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


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


This creates an evolutionary paradox due to the existence of PKET pathway in obligatory photoautotrophs. We aim to answer the paradox with the aid of bioinformatic analysis along with metabolic, transcriptomic, fluxomic and mutant data integrated into a multi-level kinetic model. We discussed the problems linked to neglected isozyme, pket2 (sll0529) and inconsistencies towards the explanation of residual flux via PKET pathway in the case of silenced pket1 (slr0453) in Synechocystis sp. PCC 6803. Our in silico analysis showed: (1) 17% flux reduction via RuBisCO for Δpket1 under AC-auto, (2) 11.2–14.3% growth decrease for Δpket2 in turbulent AC-auto, and (3) flux via PKET pathway reaching up to 252% of the flux via phosphoglycerate mutase under AC-auto. All results imply that PKET pathway plays a crucial role under AC-auto by mitigating the decarboxylation occurring in OPP pathway and conversion of pyruvate to acetyl CoA linked to EMP glycolysis under the carbon scarce environment. Finally, our model predicted that PKETs have low affinity to S7P as a substrate.Phosphoketolase (PKET) pathway is predominant in cyanobacteria (around 98%) but current opinion is that it is virtually inactive under autotrophic ambient CO Synechococcus elongatus PCC 79421 and butanol from Synechocystis sp. strain PCC 68032. However, certain metabolites or overproduction of intermediates can be lethal. There is also a possibility that required mutation(s) might be unstable or the target bacterium may even be able to maintain the flux distribution for optimal growth balance due to redundancies in the metabolic network, such as alternative pathways. The current dominant “trial and error” approach implies that our understanding of cellular metabolic regulatory mechanisms, including those involving isozymes, post-translational modifications or allosteric regulations, is secondary to achieve the desired effect/product. Integrating computational biology methods may help both to understand complex big data and to improve our understanding of cellular processes, improving potential biotechnological applications in the long term.Metabolic engineering of cyanobacteria provides many options for producing valuable compounds, e.g., acetone from Synechocystis sp. strain PCC 6803 (hereafter referred to as Synechocystis) is one of the more complex prokaryotes, known for its metabolic plasticity3. All known glycolytic pathways that exist in nature are accommodated in its central carbon metabolism: the Embden–Meyerhof–Parnas (EMP) pathway, the oxidative pentose phosphate (OPP) pathway, the phosphoketolase (PKET) pathway and the Entner–Doudoroff (ED) pathway. However, our knowledge about regulatory roles of PKET in Synechocystis is still limited.Cyanobacterium 4, and it is known that deactivation of PKET pathway leads to 11.5% slower growth under autotrophic ambient CO2 (AC-auto) condition4. Moreover, an engineered strain of Synechocystis with blocked glycogen synthesis and enabled xylose catabolism was reported with over 30% total carbon flux directed via PKET4 under mixotrophic condition on xylose and CO2. Furthermore, overproduction of acetyl phosphate, caused by overexpression of pket (PKET gene), can negatively influence the pentose sugar metabolism5 but may increase lipid synthesis6. Finally, recent 13C labelling study concluded that there is virtually zero flux via PKET under AC-auto7 in Synechocystis. This finding is in direct disagreement with the previous theoretical stoichiometric8 and kinetic9 studies which estimated the flux via PKET pathway to be 11.6% and 5.3% of flux via RuBisCO, respectively. Thus, we have decided to investigate this issue and to review the flux via PKET pathway based on all available data.Phosphoketolase converts xylulose 5-phosphate to glyceraldehyde 3-phosphate and acetyl phosphate, or, fructose 6-phosphate to erythrose 4-phosphate and acetyl phosphate, see Fig. Synechocystis: (1) if PKET pathway is present in cyanobacteria, especially in obligatory photoautotrophs, why it is inactive in autotrophic conditions during day, (2) what is the role of PKET2 and can we quantify its potential benefits and (3) does the role of PKET pathway change in different environmental conditions?Our study aims to provide answers to the following questions with the help of multi-level kinetic model of central carbon metabolism for Nostoc/Anabena PCC 7120 (Nostoc)10 but our multiple sequence alignment showed 78% homology among PKETs in cyanobacteria with annotated PKET isozymes (see pket1 (slr0453) of Synechocystis and pket1  of Nostoc. Interestingly, the secondary PKETs of cyanobacteria, including pket2 (sll0529) of Synechocystis and pket2  of Nostoc, are clustered also with fungi, bacteria and algae, implying a possible horizontal gene transfer. The common feature of photosynthetic and non-photosynthetic species in this heterogenous cluster is a complex metabolism, allowing to survive on organic source of carbon. Strikingly, PKET2, firstly mentioned in 201011, was ignored in the metabolic studies despite the residual (approximately 10%) flux via PKET pathway in the case of silenced pket1 under both mixotrophic4 and autotrophic7 condition. It was suggested that this residual flux is caused by the activity of phosphate acetyl-transferase (PTA) activity7. The problem with this explanation is that it considers only reactions towards TCA cycle, i.e., reversibility of PTA, even though the same residual flux was detected also towards glyceraldehyde-3-phosphate (GAP) synthesis4. Since GAP synthesis is not connected to PTA activity  whereas aldolase 2 (class I) supplies only 10% of total due to allosteric inhibition13. It can thus be speculated that, after pket1 is silenced, PKET2 is behind the residual activity of PKET pathway and PKET2 contribution does not increase due to allosteric inhibition. However, despite other known cases among isozymes in Synechocystis, allosteric inhibition is only one of possible explanations and we cannot prioritize it or exclude other options such as post-translational modification or regulation by signalling cascade(s), etc.We propose that neglected PKET2 of Synechocystis. Fortunately, our method of multi-level kinetic modelling  and ambient CO2 mixotrophic condition (AC-mixo), varying slightly in dependence of preferred substrate  PKET2 has a positive impact on growth rate in turbulent, carbon scarce environment and (iii) PKET2 is currently the only explanation of the residual fluxes via PKET pathway in the case of silenced pket1, both towards TCA cycle and GAP synthesis.In order to test the impact of PKET2 on growth in case of ΔSynechocystis was made with the aid of stoichiometric model based on flux balance analysis, suggesting flux of 14.9 (11.6% of flux via RuBisCO)8. At the time, it was believed that the model accurately describe HC-auto due to very good agreement with labeling experiment performed in HC-auto14. However, it was suggested later that the majority of fluxes are scalable in response to a changing environment9. Thus, explaining the stoichiometric model, designed for AC-auto, nearly fitting the fluxomic data for HC-auto. The rescaled value for PKET flux under AC-auto from stoichiometric model is then 4.1. This value is more than double of our previous estimate 9. However, 13C labeling experiment was recently performed for AC-auto which concluded virtually zero flux via PKET pathway7. This result implied that both theoretically estimated values of PKET flux were incorrect. The problems with such implication were: (1) stoichiometric modeling approach assumes the flux balance for maximal growth benefits so non-zero flux via PKET pathway indicates at least a potential growth benefit and (2) our previous kinetic model9, validated for different environmental conditions, also predicted non-zero flux via PKET pathway in order to improve the match between the experiments and simulations.The first attempt to assess the flux via PKET pathway in pket13. The simulated flux via PKET pathway was 3.16 (7.2% of flux via RuBisCO) under AC-auto which strongly out-performed (up to 2.5-fold) the flux via phosphoglycerate mutase, the exit point of EMP glycolysis from Calvin-Benson cycle, see Fig. 7. Since more accurate simulation actually increased the estimated flux via PKET pathway, we compared these values with original fluxes from labeling experiment7. The data from labeling experiment calculated a virtually non-existent flux via Xu5P branch (10–6) and high flux  via F6P branch of PKET pathway7 which agrees with our predicted value perfectly. However, this result was not mentioned in their discussion, probably due to zero lower bound value for flux via both branches of PKET pathway, implying theoretically inactive PKET pathway. The lower bound of PKET flux might be explained only by reversibility of PTA. In the case of PTA flux, lower bound was 16.86 which was the same as calculated flux via F6P branch of PKET pathway. Since we have already rejected the reversibility of PTA as the source of residual flux via PKET pathway (see the section above), we can conclude that the flux via PTA is mostly, if not fully, originated from PKET pathway.Therefore, we decided to elaborate the role and benefits of PKET pathway in detail. We tailored the flux via PKET1 and PKET2 to match 11.5% growth decrease under AC-auto for Δpket1 has negative impact over all fluxes, especially via RuBisCO, under AC-auto, see Fig. pket1, less amount of carbon is available in the system due to decarboxylation in OPP and conversion of pyruvate to acetyl CoA linked to EMP glycolysis. This conclusion is further supported by the data from AC-mixo  and only 0.42% of total PKET activity maintained by S7P branch15. PKET breaks down S7P into ribose-5P and acetyl-P . The target of our in silico experiment was to maximize the growth with any combination of the substrates  for PKETs. Firstly, the statistical analysis of results implies significantly higher variability in growth rate when S7P is metabolized via PKET pathway (Table pket1) is the same for both scenarios (with or without S7P), the reason behind the same value is that parameter estimation routine applied zero flux via S7P branch of PKET pathway due to its lower efficiency. Thus, our model predicts that the affinity of PKETs towards S7P in cyanobacteria is very low, similarly as shown for E. coli15. Nevertheless, experimental verification is required as the role of S7P branch of PKET pathway might be more significant under certain stress conditions.Recently, sedoheptulose-7P (S7P) has been identified as a substrate of PKET in  KM = 68. mM and oay Table . When thay Table . Thus, iSynechocystis: (1) bacteria have PKETs that are regulated by allosteric inhibition12 and pket2 is in the cluster with bacteria (in contrast to pket1), opening the possibility for horizontal gene transfer; thus we can speculate that PKET2 is also allosterically inhibited by phosphoenolpyruvate, oxaloacetate or glyoxylate which provides currently the only explanation for the residual flux via PKET pathway in Δpket1 and clarifies why PKET2 does not display any higher activity in such scenario; however, other alternative explanations such as post-translational modification cannot be currently excluded, (2) our in silico experiment of Δpket1 mutant with active PKET2 showed the same impact on growth as reported previously in AC-auto for silenced pket1 which was believed at the time to be a total inhibition of PKET pathway, (3) there are other known cases of allosteric inhibition in Synechocystis, including the proposed 90/10 flux contribution of isozymes, e.g., aldolase 1 and 2, and (4) we showed a clear benefit of having PKET2 in the central carbon metabolism of Synechocystis, namely, an 11.2–14.3% growth increase in the turbulent AC-auto. However, an experimental verification, if the product of gene sll0529 (pket2) indeed has phosphoketolase activity, as well as what, if any, is the impact of phosphate acetyl-transferase on residual flux via PKET pathway, is needed. Similarly, here proposed allosteric regulation of PKET should be experimentally verified.We presented the following support for the existence and role of two PKETs in 13C labelling experiment which revealed virtually no flux via PKET pathway under AC-auto. However, we have found a critical error in their analysis and their own labelling data supports our conclusion of the great importance of PKET pathway. Furthermore, we have shown that silencing pket1 has a negative impact over all fluxes, including RuBisCO and PGM in AC-auto but not in HC-auto and AC-mixo. We conclude that high flux via PKET pathway mitigates the decarboxylation occurring in OPP pathway and conversion of pyruvate to acetyl CoA linked to EMP glycolysis under AC-auto, i.e., in the carbon scarce environment. Finally, we have tested S7P as a substrate for PKETs as it was reported for E. coli. Our analysis predicted low efficiency of S7P branch of PKET pathway and low affinity towards S7P, similarly as shown for E. coli.In addition to analysing the putative PKET2 isozyme, we have also focused on the flux distribution of PKET pathway under various conditions. We showed that PKET pathway could be the major carbon flux exit point from Calvin–Benson cycle in AC-auto, overcoming the flux via phosphoglycerate mutase by up to 252%. This finding is in direct disagreement with recent Synechocystis.In the end, we did not analyse the role of PKET pathway under heterotrophic condition because its impact has already been shown and because heterotrophic condition is substantially more difficult to integrate into our light-on-only multi-state model. Our main goal was to address the purported negligible impact of PKET pathway in autotrophic condition; however, we plan to analyse the role of PKET in heterotrophic or photoheterotrophic conditions in the future, together with other isozymes within the central carbon metabolism of 16.HC-auto to AC-auto17.AC-auto to AC-mixoTranscriptomic data13C labeling data14.HC-auto7.AC-auto18.AC-mixo14, there is a line of evidence that this particular labeling experiment is describing HC-auto. Firstly, the growth rate of culture was 0.09 h−114 which is higher rate than the one observed for 3% CO2 enriched medium (0.06 h−1)7. Secondly, authors used a high concentration of bicarbonate in their study and demonstrated that level of photorespiration is very small under high CO2 conditions14; study was performed under single environmental condition, i.e., HC-auto. Finally, we showed that majority of metabolic fluxes scales based on the level of carbon9 which explains why most of fluxes are very similar for AC-auto and HC-auto after normalization. However, key exit fluxes, such as phosphoglycerate mutase, OPP or PKET pathway do not scale9 which explains significant difference for exit fluxes between14 and7, e.g., ratio between fluxes via RuBisCO and OPP is 7.8 for HC-auto14 but 1.7 for AC-auto7.We note that, despite its not clearly stated in the original work as authors are talking only about photoautotrophic condition21.HC-auto20.AC-auto17.AC-mixoMetabolic datapket1 mutant4.ΔMutant dataSynechocystis was developed and simulations were executed using the SimBiology toolbox, Optimization toolbox, Global optimization toolbox and Parallel toolbox of MATLAB . The routine for parameter estimation was a hybrid genetic algorithm. The model for HC-auto is available in the Supplement in SBML format L2V4 compatible with MATLAB 2010b and higher. We recommend to open the model either in MATLAB or user friendly COPASI (free academic license). The weight factors (applied transcriptomic data) necessary to run the model in other conditions (AC-auto and AC-mixo) is provided in Supplement.The multi-level kinetic model for 9. This simplified approach allowed to estimate the CO2 fixation and glucose uptake by comparing to relative experimental fluxes in all tested conditions as well as to keep the track of growth rate, matching the experimental levels from literature9. Moreover, the biomass in the model is assumed to be equal to the amount of fixed carbon, whereas nitrogen was not a limiting nutrient factor in the experimental setups9. The definition of biomass in the model is provided by following equation, based on the model content, in which upper index s stands for accumulation of respective metabolite in the sink  and carbohydrate synthesis. These metabolic reactions were coupled to simplified light reactions and Ci and glucose uptake as the main input parameters. Biomass production is estimated as weighted sum of sink reactionsink Fig.  and a paSynechocystis9, which was modified and extended in order to describe AC-mixo condition. We note that the justification of using fold changes in the mRNA levels as a proxy between different environmental conditions, assuming a 1:1 ratio between a change in transcriptome and enzymatic amount, is provided in our previous study, including the support from experimental studies, can be found in our previous study9.The model consists of 61 reactions, 48 metabolites and 214 kinetic parameters. The enzymatic reactions are described by Michaelis–Menten kinetics, except for the light reactions, Ci and glucose uptake, which are described by mass action kinetics. All reactions, except for the import of external carbon, are localized in a single compartment. The starting point of modelling was our previous model of max. Then, we fit the experimental fluxomics and metabolic data from HC-auto. In this process of parameter estimation, we obtain a set of parameters which will be used in all following steps or trashed and search for new set will begin. If there is a match between simulation and experiment in HC-auto, metabolic and fluxomics data are saved (including the set of kinetic parameters). Then, we simulate a transition from HC-auto to AC-auto, i.e., applying the weight factors (mRNA), run the simulation for the steady state AC-auto, save the metabolic and fluxomic data and do the same for AC-auto to AC-mixo shift. At this point, we do semi-automated check for the reasonable match between simulated and experimental data for AC-auto and AC-mixo , followed by manual curation of results. If acceptable match is found, the final step is a fine tuning of parameters and observing the changes in all three conditions. Manual tuning and curation of kinetic parameters at the different levels of parameter estimation and data integration is an essential part of modelling, because it provides a deeper understanding of system behavior and helps to detect errors in the algorithms, routines and model structure, e.g., when a new pathway is added.The integration of various omics data occurs in several steps. In the first step, transcriptomic data from HC-auto to AC-auto and AC-auto to AC-mixo are included into the model in the form of mean values of measured mRNA changes as weight factors for each Vmax and kf parameters, running it 100-times and calculating the mean value and standard deviation. Two scenarios, with one or two PKET, were tested, in all three environmental conditions.We note that we shifted our priority from metabolic levels to fluxomic data as a key reference for match fitting. The main reason is that we are analyzing now 3 environmental conditions and planning to add more. However, there is no study providing metabolic data for the particular conditions of our interest and combining multiple metabolic data sets from different studies proved to be extremely challenging. We continue observing and analyzing the metabolic levels but in the case of parameter fitting, we currently only check if metabolic levels are in the physiological range and analyze the homeostatic stability of metabolites. Finally, simulation of fluctuating environment was done by applying up to ± 20% random variation in all V9. The first application of the method, the case study of phosphoglycerate mutase, discussed the potential of this method, e.g., the verification of gene annotation22.More information about the method, parameter estimation and other details can be found in our previous studySupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Postoperative delirium is the most common complication among older adults undergoing major surgery. The pathophysiology of delirium is poorly understood, and no blood-based, predictive markers are available. We characterized the plasma metabolome of 52 delirium cases and 52 matched controls from the Successful Aging after Elective Surgery (SAGES) cohort (N = 560) of patients ≥ 70 years old without dementia undergoing scheduled major non-cardiac surgery. We applied targeted mass spectrometry with internal standards and pooled controls using a nested matched case-control study preoperatively (PREOP) and on postoperative day 2 (POD2) to identify potential delirium risk and disease markers. Univariate analyses identified 37 PREOP and 53 POD2 metabolites associated with delirium and multivariate analyses achieved significant separation between the two groups with an 11-metabolite prediction model at PREOP (AUC = 83.80%). Systems biology analysis using the metabolites with differential concentrations rendered “valine, leucine, and isoleucine biosynthesis” at PREOP and “citrate cycle” at POD2 as the most significantly enriched pathways . Perturbations in energy metabolism and amino acid synthesis pathways may be associated with postoperative delirium and suggest potential mechanisms for delirium pathogenesis. Our results could lead to the development of a metabolomic delirium predictor. The economic impact of delirium associated health care expenditures is over $164 billion per year in the U.S. alone7.Delirium is a condition characterized by an acute change and fluctuation in attention, thinking, and consciousness. Postoperative delirium complicates the courses of 15–53% of older adults undergoing major surgical procedures12; (2) alterations in plasma levels of precursor amino acids leading to dysfunction in neurotransmitter systems13; (3) oxidative stress leading to cerebral dysfunction16; (4) acute or chronic stress response resulting in aberrantly high levels of glucocorticoids18; and (5) dysregulation of the circadian rhythm21.Currently, delirium is solely a clinical diagnosis, and there is no laboratory test or biomarker to facilitate its diagnosis. Additionally, delirium etiology and pathogenesis are poorly understood. Previous research has suggested several mechanisms for delirium pathogenesis, which include: (1) increased expression of inflammatory markers found in the blood and central nervous system22. Differences in their concentration levels provide an efficient way to monitor and detect alterations in specific cellular pathways. Metabolomics can reveal transient biochemical changes that are closely aligned with the disease state of a system22.Metabolomics is a high-throughput quantitative approach to study the metabolome—the collection of small metabolites found in a system. These smaller metabolic molecules are often the end products of the biochemical processes in a cell and are particularly sensitive to endogenous and exogenous stimuli25. Targeted metabolomics approaches have been employed previously in identifying novel biomarkers and distinguishing between diseased and healthy cohorts27. Only a few prior studies have explored metabolic changes associated with delirium and illuminating potential underlying biological mechanisms31.Metabolomics has been extensively used to characterize cellular changes in disease phenotypes and facilitate identification of disease-specific markersTo obtain more insights into the pathophysiological mechanisms of delirium as well as to identify potential risk and disease markers, we performed targeted metabolomic profiling both at preoperative and postoperative time points. We identified multiple metabolites linked to delirium before and after surgery and demonstrated enrichment of several metabolic pathways. Our associative, predictive, and systems analysis may help to identify pathophysiologic pathways to prioritize for development of diagnostic and therapeutic regimens for postoperative delirium.In Fig. 32, with cases experiencing slightly more sleep disruption. The prevalence of diabetes medication use was low and similar in cases and controls. We did not control for the variables that were not used in the matching process because of the risk of over-controlling for variables that are potentially along the causal pathway to delirium34.Table 39. After data imputation, signal drift correction, and normalization, the percent relative standard deviation (RSD) was less than 5 for 87% and less than 10 for 98% of all metabolites in the pooled QC samples . Missing data accounted for 3.15% of total data acquired from present metabolites  when compared with CNT , which included three of the four metabolites that showed a binomial test BH-corrected p value < 0.05. A support vector machine (SVM) prediction model40 using these 11 metabolites as predictors yielded an area under the curve (AUC) of 83.80% for the associated receiver operating characteristic (ROC) curve  = 0.848 and predictive performance with R2Q = 0.344  at PREOP: 2-oxobutanoate, isoleucine, and valine. All three metabolites were downregulated in DEL  Table . This caAt POD2, three pathways were found to be significantly enriched with our significant metabolites N = 53) .Metabolomics provides new insights into metabolic phenotypes and dysregulations of metabolic pathways underlying human disease and adds an important avenue for biomarker and therapeutic target discovery. However, experimental and computational challenges such as sample size, batch effect, peak drift, and data normalization hamper wide-spread use30. In a blood-based metabolomics study of delirium, Guo et al. took an untargeted metabolomics approach in characterizing preoperative serum metabolites of older surgical patients (between 65 and 80 years) undergoing hemiarthroplasty for hip fractures. They identified four metabolites associated with a higher risk of postoperative delirium  with alterations in energy metabolism and oxidative stress pathways29. In a follow-up study, Guo et al. profiled both PREOP and POD1 samples and reported imbalances between branched-chain and aromatic amino acids with perturbations in tricarboxylic cycle and oxidative stress pathways31. Although the specific metabolites and direction of dysregulation were not always consistent within the three studies  metabolome of older patients (≥ 65 years) who developed delirium following elective hip or knee arthroplasty. They found that spermidine, putrescine, and glutamine were significantly upregulated in delirium patients compared to controlsl-alanine, nicotinamide, Creatine, 2-oxobutanoate, Creatinine) are involved in metabolism of amino acids as well as 2 metabolites  are linked to pyrimidine ribonucleosides degradation. Uracil was the only metabolite linked to delirium by Guo et al.31. Disruption in amino acid metabolism has been linked to Alzheimer’s disease and dementia47. Another one of these metabolites, N-acetyl-glutamate (NAG), is a crucial enzymatic cofactor for the first step in the urea cycle, essential for liver ureagenesis and reduced levels are associated with Alzheimer’s disease48.  class, which also includes the aromatic amino acids (AAA), tyrosine, tryptophan, and phenylalanine. LNAAs are key precursors for neurotransmitters that have been implicated in delirium, including glutamate, serotonin, dopamine, and norepinephrine14. BCAAs and AAAs compete for access to the central nervous system (CNS) through the large neutral amino acid transporter 1 (LAT1)52. With the absence of BCAAs, there is greater opportunity for the AAAs to reach CNS and generate their associated neurotransmitters: serotonin, dopamine, and norepinephrine.Our systems biology approach indicates possible downregulation of the valine, leucine, isoleucine biosynthesis pathway at PREOP, for those patients who would go on to develop delirium following surgery. Valine, leucine, and isoleucine are essential branched-chain amino acids (BCAA). Essential amino acids are not naturally synthesized in the body and are obtained through diet. BCAAs contribute to energy production, and are key precursors in glutamate synthesis54. Importantly, there is a well characterized reduction in activity of the rate limiting citrate cycle enzyme α-ketoglutarate dehydrogenase which converts α-ketoglutarate to succinyl-CoA56 that may be a key event in AD pathogenesis as well as various other neurodegenerative diseases due to oxidative stress54. Reduced  α-ketoglutarate dehydrogenase may result in accumulation of α-ketoglutarate. A disruption of energy homeostasis and increased oxidative stress because of increased citrate cycle activity may be a crucial event during delirium manifestation.We identified distinct metabolites and metabolic pathophysiological pathways linked to delirium development at POD2. Most significantly, potential upregulation in the citrate cycle pathway in delirium cases was predicted. This is a key pathway for energy metabolism. Six metabolites within the citrate cycle  were all increased in delirium cases, as was itaconic acid, which is generated by decarboxylation of aconitate. Interestingly, both plasma and CSF metabolomics comparison of patients with Alzheimer’s disease (AD) to matched cognitive healthy controls identified a similar increase of several citrate cycle intermediates such as citrate, aconitate, and α-ketoglutarate in AD57. Kealy and colleagues showed that energy metabolism dysregulation was sufficiently capable of triggering an acute cognitive dysfunction in a delirium mouse model44. Under normal conditions in humans, these ketones are present in plasma in an approximate 2:1 βOHB:AcAc ratio58. At PREOP, the delirium cohort displayed a decrease in βOHB , and then an upregulation of AcAc  at POD2, elucidating an alteration in energy metabolism. At POD2 our participants were in a fasting state, which supports an increase in acetoacetate, but this upregulation should be uniform across both phenotypes. This disparity points to potential energy pathway alteration in the delirium group that is not observed in the control group. These findings support previous hypotheses that energy metabolism dysregulation is a possible driver in delirium pathophysiology44.Glucose is the main fuel for the brain, but under glucose-depleted conditions like strenuous exercise, fasting, or starvation; the ketone bodies, β-hydroxybutyrate (βOHB) and acetoacetate (AcAc), are the main alternative fuel sources. These small lipid-derived molecules are converted to acetyl-CoA and then oxidized in the citrate cycle to produce adenosine triphosphate and carbon dioxidel-tryptophan, is a neuromodulator interacting with NMDA, nicotinic, and GPR35 receptors. It plays a role in various neurophysiological functions as well as neurological diseases and inflammatory processes. Overexpression of kynurenic acid in several neurological diseases is associated with confusion58, while reduction of kynurenic acid in mice improves cognitive function60. Both kynurenine and tryptophan are differentially quantified concentrations at POD2 in delirium samples at nominal, not BH-corrected, p value  with kynurenine, like kynurenic acid, being increased and tryptophan being decreased in delirium samples. Thus, kynurenic acid may be one key metabolite associated with delirium development.While most of the metabolites identified in this study have not been previously linked to delirium risk or development, a few of these metabolites have been previously linked to inflammation, cognitive function, or neurological diseases. Most prominent is kynurenic acid, which our data show to be significantly increased in delirium cases at POD2. Kynurenic acid, a degradation product of Despite our strengths in experimental design, established cohort, developed workflow, and data analysis methods, several limitations of this study are of note. The statistical power at PREOP was still insufficient to yield metabolites that passed two or more BH-corrected statistical tests implying the need for more samples. Second, the SAGES study was conducted within a single geographic region, limiting the generalizability of our results across other populations. Third, since only plasma samples were analyzed, it is difficult to assess whether similar metabolic changes occur in the brain and have functional and causal consequences with regard to delirium risk or development. Therefore, the identification of disruptions in several metabolic pathways and the link to delirium pathogenesis and pathophysiology are putative and need to be further validated. Fourth, since delirium had already occurred in most of our participants at the time of the POD2 blood draw, we are unable to completely disentangle metabolic changes that are on the causal pathway to delirium from those that result from delirium. Finally, targeted metabolomics is restricted by detection of the predefined metabolites established by the specific protocol that may result in the omission of metabolites involved in delirium.In conclusion, we have identified metabolites associated with delirium at both pre- and post-operative time points that may enhance our understanding of the etiology and pathophysiology of delirium. Our findings highlight potential dysregulation in energy production pathways at both PREOP and POD2 paving the way for future studies, which should expand upon these findings through exploration of key enzymes involved in these pathways. The metabolomic findings at PREOP have the potential to be combined with other biomarkers to develop a predictive signature, which can be used to target personalized, preventative interventions to reduce delirium incidence. Additionally, our analysis of the metabolome at POD2 could improve our understanding of the disease mechanism, which may lead to better strategies to ameliorate the delirium process. Overall, the established workflow and data analysis provide a two-faceted approach to delirium metabolomics providing a comprehensive assessment of the biochemical activity associated with this syndrome.60. SAGES enrolled patients ≥ 70 years old scheduled for major noncardiac surgery, including total knee or hip replacement, cervical or lumbar laminectomy, abdominal aortic aneurysm repair, lower extremity vascular bypass, or colectomy. Patients received either general or spinal anesthesia. Major inclusion and exclusion criteria have been published60. Patients underwent a detailed screening process to exclude dementia based on medical record review, capacity assessment, patient or family report of dementia diagnosis, and cognitive testing using the Modified Mini-Mental State Examination61. In addition, patients underwent a neurocognitive battery at baseline, which was used to compute the general cognitive performance (GCP) summary measure62. Comorbidities were identified from medical record review by trained physician abstractors and scored based on the Charlson Comorbidity Index.The Successful Aging after Elective Surgery (SAGES) study is a prospective observational study of older adults undergoing major elective non-cardiac surgery and was designed to understand novel risk factors and long-term outcomes of deliriumInformed consent for study participation was obtained from all subjects according to procedures approved by the institutional review boards of Beth Israel Deaconess Medical Center (BIDMC) and Brigham and Women’s Hospital, the two surgical sites, and Hebrew SeniorLife, the study coordinating center, all located in Boston, Massachusetts. All experiments, methods, and analyses conducted in the current manuscript were performed in accordance with relevant guidelines and regulations of the institutional review board (IRB) of all participating institutions.64. The presence of delirium by chart review was adjudicated by at least two delirium experts, and discordance was resolved through consensus65. Patients were considered delirious if delirium was present on either the CAM or the chart review method on any postoperative day; otherwise, patients were considered non-delirious. If delirium was absent during the entire hospitalization, subsyndromal delirium was defined as (i) an acute change in mental status or fluctuation, (ii) at least one CAM core feature , and (iii) at least one other CAM supporting feature 66.Postoperative delirium was determined daily throughout hospitalization, supplemented with a validated chart review to identify cases that may have been missed during daily interviews. Delirium was assessed using the Confusion Assessment Method (CAM) diagnostic algorithm68. Delirium cases (DEL) were defined as patients with delirium on POD2. No delirium controls (CNT) were defined as patients with no delirium or no subsyndromal delirium during their hospital stay. DEL/CNT pairs were matched on six variables   Table . Vascula59 as previously described68. PREOP and POD2 timepoint plasma was utilized in the present study. For all of the samples, blood was collected in a non-fasting state at PREOP and in a fasting state at POD2. A pooled control sample was created from twenty PREOP samples  from patients de-enrolled from the SAGES study. Metabolites were extracted from equal volumes of heparinized plasma (100 µl) by ice-cold methanol precipitation added to a final concentration of 80% (vol/vol).Heparinized plasma samples were collected and stored as part of the SAGES study69. This protocol utilizes a targeted selected reaction monitoring (SRM), positive/negative ion-switching, mass spectrometry-based metabolomics platform suitable for bodily fluids, cells, and fresh and fixed tissue69.Equal volumes per sample of methanol extracted metabolites were run on a 5500 QTRAP  mass spectrometer using a previously published targeted methodology developed by the BIDMC Mass Spectrometry CoreTo accommodate for the maximum sample loading capacity of the 5500 QTRAP 60 samples, six unique sample preps and mass spectrometry runs were performed for a total of 52 matched DEL/CNT pairs at PREOP and POD2 that resulted in analysis of a total of 208 samples. Each experimental run—except for the final one, which was abridged due to sample limitations—included ten matched pairs, three internal spike-in standards, thirteen pooled quality control plasma samples (QC), three conditioning samples and four blanks Fig. . In the 69. A metabolite was considered “present” if it was measured in at least 50% of the samples within a phenotypic group .Overall, 315 metabolites were measured using previously published targeted methodology  method72.Signal drift was corrected with statTarget using a quality control-based machine learning algorithm: random forest signal correction (QC-RFSC)74.NormalizeMets R package was used to normalize the metabolomics data matrix to previously selected internal standards using the NOMIS: normalization using optimal selection of multiple internal standards technique75. These results were further tested for robustness using tenfold split analysis at a 5% significance level, with 80% power, and run 10,000 times , the Benjamini–Hochberg (BH) procedure was applied to correct for multiple hypotheses testing77. Potential outliers were identified using principal component analysis (PCA) and Mahalanobis distance78 and were omitted prior to the application of orthogonal partial least squares-discriminant analysis (OPLS-DA), a supervised multivariate separation method79. The model was validated through sevenfold cross-validation, cross validation-analysis of variance (CV-ANOVA)80, and response permutation testing (n = 1000) using the fit metrics R2X, R2Y, and Q2.Multivariate statistical analysis was performed using the ropls package in the statistical program R83, which takes a bifurcated analytic approach that includes metabolite over-representation and pathway topology84. A metabolite was included in biological systems analysis if it showed significant differential concentration , BH p value < 0.05 (POD2)) in two or more statistical tests. At PREOP, nominal p value was used in lieu of BH p value because systems analysis requires more metabolites than those that met the BH significance cut-off < 0.05.Systems biology analyses of delirium-related metabolites were performed using MetaboAnalyst 4.0 82. A pathway was considered enriched if the false discovery rate (FDR) adjusted p value was < 0.05. In topology analysis, perturbed metabolites located in critical or central positions are deemed to have a greater impact on a pathway.Hypergeometric testing was used to assess if delirium-related metabolites were overrepresented in a pathway more than expected by chanceSupplementary Tables.Supplementary Figures.