introducing the isoprene synthase (ispS) obtained from various plants into the Synechocystis sp. PCC 6803 genome by homologous recombination, a maximum of 4.3 mg/L/h isoprene production rate was achieved with the aid of dxs and idi overexpression [ispS, and as a result, expression of Eucalyptus globulus ispS with Ptrc promoter showed the highest isoprene production. As demonstrated in various studies, cyanobacteria can produce industrially valuable biomaterials by utilizing light and CO2. Therefore, cyanobacteria have the potential to be developed into an eco-friendly and economical photoautotrophic biofactory.Isoprene is a plant-derived building block, mainly used in the manufacturing of synthetic rubber. While most of the current synthetic rubber production depends on the petrochemical source, there have been efforts made to increase the cyanobacterial production of isoprene directly from COThe value-added biochemicals that we have discussed are all products of photosynthesis. Thus, enhancement of the photosynthetic efficiency is crucial for supplying sufficient energy and reducing power for productivity increment. Some of the strategies for improving photosynthesis include expansion of the absorption spectra to capture more light energy, downsizing of the antenna to increase high illumination tolerance, and optimization of the electron transport chain. Furthermore, efficient use of photosystem-generated energy is another strategy that can be achieved by enhancing carbon fixation or reducing carbon loss .E. coli or Saccharomyces cerevisiae. In the case of E. coli, which is most widely used engineering host with a lot of information about genetic features and metabolic network, it is easy to apply knowledge-based engineering approaches such as enzyme structure modification, feedback inhibition removal, and precursor pool or cofactor level increasement [E. coli. On the other hand, since cyanobacteria is a photoautotroph, more complex energy generation and distribution, and redox state should be considered when manipulating the metabolic network, and thus a more systematic insight is required. Additionally, when engineering multiple targets in the metabolic pathway, it is difficult to fine-tune the relative expression levels of the genes due to the lack of available bioparts such as neutral site, promoter, and RBS. In order to overcome these limitations, it is essential to systematically understand the complex metabolic network within the cell and to develop various genetic tools by genome-scale screening of native promoters and RBSs or constructing synthetic bioparts. In recent studies, a systematic approach through genome-scale modeling (GEM) has been successfully applied to engineering cyanobacteria [Until now, we have presented studies showing the potential of cyanobacteria in producing various metabolites and that continuous engineering efforts can enhance the native and non-native metabolite production from cyanobacteria. However, despite the proposed and demonstrated potential as a production host, the production levels in cyanobacteria are not compatible with those in model organisms such as easement . In addibacteria ,125. ForSynthetic biology involves development of genetic parts, combination design to fulfill the desired function, and application of the combined tool into an organism. Quantification and standardization of the genetic parts represented by promoters, RBS, untranslated region (UTR) sequences, and terminator sequences are critical for proper employment of synthetic biology. Systems biology deals with the living system as an interactive network more than just a collection of reductive components. Therefore, understanding of the organism as a system is required for precise designing of the synthetic biology tools, and the introduction of synthetic biology tools into an organism affects the system, making the two biological approaches inseparable. The general synthetic and systems biology research flow is represented as the design–build–test–learn cycle . In the E. coli), genetic parts such as promoters and RBS with varying strengths are available, there has been a significant lack of information and diversity concerning the cyanobacterial genetic parts. Currently used genetic parts are cyanobacterial endogenous promoters (PpsbA and Pcpc) and E. coli origin promoters  [T7 derivatives, PpsbA* derivatives, Ptrc-based hybrid, and synthetic promoters have been developed and tested in Synechocystis sp. PCC 6803 [psbA and Pcpc of S. elongatus PCC 7942, were generated to achieve promoters of varying strength [S. elongatus strains, expanding the cyanobacterial synthetic biology toolbox. The RBSs for cyanobacterial gene expression are mostly wild-type RBS associated with native promoter, or RBS of highly expressed genes such as psbA2 and rbcL [Synechocystis sp. PCC 6803 genome sequence (RBS*) are also used in several studies [Synechocystis sp. PCC 6803 were additionally identified, and 13 RBSs were rationally designed based on the known strong RBS sequences [Development of the genetic parts is critical for applying synthetic biology to metabolic engineering of cyanobacteria. While in other model species  . Promoted PnrsB) . HoweverPCC 6803 ,142. Recstrength . A colleand rbcL . In addi studies . Recentlequences .S. elongatus PCC 7942, the operation of the synthetic theophylline riboswitch confirmed that it could control translation initiation [Leptolyngbya sp. BL 0902, Nostoc sp. 7120, and Synechocystis sp. WHSyn [Bacillus subtilis phuE (adenine riboswitch). The chimeric riboswitch was validated in Nostoc sp. 7120 [Riboswitches are another class of bioparts operated based on their RNA structures. A riboswitch comprises of an aptamer and an expression part. While the aptamer directly binds to a corresponding small molecule, the expression part regulates gene expression post-transcriptionally by causing structural changes in accordance with the small molecule binding. In itiation . Recentlitiation . The thep. WHSyn . The thesp. 7120 . OverallSynechocystis sp. PCC 6803 and S. elongatus PCC 7942, are completely assembled. In particular, the complete genome sequence of Synechocystis sp. PCC 6803 was reported as early as E. coli genome sequence, thus settling as a model organism among cyanobacteria [Synechocystis sp. PCC 6803, S. elongatus PCC 7942, and Nostoc sp. 7120 [Euhalothece living in a hypersaline habitat, various salt resistance-related genes, such as Na+ transporting multiple resistance and pH adaptation systems, and glycine betaine biosynthesis enzymes were highly upregulated [S. elongatus UTEX 2973 and two types of Fischerella strains, which were used to elucidate the differences in transcriptional regulation resulting in different phenotypes [Synechocystis sp. PCC 6803 under carbon starvation condition was reported in 2018 [13C isotopically nonstationary metabolic flux analysis have been implemented [Approximately 1500 cyanobacterial genome sequences have been registered in the NCBI genome database, and 196 of them, including the genome sequences of bacteria . Since tegulated . In addienotypes ,154. Add in 2018 . In ordelemented . The masSynechocystis sp. PCC 6803 has been studied most extensively, starting with the central carbon metabolic reconstruction under heterotrophic, mixotrophic, or autotrophic conditions [Synechocystis sp. PCC 6803 GEM was recently updated with more detailed photosynthesis and electron transport chain data [Arthrospira platensis, Cyanothece sp., Nostoc sp., S. elongatus UTEX 2973, S. elongatus PCC 7942, and Synechococcus sp., are being established and further enhanced [GEM is an in silico tool that is useful for explaining the entire metabolism of an organism based on its genomic information. Given the ability to predict cellular metabolic behavior under given conditions or constraints, GEMs are mainly used for designing metabolic engineering strategies. GEM is an iteratively evolving system mended by new input information starting from the prior draft. Thus, more precise GEMs can be constructed by gathering greater types and amounts of genome information. GEM has been witnessing significant advancements with continuous accumulation of massive omics data supplied by various NGS techniques. Among cyanobacteria, nditions ,158. Theain data ,160. GEMenhanced . ThroughCyanobacteria have significant industrial value owing to their ability to generate energy from photosynthesis and to produce various secondary metabolites. However, several improvements are required for cyanobacteria to meet the industry-level expectations and to establish themselves as a potential bioproduction platform. First, when using cyanobacterial native promoter or RBS, unexpected interaction may occur within the cell, which may reduce engineering efficiency. Therefore, development and application of the variety of orthogonal tools for engineering cyanobacteria is crucial. In addition, it is essential to obtain the precise metabolic network information to design strategies for the concise use of the synthetic biology tools. For example, the optimal production conditions can be discovered through promoter and RBS combination randomization, and the kind of neutral sites that can be used for chromosome integration can be expanded based on essential gene information found with transposon mutagenesis ,211,212.


Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of  Genome-scale metabolic models are important tools for systems biology. They are used in various fields, e.g. in metabolic engineering, the identification of potential drug targets, and as knowledge libraries to understand the behavior of biological systems in detail . In thesFlux balance analysis (FBA) allows the prediction of the steady-state condition of the metabolic network of an organism under different environmental conditions . MetabolAs most applications for flux optimization focus on a reaction-centric point of view, they pay little attention to metabolite fluxes. Only a small number of previous studies showed the benefit of analyzing models from a metabolite-centric point of view, e.g. for the discovery of new drugs ,7. TherePhaeobacter inhibens DSM 17395, an ideal model organism for basic research and elucidation of stress responses because of its metabolic versatility. Experimental data for degradation pathways of different carbon sources based on metabolome and proteome measurements have been determined for P. inhibens DSM 17395 in previous studies by us and others [P. inhibens DSM 17395 are applicable for metabolic modeling. Metabolic modeling is used for a variety of applications, ranging from biotechnological applications to ecological questions . A well-d others ,12,13,14d others ,16. Protd others ,17,18. Dd others . More red others ,21. ThusCorynebacterium glutamicum DSM 20300, a widely used biotechnological production strain that was originally isolated as an l-glutamate-producing bacterium [C. glutamicum has been metabolically engineered for the production of other amino acids, organic acids, alcohols, and polymers [The second model system of this study is acterium  and is uacterium . Furtherpolymers . In this study, we present Metano, an open-source command-line toolbox for metabolic modeling, and MMTB, a web-based interface to Metano that comes with an integrative database of biochemical reactions and genome annotations. While Metano was developed for a more versed audience, MMTB was designed especially for inexperienced users or users without a strong background in bioinformatics. C. glutamicum to verify the accuracy or capability of the toolbox. In a second step, we used the TDA-negative mutant strains of P. inhibens DSM 17395, with optimal growth on three different amino acids to reconstruct a metabolic model for P. inhibens DSM 17395, based on genomic and experimental data. FBA was performed to analyze the carbon flux in the wild-type strain compared to the TDA-negative mutant strains with respect to carbon loss and growth inhibition by TDA. MMTB methods provided a metabolite-centric view on the model to determine changes in the intracellular carbon flux under different growth conditions.We applied Metano methods to the published model of Metano is a Python-based command-line tool for the analysis of stoichiometric models. Several features can be accessed online via the MMTB. Prominent examples of constraint-based metabolic modeling software include COBRA , FASIMU While flux distributions in metabolic networks are conventionally analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization (MFM) allow a metabolite-centric view on flux distributions . Split-rThe Metano toolbox includes several features for flux optimization and is optimized for processing speed. In addition to the common FBA algorithm, Metano implements the fastFVA algorithm for FVA . MetabolIn addition to the widely used SBML format, Metano models can be represented by simple lists of reactions that are readable by humans and can be easily modified by the user with any text editor. An additional parameter file sets the constraints for a simulation scenario so that the variables, e.g., substrate boundaries and objective function, are detached from the model itself. Easy exchange of models is ensured by offering export and import tools for the widely accepted standard format SBML .Metano Modeling Toolbox (MMTB) is a web-based tool for the generation and analysis of metabolic models. MMTB closes the gap between BKMS-react  and EnzyOn the one hand, MMTB assists during model creation. The key feature is an intuitive search tool that gives access to reaction and metabolite information. The user can search for reactions by either entering parts of the biochemical reaction, EC numbers, database IDs, or by selecting a BRENDA, MetaCyc, or KEGG pathway. The resulting reactions originate from the integrative database BKMS-react  that incOn the other hand, MMTB assists during model analysis via an interface to the Metano toolbox. The user can upload a metabolic model and apply one of the following analysis methods: (1) FBA, (2) FVA, (3) dead-end analysis, (4) split-ratio analysis, (5) metabolite flux minimization, (6) a metabolite-centric visualization that shows the network as an interactive bipartite graph with consuming and producing metabolite fluxes, (7) a model verification tool where all metabolites in the uploaded model are replaced by their recommended names from BKMS-react. Because misspelled metabolites or synonyms are not treated as identical metabolic nodes, this unique method helps to avoid mistakes. Additionally, MMTB has a model exchange platform, where users can upload and publish their own models. These models can be viewed, analyzed, visualized, and downloaded, thus promoting the reuse of models that are already published. A model conversion tool for SBML and JSON models allows the use of models that were generated with other software. C. glutamicum model iMG481 that consists of 550 reactions [The Metano modeling toolbox was evaluated using the published eactions . The onl−1, as well as key fluxes described in the paper. Furthermore, the MFM method revealed that of a total of 408 metabolites, 195 were not essential for biomass production of 95% of the optimal value. When the biomass function is restricted to 10%, 205 metabolites are non-essential. By using the Metano toolbox, we were able to reproduce the in silico growth on glucose as it was described by Graf and colleagues. When using maximal biomass production as objective function, Metano was able to reproduce the growth rate of 0.337 hThe model iPin571 was manually assembled with the assistance of the MMTB database of metabolic reactions and the integrated EnzymeDetector annotation . The modl-phenylalanine as the precursor for TDA biosynthesis, (2) l-alanine as an amino acid with immediate access to the central carbon metabolism, and (3) l-leucine as an amino acid with a more complex degradation pathway, slower growth, and a reduced TDA production compared to l-alanine. TDA-negative strains (Δ262-kb plasmid-cured and tdaE transposon mutant strains) were cultivated as a reference to compare optimal growth with inhibited growth, and furthermore, to analyze the influence of carbon loss due to secreted metabolites. Nine of the proteinogenic amino acids were previously analyzed with respect to growth physiology and degradation pathways . For theThe model iPin571 was able to simulate growth for the TDA-negative mutants on different carbon sources and the wild-type strain on leucine . l-phenylalanine, indicating reduced carbon yields due to TDA biosynthesis , 1.3-fold (l-leucine), and 1.6-fold  higher compared to that of the wild-type strain. The tdaE transposon mutant strain showed almost the same growth yields as the Δ262-kb mutant strain, indicating that the lower growth yields of the wild-type strain were connected to the inhibitory effect of TDA with a necessity for an increased respiratory activity resulting in more carbon loss by CO2 production [The wild-type strain showed the lowest growth yields on each of the amino acids as a nutrient, especially on ynthesis . Growth n source . The grooduction ,21.2 , and cells were transferred at least for one adaptation cycle in the respective amino acid before inoculating the main cultures. All main experiments were conducted with three biological replicates in 500 mL Erlenmeyer flasks with three baffles at 150 rpm and 28 °C in an orbital incubator as previously described [g for 5 min. Cells were re-suspended in 3.7% sodium chloride and split to get 1–3 mg cell dry weight (CDW) for DNA, RNA, protein and polyhydroxybutyrate (PHB) each, 10–30 mg for lipid and 10–30 mg for determination of CDW  as previously described . For detn of CDW .iMG481 was obtained from the The metabolic model iPin571 was reconstructed based on genome and experimental data [The metabolic model tal data ,17,18,46tal data . We inclMetano is a software toolbox for metabolic modeling implemented in the Python programming language. Metano is easily extensible by prospective functional extensions via well-documented interfaces in a structured object-oriented framework. This allows the adaptation of the software to different requirements by users with basic programming skills. Additionally, all functionalities of Metano can be accessed by standalone Python programs via the command line, without the need to write programs. The Metano toolbox implements many well-established analysis tools, including FBA, FVA, MOMA, knockout simulation, and dead-end analysis . AdvanceMFM first performs a standard FBA and then adds a variability analysis comparable to FVA by the following algorithm: (1) Set a constraint for the objective function (default 95% of FBA flux). (2) Generate coefficient vectors expressing the (producing) metabolite fluxes as linear combinations of the reaction fluxes. (3) Perform linear programming with the linear objective functions defined by these coefficient vectors to successively minimize each objective function.Split-ratio analysis computes the split ratios for the fluxes entering and leaving each metabolite as totals fluxes and percentage values. This method is used to calculate the network and label edges and nodes for the visualization available at MMTB.github.com/JuliaHelmecke/metano (accessed on 9 Febraury 2021).All methods are described in more detail on the software website and in the Metano includes interfaces to numerous freely available solvers for mathematical optimization problems. For linear programming, the supported solvers are lp-solve and the GNU Linear Programming Kit (GLPK), interfaced to Python via Swiglpk and PyMathProg, respectively. Quadratic programming problems are solved by using the quadratic cone program solver of CVXOPT, interfaced via CVXPY.mmtb.brenda-enzymes.org (accessed on 9 Febraury 2021)). Metano does not rely on commercial software packages and can be installed on various Linux distributions via the Python Package Index (PyPI) and the related package installation system pip. A current version of Metano is also available for OS-independent use as a Docker image on Docker Hub.The software is published under the GNU General Public License version 3 and is available via the MMTB website (


Our study improves the current understanding of microbial interactions. Because “assistant” microbes showed importance in communities in addition to the functional microbes, our findings also suggest a useful “assistant-microbe” principle in the design of microbial communities for either bioremediation or chemical production.Many natural and synthetic microbial communities are composed of not only species whose biological properties are consistent with their corresponding communities but also ones whose chemophysical characteristics do not directly contribute to the performance of their communities. Even though the latter species are often essential to the microbial communities, their roles are unclear. Here, by investigation of an artificial two-member microbial consortium in  Dietzia sp. strain DQ12-45-1b and Pseudomonas stutzeri SLG510A3-8, had a synergistic effect on hexadecane (C16 compound) biodegradation, even though P. stutzeri could not utilize C16 individually. To gain a better understanding of the roles of the alkane nonconsumer P. stutzeri in the C16-degrading consortium, we reconstructed a two-species stoichiometric metabolic model, iBH1908, and integrated in silico prediction with the following in vitro validation, a comparative proteomics analysis, and extracellular metabolomic detection. Metabolic interactions between P. stutzeri and Dietzia sp. were successfully revealed to have importance in efficient C16 degradation. In the process, P. stutzeri survived on C16 metabolic intermediates from Dietzia sp., including hexadecanoate, 3-hydroxybutanoate, and α-ketoglutarate. In return, P. stutzeri reorganized its metabolic flux distribution to fed back acetate and glutamate to Dietzia sp. to enhance its C16 degradation efficiency by improving Dietzia cell accumulation and by regulating the expression of Dietzia succinate dehydrogenase. By using the synergistic microbial consortium of Dietzia sp. and P. stutzeri with the addition of the in silico-predicted key exchanged metabolites, diesel oil was effectively disposed of in 15 days with a removal fraction of 85.54% ± 6.42%, leaving small amounts of C15 to C20 isomers. Our finding provides a novel microbial assembling mode for efficient bioremediation or chemical production in the future.Biodegradation of alkanes by microbial communities is ubiquitous in nature. Interestingly, the microbial communities with high hydrocarbon-degrading performances are sometimes composed of not only hydrocarbon degraders but also nonconsumers, but the synergistic mechanisms remain unknown. Here, we found that two bacterial strains isolated from Chinese oil fields, IMPORTANCE Many natural and synthetic microbial communities are composed of not only species whose biological properties are consistent with their corresponding communities but also ones whose chemophysical characteristics do not directly contribute to the performance of their communities. Even though the latter species are often essential to the microbial communities, their roles are unclear. Here, by investigation of an artificial two-member microbial consortium in n-alkane biodegradation, we showed that the microbial member without the n-alkane-degrading capability had a cross-feeding interaction with and metabolic regulation to the leading member for the synergistic n-alkane biodegradation. Our study improves the current understanding of microbial interactions. Because “assistant” microbes showed importance in communities in addition to the functional microbes, our findings also suggest a useful “assistant-microbe” principle in the design of microbial communities for either bioremediation or chemical production. Afterward, we did in vitro cultivation, comparative proteomics analysis, and extracellular metabolomics analysis based on the in silico prediction of the metabolic exchange between the two strains on hexadecane (C16) to understand the mechanism of communication between the two strains. Our finding on the mutualistic interaction between the leading and supporting members reveal a novel assemblage strategy to construct synthetic microbial consortia with high-performing functions.In this study, we constructed a simple microbial consortium consisting of an alkane-degrading bacterial strain, l fields –22. Dietradation . Among te strain –26. P. sse genes , indicatronments –30, its Dietzia sp. strain DQ12-45-1b and P. stutzeri SLG510A3-8 were aerobically cocultivated on C16. When P. stutzeri was exposed to C16 individually, the percentage of C16 removed by P. stutzeri was low in a 12-day culture period (5.21% ± 2.88%)  was significantly higher than those by each individual strain (P < 0.05). On the other hand, both P. stutzeri and Dietzia sp. in the coculture system grew much better than those in monocultures, even though the cell density of P. stutzeri was an order of magnitude less than that of Dietzia sp.  than in its monoculture system (0.71 ± 0.16 pg day−1 CFU−1) (P < 0.05). Based on the efficient performance in C16 degradation by the coculture of P. stutzeri and Dietzia sp., a synergistic effect on C16 biodegradation through metabolic communication was proposed for the coculture system, in which P. stutzeri might survive on the by-products of Dietzia sp. under the C16 exposure condition and, in return, secretory metabolites of P. stutzeri are able to induce a higher C16 recovery efficiency of the Dietzia strain.Bacterial strains ± 2.88%) , and theow level . This inl on C16 , with 79oculture . Neverthtzia sp.  and c. BP. stutzeri and Dietzia sp., two-species metabolic network reconstruction and consortium-level flux balance analysis (FBA) were carried out. First, we reconstructed the single-species GEM of Dietzia sp. DQ12-45-1b, namely, iBH925 , and the GEM of P. stutzeri SLG510A3-8, namely, iBH983 . The accuracy of these two models was validated by the consistency between in vitro experimental data and in silico prediction of growth rates and available carbon source spectra . Afterward, iBH925 and iBH983 were reconciled to unify the metabolites and reaction formats, followed by network integration. The resulted model for the microbial consortium, namely, iBH1908, contained 1,908 genes, 1,273 metabolites, and 2,670 reactions . In iBH1908, the metabolic networks of Dietzia sp. and P. stutzeri were connected to each other through 68 shared metabolites (see Table S1 in File S1). Details of the modeling and analyzing processes of iBH925, iBH983, and iBH1908 are described in File S1.To elucidate the interspecific metabolic communication between Dietzia sp. and P. stutzeri was simulated for individual growth or coculture in C16-exposed minimal medium. A maximum growth rate μ0.0718 = 2.38 × 10−2 h−1 (see Table S2 in File S1) for Dietzia sp. in monoculture was predicted by FBA on Dietzia sp. GEM iBH925 at the C16 uptake rate of 7.18 × 10−2 mmol g−1 h−1, which was determined by batch culture . Similarly, with the same nutrient constraints, a maximum growth rate of 0 h−1 for P. stutzeri was predicted by FBA on P. stutzeri GEM iBH983. In contrast, FBA on the two-species GEM iBH1908, in which the Dietzia sp. growth rate was constrained to be 2.38 × 10−2 h−1 while the C16 uptake rate was fixed at 8.00 × 10−2 mmol g−1 h−1 based on our previous measurement (File S1), predicted a maximum growth rate of 5.60 × 10−3 h−1 for P. stutzeri. Taken together, these predictions were in agreement with our previous findings that no P. stutzeri cell grew on C16 individually but its cell density gradually increased in the presence of Dietzia sp.  was almost unaffected when formate and glycolaldehyde uptake rates were both constrained to 0 mmol g−1 h−1. However, the drops were significant when each of the other three compounds was constrained, suggesting that R-3-hydroxybutanoate, α-ketoglutarate, and hexadecanoate were key compounds secreted by Dietzia sp. to support P. stutzeri growth in the C16-exposed minimal medium. Similarly, the 10 P. stutzeri secreted metabolites in iBH925. Addition of the 10 compounds induced a cell growth rate rise from 2.38 × 10−2 to 5.53 × 10−2 h−1. Acetate and l-glutamate were found to be the key metabolites responsible for the significant rise, as the maximum predicted growth rate of Dietzia sp. decreased to 2.56 × 10−2 h−1 if the two metabolites were excluded.By using FBA, the flux distribution in tzia sp. . The glolization . P. stutP. stutzeri SLG510A3-8 were inoculated in the minimal medium supplemented with R-3-hydroxybutanoic acid, α-ketoglutaric acid, or hexadecanoic acid. As shown in P. stutzeri biomass accumulated on each of the three compounds after 24 h of incubation, of which α-ketoglutaric acid was preferred by P. stutzeri more than the other two compounds. The in vitro result confirmed that in silico-predicted key exchange compounds were able to be utilized by P. stutzeri and suggested that the three compounds might be the reason why P. stutzeri could survive in the coculture system when C16 was the sole exogenous carbon source.Cells of 16 biodegradation by Dietzia sp. strain DQ12-45-1b, Dietzia sp. cells were cultivated in the C16-exposed minimal medium with the addition of low concentrations of sodium acetate or sodium glutamate, respectively. Higher Dietzia sp. biomass and lower residual C16 contents in the cultures supplemented with acetate and glutamate  , on Dietzia sp. cell growth and C16 biodegradation. The specific C16 removal rates in Dietzia sp. monoculture systems with the addition of these three compounds were not significantly different from those in the monoculture system without any additives (in vitro data mentioned above. This result indicated that C16 biodegradation by Dietzia sp. could be enhanced through the in silico-predicted P. stutzeri secretions but not by some other compounds.In order to test if acetate and glutamate promoted Clutamate  indicate < 0.05) , which wdditives , which wDietzia sp. and P. stutzeri from their monoculture to the coculture states, Dietzia sp. and P. stutzeri cells were separately inoculated in each side of a two-chamber bioreactor , cultivated aerobically on C16-exposed minimal medium, and harvested at the mid-exponential phase for proteomics analysis, with the monocultures of each strain as the controls. The results showed that a total of 1,541 Dietzia sp. proteins and 1,292 P. stutzeri proteins were detected from trypsin-digested protein mixtures of the coculture and monoculture samples, with a false-discovery rate (FDR) of <0.05. As shown in P. stutzeri proteins were significantly upregulated compared with those in P. stutzeri monoculture system (P < 0.05), among which 31 proteins showed at least a 2-fold increase in expression values and were termed high-level upregulated proteins, while the remaining 37 proteins were designated low-level upregulated proteins. On the other hand, a total of 91 and 39 P. stutzeri proteins were significantly high-level and low-level downregulated, respectively, in the coculture system (P < 0.05). Similarly, we found that 59 and 30 Dietzia proteins were high- and low-level upregulated in the coculture system, respectively, while 74 and 54 Dietzia proteins were high- and low-level downregulated, respectively . Among the differentially expressed proteins, nine proteins in Dietzia sp. and 13 proteins in P. stutzeri were involved in the metabolism of the six compounds; the proteins and their fold changes are listed in Tables S3 and S4. As shown in Dietzia sp. and P. stutzeri in the in silico-predicted directions. Generally, the central metabolism of P. stutzeri became relatively less active in the coculture system than in its monoculture system. In contrast, the tricarboxylic acid (TCA) cycle of Dietzia sp. turned out to be more active when the cells were incubated together with P. stutzeri. This indicated that the P. stutzeri cells grown on Dietzia sp. secretions might reorganize the flux distribution to produce more acetate and l-glutamate for their partner via stripping their intracellular active metabolism unrelated to acetate and l-glutamate biosynthesis down to essentials while expressing more enzymes relating to acetate and l-glutamate biosynthesis.Among the differentially expressed proteins, we focused on proteins relating to the metabolism of Cin silico-predicted five key exchanged compounds were all detected in their original or derivatized forms in the supernatant from the coculture system, which was in agreement with above-described proteomic analysis . Among the 43 metabolites, the analysis . Some otediction . HoweverDietzia alkane hydroxylases AlkW1, AlkW2, and CYP153 showed no significant difference between the monoculture and coculture systems (P ≥ 0.05) (see Table S6 in File S1), indicating that the synergistic C16 biodegradation did not depend on the expression level of Dietzia alkane hydroxylases. The key metabolic pathways affecting the C16 consumption rate of Dietzia sp. in the coculture system should be among the 10 metabolic reactions which were catalyzed by the nine differentially expressed Dietzia sp. enzymes , in which the biomass growth rate μ0.0718 (2.38 × 10−2 h−1) (Table S2) was present; after that, the flux rates of the 10 metabolic reactions mentioned above were constrained to 2-fold (for high-level upregulation), 1.5-fold (for low-level upregulation), 1/1.5 (for low-level downregulation), or 1/2 (for high-level downregulation) of the responding flux values in V0.0718; next, the C16 uptake rate in iBH925 was modified to be equal to that in iBH1908 (8.00 × 10−2 mmol g−1 h−1) as mentioned above; and finally, the maximum predicted biomass growth rate μ0.08 was computed via FBA again, and μ0.08 was compared with μ0.0718. If μ0.08 with some special reaction flux constraints was similar to μ0.0718, the enzymes which catalyzed the special reactions would be considered the key enzymes for the synergistic C16 biodegradation in the synthetic microbial consortium. It was found that the value of μ0.08 (2.43 × 10−2 h−1) was similar to that of μ0.0718 when the flux rate of Rxn13 catalyzed by succinate dehydrogenase was constrained, but the value was obviously different from that of μ0.0718 when the flux rate modification was operated on any one of the remaining nine metabolic reactions. This suggested that the synergistic C16 biodegradation in the synthetic microbial consortium might be due to the higher-level expression of succinate dehydrogenase induced by the substrates produced from P. stutzeri. The potential molecular mechanism under which the succinate dehydrogenase positively affects C16 biodegradation by Dietzia sp. was then examined.According to our proteome data, the relative amounts of the known  enzymes . FBA on 16 but also included the synthetic n-alkane mixture of C14, C16, and C28 , and the synergistic effect of the consortium on various n-alkanes was proved in silico to be enhanced by the sustained supply of acetate and glutamate from P. stutzeri (see Table S7 in File S1). Here, we inferred that the microbial consortium of Dietzia sp. and P. stutzeri might have synergistic biodegradation capability on diesel oil, a specific fractional distillate of petroleum composed mainly of alkanes with chain lengths between C8 and C25 , taking the Dietzia sp.-P. stutzeri coculture without or with the additives as two positive controls  and cell-free culture and the P. stutzeri monoculture as two negative controls . All the cultures were harvested on day 15 for Dietzia sp. cell density measurement and determination of the diesel removal percentage. As shown in Dietzia sp. had a significantly higher increase in the coculture systems than in the Dietzia sp. monoculture system, especially in treatment 6. As shown in P. stutzeri was likely incapable of diesel biodegradation, while Dietzia sp. was effective for diesel removal. Notably, the coculture system with the addition of slight amounts of acetate and glutamate had the highest removal percentage (85.54% ± 6.42%), which was over 10% higher than that of the Dietzia sp. monoculture (73.53% ± 3.27%). Afterwards, we analyzed the hydrocarbon compositions of the disposed diesel oil and found that the composition profile in the coculture system with additives was much simpler than those in the cell-free culture and Dietzia sp. monoculture . According to the composition profiles, n-alkanes and the isomers with chain lengths between C12 and C14 were completely removed from the diesel samples in treatments 3 and 6, suggesting a very effective removal of the medium-chain-length n-alkanes and their isomers by Dietzia sp.; for the n-alkanes with chain lengths between C15 and C25, they were partially removed from the diesel sample in treatment 3 but were completely disposed of in treatment 6, leaving small amounts of C15 to C20 isomer residues. Our results suggested that the strategy using the synergistic microbial consortium plus in silico-predicted interspecific regulators was very efficient for diesel disposal.Accordingly, the synergistic biodegradation capability of the microbial consortium was not limited to C and C25 , and thaoculture , as wellDietzia sp. DQ12-45-1b, and a potential alkane nonconsumer, P. stutzeri SLG510A3-8, was investigated. Surprisingly, the microbial consortium was found to have a greater C16 removal rate than Dietzia sp. alone. The synergistic effect was not limited only to C16 degradation but also included degradation of synthetic and natural n-alkane mixtures. This finding was in agreement with cases of bioaugmentation with fungal-bacterial consortia, in which the bacterial members acted as the pollutant degraders while the fungal members did not   under accession numbers 1.10709 and 1.15316, respectively. After the two strains were grown in GPY  and LB  media, respectively, at 150 rpm and 30°C until the mid-exponential phase, they were centrifuged and washed with the minimal medium  of 0.1. The culture without inoculum was taken as the control. As described in File S1 in the supplemental material, cocultivation of the two strains was also done in minimal medium supplemented with a synthetic n-alkane mixture consisting of C14, C16, and C28 or with diesel oil. Since the colonies of Dietzia sp. were orange and smooth, being significantly different from those of P. stutzeri, which were brown and wrinkled, samples were taken every 1 to 2 days for cell density counting by CFU until the early stationary phase. The collected data were processed using nonlinear curve fitting followed by differentiation in Origin8.5  to obtain the time courses of specific growth rates.l medium  three ti16 in each flask was extracted using 20 ml n-hexane in a separating funnel and then was analyzed using a 5975C gas chromatograph (GC) coupled with a 7890A mass spectrometer (MS) detector . Helium was applied as the carrier gas at a flow rate of 25 ml min−1. The oven temperature was set to 100°C for 1 min and then increased to 260°C at a rate of 20°C min−1 and held at that temperature for 5 min. The external standard method was utilized to obtain the C16 concentrations in each sample. Afterward, the residual C16 concentration data were processed in Origin8.5 to obtain the time courses of specific C16 removal rates. The C16 removal rate was calculated as the difference in the percentage of the residual C16 concentration between the experimental and the control treatments in 0.7734 g liter−1 C16.At the end of the cultivation period, the residual CDietzia sp. DQ12-45-1b and P. stutzeri SLG510A3-8 were reconstructed as described in File S1 and then were modified by unifying the format of reactions and metabolites before being integrated to form the two-species model following the approach of Klitgord and Segrè  [t] = extracellular space of Dietzia sp., (iii) [p] = cytoplasm of P. stutzeri, (iv) [s] = extracellular space of P. stutzeri, and (v) [e] = environment shared by the two strains. FBA was used to calculate the metabolic flux distribution in the reconstructed GEMs, in which the biomass reactions accounting for all known biomass constituents and their fractional contributions to the overall cellular biomass were used as the objective to calculate the flux distribution solution in the feasible flux space for the maximal growth rate. System constraints were defined with the upper bound for each metabolic reaction as 1,000 and the lower bounds as −1,000 and 0 for the reversible and irreversible reactions, respectively, except for the exchange reactions. The lower bounds of exchange reactions relating to oxygen and the medium compounds were set to −1,000, while the remaining ones were set to 0. All calculations were performed under the linear programming algorithm using the COBRA toolbox . The mobile phase was comprised of solvent A  and solvent B (99.9% acetonitrile and 0.1% formic acid). The detection of peptide spectra was as described by Liu et al. . Seeds of u et al. . The prou et al. .Dietzia sp. DQ12-45-1b, P. stutzeri SLG510A3-8, and their mixture were obtained by centrifugation of 2-ml samples at 4°C and 10,000 rpm for 10 min to remove C16-exposed cells. For desalinization, 10 ml methanol was added to each cell-free culture, vortexed for 5 min, and kept at 4°C for 5 h. The mixtures were then centrifuged at 20,000 rpm and 4°C for 10 min, and the supernatant was lyophilized  for 4 h. The lyophilized samples were derivatized as described by Mujahid et al.  and Wiley 9 , with a match threshold of >80 . Relative metabolite abundances were calculated from peak areas (unique mass) of identified metabolites from GC-MS data and then were amended using the peak area of methyl tetradecanoate to minimize instrumental errors.Cell-free cultures of d et al. . GC-MS aDietzia sp. DQ12-45-1b were inoculated into the minimal medium containing 10 g liter−1 diesel oil with or without the addition of P. stutzeri SLG510A3-8 and exogenous addition of 0.045 g liter−1 sodium acetate and 0.037 g liter−1 sodium glutamate. The culture without inoculum and the one inoculated with P. stutzeri SLG510A3-8 alone were taken as the two control treatments. Every treatment was performed in triplicates. All the cultures were incubated at 150 rpm and 30°C. Samples were taken weekly until the stationary phase or at the end of the culture period for Dietzia sp. cell density counting with CFU. At the end of the culture period, the oil removal rates were tested using the weight difference assay as described in EPA method 3500B, and diesel composition profiling was tested using GC-MS. Samples for diesel composition profiling were prepared as described previously. During the GC-MS processing, the oven temperature was set to 50°C for 1 min and then increased to 260°C at a rate of 10°C min−1 and held at that temperature for 5 min, after which it was sequentially increased to 280°C at 10°C min−1 and held at that temperature for 0.5 min.Seeds of in vitro experiments were performed in triplicates and subjected to statistical analysis. The standard deviations were calculated in Microsoft Excel to represent the errors for the triplicate data. The P values between two triplicates were also calculated in Microsoft Excel, and a P value of <0.05 was considered statistically significant. For the proteomics analysis, proteins with a |log2(fold change)| of >1 were defined as high-level regulated proteins, and those with a |log2(fold change)| of <1 were defined as low-level regulated proteins. The significantly changed proteins were annotated according to the KEGG database to identify metabolic pathways in each strain that were influenced by the other one.All CP046567.1, CP046568.1, and CP046569.1 for Dietzia sp. DQ12-45-1b and CP011854.1 for P. stutzeri SLG510A3-8. The mass spectrometry proteomics data reported in this paper have been deposited in the open-access database ProteomeXchange Consortium via the iProX partner repository (The DNA sequencing data are deposited at GenBank under accession numbers pository  with the