Heavy-ion beams of 28Si and 16O were used to bombard natural chromium and copper targets just above the Coulomb barrier at the University of Notre Dame's Nuclear Science Laboratory to produce these bromine and precursor radioisotopes by fusion-evaporation reactions. Production yields for these reactions were measured and compared to PACE4 calculations. In addition to using more robust targets for irradiation, a simple physical–chemical separation method is proposed that will lead to very high radiopurity yields. A summary of accelerator facility requirements needed for routine production of these radioisotopes is also presented.Many radioisotopes with potential medical applications are difficult to produce routinely, especially those on the proton-rich side of the valley of stability. Current production methods typically use light-ion (protons or deuteron) reactions on materials of similar mass to the target radioisotope, which limits the elemental target material available and may require the use of targets with poor thermal properties (as is the case for the production of radiobromine). These reactions may also create significant amounts of proton-rich decay products which require chemical separation from the desired product in a highly radioactive environment. A promising alternative method using heavy-ion fusion-evaporation reactions for the production of the medically relevant bromine radioisotopes  Both have been used extensively in various molecular formulations (2 and references therein), and can in principle be used in any compound in which radioiodine is used, with the benefit of a stronger bond to carbon and no dosimetric build-up in the thyroid in the event of in vivo dehalogenation4.Radioisotopes of bromine are uniquely suited for use in theranostic compounds (compounds which can be used for both therapeutic and diagnostic purposes). 5. Targets using compounds of selenium and copper7, nickel9, zinc7 and cobalt5 have been attempted, but, while significantly better at withstanding irradiation than pure selenium targets, proton intensities must still be limited to 40µA, leading to limitations on the achievable yield of 76/77Br. In addition, target degradation ensures that new targets must be produced after using the target a limited number of times. Reported yields have been up to 10.6 GBq per 3-h irradiation (88 MBq∙μA−1∙h−1) for 76Br and 2.8 GBq per 3-h irradiation (23 MBq∙μA−1∙h−1) for 77Br5. With alpha beams, elemental arsenic targets have been used, with similar issues of target degradation. 76Br cannot be produced by this method without the co-production of an at least equal amount of 77Br. Yields of 9.1 MBq∙μA−1∙h−1 for 77Br have been reported from 30-min irradiations with 70 nA currents10. Taken together with radiobromine’s promising radiochemistry and in vivo applications, there is a clear need for alterative production methods for these promising radioisotopes.Current methods of production for both bromine radioisotopes use energic light-ion beams  to bombard upon targets close to bromine on the periodic table. With proton beams, isotopically enriched selenium targets can be used to produce the desired bromine isotopes, but elemental selenium does not withstand irradiation well; the low boiling point, high vapor pressure and poor conductivity  ensure that any target will substantially degrade even with limited proton flux76Br and 77Br: symmetric heavy-ion reactions. Symmetric heavy-ion fusion-evaporation reactions have been proposed to produce other medically relevant radionuclides12 and have several advantages over proton or alpha spallation reactions. The most significant advantage is the isotopic yield specificity since the amount of energy lost through light charged particle evaporation restricts the number of resultant radionuclides formed in each reaction. More symmetric heavy ion entrance channels also increase the ability to produce proton-rich compound nuclei far from the valley of stability. Two promising systems were chosen for exploration into producing radiobromine: a 28Si beam bombarding a natural chromium target and an 16O beam bombarding natural copper.This work demonstrates the feasibility of a different approach to produce 28Si beam, the primary exit channel of interest is a (2p + n) fusion-evaporation reaction on 52Cr:52Cr makes up 84% of natural chromium, so isotopically enriched targets are not required. Reactions on the other chromium isotopes have similar products, although with reduced production of the mass-77 isobars. The only strongly produced contaminant solely from the non-52Cr isotopes is short-lived 74Br (t1/2 = 25 min), which would not interfere with routine production of 77Br. The PACE414 fusion-evaporation code was used to predict production cross-sections. Calculations were performed at 10,000 cascades per energy to minimize simulation error, which is on the order of one millibarn. The cross-sections for various relevant radionuclides (bromine and radionuclides which decay to bromine isotopes) are presented in Fig. For the 63Cu and 65Cu:For the oxygen on copper system, the reactions of interest are multi-nucleon-evaporation reactions on both 76/77Br is not desirable, then careful energy selection is required to favor the production of one isotope over the other. In that case, isotopically pure targets of either 63Cu or 65Cu could be advantageous. For the purposes of this proof-of-principle study, a mixed source was studied. The Stopping Range of Ions in Matter (SRIM15) program was used to calculate the energy-loss-profile for the beam in the target. The energy loss was then used to calculate the beam energy as a function of target penetration depth, which was then combined with the PACE4 cross sections and a delivered-current profile to produce predicted yields. Figures 16O bombardment of natCu as a function of laboratory bombarding energy . The mass-77 and mass-76 isobars can both be strongly produced.These reactions are all available on natural copper, and again isotopically enriched targets are not required. If a mixed source of in vacuo via dry distillation5 and differential cold-trapping. By carefully selecting when this separation occurs, it is possible to isolate specific radionuclides with high radiopurity. Figure 16O bombarding natCu for 20 min). At end-of-beam (EOB), all of 76Br and 76/77Kr are present in abundance, as are contaminant isotopes of both krypton and bromine. Of the krypton isotopes present, the three shortest half-lives are 75Kr (276 s), 77Kr (74.4 min) and 76Kr (14.8 h). 76Kr and 77Kr decay to the bromine isotopes of interest, while 75Kr and its daughter 75Br are contaminants. After 900 s, almost all (90%) of the 75Kr will have decayed away, with a minimal amount of 77Kr (13%) and 76Kr (1%) having decayed. At this point, bromine and krypton can be separated. The bromine will contain many isotopes but only three with a half-life longer than 97 min can be produced in this reaction: 76Br (16.2 h), 77Br (57 h) and 79Br (stable). After the bromine is allowed to decay for some amount of time, it will contain only 76Br and 77Br with relatively high radiopurity and a small amount (on the order of 1%) of stable 79Br. The exact decay time can be varied as needed to select acceptable radiopurities for a given application.Theoretically, krypton and bromine isotopes can be easily and rapidly separated from one another 77Kr will decay to 77Br, while 16% of the 76Kr will decay to 76Br and 7% of the 79Kr will decay to 79Br. Bromine and krypton can then be separated again, producing 76Br and 77Br with 51% and 48% respective radiopurity as measured by activity at 4 h EOB. The same procedure can be repeated after further decay to isolate 76Br from the decay of 76Kr. The exact decay lengths and radiopurities can again be tuned as needed for a given application.The krypton can simultaneously be allowed to decay for approximately 4 h, during which time 89% (of EOB amount) of the To independently verify the statistical model predictions for these two proposed reactions mechanisms for radiobromine production, a set of complementary experiments were performed to measure the productions yield experimentally at relevant bombarding energies.28Si or 16O ions. The experimental parameters used for each experiment are listed in Table The irradiations were performed at the Nuclear Science Laboratory (NSL) at the University of Notre Dame on the 10 MV High Voltage Engineering Corporation FN Tandem Pelletron accelerator. A Source of Negative Ions from Cesium Sputtering (SNICS) ion source was used to produce negatively charged The targets—commercially available, high-purity sputtering targets —were mounted in electrical contact with an approximately 1 foot long, closed, conducting beampipe that was itself electrically isolated from the rest of the experimental set up  dead-time losses were corrected by using the detector live-time in activity calculations. The target from the 28Si run (run 1) was counted beginning approximately 5-h after end-of-beam (EOB), with 30-min measurements continuously repeated for 10 days. Due to the high activity immediately post irradiation, the 72 MeV 16O irradiated copper target (run 2) was counted beginning approximately 18-h after EOB, with 30-min measurements continuously repeated for 10 days. Since much of the shorter-lived precursors would have decayed during the cooling period, reported activities (corrected to EOB) for these runs will over-predict the direct production of longer-lived products and late-produced daughters (and consequently under-predict the production of shorter-lived products and precursors) and represent net yields—that is, how much of a given radionuclide can be produced if harvesting is delayed until each decay chain of interest has settled into some long-lived species. Offline counting for the 55 MeV 16O run (run 3) began approximately 7 min after EOB with 1-min runs, so that over/under-attribution error does not apply to that run.Activity measurements were conducted at Notre Dame via off-line gamma spectroscopy using a high-purity germanium detector. Detector efficiency was determined using 28Si on natCr system 5-h after EOB. The dominant features are peaks from 77Kr, 75Br and positron annihilation. Figure 77Br, which retains 88% of the activity it had in 6a, 83% of EOB.Figure 16O on natCu system. The first measurements were further from EOB, so the initial precursors and contaminants are not visible, but the system decays such that eventually the desired products represent the majority of the radioactivity. A complete list of radioisotopes identified by gamma spectroscopy for each run is given in the supplemental information , so some additional physical effect on these isotopes must be in play. Since both gamma energies decay in tandem, the cause of the decreased lifetime must be affecting both peaks equally, so it cannot be independent contaminants for each energy. Since no other species have been identified which match this gamma signature, the cause cannot be spectral interference. Therefore, the most likely cause is krypton gas physically leaving the target prior to its radioactive decay. At later times , the half-life of decay for these gamma peaks is measured to be t1/2 = 14.7 ± 0.2 h, which is consistent with the established lifetime for 76Kr. These observations are consistent with krypton which was produced near the surface of the target evaporating away, but krypton deeper in the target remaining trapped and decaying in place. In future experiments, this effect might be mitigated by cooling targets during irradiation to minimize evaporative losses.It is apparent from Table 16O run. This is likely a combination of more immediate gamma spectroscopy and lesser beam energy, beam current and irradiation time resulting in substantially less target heating and, by extension, less evaporative loss. SRIM calculations suggest that a 72 MeV 16O beam in natural copper will reduce to 55 MeV in approximately 6.4 μm, which implies that the increased krypton production would occur closer to the surface for the increased bombarding energy, and evaporation could justify the suppressed 76Br activity as well.These effects are not seen in the 55 MeV An attempt was also made to perform an alpha bombardment of an arsenic target as an additional point of comparison, but even with only a few hundred nanoamperes of alpha-beam, target degradation was sufficient to severely impact the accuracy of post-irradiation gamma spectroscopy. Because of the difficultly of reproducing measurements with such a friable target, these data were excluded from this work.These results demonstrate the utility of making proton-rich isotopes with heavy-ion reaction channels. Simple target preparation and simple radioisotope separation requirements offer advantages over current production methodologies, and do not require radiochemical purification. Although production yields are lower by 100-fold than current proton reaction methods  of the heavy-ion projectiles means that all products are near to the target surface and should be recoverable without dissolving the target. With enriched targeted materials , heavy ion reactions also offer routes to pure in vacuo) for routine production. With the observation of evaporative losses from the room temperature target, the possibility of continuous irradiation and on-line separation emerges. Krypton evaporating from the target could be pumped away and cold-trapped, and harvesting could potentially take place without ever having to stop irradiation, access the target or perform separation chemistry with hot targets. With modern ion sources capable of delivering abundant silicon or oxygen beams  it is feasible to construct a dedicated production facility that could routinely produce amounts of radiobromine suitable for clinical use. Such a facility may also be able to make large-scale production of several other proton-rich radioisotopes economically feasible.Targets made from natural copper are also reusable, meaning that targets can be cycled between irradiation and extraction (Supplementary Information 1.


Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.The online version contains supplementary material available at 10.1186/s12866-021-02357-1. Metabolism is the basic process of living organisms which provides building blocks and energy for cellular growth. The availability of genome sequences for a great number of organisms enables the reconstruction of genome-scale metabolic networks (GSMNs) containing thousands of enzyme-catalyzed reactions and metabolites , 2. UndeE. coli metabolic network model iML1515 [E. coli metabolic network. However, we also noticed that the obtained production pathways for acetyl-CoA and succinyl-CoA were very complex ones, rather than the commonly recognized pathways (e.g. pyruvate to acetyl-CoA through pyruvate dehydrogenase) but very complex ones. The majority of the pathways are actually used for the production of the CoA group. Therefore, in order to obtain the pathways for the acyl moiety of acyl-CoA, the demand reaction (used as the optimization objective in FBA) for a CoA-containing metabolite was changed from “acyl-CoA ◊” to “acyl-CoA ◊ CoA” when its production pathway was calculated. This way, the CoA group can be recycled and does not need to be produced from the substrate. Similarly, we added demand reactions for other functional groups such as acyl carrier protein (ACP), THF (for transferring one carbon unit), UDP, ADP, CDP (for sugar nucleotides) etc. and modified the demand reactions for metabolites containing such groups to allow their reutilization in the pathways.The  iML1515  from the iML1515  was firsE. coli metabolic network. Compared to GBA, one advantage of the FBA method is that the pathway conversion efficiency (represented as pathway yield) can also be calculated as rp/rs, where rp is the rate of production and rs is the substrate consumption rate. To consistently compare pathway yields between pairs of metabolites, we used the carbon yield as an indicator for pathway conversion efficiency, expressed as the total carbon atoms produced in the objective product divided by the consumed carbon atoms from the substrate. The carbon yield of a pathway connecting two carbon-containing molecules should be between 0 and 1. Among the 132 pathways between the 12 precursors, 80 of them were revealed to have a carbon yield of 1 (100 %), indicating that all carbon atoms from the substrate are transferred to the product . This implies that the target metabolite may not be the main product of the pathway. Thus, the existence of such conversion pathways is questionable. We then carefully examined the reactions in the calculated pathways. For example, the pathway from chorismate to pyruvate (carbon yield 6 %) is very complex, with over 60 reactions  did reach the maximal carbon yield, meaning that all the carbon atoms in the substrate are transferred to the product through the pathway, i.e. without carbon loss. More than 90 %  of the pathways had carbon yield values above 50 %, implying that the target products are the main products of the pathways. By contrast, the majority of the pathways with a carbon yield of less than 50 % had two main products. For example, the carbon yield from pyruvate to 5-deoxy-ribose is only 30 %. By checking the calculated pathway, we found another product in the pathway 4-cresol, with a high carbon yield of 42 % , 4-hydroxyphenylpyruvate is the only main product and directly released through its demand reaction. By contrast, the NADH produced in this reaction can drive the fixation of CO2 through the threonine cycle to produce pyruvate. Although the carbon atoms in pyruvate do come from prephenate through the released CO2, it is quite a stretch to call this a conversion pathway from prephenate to pyruvate. Therefore, we excluded those pathways with a carbon yield smaller than 20 % in further global connectivity analysis.Even after adding the demand reactions to allow the co-production of several metabolites, there were still many pathways with a very low carbon yield. For example, in the calculated pathway from prephenate to pyruvate, the carbon yield is only 6 %. After examining the obtained pathway, we found that it was actually not a conversion pathway from prephenate to pyruvate but a COne cycle . In the Based on the described analysis, proper processing of the many metabolites containing certain functional chemical groups such as CoA, allowing ATP generation from the maintenance reaction, adding demand reactions for carbon-containing molecules, as well as examining and excluding low-carbon-yield pathways are all necessary steps for reliable pathway calculation.E. coli metabolic network is shown in Fig. The comprehensive FBA pathway analysis yielded over 2,000 pathways connecting 1,458 metabolites with pyruvate. Among them, 747 can be produced from pyruvate and also consumed to produce pyruvate. They can also be interconverted at least through pyruvate . These 748 fully connected metabolites (including pyruvate) form the giant strongly connected component (GSC) of the network. The GSC is the central part of the metabolic network and most metabolic conversions occur in the GSC. The 369 metabolites which can only be consumed to produce pyruvate  form the IN of the bow-tie structure. Correspondingly, the 342 metabolites that can only be produced from pyruvate form the OUT. The remaining 254 of the total 1713 carbon-containing metabolites are not connected with the main part of the metabolic network and form the IS. Many of these metabolites are only involved in one reaction (dead-end metabolites), and thus cannot take part in any mass-balanced pathways. This is in agreement with previous findings that many reactions are actually blocked and cannot carry nonzero steady state fluxes . HoweverAs the bow-tie structure has been studied by GBA, it was interesting to compare the structures obtained for the same network using the two different methods. For a like-to-like comparison, we also only considered the carbon-containing metabolites as nodes in the metabolite graph. One important issue in graph analysis of the metabolite network is the removal of links through currency metabolites . As discE. coli [With the generated graph, the bow-tie structure was calculated using the method mentioned in our previous study , and a sE. coli . As an eIn fGSC, the co-product of this reaction is S-methyl-5’-thioadenosine, and it can thus be utilized to produce pyruvate. However, none of the carbon atoms in S-methyl-5’-thioadenosine are derived from cadaverine, and the pathway for lysine uptake obtained from GBA using this link is actually wrong. Scientists have developed atom mapping methods to address such problems, but those methods require detailed atom mapping information between the reactants and products of a reaction , 27. HerThe 21 metabolites presented in fGSC but not in gGSC are mainly related to the metabolism of ATP, NADH and NADPH. Many links through these metabolites were removed in the first stage of currency metabolites processing. It is very tricky to identify which links should be regarded as currency metabolite links. Even though complex rules were utilized in this study, it is still not possible to correctly identify all currency metabolite links. Various approaches have been proposed to generate metabolite graphs without meaningless currency metabolite links, but no commonly recognized standards exist to deal with the problem at the moment. This leads to inconsistencies in graph analysis of metabolic networks, especially when it is used for biological function analysis. By contrast, the FBA pathway analysis is more reliable and the calculated pathways can reflect the real mass flow in the network. Therefore, the true global connectivity structure is better represented by the bow-tie structure obtained from FBA pathway analysis.Methanosarcina barkeri str. Fusaro to 2,153 metabolites for Pseudomonas putida KT2440. The ATP maintenance reaction (ATP → ADP + Pi) was missing in a few models, and was added it to make these models consistent with the pathway calculation method used for iML1515.To assess the broader applicability of our approach, 31 metabolic network models for different microorganisms were obtained from the BiGG database . We firsE. coli metabolic network analysis  as studied recently by Basan et al [We present a new method to identify the bow-tie connectivity structures of metabolic networks by calculating biological pathways with FBA rather than GBA to ensure the biological feasibility of the pathways. Our study shows that certain functional chemical groups such as CoA should be processed properly and correct demand reaction should be added in order to obtain pathways representing the real mass flow, either with GBA or FBA for pathway calculation. The obtained bow-tie structure is quite different in the classification of individual metabolites. The fully connected GSCs are smaller than those from GBA due to the exclusion of many biologically impossible pathways. The combination of FBA-based pathway analysis with the study of the global connectivity structure considering pathways between all pairs of metabolites in a network offers hope for a better understanding of the impact of system level-structure on the functions of biological systems. GSC is the core part of the metabolic network where metabolic fluxes can be rerouted corresponding to different environmental conditions. In contrast fluxes in other subsets can only go in one way and there is no backward flux. This bow-tie organization principle has been used to develop a new algorithm for determination of internal fluxes using ing data . As theran et al .http://bigg.ucsd.edu/) [Escherichia coli, iML1515, was used as an example to illustrate the analysis methods used in this study. Element balance for all the reactions in these models was checked using the check_mass_balance function in the COBRApy package [We downloaded the latest genome-scale metabolic models in SBML format from the BiGG Models database (sd.edu/) . There a package .For global connectivity analysis, we focused on the carbon flow and only calculated pathways between pairs of metabolites containing carbon atoms. These metabolites can be easily extracted based on their chemical formulas. As the original models normally use glucose as the substrate and biomass production as the objective function, we had to modify the model by updating the lower boundary of the glucose exchange reaction (EX_glc__D_e) to zero, and setting another objective function. For calculating a pathway from metabolite A to metabolite B, we added a sink reaction for A (as substrate) and set its lower boundary at -10 mmol/g/h as well as a demand reaction for B (as product) used as the optimization objective. The pFBA  function in the COBRApy package  was used2O + pyruvate → pep + AMP + 2.0 h + Pi). This simple conversion pathway is biologically relevant as all carbon molecules from pyruvate are transferred to pep. For structure analysis, we should focus on the carbon flow and the reactions for energy balance can be ignored. To this end we added a reversible energy balance reaction (ADP + Pi <=> ATP) so that ATP can be generated from this reaction instead of a large set of reactions for ATP production from the substrate. This modification enables us to obtain the simple pathway from pyruvate to pep same as that from GBA. This made it easy for us to focus on pathways representing the actually carbon flow rather than energy production, and the calculated pathways were biologically more understandable than the complex pathways with many reactions for energy balance.One problem in FBA based pathway analysis is that we often obtain more complex pathways than those from GBA. For example, the pathway from pyruvate to phosphoenolpyruvate (pep) calculated by FBA contains many reactions involved in the TCA cycle and respiratory chain for producing ATP to satisfy ATP balance. In comparison, in GBA, a simple one step pathway from pyruvate to pep can be obtained from reaction  to ensure that the calculated pathways are for the production of the converted carbon-containing parts of the metabolites rather than the large carrying groups. For example, the demand reaction for an acyl-CoA metabolite was added as “acyl-CoA → CoA” so that the CoA group can be reutilized and thus not need to be synthesized from the substrate. The Sink reactions for such metabolites were updated in a similar way so that the carbon atoms in the special group of the substrate did not need to be consumed. In addition to the demand reaction for the target metabolite, we also added demand reactions for all carbon-containing molecules so that the byproducts in a reaction can be balanced in addition to the objective metabolite. The complete workflow for model preprocessing and bow-tie structure determination is shown in Fig. Many metabolic networks contain over a thousand carbon-containing metabolites, leading to over a million metabolite pairs for pathway calculation. To reduce the calculation time, we simplified the process based on the transitive property of the network. Thus, if there is a pathway from A to B and a pathway from B to C, then there is also a pathway from A to C. Based on the bow-tie connectivity structure of metabolic networks discovered by GBA, we can choose any metabolite in the core part of the metabolic network (GSC) to calculate the pathways to/from it. As all metabolites in the GSC are fully connected, the connectivity of the whole GSC can be represented by one metabolite in it. However, it is problematic that one does not know which metabolites should be in the GSC beforehand. Based on previous knowledge of biochemistry, the metabolites in central pathways are more likely to be in the GSC. Therefore we chose 12 biosynthesis precursors from the central pathways  as starting metabolites. Conversion pathways between these metabolites were first calculated to evaluate the connectivity. If these metabolites are fully connected, we can choose any one of them as a seed metabolite for pathway calculation. If not, one metabolite in the largest connected component was chosen.After calculating all pathways to/from a seed metabolite, we classified all other metabolites based on their connectivity with the seed metabolite. Similar to the definition of the bow-tie structure in GBA, a metabolite is in the GSC if it can be produced from the seed metabolite and can also be consumed to produce the seed metabolite. It is in the IN if it can only be consumed or OUT if it can only be produced. All other metabolites are in the IS.2, HCO3 and ACP).To compare the connectivity structure revealed by FBA-based pathway analysis with that from GBA, we converted the metabolic models in SBML format into directed graphs, in which nodes represent metabolites and links represent reactions. Only carbon-containing metabolites were considered. We used a ranked metabolite pair based approach to remove most currency metabolite pairs. These pairs often appear on two sides of a reaction for transferring a chemical group such as hydride (e.g. NADH/NAD), phosphate (e.g. ATP/ADP), amine (e.g. glutamate/ketoglutarate), one carbon unit (e.g. METHF/THF), acetyl (Acetyl-CoA/acetate) etc. A complete list of such pairs can be seen in additional file https://networkx.org/) was used to calculate the bow-tie structure using methods based on graph theory. We first calculated the strongly connected components in the network to identify the GSC. Then, we chose a node in GSC to calculate its output domain (O) and input domain (I). The IN includes nodes in I but not in the GSC, and the OUT contains nodes in O but not in the GSC. All other nodes are in the IS.The python-based network analysis package NetworkX  and other metabolites in iML1515Additional file 3. The calculated pathway from chorismate to pyruvate before adding demand reactions for all carbon-containing metabolites.Additional file 4. Classification of metabolites in the bow-tie structures of the models.Additional file 5. List of currency metabolites and metabolite pairs.Additional file 6. Comparison of the bow-tie structures obtained using GBA and FBA methods.


The findings of this study show that a series of metabolic pathways associated with the glycine-serine-threonine (Gly-Ser-Thr) metabolic axis affects carbohydrate patterns and energy utilization efficiency and ultimately delays disc degeneration through antioxidant effects.Although intervertebral disc degeneration (IDD) can be described as different stages of change through biological methods, this long and complex process cannot be defined in stages by single or simple combination of biological techniques. Under the background of the development of nuclear magnetic resonance (NMR) technology and the emerging metabonomics, we based on animal models and expanded to the study of clinical human degeneration models. The characteristics of different stages of IDD were analyzed by omics. Omics imaging combined with histology, cytology, and proteomics was used for screening of the intervertebral disc (IVD) of research subjects. Furthermore, mass spectrometry nontargeted metabolomics was used to explore profile of metabolites at different stages of the IDD process, to determine differential metabolic pathways and metabolites. NMR spectroscopy was used to qualitatively and quantitatively identify markers of degeneration. NMR was combined with mass spectrometry metabolomics to explore metabolic pathways. Metabolic pathways were determined through protein molecular biology and histocytology of the different groups. Distinguishing advantages of magnetic resonance spectroscopy (MRS) for analysis of metabolites and effective reflection of structural integrity and water molecule metabolism through diffusion tensor imaging (DTI) were further used to verify the macrometabolism profile during degeneration. A corresponding model of  Lumbar disc degeneration causes lumbar disc herniation, lumbar spinal stenosis, and other lumbar disc degeneration diseases . DegenerThe intervertebral disc (IVD) is located between the vertebral bodies. It facilitates in intervertebral motion and distributes compressive load in adjacent vertebral bodies . IVD con2WI images [Clinical diagnosis of IDD is mainly based on magnetic resonance imaging (MRI) as the first noninvasive examination. Degree of degeneration is defined by Pfirrmann grading, which is based on TI images . The oveI images , and curI images  enables I images –16.This study used mass spectrometry and nuclear magnetic resonance (NMR) metabolomics to explore metabolic characteristics of each process of IDD to understand the biological characteristics of the IVD. In addition, the findings of this study show the degeneration process from the level of material metabolism. The findings were verified through omics imaging, molecular biology, and histopathology thus establishing a complete metabolic profile. These findings showed the degenerative mechanism of antioxidation attenuation of the IVD.ad libitum. All experimental procedures were approved by the Ethics Committee of Qingdao University  and were implemented in accordance with relevant guidelines and regulations.48 healthy Sprague-Dawley (SD) rats were purchased from Shandong Jinan Animal Experiment and Breeding Center. The weight of animals ranged between 557 ± 31.5 g for males and 509 ± 26.3 g for females. Male rats were raised in different cages from female rats. Animals were maintained under suitable temperature and light control conditions  and provided with rodent food and water 2WI sequence and computed tomography (CT) scan confirmed establishment of the model.Twenty-four 6-month-old healthy SD rats of the same strain were selected and were similar in size and weight. Three consecutive segments of intervertebral space were selected for ultrasound-guided puncture (the segment was Co3-6), with no intervention for grade I IVD, unidirectional puncture with a fine puncture needle for grade II intervertebral disk, and multidirectional puncture with a thick puncture needle for grade III intervertebral disk. Three weeks later, 3.0 T magnetic resonance imaging (MRI) TIn vitro samples were obtained from 16- to 35-year-old patients with acute trauma. In this study, in vivo and in vitro experiments were performed after obtaining informed consent of patients and their families. All participants met ethical standards.Selection criteria for experimental subjects were as follows: participants who presented with degenerative disease or acute injury of the lumbar spine that required IVD ectomy or discectomy and MRI can be performed at the corresponding time node before and after surgery were included in this study. Patients with tumors, infections, malformations, immune diseases, genetic diseases, metabolic diseases, and the young (<16 years) and elderly (>80 years) were excluded from the study. 1WI, SAG-FSE-TSE, T2WI, diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). The corresponding relationship database of image data was established using patient information. Samples were collected by conventional surgical methods within 24 hours of injury or onset based on the preoperative MRI lesion responsibility segment. Samples were divided into four parts  and washed with distilled water. Samples were stored in vacuum packages and stored at low temperature.Image data was obtained by GE®3.0 T for preoperative MRI Tμm) were obtained.Paraffin-embedded sections: the attached tissue was separated from the spine, and tissues sections were fixed with 4% paraformaldehyde  for 72 h. EDTA , NaOH , and HCL  were used to produce decalcification agent (pH 7.3-7.4) for decalcification. The decalcification agent was changed every week, for 4 weeks. After decalcification, paraffin-embedded sections of decalcified tissue . Paraffin sections were deparaffinized with xylene  and washed with gradient ethanol  and then rinsed with distilled water for hydration. Tissue sections were then stained with hematoxylin staining solution, differentiation solution, and eosin staining solution. After staining, gradient ethanol was used for dehydration, and then, tissues were treated with xylene : carbolic acid (3 : 1) to make them transparent and then sealed with neutral resin. Tissue sections were then observed under a light microscope.To explore distribution and content of collagen fibers in tissues, paraffinized sections were stained with the Masson kit (Solarbio). In summary, paraffin sections were deparaffinized with xylene  and stained with Weigert iron hematoxylin. Fluid staining was used for acidic differentiation. Further, tissues were stained with Ponceau-magenta staining solution. Tissue sections were then washed with weak acid working solution and phosphomolybdic acid solution. Tissues were stained with aniline blue staining solution, rinsed with weak acid working solution, and dehydrated with 95% ethanol . Xylene solution was added to make tissues transparent, and tissues were then sealed with neutral resin and observed under a light microscope.To assess the relative distribution of bone and cartilage in the tissue, paraffinized sections were stained with the Safranin O-Fast Green Kit (Solarbio). Paraffin sections were deparaffinized with xylene . Weigert iron hematoxylin staining solution was then used for staining and acidic differentiation solution for differentiation. Fast green dye solution was used for dip dyeing, and tissues were washed with weak acid working solution. Tissues were dip-stained with Safranin O and dehydrated with gradient ethanol . Xylene was used to make tissues transparent, and the film was mounted with neutral resin. Tissues were then observed under a light microscope.Immunohistochemistry was used to evaluate the expression levels of MMP3/TIMP1 in tissues. Paraffin wax slices were baked overnight. Xylene  was used for dewaxing, washed with gradient ethanol , and TBST buffer was used for hydration. 3% hydrogen peroxide solution was added for blocking, and tissue sections were then washed with TBST solution. The slices were washed with TBST solution after antigen retrieval. Tissue slices were placed flat in the wet box, and the tissue boundary indicated with a hydrophobic pen, and then, 3% BSA solution was added. The blocking solution was aspirated, and primary antibody was added (MMP3/TIMP1 antibody purchased from Santa Cruz), and then, the mixture was incubated overnight at 4°C. Sections were warmed to room temperature and washed with TBST solution. Tissue sections were then incubated with secondary antibody at room temperature, and color was developed with diaminobenzidine (DAB) solution, under controlled reaction time. Tissues were then observed under a microscope. Hematoxylin solution was used for dyeing and counterstaining, and then, tissues were alkalized with alkalized water. Tissue sections were dehydrated with gradient ethanol , and xylene was used to make sections transparent. The slide was mounted with neutral resin, and then, tissue sections were observed under a light microscope.2 incubator. The flask was taken out on days 1, 3, 5, and 7 for observation of cell morphology. The CCK-8 method was used to quantitatively analyze proliferation activity of NP cells at various levels. Cells were counted and seeded in a 96-well plate. Cells were then incubated in a 5% CO2 incubator at 37°C, for 4 hours; 10% CCK-8 was added by changing the medium. After addition of CCK-8, cells were incubated for 2 h in the incubator, and the absorbance was determined at 450 nm using a microplate reader.AF and EP were separated from isolated IVD tissues. Primary NP cells were extracted from the NP tissue using the type II collagenase method. Cell culture flask containing NP cells was placed at 37°C, under 5% COβ-actin (bs-0061R). Samples were subjected at constant voltage 80 V electrophoresis. After entry of bromophenol blue dye in the separation gel, the constant voltage was changed to 100 V and electrophoresis was performed until the bromophenol blue dye reached the bottom of the separation gel. After cutting the glue, the membrane was transferred in a transfer box. PVDF membrane was sealed with skimmed milk powder, and primary antibody was added at 4°C overnight. After washing samples in the TBST shaker, secondary antibody was added to samples and incubated for 1 h. Liquid A and liquid B in the ECL kit were used to prepare a luminescent liquid 1 : 1. The liquid was added to the PVDF membrane and then placed on the Bio-Rad developing instrument for development. Expression of the target protein was observed, and images were obtained for analysis.Western blot was used to detect degenerative factors in IVD samples. In summary, separation gel and concentrated gel were prepared, and samples were loaded according to the measured protein concentration. The application antibody product number information is TIMP1 (bs-0415R), TIMP2 (10396-H01H), TIMP3 (ab159704), MMP1 (bs-4597), MMP9 (sc-13520), MMP13 (310471-T40), MATN3 (ab106388), IL11 (ab76589), Aggrecan (bs-11655R), Collagen II (bs-11929R), and 1WI, T2WI, and other sequences after setting the relevant scanning parameters. Pfirrmann grading of the IVD was determined based on T2WI.A 3.0 T MRI machine  was used to perform one-time detection of TA 3.0 T MRI machine  magnetic resonance imager was used to perform DWI imaging. Data were imported into PACS system to determine apparent diffusion coefficient (ADC) and partial anisotropy index ) of region of interest (ROI) related to the intervertebral disc nucleus pulposus.1H spectrum was mainly the H atom signal distribution of characteristic groups. Distribution of the compound represented by each peak was assigned a compound name based on the identification of synchronous mass spectrometry. The assignment was determined by standard compound NMR spectrum database (https://sdbs.db.aist.go.jp/sdbs/cgi-bin/cre_index.cgi?+lang=eng). The assignment indicated the 1H spectrum representative peaks of each level of IVD.GE®3.0 T DISCOVERY MR750 was used to obtain the original data and which was then preprocessed with jMRUI_v6.0 beta to obtain MRS fitting spectrum. Peak calibration was performed on the final curve to obtain relevant compound information. Analysis of Ultrahigh-resolution performance liquid chromatography-Q-HF-X hybrid-high-resolution mass spectrometer detection (UHPLC-Q-HFX-HRMS): samples were washed and dried and then pretreated, extracted, and homogenized with a homogenizer and then sampled for testing. Mass spectrum platform was used for analysis of samples. Total ion chromatogram (TIC) was obtained from the raw data after noise reduction, baseline correction, peak alignment, data binning, peak standardization, and standardization.Metabolite difference analysis: multivariate statistical analysis was performed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify labeled metabolites, and heat maps were generated. Permutation test and S-plot scatter plot were used to analyze significance of metabolite differences.Metabolic pathway analysis and calibration: functional analysis was performed based on the differential metabolites. Related metabolic pathways were identified through enrichment analysis and topological structure. The five-level IVD groups were compared to draw statistical charts.The microarray dataset of IDD was retrieved from the NCBI GEO database. Metabolic pathways of differentially expressed proteins were analyzed. Machine learning algorithms were used to further screen and determine the metabolic pathways of IDD. The ssGSEA tool was used to analyze the different subgroups of RNA-seq data. The proportion of each metabolic pathway was determined, and the correlation between metabolic pathways and specific genes was analyzed.P < 0.05 was considered as statistically significant.The ssGSEA tool was used to analyze RNA-seq data of different subgroups of patients to infer the relative proportions of 43 metabolic pathways. The “pheatmap” package was used to generate a heat map of metabolic pathways and compare distribution of metabolic pathways between the two groups. The “vioplot” package was used to determine the relative content of metabolic pathways, and the “corrplot” package was used to determine correlation coefficients between metabolic pathways and specific genes. NP tissue was lysed with RIPA and then ground. Tissue sample was resuspended in dichloromethane and methanol and then homogenized. Original data was obtained by computational analysis. Preprocessed data was based on the iconic lactate peak. The spectrum was aligned, and the full spectrum was labeled and integrated to obtain the full spectrum peak data and peak shape distribution. After identification of the coupling situation of each peak, substance integration quantitative analysis was performed to obtain a compound prediction list.f1, f2 phase correction, Fourier transform, window function, and zero function adjustment and signal suppression. After processing, the lactic acid (Lac) peak  was calibrated and a total correlation spectroscopy (TOCSY) spectrum generated. The newly identified compounds represented by each peak were then identified and assigned.The “two-dimensional correlation spectrum” experiment was used to label the IVD qualitative IVD marker compounds. The original data was processed by Mestrenova 14.0.0 through spectral baseline correction, X ± S and parametric testing was done using a t-test. Nonparametric was represented as median and interquartile plotting rank sum test was used for nonparametric data. Multigroup analysis of quantitative data was performed using one-way analysis of variance followed by Kruskal-Wallis test. Correlation analysis was performed to compare expression in the different groups. P < 0.05 was considered as statistically significant.Data obtained were analyzed based on their respective schemes and methods for intragroup difference statistics and between-group difference statistics. Quantitative data were represented as 2WI imaging showed that the model met the preset imaging conditions. In addition, CT showed significant differences in the height of the intervertebral space between models of different levels . Tissue inhibitor of metalloproteinase 1 (TIMP1) was light brown in the NP tissue .Matrix metalloproteinase-1 (MMP-1) stained deeper in the NP-like cell with IDD , and itsP tissue . TIMP1 eWB results showed a decrease in the expression of structural characteristic proteins Aggrecan and Collagen II, whereas the expression of TIMP1 and MMP1 proteins related to ECM metabolism was higher . NotablyUHPLC-Q-HFX-HRMS was used to perform mass spectrometry metabolic analysis on rat IVD samples of different levels . The supPCA was used to perform dimensionality reduction processing of data features. Analysis showed that the distribution ranges of groups B and C in PCA anion and cation mode were mostly overlapping, whereas the areas of group B and C were separated from the area of group A . This fiP value)  . The folA total of 43 metabolic pathway subgroups in the 48 human samples of the GSE70362 dataset were retrieved from the NCBI GEO bioinformation database. The expression of the 43 metabolic pathways was analyzed and summarized . FunctioIVD of patients was analyzed by mass spectrometry metabolomics . IVD wasMetabolic pathway enrichment analysis showed significant differences in metabolic pathways in the A-E group . NotablyAnalysis of degeneration-related factors of these key pathways showed that metabolism of glycine-serine and threonine  after the degeneration decreased expression of related molecules compared with the levels before degeneration . In addi1H spectrum of each level of IVD were identified based on the attribution and data from the standard compound NMR spectrum database. The V-level disc was used as a representative sample . Notably, grade V IVDs were not detectable in DTI imaging. In grade V, ADC and FA values were not obtained, implying that the measured value range exceeded the high limit or cannot be imaged. This finding shows that the ADC value of degenerative IVDs gradually decreased, whereas the FA value increased with degeneration. Notably, analysis showed significant differences in ADC and FA values between groups .Analysis of DTI imaging axial map of each level of the IVD showed that Pfirrmann I-V level represents gradual decrease of the high signal of water molecule activity . ADC valPfirrmann grade IV disc spectrum peak identification was similar, from low field to high field . The valDegeneration is a multistage physiological process with complex factors. IVDs at different degeneration stages are characterized by differences in morphology, pathology, and cell biology . Clinicain vivo metabolism model of noninvasive detection of degeneration.Currently, MRI is the main method for noninvasive diagnosis of IDD . Its priAnalysis showed that the main metabolic processes were carbohydrate and protein metabolism, mainly involving matrix components such as collagen and proteoglycans. Therefore, this study focuses on amino acid metabolism and sugar metabolism. Analysis of TIC, OPLS-DA, and the corresponding permutation test and S-PLOT load diagram showed that group A without intervention in the overall metabolic pattern of rat IVDs was significantly different from other groups. On the contrary, groups B and C showed similar trends in expression of metabolites. Therefore, the two stages before and after degeneration were explored.The main different metabolic pathways before and after degeneration were mainly related to three types of biological activities including ① carbohydrate utilization , ② antioxidant pathways , and ③ structural protein synthesis and degradation (Gly-Ser-Thr metabolism) , 28. NotAnalysis of the difference in metabolites showed that marker metabolites in the predegeneration stage were mainly implicated in carbohydrate metabolism pathways including pentose phosphate pathway, glycolysis/xenogenesis, and amino sugar and nucleotide sugar metabolism and protein metabolism pathways including serine and threonine metabolism, cysteine and methionine metabolism, and small molecule metabolism mainly inositol phosphate metabolism. In addition, marker metabolites in the late stage of degeneration were mainly involved in sugar metabolism pathways such as amino sugar and nucleotide sugar metabolism, proteoglycan structural metabolism, and tricarboxylic acid (TCA) cycle; protein metabolism pathways such as cysteine and methionine metabolism and serine and threonine metabolism; and small molecule metabolism including niacin and nicotinamide metabolism and PGI2 metabolism. Consistent with the findings on metabolic pathways, the main biological processes involved in rat IDD were energy metabolism , biologiCysteine plays a role regulating glutathione synthesis. Glutathione is an important antioxidant and oxygen free radical scavenger in the body. Methionine is involved in destruction of membrane lipids produced by oxidative free radicals through multiple channels of oxidation thus protecting membrane-containing structures such as cells and mitochondria . DownregAlthough the rat genome is highly homologous to human genome and the metabolic pathways are similar, it cannot be inferred that the metabolic profiles of the two organisms are the same. Therefore, human metabolic pathway subgroups were retrieved from the NCBI GEO database and used for induction. A heat map on the correlation between core genes and metabolic pathways showed that main changes are changes in the metalloproteinase families , 41 and 1H spectrum identification and quantitative analysis showed that consistent with the mass spectrum metabolism findings, there was no significant change in the types of metabolites. Notably, Lac levels increased significantly with degeneration. Decrease in the level of phenylalanine, tyrosine, and other sugar metabolism regulating substances [3, is an intermediate metabolite of phospholipids [1H spectrum has poor specificity in identifying compounds. In order to separate effective compound components from the overlapping peaks and peaks with small amplitude, TOCSY two-dimensional spectrum was used to transfer the magnetization vector using the direct coupled spin method [NMR is an effective tool for mass spectrometry metabolomics due to its universality and high throughput for hydrogen-containing metabolites . Advantabstances  with theholipids , and itsn method  thus allIn vitro NMR 1H spectrum and the TOCSY two-dimensional spectrum divide the stages of human IDD based on levels of amino acid residues and macromolecular compounds, highlighting the specificity of the metabolism process at different stages. The phase difference of metabolites can be explained by molecular biology regulation mechanism. Further studies should explore whether NMR analysis results are consistent with the corresponding MRS quantitative results and whether the macrobiological process can be explained by DTI.Conventional NMR, DTI, and MRS were used to describe the classification of human IVDs from three levels including anatomical shape, water molecule pathophysiological behavior, and metabolic level adjustment. The process of degeneration was verified through metabolomics analysis.2WI-weighted images of the IVDs showed that the imaging features of the IVDs of each grade conformed to the Pfirrmann classification. In addition, imaging confirmed that there was no objection to the sorting of human IVD models. This study used functional nuclear magnetism to observe water metabolism based on morphological changes of T2WI [T of T2WI . DecreasIn vitro metabolomics were linked with in vivo imaging mimics through spectrogram identification and spectral peak analysis. These findings were then confirmed through histopathological and molecular biology analysis.Qualitative analysis of NMR showed that the metabolites in the metabolic pool of the IVD were stable and the overall metabolic profile was consistent. Notably, lactate ratio represented accumulation of Lac in the unit structure substance, and its accumulation shows a decrease in energy utilization efficiency in the late stage of degeneration and that the sugar utilization pattern in mass spectrometry metabolomics was changed from the previous glycolysis. In addition, change in metabolites showed transformation from xenobiotics and pentose phosphate to fructose and mannose pathways in the late stages. N-Acetyl resonance compound is an important identification peak of MRS. N-Acetyl/Carb is interpreted as the ratio of collagen content to carbohydrate and hydrocarbons. Carbohydrate content is a marker of matrix structure and carbohydrate metabolism of proteoglycans. The significant decrease in the ratio value indicates decrease of the collagen content in the structural material, which is consistent with the findings on the progressive fibrosis of the NP tissue. On the other hand, N-Acetyl/Cho represents the proportional relationship between the collagen content and the density of the IVD NP cells. The ratio of IDD showed significant decrease indicating that the unit number of cells matching ECM decreased with progress in degeneration. This implies that the proportion of NP cell apoptosis and degeneration increases, and its secretory function decreases. N-Acetyl/Cr represents the proportional relationship between collagen content and energy buffer mechanism, because Cr is an important substance for ATP buffering  and it mIn summary, this study described the relatively complete IDD through correlation analysis of rat and human metabolomics (mass spectrometry metabolomics and NMR metabolomics) combined with imaging, molecular biology, and histopathology verification. The findings of this study show tissue metabolism during degeneration. The findings showed that the process of IDD is closely related to the functional state of the Gly-Ser-Thr metabolic axis. Changes in carbohydrate utilization patterns around this metabolic axis and reduction of the antioxidant capacity of the IVD environment promote degeneration. The process eventually leads to disintegration of the annulus fibrosus and loss of water-fixing groups.


In addition, they have metabolic capacities to produce novel secondary metabolites of pharmaceutical, nutraceutical and industrial applications. Studies have explored the inherent metabolic capacities of microalgae with altered growth conditions for the production of primary and secondary metabolites. However, the production of the targeted metabolites at higher rates is not guaranteed just with the inherent genetic potentials. The strain improvement using genetic engineering is possible hope to overcome the conventional methods of culture condition improvements for metabolite synthesis. Although the advanced gene editing tools are available, the gene manipulation of microalgae remains relatively unexplored. Among the performed gene manipulations studies, most of them focus on primary metabolites with limited focus on secondary metabolite production. The targeted genes can be overexpressed to enhance the production of the desired metabolite or redesigning them using the synthetic biology. A mutant (KOR1) rich in carotenoid and lipid content was developed in a recent study employing mutational breeding in microalgae . There are lot of challenges in genetic engineering associated with large algal diversity but the numerous applications of secondary metabolites make this field of research very vital for the biotech industries. This review, summarise all the genetic engineering studies and their significance with respect to secondary metabolite production from microalgae. Further, current genetic engineering strategies, their limitations and future strategies are also discussed.Microalgae are highly diverse photosynthetic organisms with higher growth rate and simple nutritional requirements. They are evolved with an efficiency to adapt to a wide range of environmental conditions, resulting in a variety of genetic diversity. Algae accounts for nearly half of global photosynthesis, which makes them a crucial player for CO Microalgae are eukaryotic organisms that have evolved through a series of primary and secondary endosymbiosis . They arJust like plants, carbohydrates and other primary metabolites are required for cell development and division in microalgae. This is especially more important in the early phases of the growth, when the algal cells are actively growing. Secondary metabolites are usually produced near the end of the growth cycle, when the organism is approaching the end of life cycle. This is because cells will be encountered with stress due to environmental factors such as nutrient availability and toxin exposure at this stage. These secondary metabolites have features that assist cells in coping with stress and initiate certain signalling pathways involved in the defence responses . Up tillMicroalgae can serve as a sustainable feedstock for production of several secondary metabolites to meet the requirements of growing population. These metabolites can be of pharmaceuticals, cosmeceuticals or nutraceuticals applications. In particular, the wide spectrum of applications are due to their key features such as antioxidant, anti-inflammatory, antimicrobial, hepatoprotective, neuroprotective and anticancer properties . Here we2 and carbon containing organic substance into primary and secondary metabolites. The primary metabolites  are key compounds, which are essential for the survival of the organism  is recently discovered MAA and is sole MAA present in Aphanothece halophytica  to pass across the membrane . Synechoormation . The voloration. .This method works on accelerated or high-speed gold/tungsten microparticles, which are coated with DNA and shot using high pressure to transform the target cells . This meAgrobacterium tumefaciens is used in this method to transfer DNA segment into the target cells by natural infection process. The T-DNA sequence (part of Ti plasmid) of the bacterium, which allows insertion of the desired DNA construct is utilized for the transformation , which in turn increased their transformation frequency  are some of the main factors, which can affect the transformation efficiency. Agitation of the cells using silicon-carbide whiskers is also a popular method used in which the cells are vortexed using ceramic microparticles composed of silicon and carbon. Cell walled, Amphidinium sp, Symbiodinium microadriaticum and C. reinhardtii are also transformed using this technique. Although the transformation efficiency is much better but the health hazard caused by inhaling silicon while handling is one of the major drawback  and glass beads of about 0.5 mm in size, followed by agitating the mixture. PEG acts as a membrane fusion agent . Microaldrawback .Synechocystis PCC 7942, Synechococcus PCC 7002, Synechocystis sp. PCC 6803 and Thermosynechococcus elongatus BP-1 are naturally transformable cyanobacterial species , mob genes, which are required for the DNA mobilization from the bacterial donor to the microalgal cells, an antibiotic resistance gene, which acts as a selectable marker and the Clover gene as a green fluorescent reporter , Vde-related (Vdr) and Zeaxanthin epoxidase 3 (Zep3). Studies were performed to evaluate the change in the pigment content and the results suggest that the triple transformants showed 4-fold increase in fucoxanthin content , Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), Zinc Finger Nucleases (ZFNs), etc. which can alter the metabolite production rates at higher rates. RNAi and riboswitch engineering are also efficient in microalgal research.Along with the advancements in gene editing techniques, sequencing technologies should also improve to make sequences more accessible and to study non-model organisms that are industrially important. Further, advanced bioinformatics approach in combination with genomic and metabolomic data analysis should progress to study the metabolic drifts. Such drifts may cause due to the non-orthologous gene displacement or a change in the main enzyme resulting in the same phenotype . These dThe gene delivery systems are critical for successful transformation of microalgal cells to insert foreign genes. The chosen system must be appropriate for the organism being studied, which is selected depending on the cell wall composition. Recent research has found the ideal approaches for algal cells with hard cell walls, such as the zinc oxide nanowire array microdevice system, are much more efficient than conventional transformation methods . Algal nPlants are more complicated than algae, with advanced metabolic pathways capable of producing a variety of biotechnologically relevant metabolites. However, gene editing studies are difficult to conduct due to their complexity. To obtain higher yields of the plant metabolites, respective genes can be transformed into organisms with higher growth rate like microalgae. The genes can be inserted into either the nuclear or chloroplast genomes. Chloroplast transformation results in a larger accumulation of recombinant proteins in algae and plants than nuclear transformation . OptimisAll the above mentioned strategies are mostly performed on only a few organisms for commercial product productions due to the ease of cultivation and harvesting of some common species. There is a need for more research to be performed to explore the potential of other species as well.Microalgae have the ability to produce a variety of secondary metabolites of commercial importance. Using genetic engineering, large-scale microalgae production can be made feasible. There are several studies performed on algal gene manipulations which resulted in higher yields of the target metabolites. The new technologies, when coupled with metabolic, genetic engineering methodologies and optimised culture conditions will help to improve metabolite production efficiency. In addition to it, accurate metabolic profiling also should progress to explore several secondary metabolites of industrial importance, whichin remain undiscovered till today. The proper handling and disposal of residual biomass can make industrial production of genetically altered microalgae eco-friendly.


Moreover, the resultant 5% NiO/CdS composite displays excellent stability over four cycles for photocatalytic hydrogen production. The significantly enhanced photocatalytic activity of the 2D/1D NiO/CdS heterojunction can be attributed to the efficient separation of photogenerated charge carriers driven from the formation of p-n NiO/CdS heterojunction. This study paves a new way to develop 2D p-type NiO nanosheets-decorated n-type semiconductor photocatalysts for photocatalytic applications.Designing low-cost, environment friendly, and highly active photocatalysts for water splitting is a promising path toward relieving energy issues. Herein, one-dimensional (1D) cadmium sulfide (CdS) nanorods are uniformly anchored onto two-dimensional (2D) NiO nanosheets to achieve enhanced photocatalytic hydrogen evolution. The optimized 2D/1D NiO/CdS photocatalyst exhibits a remarkable boosted hydrogen generation rate of 1,300 μmol h This current research highlights the advantages of the 2D/1D p-n heterojunctions toward improved photocatalytic hydrogen generation performance.2·2H2O] and 4 mmol of thiourea (CH4N2S) were initially dissolved into 50 ml of ethylenediamine. The mixture was then placed in a 100-ml Teflon-lined autoclave and heated to 180°C for 16 h. The product was obtained by using centrifugation and washed with DI water and ethanol before vacuum drying.First, 2 mmol of cadmium acetate  and 2 mmol of urea (CH4N2S) were dissolved in 35 ml of ethanol. The mixture was then transferred into 50 ml of Teflon-lined autoclave and kept at 120°C for 8 h. The α-Ni(OH)2 nanosheets were obtained after centrifugation, washed, and vacuumed dried. Finally, the NiO nanosheets were prepared by annealing the as-obtained α-Ni(OH)2 nanosheets at 350°C for 1 h under air with a heating rate of 1°C/min.The NiO nanosheets were prepared by the calcination of α-Ni(OH)x% NiO/CdS, in which x% represented the mass ratio of NiO to the NiO/CdS composite.The NiO/CdS composites were synthesized through a solution-phase method. Firstly, NiO nanosheets and CdS nanorods were dispersed in 40 ml of ethanol. Then, the mixture was magnetically stirred for 12 h. Finally, the product was obtained after the mixture was washed and vacuum dried. The final products were denoted as 2SO4 solution, where a Pt sheet, Ag/AgCl, and photocatalyst-coated ITO glass were used as the counter electrode, reference electrode, and working electrode, respectively.The crystal phases of the samples were carried out using a powder X-ray diffractometer . The scanning electron microscopy (SEM) image was obtained using a HITACHI S-3400 N microscope. The images from transmission electron microscopy (TEM) were acquired from a TECNAI F-30 microscope . The UV-visible spectra of the samples were obtained on a PerkinElmer, Lambda 750. The photoluminescence (PL) spectra were obtained using FL3C-111 (HORIBA Instruments Inc.). The x-ray photoelectron spectroscopy (XPS) measurements were acquired using an ESCALAB 250Xi spectrometer. For the photoelectrochemical (PEC) tests, photocurrent responses, electrochemical impedance spectra (EIS), and Mott–Schottky plots were performed on a Bio-Logic VSP-300 potentiostat in a 0.1 M Na2 production activities were measured on a closed-circulation apparatus. Typically, the photocatalyst (40 mg) was suspended into a 0.2 M Na2S/0.35M Na2SO3 aqueous solution (60 ml). The mixture was then irradiated under a 300 W Xe lamp . The amount of H2 was determined using gas chromatography (GC-7920) using a thermal conductivity detector. After the H2 in the previous cycle was fully removed, the cycling tests for the photocatalytic H2 production were carried out under identical conditions.The photocatalytic H2  for the conversion of Ag/AgCl to the reversible hydrogen electrode (RHE) scale. The positive and negative slopes of CdS and NiO were observed, corresponding to n-type and p-type semiconductors, respectively. Furthermore, based on the Mott–Schottky plots, the conduction band (CB) of CdS and the valence band (VB) of NiO were estimated to be ~−0.58 and 2.06 eV, respectively (Qiu et al., As depicted in A possible photocatalytic hydrogen evolution mechanism of the 2D/1D NiO/CdS heterojunction is proposed in −1 g−1, which is more than eight times higher than that of the CdS nanorods. Moreover, the NiO/CdS composite displays excellent stability over four cycles of photocatalytic hydrogen production. According to the results from the PL, the TRPL, the PEC, Mott–Schottky plots, the improved photocatalytic hydrogen evolution performance was mainly attributed to the efficient separation of charge carriers caused by the formed p-n NiO/CdS heterojunction. This study provides new opportunities in improving photocatalytic activity by constructing 2D/1D p-n heterojunction photocatalysts.In summary, the CdS nanorods were uniformly anchored onto the NiO nanosheets through a solution-phase hybridization method. The 2D/1D NiO/CdS heterojunction photocatalyst unveiled efficient photoactivity toward hydrogen production under visible light. The resultant 5% NiO/CdS exhibited a photocatalytic hydrogen production rate of 1,300 μmol hThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LW and DZ planned the experimental work, wrote the manuscript, and helped in the analysis. ZX, QZ, and HZ methodology, formal analysis, and data curation. DZ, TF, and YW review, editing, and funding acquisition. All authors contributed to the article and approved the submitted version.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.


Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines  from predicted annotation features  to the metabolic network-based analysis of symbiotic communities . While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases. Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. These bacteria are important for the brown algal host e.g., by promoting its growth and by providing defenses against pathogens  platform . Both the list of producible metabolites and the selected microbial communities were compared for all tested annotation pipelines. The detailed description of the code and data used for the above steps is available on GitHub: https://github.com/ElhamKarimi/Metabolic-predictions_different-Pipelines.The SBML files were utilized to predict potential metabolic complementarity between the algal host and the bacteria using the MiSCoTo tool . Based o2-acceptor, H2-donor). Finally, for each graph, strongly connected components were found with the Networkx strongly_connected_components function. Then, for each metabolic network, the ratio between the size of the largest strongly connected component and the total number of metabolites was computed.To gain insight into the quality of metabolic networks generated based on the different pipelines, dead-end metabolites were computed for each pipeline using Memote . FurtherIn addition to these indirect measures, we randomly chose 100 reactions that were exclusively found in one pipeline (20 per pipeline) for manual curation. Protein sequences of the bacterial genes that led to the prediction of these reactions were aligned with the Swissprot/Uniprot database using blastP and were then classified as high confidence , low confidence , or false .All statistical analyses were carried out using RStudio (v.1.1.463). Differences in the number of annotation features per pipeline were determined using an ANOVA followed by a Tukey’s HSD (honestly significant difference) test, considering each genome as a replicate. Nonmetric multidimensional scaling (NMDS) was performed on a presence/absence matrix comprising all reactions for each genome/annotation pipeline. The Bray-Curtis index was used as a measure of quantification of compositional dissimilarity between genomes annotated by different pipelines. To verify if the annotation pipeline had an impact on the reaction, an ANOSIM test (number of permutations = 9999) was performed using the Vegan package . UpSet d81 bacterial genomes were annotated using the Prokka, RAST, IMG, MaGe, and DFAST pipelines, and the number of predicted features (Coding Sequences (CDSs), Enzyme Commission (EC) numbers, hypothetical proteins) are shown for each genome and pipeline in Prokka predicted more EC numbers than the other pipelines, regardless of the e-value cutoff used. The precision of the predictions was similar in Prokka, IMG, MaGe, and DFAST with 85, 88, 84, and 85% of all EC numbers being complete. The only outlier was RAST, which provided only complete EC numbers . We did,Actinobacteria by DFAST were lower compared to other pipelines, and CDS prediction for Firmicutes by IMG were slightly higher. Finally, we examined three indirect measures of quality for the network reconstructions: the percentage of dead-ends, i.e., metabolites which can be produced but are not consumed; orphan metabolites, i.e., metabolites that are consumed but not produced in the metabolic networks; and the ratio between the size of the largest strongly connected components and the total number of metabolites, a direct estimate of the “connectedness” of the network. The percentage of dead-end metabolites was similar across all pipelines: 30.35% for Prokka, 29.66% for IMG, 29.17 for DFAST, 29.02% for MaGe, and 28.4% for RAST. The percentage of orphan metabolites was also similar: 30.63% for Prokka, 30.49% for IMG, 29.81% for DFAST, 30.37% for MaGe, and 29.48% for RAST. Lastly, this was also true for the ratio between the size of the largest of strongly connected components and the total number of metabolites: 26% for Prokka, 28% for IMG, 29% for DFAST, 27% for MaGe, and 29% for RAST.In terms of predicted metabolic reactions, IMG predicted the highest numbers (4.2% more than average) closely followed by Prokka with default settings (3.1%), while DFAST annotations resulted in the fewest reactions (7.7% fewer than average). Similar patterns were also obtained when running the analyses separately for the different bacterial phyla , except p < 0.0001 in both cases), but confirm that the effect was stronger for phylum (R = 0.93) than for annotation pipeline (R = 0.41).To determine how similar networks based on different annotation pipelines were, the Bray-Curtis dissimilarity, a unidimensional distance measure between two matrices or networks, was calculated for all network comparisons. Non-Metric Multidimensional Scaling (NMDS) was then used to display the resulting distance matrix in the two-dimensional space. In the resulting graph  the distTo further explore the differences in the metabolic network reconstructions and to highlight the specificities of each annotation pipeline we aimed to determine the conserved and unique content between the genome annotations from these different pipelines. This was done using the UpSetR package. Besides generating lists of reactions specific to different subsets of pipelines, this package also generates an UpSet diagram , which dTo get an overview of the dominant metabolic functions specific to the networks based on the different pipelines, we examined pipeline-specific metabolic reactions at the level of the pathways they participate in. In total, 180 pathways comprised the reactions specific to Prokka, and among them, 28 were constituted of >50% Prokka-specific reactions, including nitrobenzene degradation (PWY-5637) I, taurine degradation I (PWY-1263), albaflavenone biosynthesis (PWY-5887), and novobiocin biosynthesis (PWY-7287). Similarly, for MaGe the specific reactions could be associated with 127 pathways, and 11 were constituted of >50% specific reactions, including nitroethane degradation (PWY-5355), sulfite oxidation I (PWY-527), and sulfite oxidation IV (PWY-5326). For RAST 99 pathways including 6 pathways >50% complete presented the specific reactions, e.g., acetaldehyde biosynthesis II (PWY-6330) and trypanothione biosynthesis (TRYPANOSYN-PWY). IMG-specific reactions represented 52 pathways with only 4 >50% complete, including tRNA splicing II (PWY-7803) and aldoxime degradation (P345-PWY). Finally, reactions predicted specifically by DFAST corresponded to 42 pathways with only Nitrogen fixation II (PWY-7576) and Fatty acid biosynthesis (PWY-5970) being >50% complete .To evaluate the accuracy of the annotation pipelines, manual curation of 20 randomly selected reactions that were unique to each pipeline was carried out. DFAST has the highest number of high confidence assignations (10) followed by Prokka and MaGe (6), IMG(4), and RAST(1). RAST, on the other hand, did not produce any annotations that could be clearly identified as false, followed by Prokka (1), DFAST (2), IMG (3), and MaGe (3). In Prokka, increasing the stringency of the e-value cutoff did not affect the proportions of high confidence, low confidence or false reactions. In DFAST, although the correct reaction was associated with the gene, we observed discrepancies between the product name of genes and the EC number associated in 3 cases. In MaGe and IMG we found 1 and 3 predicted proteins without any similarity in the database and classified them as low confidence .To assess how these differences in the draft metabolic networks impacted the function of the predicted metabolism of algal–bacterial holobionts, we next examined the list of metabolites that could be produced by the algal metabolic network when combined with the 81 draft bacterial networks for each annotation pipeline . As shown in Pseudomonas 9AG, was included in minimal communities only when they were calculated from merged data from all annotation pipelines. Most strains were jointly selected by two or more pipelines, but even Prokka and IMG, for which minimal communities were based on the smallest subset of bacteria  had only six strains in common. However, we also found commonalities between the selected communities: all communities comprised at least one representative of the Actinomycetales (Actinobacteria), Bacillales (Firmicutes), Rhizobiales (Alphaproteobacteria), Sphingomonadales (Alphaproteobacteria), and Pseudomonadales (Gammaproteobacteria). Lastly, one strain, Maribacter strain 151 , was consistently selected in all data sets regardless of the annotation tool  to produce the highest proportion of high-confidence prediction, as well as the most connected networks in terms of dead-end metabolites. The overall high proportion of dead-end metabolites can be explained by the facts that (i) during the reconstruction process we discarded reactions without genetic support (gene association), and that (ii) metabolic networks underwent no or very little curation. Furthermore, the genomes analyzed here belong to non-model organisms, and hence their metabolism is more difficult to reconstruct than in well-established models.Given these differences, a key question is how reliable functional analyses based on these draft metabolic networks are, and how much their results change according to the annotation pipeline employed. Here our results may seem contradictory at first: on one hand, variability at the level of producible metabolites was low; on the other hand, during the selection of microbial communities based on metabolic complementarities, even these small differences in the draft metabolic networks resulted in largely different consortia. These small differences, whether they are erroneous or missing annotations, accumulate across the 81 annotated genomes and are particularly likely to impact the overall set of producible metabolites and hence community selection, as any possible cooperation with an added value to the host is selected. Indeed, although small, differences in the producible metabolites require complementing the bacterial community with strains that specifically enable the production of these metabolites. Overall, however, the low number of pipeline-specific metabolites indicated that all of the selected consortia were functionally similar. Furthermore, we have shown that a given minimal bacterial community (in our case one generated with Prokka data), also yields similar producible metabolites regardless of the annotation pipeline used for the generation of the metabolic networks. This means that the different selected bacterial consortia are likely able to fulfill the same or similar metabolic roles in our metabolic model. This phenomenon has previously also been described in natural alga-associated bacterial consortia  and was Regarding the use of metabolic complementarity as a criterion to select microbial communities, this implies that, as also confirmed experimentally by Another application of metabolic complementarity using different annotation pipeline could also be in the context of metagenome analysis, on Metagenome Assembled Genomes (MAGs) of uncultured bacteria. In the long run, continued and extensive experimental validation of bioinformatic predictions in both a culture and a metagenomic context will be key to evaluate and refine the pipelines  and need10.7717/peerj.11344/supp-1Supplemental Information 1The list of genomes used in the study and their properties (S1), the parameters used for genome annotation (S2), the EC numbers (S3) and reactions (S4) predicted in each genome/annotation pipeline, the pipeline-specific reactions (S5) and pathways (S6), the results of the manual curation for 100 reactions (S7), the metabolites producible by the cooperation of algal network and bacterial networks with each of the pipelines , and the results of the detailed outcomes of the ANOVA test corresponding to Fig. 1 (S10).Click here for additional data file.10.7717/peerj.11344/supp-2Supplemental Information 2The graphs show the deviation of each genome annotated with everyone pipeline from the mean across all pipelines in %.Click here for additional data file.


The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO Howeverproducts , such asproducts , ethylenproducts , ethanolproducts , or omegproducts .2 limitations , allows for the characterization of in vivo fluxes of central metabolism in photosynthetic organisms , with the goal of capturing light-limited, light saturated and light-inhibited growth , a “pseudo-flux control coefficient” (PFCC), To further quantify the relationship between metabolic fluxes and individual enzyme abundances, the Pearson correlation coefficient and associated reaction . Based oi, around a reference state (–2 s–1 steady state. The non-linear fitting method (Synechocystis (p > 0.1), as it was not possible to perform any accurate non-linear fitting between J and E.In metabolic control analysis, a flux control coefficient, ce state . Typicalce state , and do g method  was usedg method , as theyhocystis . Intereshocystis , meaninghocystis , p > 0.113C labeling kinetics wherein downstream metabolites had higher 13C labeling than their precursors at the steady state condition. A proposed explanation for this phenomenon is that CBB cycle metabolites may be subjected to metabolite channeling, a phenomenon where enzymes are organized into complexes that pass metabolites to each other, thereby potentially increasing the local concentration resulting in higher enzymatic reaction rates , and significantly higher when light was either limited or inhibiting growth.Our results reinforce these observations, as we consistently found lower and RUBP . The preand RUBP  (Ma et aand RUBP . In gene–1 h–1 (assuming 0.0412 mol C gDW–1 based on the WT biomass equation). In all tested conditions, as expected, the majority of carbon (∼95%) was fixed from RuBisCO activity as opposed to other CO2 incorporating reactions such as PEP carboxylase. In general, it is difficult to statistically differentiate between small CBB cycle fluxes due to relatively large error bounds   , of whiclass II) . PKT catlass II) . The ballass II) .Synechocystis 6803, but the use of proteomics over transcriptomics represents an iterative improvement. The use of proteomics and fluxomics together presents an opportunity to examine correlations between fluxes and enzyme abundances. Based on statistical correlation analysis  between those conditions despite a similarly labeled RUBP   . To a lectively) . SimilarBacillus subtilus to enhance N-acetylglucosamine synthesis  expression was relatively insensitive to light level, a result corroborated in a recent proteomic study  and the small protein CP12, forming a multi-enzyme complex that appears to be conserved in both cyanobacteria and plants . This coactivity . Of thesr plants . Curiousic study . This coproteins .During light-inhibiting conditions, the production of radical oxygen species becomes significant and may promote PRK/CP12/GAPDH formation similarly to a low light or dark condition as reflected by the lower growth rate. Therefore, under light-inhibiting conditions where oxidative stress could be high, the formation of PRK/CP12/GAPDH complex can be an additional way to explain the reduced flux through PRK and GAPDH. The PRK/CP12/GAPDH complex has much lower activity as compared to the dimeric PRK or GAPDH alone . Based oSynechocystis central metabolism. In comparison with earlier INST-MFA flux maps performed under several different light conditions  was provided by a cold LED light . The light spectra is provided as a 730 0.1 to 0.7. 13C was introduced to the bioreactor after OD730 reached at least 0.6.PC media  with no 13C into cellular metabolism  were removed and quenched at separate time intervals  into 40 mL pre-chilled 60% methanol in a dry ice-ethanol bath and rapidly pelleted at 8000 g, −20°C and then extracted. The MATLAB-based software INCA v1.8  allows for estimation of flux distribution throughout photoautotrophic central carbon metabolism by tracking the transient incorporation of tabolism . NaH13CONCA v1.8  was usedNCA v1.8 . Flux es4OH (2 and then reconstituted in 60 μL ultrapure water. An QTRAP 5500 (AB Sciex) linked to HPLC (Shimadzu) was used to identify central carbon metabolites using an established ion-pairing method (Pellets were extracted using 500 μL chloroform/methanol (3:7 v/v) under −20 °C for 2 h followed by two rounds of 500 μL 0.2 M NH4OH . The uppg method  using a Immediately prior to labeling experiments, a 20 mL aliquot of culture was withdrawn and pelleted for use in non-targeted proteomics. Cell pellets were washed twice in 20 mM PBS and then stored at −80°C until sample preparation for proteomic analysis. Cell pellets were re-suspended in 400 μL of 100 mM ammonium bicarbonate containing 1 mM phenylmethylsulfonyl fluoride and homogenized at high pressure  at 6500 rpm for 90 s (Bertin Technologies SAS). Homogenate was transferred to new tubes and centrifuged at 13,500 rpm for 15 min at 4°C. The supernatant was transferred to a new tube and treated as a soluble fraction, and the pellets were treated as insoluble fraction. Proteins in the soluble fractions were precipitated overnight at −20°C using 4 volume of cold (−20°C) acetone and precipitated proteins were pelleted by centrifugation at 13,500 rpm for 15 min at 4°C. The soluble and insoluble fractions were dissolved in 8 M urea at room temperature for 1 h with continuous vortexing. The protein concentration was determined by Bicinchoninic Acid BCA assay and 50 μg of the protein  was used for proteomics sample preparation. Samples were incubated in 10 mM dithiothreitol at 37°C for an hour for reduction followed by incubation in 20 mM iodoacetamide for an hour at room temperature in the dark for alkylation. Samples were digested using Trypsin/LysC protease mix at 1:25 (enzyme to substrate) ratio at 37°C. After 3 h of digestion, additional Trypsin/LysC protease mix was added at 1:50 (enzyme to substrate) ratio and digestion was allowed to proceed overnight at 37°C. Peptides were desalted using C18 micro spin desalting columns . Eluted peptides were dried and re-suspended in 3% acetonitrile/0.1% formic acid to a final concentration of 1 μg/μL and 1 μL (1 μg) was loaded used for LC-MS/MS analysis.Peptides were analyzed in an Dionex UltiMate 3000 RSLC nano System coupled on-line to Q Exactive Orbitrap High Field Hybrid Quadrupole Mass Spectrometer  as described previously . BrieflySynechocystis sp. PCC 6803 database downloaded from the NCBI1. We edited the following parameters for our search: precursor mass tolerance of 10 ppm; enzyme specificity of trypsin/Lys-C enzyme allowing up to 2 missed cleavages; oxidation of methionine (M) as a variable modification and carbamidomethylation (C) as a fixed modification. False discovery rate (FDR) of peptide spectral match (PSM) and protein identification was set to 0.01. Proteins with LFQ >0 and MS/MS  ≥2 were considered as true identification and used for further downstream analysis. In total, 1,202 proteins were detected in at least one of the four tested conditions.LC-MS/MS data were analyzed using MaxQuant (version 1.6.3.4) against the 2  intensities were used. The intensities of the soluble and insoluble fractions were combined for each replicate. InfernoRDN2  was usednditions .https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp), under accession number MSV000087721.The original contributions presented in the study are publicly available. This data can be found here: the MassIVE repository (NY designed and conducted the isotopic labeling experiments, analyzed all the data, and wrote the manuscript. UA conducted the proteomic study including sample preparation, data acquisition, and analysis, and edited the draft manuscript. JM assisted with the experimental design, evaluated the experimental results, edited the manuscript, and acquired funding. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.