Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle.  Methane is a major component of natural gas and a potent greenhouse gas. As a result, methane has significant economic value and environmental importance, particularly as global warming concerns escalate worldwide. The last few decades have seen increasing methane emissions, with methane concentration in the atmosphere increasing substantially since pre-industrial times, from 722 ppb to 1803 ppb , while aMethanomicrobials and Methanosarcinales)  clusters provided channels for electron transfer -hydrogenase complex (HdrABC-MvhAGD) from transfer . In M. n cluster . To valiM. nitroreducens was not able to grow. The incapability to grow was caused by the unbalanced ferredoxin redox cycle, as no oxidized ferredoxin generation mechanism existed in the network. In addition, the large potential difference between ferredoxin (E0′ = −520 mV) and CoM-SH and CoB-SH (E0′ = −143 mV) would cause waste in ∆G potential. If the electron confurcation was used only to overcome the ∆G barrier of heterodisulfide reductase, we could also consider using F420 reduction to drive CoM-S-S-CoB oxidation without confurcation.However, under this variation, the model of Though it was more thermodynamically favorable, the model would not be stoichiometrically feasible due to ferredoxin imbalance. If a separate reaction of ferredoxin-F420 oxidoreductase (R3) was added, it could help. However, this reaction has not yet been identified.This represents a true stoichiometric decoupling of two confurcation half-reactions. However, the ∆G of the half-reaction was highly positive (approximately 90 kJ/mol) and probably infeasible. Thereby, after comparing with the possible reactions in the Hdr-Frh complex, the electron confurcation was demonstrated to be an essential mechanism in the nitrate-driven AOM. Model simulations showed that the electron transfer direction was consistent with the hypothetical confurcating pattern, and the growth yield predicted by this condition was also in accordance with the experimental results shown in Haroon (2013) .M. nitroreducens has been reported to be extremely low, and only about 1 percent of the consumed methane is utilized for biomass synthesis [To further investigate the energetics of the metabolism, we performed flux balance analysis to simulate fluxes for the entire network. The model was constrained with experimental data from methane and nitrate uptake . Simulatynthesis .+ while transferring electrons from menaquinone to the main redox cofactor F420. One more H+ was proposed to be driven by Fqo compared with F420H2: methanophenazine oxidoreductase (Fpo), as the higher potential difference between menaquinone (E0′ = −80 mV) and F420 (E0′ = −380 mV) than methanophenazine (E0′ = −165 mV) and F420 could raise the ion-pumping ability [+ and H+ gradients were co-utilized by ATP synthase with the optimal Na+/H+ stoichiometric ratio updated in Methanosarcina [+/H+ antiporter Mrp assisted in optimizing the ATP synthase efficiency.In anaerobic methane oxidation, the energy-producing process has already been suggested to be chemiosmotic coupling, as no other obvious oxidative phosphorylation methods in reverse methanogenesis were known. Hence, these membrane-bound electron transport enzymes were essential in connecting the whole process of nitrate-driven methane oxidation with the chemiosmotic energy conservation system. Their capability of translocating protons or sodium ions across the membrane constituted chemiosmotic potential and generated ATP synthesis. In the metabolic network , the app ability ,46. Besiosarcina , and a NMethanosarcinales which also involved two classes of heterodisulfide reductase in the networks [Using FBA, a quantitative prediction of flux distribution was obtained, which discovered important unknown activities of various enzymes in energy conservation. Prominently, the flux value of the membrane-bound HdrDE was much smaller than the cytoplasmic HdrABC complex in this reverse methanogenesis pathway  comparednetworks ,35. The However, the primary cause of HdrABC dominance was not the lack of Rnf, but the difference in bioenergetic efficiencies. In fact, HdrABC could even give an energy-conserving advantage over the Rnf complex in nitrate-driven AOM. To compare the energetic efficiencies of HdrABC and Rnf, under the assumption that the cell seeks to maximize energy production under a steady state, the energy production mechanism was simulated by maximizing ATP synthesis .oxi for methane oxidation was both supplied by cofactor F420 reduction. In the F420 redox cycle, cytoplasmic HdrABC and the two successive F420-dependent steps in central reverse methanogenesis both generated high flux F420H2 according to the model, except for the F420-dependent NADP reductase (F4NR), which catalyzed a small amount of cofactor F420 oxidation with NADP+ for anabolic activities. The majority of reoxidation was processed by Fqo, accompanied by 13.2 protons being pumped out of the cell. With 17.6 protons translocated through the Nar/cytochrome complex and 2.2 Na+ pumped into the cell catalyzed by Mtr, an ion gradient of 28.6 H+/Na+ was driven by this process. The HdrABC available nitrate-driven AOM was predicted to yield 7.15 ATP theoretically via the model simulation. However, the alternative hypothesis with a pseudo-Rnf reaction instead of HdrABC was suggested to form a different energy-conserving mechanism. As the potential difference between ferredoxin (E0′ = −520 mV) and menaquinone (E0′ = −80 mV) was large, the Rnf complex was supposed to be an efficient enzyme translocating four sodium ions across the membrane when catalyzing ferredoxin oxidation by menaquinone. Under the hypothesis of the ∆hdrABC mutant, our model suggested that the membrane-bound HdrDE carried all the CoM-S-S-CoB oxidation activity, which caused 2.2 H+ to be pumped into the cell, resulting in a directly opposite effect on ATP synthesis. The flux value of Fqo also decreased under the ∆ hdrABC mutant, as no new cofactor F420 could be reduced via CoM-S-S-CoB and ferredoxin oxidation, which resulted in a lower proton gradient. The Nar/cytochrome complex and Mtr enzyme translocated the same amount of protons, as the redox balance in the quinone pool was constant. Thereby, though the sodium-pumping Rnf was responsible for energy conservation and oxidized ferredoxin regeneration, this whole hypothetical process could only gain 23.2 H+/Na+ ion gradient and was predicted to generate 5.5 ATP. Approximately one less ATP was synthesized than HdrABC variant metabolism. Therefore, even if the Rnf is available in the network for Fdoxi regeneration, the metabolism was still predicted to carry flux through HdrABC  and homolytically cleaved. According to the structural data, the catalytic mechanism of CoM-S-S-CoB reduction occurred via a one-by-one electron transfer to successively release CoB-SH and CoM-SH . ConsequIn the process of CoM-SH/CoB-SH oxidation, the two heterodisulfide reductase classes not only generated two energetic mechanisms but also resulted in two different electron transfer modes. In the HdrDE-centred electron transfer route, the electrons released from Mcr and Mtr, which flow via coenzyme B and coenzyme M redox loops, were directly transferred to the in-membrane electron carrier, menaquinone, via membrane-bound HdrDE. Subsequently, the electrons transmitted by a quinone-loop could be accepted by nitrate A. The cyG = −503 kJ mol−1) [To reverse the canonical methanogenic pathway, AOM must be thermodynamically feasible. This is achieved by coupling suitable terminal electron acceptors such as nitrate, sulfate, or iron to achieve thermodynamic feasibility. When nitrate is the only terminal electron acceptor driving AOM, the change in the Gibbs free energy is exergonic (∆J mol−1) . HoweverM. nitroreducens, as illustrated in G of the reactions are shown in To assess the thermodynamic feasibility of the electron flow, we explored options for the AOM electron transfer chain in G value shows that, though the initial process of oxidizing methane to methyl-H4MPT in AOM was endergonic, the reduction of nitrate was sufficient to drive the endergonic reactions. We speculate that the endergonic methane oxidation reactions (Mcr and Mtr) in other methanotrophic archaea might follow a similar thermodynamic path. To further analyze the thermodynamic feasibility of reverse methanogenesis, we suggest the use of a thermodynamic MFA (tMFA) [The negative ∆A (tMFA) .Candidatus Methanoperedens nitroreducens strain ANME-2d from NCBI (NCBI Reference Sequence: NZ_ JMIY01000000). The genome sequence was annotated using the RAST server on default settings [https://kbase.us/, accessed on 11 July 2020) [https://modelseed.org/, accessed on 11 July 2020) [The process of metabolic network reconstruction thoroughly followed the main steps listed in Thiele and Palsson (2010) , we begasettings , and thely 2020)  and Modely 2020) . The draly 2020) , Bio-Cycly 2020) , BRENDA ly 2020)  and Uniply 2020) . The dirly 2020)  and liteM. nitroreducens ANME-2d was constructed, including 684 unique metabolites and 813 metabolic reactions. A complete table containing all the reactions, metabolites and biomass compositions is available in https://github.com/computer-aided-biotech/iMN22HE (accessed on 8 February 2022).Finally, the genome-scale metabolic model of M. nitroreducens metabolic network were connected by the stoichiometric matrix S (m*n), where m is the number of metabolites and n is the number of reactions. In FBA, the cellular system was assumed to be under pseudo-steady-state growth and could be represented by the equation:v (n*1) is a vector of reaction flux. Upper and lower bounds of flux through individual reaction were imposed as additional constraints as follows:The model simulation was performed using flux balance analysis (FBA), an established technique applied to predict phenotypes for metabolism reconstruction . ReactioiMB745 model [−1, based on the previous model of Methanococcus maripaludis iMR539 [https://www.gurobi.com/, accessed on 9 October 2020) using COBRA Toolbox 3.0 [To find feasible flux distributions that simulate the likely physiological conditions, FBA used linear programming (LP), which is subject to mass balance and flux constraints dealing with an optimization problem. In this paper, the model growth was maximized by maximizing both ATP demand and biomass production. The biomass equation formulation was adopted from the 45 model , and lim45 model . The gros iMR539 , as the lbox 3.0  in Matlalbox 3.0 ; a map oThe standard Gibbs free energy of each reaction in methanogenesis, including the canonical reverse methanogenesis and electron transport chain, was calculated based on the published standard midpoint potentials of redox couples in standard conditions . The equF is the Faraday constant, 0.09648 kJ/eV, and n is the number of moles of electrons transferred in the reaction. The Gibbs free energy of the reactions in the reverse methanogenesis pathway in M. nitroreducens are presented in


This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of 13C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of 13C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed. Then, we introduced the components of fluxomics, including the modeling framework, experimental measuring technique, and optimization techniques. Finally, the application of 13C-based metabolic fluxomics have evolved into a large family of diverse methods as shown in Recently, In qualitative fluxomics, an isotope-labeled tracer is incorporated into the metabolic system, leading to variation in the isotopic pattern of the metabolites . Qualita13C measurements by dedicated machine learning predictors  method assumes that the labeled fraction of the metabolite pool changes exponentially during the labeling process. As long as the pool size is accurately measured, this method can estimate the absolute flux through sequential linear reactions according to the kinetic elution equation . KFP is nogaster .13C-MFA can accurately determine the absolute value of the flux of the global metabolic network  of the corresponding elementary metabolite unit (EMU) fragment with n carbon atoms. Yn is a matrix similar to Xn, in which its rows are the vectors of the ILM of the corresponding input substrate and/or the calculated EMU fragment with 1 ∼ (n-1) carbon atoms. In the objective function, x is the vector of the isotope-labeled molecules in X1,…, Xn, and Mx is the experimental counterpart to x. An and Bn represent the system matrix determined by the corresponding metabolic reaction topology and atomic transfer relationship. Σe represents the covariance matrix of the measured values.13C-MFA as depicted in 13C-MFA method first involves a carbon labeling experiment  and nuclear magnetic resonance (NMR) spectroscopy    in According to formula (1), metabolic flux analysis can be divided into three categories, stationary state(SS-MFA) , isotopiNST-MFA)  and metaXi/dt of the constraint conditions in formula (1) are equal to 0 and v is constant. The method is suitable for systems in which metabolic flux and isotope labeling state do not change with time  are equal to 0 and v is constant. It can be applied to systems in which the metabolic flux and metabolite concentration do not change with time while the isotopic labeling fraction changes with time  are equal to 0 and v is a time-varying function. This method fits well with systems in which the metabolite concentration, metabolic flux and isotope labeling fraction all change with time . The same 13C labeling experiment can be simulated with different ILMs  determines the objective function. The appearance of 13C at different positions classifies a molecule as different isotopic molecules. Differences in the physical and chemical properties of 13C and 12C are the basis for discriminating these isotopic molecules. They have at least two differences. One is that 13C has a half integer nuclear spin, so it can be detected by NMR    and 3D tC-TOCSY) . This setrometry .Gas chromatography-electron ionization (GC-EI)-quadrupole can quantify the mass isotopomers of amino acids , organicFlux estimation solves the entire optimization problem of formula (1). This process depends on local or global optimization methods. As a heuristic method, an evolutionary algorithm was employed early in the optimization of S-MFA . Some ofA hybrid algorithm combining the trust-region method and sequential quadratic programming (SQP) facilitates numerically stable and accurate flux estimation of S-MFA . The LevThe sum-of-squares residual error (SSE) between the measured and simulated data is the main form of the objective function in formula (1). AdditioSince metabolic flux analysis is a parameter estimation problem, there are uncertainties in the obtained parameters. Flux uncertainty analysis should become part of routine analyses. Local linear error propagation based on the chain rule is an earlier method for determining the confidence interval of flux values . In cont13C MFA has also been extended to solve metabolic networks at the genome scale by incorporating a large set of secondary metabolic reactions  to generate a new derivative of MFA, which can expand the scope of metabolic flux analysis or make it more accurate in some cases. Compartment-specific metabolic flux in mitochondria and cytosol can be quantified by a spatial-flux analysis with rapid subcellular fractionation and quenching of metabolism . A flux eactions . In addieactions . The flueactions . Gebresea system .13C data  and observes an increase in flux through the pentose phosphate pathway, with subsequent increases in gluconeogenesis and lipid biosynthesis pathway in Apolipoprotein E4 astrocytes (As outlined in ral cell . INST-MFral cell . Isotoperal cell . FR analral cell . By reviral cell . Patel etrocytes .Stable isotope metabolic flux analysis has been successfully used in applications from homogeneous cell systems to heterogeneous cell systems, even at the level of animal and plant organs. It has now become the gold standard for measuring metabolic flux values. Several technologies that it includes are also developing rapidly, so the metabolic fluxomics method is evolving into a family with different members that have higher accuracy, wider coverage, more application scenarios, and shorter time consumption.At present, the development of stable isotope metabolic flux analysis should focus on improving the temporal and spatial resolution. This requires the continuous introduction of updated detection technology.BT, MC, and LL drafted the manuscript. TS and ZZ conceived of the study. TS, ZD, and BR helped to draft the manuscript. All authors read and approved the final manuscript.


However, for sodium butyrate (SB), the improvement of the methylmalonyl-CoA and NADPH availability was a potential reason for the rapamycin overproduction. Further, the responsive metabolic pathways after chemical elicitor treatments were selected to predict the potential key limiting steps in rapamycin accumulation using a genome-scale metabolic network model (GSMM). Based on the prediction results, the targets within the reinforcement of the DHCHC and NADPH supply were selected to verify their effects on rapamycin production. The highest rapamycin yield improved 1.62 fold in the HT-aroA/zwf2 strain compared to the control.Rapamycin is a polyketide macrocyclic antibiotic with exceptional pharmacological potential. To explore the potential mechanism of rapamycin overproduction, the intracellular metabolic differences of three chemical elicitor treatments were first investigated by combining them with dynamic metabolomics and network analysis. The metabolic response characteristics of each chemical elicitor treatment were identified by a weighted gene co-expression network analysis (WGCNA) model. According to the analysis of the identified metabolic modules, the changes in the cell membrane permeability might play a key role in rapamycin overproduction for dimethyl sulfoxide (DMSO) treatment. The enhancement of the starter unit of 4,5-dihydroxycyclohex-1-ene carboxylic acid (DHCHC) and the nicotinamide adenine dinucleotide phosphate (NADPH) availability were the main functions in the LaCl Streptomyces hygroscopicus, Streptomyces iranensis, Actinoplanes sp. N902-109) [Rapamycin is a 31-membered polyketide macrocyclic antibiotic, which can be produced by several actinomycetes  , and lif902-109) . With it902-109) .Nowadays, the details of rapamycin biosynthesis have been clarified , and it Streptomyces tsukubaensis [In fact, some secondary metabolites are likely only synthesized under specific conditions. To explore the potential mechanism of secondary metabolite synthesis, therefore, some stress responses were applied to trigger the expression of secondary metabolic genes  . Some crubaensis . Similarubaensis , and theStreptomyces species, including S. hygroscopicus [S. tsukubaensis [S. coelicolor [S. lydicus [In recent years, metabolomics have acted as an effective strategy to directly discover the metabolic differences caused by environmental disturbances, such as carbon repression , salt stscopicus ,18,22, Subaensis , S. coelelicolor , S. lydi lydicus ,24, and 3, and sodium butyrate (SB)) were selected to investigate their effects on rapamycin accumulation. Subsequently, the metabolomics associated with different multivariate statistical analyses were applied to analyze the dynamic characteristics of the intracellular metabolism, including the key biomarker, and the potential key metabolic pathways and nodes. The potential key limiting steps were further identified in the above key metabolic pathways based on a genome-scale metabolic network model (GSMM) of S. hygroscopicus constructed by our group before. After the corresponding gene manipulation, these identified targets were verified in experiments. This strategy is not only helpful for a better understanding of rapamycin biosynthesis and its potential intracellular metabolic regulation but also provides valuable guidance on other secondary metabolite overproduction.In this work, three kinds of chemical elicitors 2SO4, and 1 g/L K2HPO4 and adjusted to pH 7.0 before autoclaving. After 10% (v/v) seed culture was added into the fermentation medium, the batch fermentation was performed at 28 °C, 220 rpm for 7 days. Except for DMSO, the rest of the chemical elicitors were first dissolved by distilled water. Then, all of the chemical elicitor solutions were filtered with sterile filtration (0.22 μm) before being used. All plasmids and strains used in the gene-engineered manipulation are described in E. coli cultivation.The parent strain of p before . The fer3, and sodium butyrate at high purity were of analytical grade and purchased from Tianjin Guangfu Technology Co., Ltd. . All of the antibiotics  used in gene manipulation were purchased from Sangon Biotech (Shanghai) Co., Ltd. . The standard of rapamycin was purchased from Sigma-Aldrich . All of the restriction endonucleases were purchased from Fermentas Inc. , and the rest of the enzymes were purchased from TransGen Biotech Co., Ltd. . All of the chemicals (chromatographic purity) used in metabolomics analysis were purchased from Sigma-Aldrich. Except for the above-mentioned chemical reagents, the rest of the chemicals were of analytical grade and purchased from Tianjin Bodi Chemicals Co., Ltd.  and Chengdu Kelon Chemical Reagent Co., Ltd. .DMSO, LaClStreptomyces DNA manipulations and transformations were performed using the standard protocols [aroA, tktB, and zwf2 were first amplified by PCR using primers of aroA-F1/aroA-R1, tktB-F1/tktB-R1, and zwf2-F1/zwf2-R1. The above PCR products were digested by NdeI-XbaI and cloned into pIB139 to generate pAROA, pTKTB, and pZWF2, respectively. In the construction of the vector pIB139-aroA/tktB (pAT), the genes of aroA and tktB were amplified using the primer pairs of aroA-F2/aroA-R2 and tktB-F2/tktB-R2, respectively. The aroA PCR product was first digested by NdeI-HindIII and cloned into pUC18 to generate pUC18-A. The plasmid of pUC18-A was further cut by HindIII-XbaI and linked with the tktB PCR product  to form pUC18-AT. Finally, the plasmid pUC18-AT was excised with NdeI-XbaI and cloned the two genes into pIB139 to generate pAT. Similarly, the plasmids of pUC18-AZ and pAZ were also constructed using the above methods. All of the overexpression plasmids  were introduced into the parental strain using the previous described methods [All rotocols . All of  methods . All of gdhA, the upstream flanking fragment of gdhA was first amplified by PCR using primers of gdhA-LF/gdhA-LR, and its downstream flanking fragment was further amplified using primers of gdhA-RF/gdhA-RR. Then, the above fused fragments were ligated into HindIII-EcoRI-digested pKC1139 to obtain the gdhA deletion vector p△GCDH. Transformation of p△GCDH into the parental strain was carried out as described before [gdhA deletion mutant was verified by the same methods used in the above single gene overexpression.To delete the gene d before . The putFor the combined gene overexpression and knockout manipulation, the vector pAROA was transferred into the HT-∆gdhA strain to generate HT-∆gdhA-aroA strain, using the reported methods . The pos4+ form), and phosphorus (PO43+ form).Approximately 15 mL of fermentation broth was used at each sample point for the following detection. Biomass was detected by the method of dry cell weight . Rapamyc3, and sodium butyrate (SB) at different time points were collected for intercellular metabolite analysis, respectively. The corresponding methods in the extraction and detection of intracellular metabolites were all consisted with the reported methods by our group [To explore the dynamic changes of intercellular metabolism under different chemical elicitor treatments, the fermentation samples treated with DMSO, LaClur group , includiS. hygroscopicus by our group before was used to predict the potential targets of enhancing rapamycin production in the identified metabolic pathways of metabolomic analysis [PHf (the ratio of weighted and dimensionless specific growth rate and specific rapamycin production rate) values of each target were compared based on the reported methods [The previous constructed GSMM model of analysis . Combineanalysis ,27. Durihttp://www.metaboanalyst.ca, accessed on 10 January 2021) was used for the pathway enrichment analysis of the metabolites identified by WGCNA [p < 0.05 was considered as significant. Four biological replicates were performed in the above detection.According to the previously reported method , all of by WGCNA . Signifiv/v), 161.16 mg/mL), followed by LaCl3  and SB . Interestingly, no obvious differences were observed in the biomass contents between the control and the treatment groups with the optimal concentration conditions. This indicates that the intracellular metabolism might be slightly disturbed under the current feeding strategy of all of the selected chemical elicitors.As an effective method in enhancing secondary metabolite production, chemical elicitors play important roles in the activation of cryptic pathways or large and complex regulatory networks . Therefo3 at 96 h (205.67 mg/mL), followed by DMSO  and SB , respectively  generated by the PLS-DA were identified, which could be recognized as the potential biomarkers under chemical elicitor treatments , SB (M2 and M3), and LaCl3 (M4) treatments, respectively  and Module 3 (11 metabolites) B,C. Takeubaensis . In factubaensis . Therefo3 treatment, only one metabolic module (Module 4) was identified, which contained 17 characteristic metabolites. Using MetPA analysis, three metabolic pathways were successfully enriched, including phenylalanine, tyrosine and tryptophan biosynthesis, PP pathway, and aminoacyl-tRNA biosynthesis (3 treatment.For the LaClynthesis . Interes4+ form), and inorganic phosphorus (PO43+ form) were set to 1.08, 0.132, and 0.0074 mmol/(g DCW•h), respectively. The experimental specific production rate of rapamycin (0.021 μmol/(g DCW•h)) was set to the lower bound of the rapamycin exchange reaction. Using these experimental constrains, the specific growth rate was predicted to be 0.0565 h−1, which was close to the experimental data (0.061 h−1).To ascertain the key limiting steps in the above enriched metabolic pathways, the GSMM model constructed by our group before was further performed to predict the potential targets. Under the current fermentation conditions, the parts of the basic fermentation parameters were first determined and set as the constrain conditions of the simulation . For exaPHf value were listed  would be beneficial for the synthesis of the start unit of rapamycin . In addition to NADPH, the intermediate metabolite  derived from the PP pathway is the direct precursor of the shikimate pathway. Therefore, the overexpression targets of r47 and r51 would achieve the same aims in improving the supply of the DHCHC and NADPH levels. Similarly, the knockout of the target r75  could directly increase the level of NADPH availability, which was confirmed in other antibiotics overproduction  [Among the selected metabolic pathways , the first 10 potential targets with a higher e listed , and therolimus) .For the rest of the predicted targets, the potential reasons for the higher rapamycin production can also be divided into two kinds of patterns. The first one is the regulation of the carbon flux distribution in the key metabolic nodes of PEP and pyruvate. For example, the inactivation of the targets r18 and r23 can decrease the branch metabolism of the above two metabolites. However, it would be beneficial for the accumulation of the intermediate precursors of rapamycin synthesized from these two metabolites . The other is to increase the level of pipecolate by enhancing the metabolic flux of aspartate (or lysine) biosynthesis  directly or by blocking the branched metabolic pathway of the lysine degradation . As the key precursor of rapamycin biosynthesis, the transformation of lysine is the only source of pipecolate biosynthesis. Therefore, the enhancement of aspartate metabolism  could improve the yields of rapamycin.PHf values clearly indicated that the inadequate supply of DHCHC and NADPH might be the key limiting steps in rapamycin biosynthesis. Therefore, the reinforcement of DHCHC and NADPH supply were first selected to verify their effects on rapamycin production by single or multiple gene manipulation. Among the single gene manipulation, the final rapamycin yields showed the significant improvement, to some extent, in all of the engineered strains compared with the control (112.59 mg/L)  with the highest 59 mg/L) . Furtherr-butyrolactones and N-acetylglucosamine). Therefore, SB (HDAC inhibitors), LaCl3 (rare earth elements), and DMSO (sulfo-compounds) were finally selected to investigate their effects on the intracellular metabolism during rapamycin accumulation.As a type of secondary metabolite synthesized by multiple precursors, the enhancement of its yield is seriously restricted by the complex intracellular metabolic regulatory system. Nowadays, the classical strategies of secondary metabolite overproduction are to strengthen the supply of the precursors and the gene cluster expression of secondary metabolism. However, the large-scale improvement of secondary metabolite yields  is still seriously restricted. An important reason is the insufficient understanding of the distribution and transformation in the intracellular metabolic fluxes during the processing of secondary metabolite biosynthesis. Therefore, in addition to the gene cluster of secondary metabolites, more attention on the global and pleiotropic regulators should be enhanced during the intracellular metabolism. Nevertheless, most of these regulators are often cryptic or exist with a silent/poor expression state under ordinary laboratory fermentation conditions. Therefore, how to wake and induce (or enhance) the expression of these cryptic or silent regulators plays a key role in the further improvement of secondary metabolite yields. In previous reports, chemical elicitors were applied to find novel antibiotics, activating the expression of silent or poorly expressed gene clusters and improving the biochemical product accumulation ,37. TherThe main goal of this work was to perturb the intracellular metabolism using different types of chemical elicitors, explore the rearrangement and transformation of the intracellular metabolic fluxes, and search the potential key limiting steps in rapamycin overproduction. According to the current results, the final rapamycin yields showed obvious improvements after all of the chemical elicitor treatments. The PCA score plots of the intracellular metabolomic datasets also suggested that the intracellular metabolism exhibited the significant differences in each chemical elicitor treatment. Meanwhile, several potential biomarkers  were identified, most of which displayed a close relationship with the precursor biosynthesis of rapamycin . Notably3 treatments. In fact, amino acid metabolism not only plays important roles in maintaining the normal cellular metabolic function but also provides the crucial precursors  in rapamycin biosynthesis. However, slight differences were also observed. For example, the pyruvate family  and aromatic amino acids  were significantly correlated with the SB and LaCl3 treatments, respectively. At the same time, the PP pathway was also enriched in the LaCl3 treatments. In the intracellular metabolism, the PP pathway was the main sources of NADPH and provided a key precursor  to participate in the shikimate pathway. Therefore, the above obtained information clearly suggested that the key metabolic nodes regulated by these two chemical elicitors were different in the intracellular metabolism, such as PEP for LaCl3 and pyruvate for SB. In the previous analysis, the important roles of these two key metabolic nodes was deeply discussed, as both controlled the metabolic fluxes within several precursor synthetic pathways of rapamycin. However, from the perspective of rapamycin formation, the regulation in intracellular metabolic fluxes existed in an obvious chronological order in the SB and LaCl3 treatments. Nowadays, it is clear that the formation of DHCHC from chorismic acid (the shikimate pathway) is the first step in rapamycin biosynthesis, which is further extended with malonyl-CoA or methylmalonyl-CoA by multiple successive polyketide chain elongation cycles. Notably, a total of seven methylmalonyl-CoA was required in rapamycin biosynthesis [3 treatment, the more important function is to regulate the start of rapamycin biosynthesis.Different to DMSO, amino acid metabolism was significantly enriched in both of the SB and LaClynthesis , which iBased on the above analysis, the current work further identified several potential key limiting targets within the enriched metabolic pathways using the GSMM model . After t


This photomixotrophic mode enables fast growth and high cell densities, opening perspectives for sustainable biomanufacturing. The model cyanobacterium Synechocystis sp. PCC 6803 possesses a complex architecture of glycolytic routes for glucose breakdown that are intertwined with the CO2-fixing Calvin-Benson-Bassham (CBB) cycle. To date, the contribution of these pathways to photomixotrophic metabolism has remained unclear.Cyanobacteria receive huge interest as green catalysts. While exploiting energy from sunlight, they co-utilize sugar and CO13C metabolic flux analysis of Synechocystis sp. PCC 6803 during steady state photomixotrophic growth. Under these conditions, the Entner-Doudoroff (ED) and phosphoketolase (PK) pathways were found inactive but the microbe used the phosphoglucoisomerase (PGI) (63.1%) and the oxidative pentose phosphate pathway (OPP) shunts (9.3%) to fuel the CBB cycle. Mutants that lacked the ED pathway, the PK pathway, or phosphofructokinases were not affected in growth under metabolic steady-state. An ED pathway-deficient mutant (Δeda) exhibited an enhanced CBB cycle flux and increased glycogen formation, while the OPP shunt was almost inactive (1.3%). Under fluctuating light, ∆eda showed a growth defect, different to wild type and the other deletion strains.Here, we developed a comprehensive approach for 13C tracer studies with GC–MS analysis of amino acids, sugars, and sugar derivatives, optionally adding NMR data from amino acids, is valuable to study fluxes in photomixotrophic microbes to detail. In photomixotrophic cells, PGI and OPP form glycolytic shunts that merge at switch points and result in synergistic fueling of the CBB cycle for maximized CO2 fixation. However, redirected fluxes in an ED shunt-deficient mutant and the impossibility to delete this shunt in a GAPDH2 knockout mutant, indicate that either minor fluxes  might exist that could provide catalytic amounts of regulatory intermediates or alternatively, that EDA possesses additional so far unknown functions. These ideas require further experiments.The developed approach, based on parallel The online version contains supplementary material available at 10.1186/s12934-022-01790-9. It therefore appeared important to integrate the complementary labelling information from sugars and sugar derivatives. This was likely due to the fact that the analytes glucose (from storage carbohydrates), glucosamine  and ribose (from RNA) represented the 13C labelling pattern of glucose 6-phosphate, fructose 6-phosphate, and ribose 5-phosphate, respectively, which are central hubs in the photomixotrophic metabolism of Synechocystis 6803 , passing the chi-square test at 95% confidence level) , indicating that CO2 was formed in metabolism via decarboxylating reactions. No flux through ED pathway toward 2-keto-3-desoxy-6-phosphogluconate (KDPG) was detected (0%). This was a big surprise, after it has been concluded that this pathway would serve as a previously overlooked glycolytic route in photomixotrophic Synechocystis 6803  glucose , (ii) 99% [3-13C] glucose , (iii) 99% [6-13C] glucose , and (iv) 99.9% [13C6] glucose , respectively.For isotopic 2 s−1) and resuspended in 1 mL fresh medium. An appropriate amount of this suspension was used to inoculate a pre-culture to an initial cell concentration of OD750 = 0.1. For photomixotrophic growth studies, the pre-culture was then incubated under light in 250 mL baffled shake flasks  on a rotary shaker . The in-built illumination panel at the ceiling of the incubator provided LED warm white light (50 µE m2 s−1) with an even distribution across the tray (± 6%). The translucent caps were derived from aluminum caps, routinely used in microbial cultivation for contamination-free gas exchange with the surrounding ambient atmosphere. They were constructed as follows. The aluminum caps were cut out in a circular shape so that the top cap had a round hole about 3 cm in diameter. The hole in each cap was then covered by a translucent foil  which was glued to the outer aluminum ring. During the exponential growth phase, cells were collected , washed, and used to inoculate the main culture to an initial cell concentration of OD750 = 0.1, which was then incubated as described above. In addition, heterotrophic growth studies were conducted, using the same set-up, except that all cultures were conducted in the dark. (n = 4). Growth studies of ∆gap2 were performed as previously described  = 0.261 × OD750.Cell growth was monitored spectrophotometrically at 750 nm. Hereby, culture samples with higher cell densities were diluted with medium to optical densities below 0.3 to ensure a liner correlation between cell concentration and optical density reading. In addition, cell dry mass (CDM) was quantified by gravimetric analysis. Cells were collected by filtration , washed once with deionized water, and dried at 80 °C until constant weight. The obtained correlation factor was CDM . The contained amino acids were converted into the corresponding at 80 °C . The mas13C metabolic flux analysis with Synechocystis sp. PCC 6803 , and transferred into 3 mm NMR tubes for analysis. The biomass hydrolysates were analyzed in duplicate by 1D 1H NMR, 1D 13C NMR, 2D ZQF-TOCSY NMR and 2D HSQC  glucose (left) and [U-13C] glucose (right). Figure S3. Computational evaluation of previous approaches, based on single isotope experiments and (mainly) GC/MS analysis of proteinogenic amino acids, for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803. The tested approaches had been applied for photomixotrophic flux analysis of Synechocystis, before the full network was known [44] or had focused on a subnetwork of the microbe [45]. A third approach had been derived to analyze heterotrophic Synechocystis sp. PCC6803 without an active CBB cycle [55]. There, the approaches analyzed for the achievable precision and accuracy to determine fluxes in a scenario with 0% (left), 5% (middle), and 50% (right) flux through the ED and the PK pathways. The show the outcome of a Monte-Carlo simulation that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. Displayed are key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. The color indicates the determinability of a flux parameter. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S4. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with low flux (5%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. Double, triple, and quadruple tracer studies were evaluated. The substrates shown here, were [1-13C], [3-13C], [6-13C], and [13C6] glucose for the following reasons. The combination of [1-13C] glucose and [6-13C] glucose well discriminated the fluxes through the EMP, the PP, and the ED pathway in glucose-grown pseudomonads, revealing a similarly cyclic pathway architecture as cyanobacteria [42]. Metabolization of [3-13C] glucose (based on the underlying carbon transitions) via the ED route should selectively lead to 13C label enrichment at the C1 of pyruvate (and amino acids derived therefrom), providing a sensitive readout, should this pathway be active. The use of [13C6] glucose appeared beneficial, likely because it helped to estimate the relative uptake of 13C sugar versus (non-labelled) CO2, as previously demonstrated for Basfia succiniciproducens, grown on sucrose under high rates of CO2 assimilation [22]. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S5. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with medium flux (25%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S6. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with high flux (50%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S7. Sensitivity of selected mass isotopomer ratios to a variation of individual flux parameters using alternative single 13C labelled glucose as input. The most sensitive change is highlighted. Figure S8. Goodness-of-fit for 13C metabolic flux analysis of Synechocystis 6863. The data reflect measured and model predicted (simulated) data for the best-fit solution: 388 mass isotopomers from amino acids, sugars, and sugar derivatives, measured by GC-MS (A) and on basis of 388 mass isotopomers from amino acids, sugars, and sugar derivatives, measured by GC-MS, plus 168 positional 13C enrichments, obtained by NMR (B). Figure S9. In vivo flux distribution of Synechocystis 6803 during photomixotrophic growth on glucose and CO2 determined by GC-MS and NMR based 13C metabolic flux analysis. Fluxes are normalized to the glucose uptake . The thickness of the arrows denotes the amount of flux. The errors for the fluxes reflect standard deviations, estimated by Monte-Carlo simulation. The anabolic fluxes into biomass are shown as triangles. The complete flux data set is given in Table S2, where also the 95% confidence intervals from the Monte-Carlo analysis are provided. GLC_ex extracellular glucose; G6P glucose 6-phosphate; F6P fructose 6-phosphate; DHAP dihydroxyacetone phosphate; GAP glyceraldehyde 3-phosphate; 3PG 3-phosphoglycerate; PEP phosphoenolpyruvate; PYR pyruvate; AcCoA acetyl coenzyme A; ICI isocitrate; 2OG 2-oxoglutarate; SucA succinate-semialdehyde; SUC succinate; FUM fumarate; MAL malate; OAA oxaloacetate; 6PG 6-phosphogluconate; KDPG 2-keto-3-deoxy-6-phosphogluconate; Ri5P ribose 5-phosphate; Ru5P ribulose 5-phosphate; X5P xylose 5-phosphate; S7P sedoheptulose 7-phosphate; E4P erythrose 4-phosphate; CO2_EX extracellular carbon dioxide;CO2 intracellular carbon dioxide. The flux estimation yielded an excellent quality of fit for the considered mass isotopomers of amino acids, sugars, and sugar derivatives and NMR-derived positional enrichments  for strains Δeda (A), ΔpfkAB (B), and Δxfp1/xfp2 (C). Figure S11. Evaluation of the light supply during cultivation in glass tubes with 3.5 cm diameter that were illuminated from the front and the back side and were mixed by air, bubbled from the bottom [37]. Simulating the light supply for this geometry, using the obtained Lambert-Beer correlation  and Δgap2. The Southern blot was performed in order to verify the completed segregation of Δgap2. The probe detected a fragment in the size of 4916 bp in the wildtype (WT) and of 780 bp in Δgap2 as expected. This result confirmed that Δgap2 was segregated and that no wild type copies were left. In addition, an unspecific fragment of about 4500 bp was detected in Δgap2 as well. Figure S13. Southern blots of wildtype (WT), Δxfp1 and Δxfp1/Δxfp2. Southern blots were performed with probes against xfp1 and xfp2 in order to check segregation of Δxfp1, Δxfp2, and Δxfp1/Δxfp2. The probe against xfp1 was expected to detect a fragment size of 1420 bp in the wildtype and of 2013 bp in Δxfp1 (top). The probe against xfp2 was expected to detect a fragment in the size of 970 bp in the wildtype and of 731 bp in Δxfp2 (bottom). Lanes 4 and 5 in the bottom gel, right from the three strains, are not relevant. The southern blots thus confirmed that Δxfp1, Δxfp2, and Δxfp1/Δxfp2 were segregated and that no wildtype copies were left. Table S1. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit was statistically acceptable. The variance-weighted sum of square residuals (SSR) was 377 and thus within the expected range  of the chi-square test at 95% confidence level. Table S2. Flux distributions in Synechocystis 6863 and related deletion mutants. The data represent the best-fit-solution for each strain and include the estimated fluxes (Mean), the standard deviation (SD) and the corresponding 95% confidence intervals . The 13C labelling data, considered for flux estimation, were taken from GC-MS and from GC-MS plus NMR analysis. The reaction numbers refer to the biochemical network model  and model simulated (sim) GC-MS mass isotopomer distributions of amino acids, sugars, and sugar derivatives plus positional enrichments from NMR analysis. Regarding GC-MS analysis, the specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. Data has been corrected for natural occurring isotopes. For NMR, the assessed carbon atom is given. The flux fit was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 583 and thus within the expected range  of the chi-square test at 95% confidence level. Table S4. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 Δeda. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 394 and thus within the expected range  of the chi-square test at 95% confidence level. Table S5. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 ΔpfkA/ΔpfkB. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 401 and thus within the expected range  of the chi-square test at 95% confidence level. Table S6. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 Δxfp1/Δxfp2. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 415 and thus within the expected range  of the chi-square test at 95% confidence level. Table S7. Primers used to construct a phosphoketolase double deletion mutant Δxfp1/Dxfp2 and a single gene deletion mutant Δgap2 from wild type. In addition, the corresponding annealing temperature (AT) is given. Table S8. Cellular composition used for metabolic flux analysis of Synechocystis 6863. The data for wild type (WT) were also used for the strains ΔpfkAB and Δxfp1/Δxfp2. For strain Δeda, the data reflect the increased glycogen content. Table S9. Biochemical reaction network for 13C metabolic flux analysis of Synechocystis 6803 including reaction stoichiometry, atom transition, and reaction directionality. F unidirectional (forward only) reaction; FR reversible reaction; B biomass. The reactions R1 (v1) to R34 (v34) refer to the carbon core network of the microbe (Fig.


Chlamydomonas reinhardtii, using time-course RNA-Seq data. We show that OVERLAY can compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. OVERLAY can also suggest possible ‘bottleneck’ proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discuss other TAG-overproduction strategies.Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Finding a reliable method to predict cell metabolism for expression data will greatly benefit metabolic engineering. We have developed a novel pipeline, OVERLAY, that can explore cellular fluxomics from expression data using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model (PC-model) by integrating protein and enzyme information into the metabolic model (M-model). Secondly, overlay the expression data onto the PC-model using a novel two-step nonconvex and convex optimization formulation, resulting in a context-specific PC-model with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or M-model alone. We apply the OVERLAY to interpret triacylglycerol (TAG) overproduction by  Chlamydomonas reinhardtii is the best-studied organism in terms of genome annotation and molecular mechanisms, making it the reference organism for studying algal lipid metabolism [C. reinhardtii, including its chloroplast, have also been developed by the research community, making its production of recombinant proteins easier than any other algae [C. reinhardtii has been utilized to make a wide variety of chemicals in the lab and industry. On the high value-added end, non-native proteins are expressed and produced in C. reinhardtii as pharmaceutics such as vaccines, antibiotics, and nutritional supplements [C. reinhardtii lipid and starch contents by up to 2.5-fold by relatively simple modifications [C. reinhardtii, using gene knock-out, heterologous expression, and triparental conjugation technique [aas, thus preventing fatty acid consumption for cellular acyl-ACP replenishment [C. reinhardtii to various medium conditions, such as nitrogen deprivation, acetate concentration, or light intensity, is a practical topic to increase TAG production further. Bogaert et al. showed that all biomass components including fatty acids increase in concentration per cell in response to supplementation with high acetate concentration [Microalgae have long been a promising class of organism as a synthetic biological chassis due to their high growth rate, efficient photosystem, and simplicity in cultivation. Because of its carbon fixation capability, it is also believed that algal products have less carbon dioxide emissions and are more sustainable in large-scale production . Within tabolism . Numerouer algae , 4. As aplements . This haplements . On the plements . Some reications . For exaications . Some soechnique . Specifiechnique . They alnishment . Moreoventration .Despite these findings, algal starch derivatives, lipid derivatives, and hydrogen are not yet economically feasible substitutes for fossil fuels on the market. Most of the experimental studies focus on modifying a few genes or adding a few chemical species into the medium without dramatically changing the cell from the wild type. It is a missed opportunity, as optimizing these new strains or potentially applying them in conjunctions can achieve a much higher yield with minor added costs. The optimization usually requires quantitative measurements from the phenotype, such as RNA-sequencing, proteomic data, and extracellular metabolomics, which are available from many existing studies. Developing an in-silico workflow would greatly assist researchers in systematically understanding the cellular metabolism from phenotype measurements, which is critical to optimize current biofuel-producing strategies and suggesting novel gene targets.Genome-scale modelling (GEM) is an in-silico tool to systematically simulate cellular expression and metabolism, which is now widely used in biotechnology and infectious disease research. GEM is a species-specific biological reaction network, usually reconstructed by researchers from an annotated genome of the organism. Genome-scale metabolic model (M-model), the most basic yet accessible GEM that focuses exclusively on predicting metabolic fluxes, is the metabolic sub-network with equilibrium constraint applied. M-model is a mathematical linear optimization problem (LP) ) and) and3)) C. reinhardtii M-model iCre1355 and chloroplast specific M-model iGR774 [C. reinhardtii, and the TAG accumulation case study is done using RNA-seq data from other studies to show the efficacy of OVERLAY.In this study, we develop a computational pipeline, OVERLAY to better address these challenges. We first formulated a protein-constrained metabolic model (PC-model) starting from the published l iGR774 , 16. On l iGR774 . MoreoveC. reinhardtii PC-model to be a superior version of the basic M-model, as it can be used for simple analyses such as FBA with better accuracy and more utilities. The total proteome budget has defaulted to a constant of 150 mg per gram of dry cell weight (gDW)  of photosystem I only and linear electron flow (LEF) through both photosystem II and photosystem I, while LEF is the more energy-efficient option [Our model qualitatively reproduced major and subtle shifts determining the optimal electron flow through photosystems I and II in different conditions. t option . Consistt option .Another observation is that, assuming optimal growth, the mixotroph has a higher photosystem activity than the other growth modes: 13.10 mmol/gDW/h electron flux from Cyt-b6f to PC for mixotroph compared to 11.89 and 7.18 mmol/gDW/h for autotroph and heterotroph, respectively Fig. a. These PC-FBA also offers insightful chloroplast metabolism and transport simulations, especially regarding carbon fixation and triose-phosphate transport. As verified by other studies, 9 moles of ATP and 6 moles of NADH are required to produce 1 mole of triose-phosphate from carbon dioxide through the Calvin cycle . Due to Under heterotrophic growth, the chloroplast is a net consumer of organic carbon, which is transported as 3-phosphoglycerate. Noticeably, the chloroplast in all phototrophic modes uptakes 3-phosphoglycerate while excreting other triose-phosphate Fig. a. Being C. reinhardtii [Our PC-model provides valid mitochondrion fluxomic and cellular exchange simulations for all growth modes. The mixotroph and heterotroph simulations used the glyoxylate shunt in the mitochondria Fig. a, as repnhardtii , 28. In Meanwhile, the autotroph shows several contrasting metabolic activities to the heterotroph. The autotrophic mitochondrion has very little activity, mostly powered by importing pyruvate from the cytosol. Instead, it generates phosphoenolpyruvate in the chloroplast, which is transported to the mitochondrion Fig. a. The auC. reinhardtii by inducing nitrogen deprivation. In particular, Goodenough et al. investigated a sta6 (unable to form starch) strain of C. reinhardtii, which showed enhanced TAG accumulation under nitrogen deprivation with acetate boosting 48 h later [Based on insights gained from PC-FBA simulations, we further investigated mixotrophic conditions for bulk metabolite overproduction. The accumulation of TAG, a useful and value-added industrial compound, has been studied extensively in  h later . The stu h later . Here, wWe first used convex QP only to fit each of the 16 time-course samples onto the PC-model, resulting in 16 context-specific PC-models. Noticeably, enzymatic rate constant , which Additionally, OVERLAY helps to quality control the metabolic reconstruction, especially regarding its gene-reaction association. We identified a set of proteins whose abundances could not match measurements across all samples. Because we allowed for adjusted rate constants, we hypothesized that the reason for these inconsistencies is due to mis-annotations in the original model reconstruction. We manually inspected all 15 proteins that are far outliers in at least 8 out of 16 samples and compared them with their annotated functions in ChlamyCyc and ALGAEPATH , formate C-acetyltransferase (PFLACTm), and phosphotransacetylase (PTArm). Of these reactions, ACALD is the only expression-dependent  and mitochondrion ATP synthase (ATPSm), while the chloroplast ATP synthase (ATPSh) activity is relatively low across all samples. This mode of ATP synthesis observed for TAG production contrasts sharply with PC-FBA simulations of optimal growth that showed large proteome allocation toward photosystems I and II Fig. a. The acis found .PYK produces ATP by converting phosphoenolpyruvate to pyruvate. PYK flux and protein allocation differ notably between the optimal mixotrophic and heterotrophic flux networks . The two diacylglycerol acyltransferases (ACOADAGAT and PLDAGAT) are also required to maximize TAG flux, but because either reaction can be used, each flux has a minimum requirement of zero.Our simulations are consistent with these observations in that maximum TAG accumulation requires elevated expression of the acyltransferase proteins. Namely, the minimum required flux of AGPATCOA was above De novo synthesis). Finally, the ACOAR reaction is uncorrelated with gene expression , 3-hydroxyacyl-CoA dehydrogenase (HACD), and enoyl-CoA hydratase (ECOAH), and trans-2-enoyl-CoA reductase (ACOAR). These reactions showed time-course flux patterns that were nearly identical to the TAG dilution reaction (EX_TAG) Fig. a. Three E). The optimal solution to this problem provides the set of highest priority targets for protein overexpression. Using this tool, we identified protein overexpression strategies that were consistent with the transcriptome changes observed in the acetate-boosted experiment. For example, all three free fatty acid synthesis genes  that were highly correlated with reaction flux  were identified as overexpression targets : TAG production is maximized subject to all context-specific PC model constraints while also allowing for a user-defined total protein overexpression “budget” , corresponding to ACKrm and PTArm reactions, were identified as overexpression targets. Interestingly, the algorithm did not identify the gene for ACALD as an overexpression target, despite it being a key step in Acetyl-CoA synthesis. This result is consistent with the high expression levels of ACALD-associated transcripts , phosphate (PTA3), and amino acids (AOT7) , glycolysis (GAP4), redox balance (NNT), and glyoxylate metabolism (HAO)  Fig. b. Other lysis GAP, redox bC. reinhardtii exhibit significantly more TAG accumulation [C. reinhardtii lipid productivity by up to C. reinhardtii simulation, we observed a different but similar phenomenon, wherein the reaction fatty acid CoA ligase hinders TAG production  and product yields (DHA production by 90%) [C. reinhardtii may become an economically-efficient cell factory for bulk chemicals after multiple iterations of optimization. On the other hand, the optimization for non-native products is more complicated, yet it can be highly impactful for the (bio)chemicals industry due to the potential for sustainable production of high-value products, especially when non-biological synthesis routes are unavailable.Microalgae  by 90%) . Using gC. reinhardtii and algae in general, Escherichia coli has also been extensively studies as a synthetic cell chassis. The main advantages of C. reinhardtii over E. coli are its lower carbon emission and simplicity in cultivation, which are both more significant in bulk chemical productions setup. Therefore, biofuel and potential food production has been the main interest in C. reinhardtii cell factories. On the other hand, E. coli is the best studies microorganism with even more established knowledge and synthetic biology tools available to researchers. The production of certain value-added chemicals by E. coli, including various vitamins and nutraceuticals, has reached commercialization stage [E. coli cell factories.Besides on stage , 44. We E , which can be determined using a simple procedure. Finally, our method enables using transcriptomics to quality-control genome-scale reconstructions and their annotations, which are found through persistent discrepancies between optimal protein and measured transcript abundances.Our PC-model formulation and OVERLAY pipeline provide several advantages over existing methods. First, our PC-model computes optimal fluxomes in response to changes in the allocation of the proteome. This proteome, in turn, is highly consistent with measured transcriptomes through a sequence of convex and nonconvex quadratic optimization problems. Second, to perform context-specific simulations, we do not require choosing an arbitrary gene expression threshold for turning on/off reactions based on transcript abundance—this has been a challenge in existing methods . Third, Chlorella vulgaris [C. vulgaris metabolic reconstruction iCZ843 using OVERLAY for deeper metabolic insights [Given all the advantages offered, the application of OVERLAY can be extended from metabolic engineering to a broader scientific inquiries. Importantly, OVERLAYovercomes the shortcomings  of FBA and PC-FBA by imposing constraints and calibrating rate constants using transcriptomics measurements that are consistent with observed phenotypes. For example, mixotrophic regulations and metabolisms in algae are a research focus for scientists. A recent study by Vidotti et al. measured time-course transcriptomic and proteome data for autotrophic, mixotrophic, and heterotrophic vulgaris . These minsights . We antiIn general, PC-model and OVERLAY is a simple yet effective tool for understanding and manipulating cellular metabolism through gene expression, making it potentially valuable for various applications. The prediction results can be used for practical decision-making in various research fields such as biotechnology, infectious disease, and cancer. We believe OVERLAY will benefit the system biology and metabolic engineering community.With more multi-omics data measurements in recent studies, we think it is worth mentioning the potential of OVERLAY to uniquely incorporate more omics data to achieve higher modelling performance.C. reinhardtii modelling without kinetome data. We believe OVERLAY can incorporate available kinetome data by explicitly setting and fixing individual rate constants in the PC-model before nonconvex QP. Available kinetome data would likely increase the modelling performance.Kinetome refers to the collection of cellular enzymatic rate constants . Due to Proteome profiling data, if available, is a substitute of transcriptomic data for OVERLAY. In this study, OVERLAY uses transcriptome data to approximate cellular proteome, which is potentially less accurate than using proteome data directly. However, RNAseq remains more accessible than proteome profiling, making it more practically useful in modelling for biotechnological applications. If proteome profiling is available but not for all proteins, it can still be useful in partially constraining metabolic flux or verifying its consistency with transcriptomic data.Metabolomic data refers to the presence or concentration of intracellular metabolites. Recently, the GEM community has developed various protocols, such as MetaboTools and matTFA toolbox, to incorporate metabolomics data into M-model , 49. TheC. reinhardtii using published time-course transcriptomics data. Using PC-FBA, we recapitulate metabolic hallmarks of autotrophic, heterotrophic, and mixotrophic growth. Importantly, the protein constraints are required to accurately simulate respiro-fermentation (overflow) metabolism. We then use time-course RNA-Seq data to investigate the over-production of triacylglycerol (TAG) in response to acetate supplementation under nitrogen limitation [In this work, we developed a computational pipeline OVERLAY for building a context-specific, protein-constrained genome-scale model (PC-model), starting from metabolic reconstruction and transcriptomics data. We showcase the utility of PC-model for deciphering how complex gene expression dynamics drive system-level fluxome shifts in mitation . Our pipTo enable researchers to systematically identify optimal overexpression targets, we developed a novel optimization-based tool. Using the tool, we identified key gene expression bottlenecks for TAG overproduction. The tool recapitulated known bottlenecks . Furthermore, we identified several novel overexpression targets to improve TAG overproduction further, including genes encoding sulphate, phosphate, and amino acid transporters; glyoxylate metabolism, and redox balancing.C. reinhardtii M-model iCre1355 only, we decided to plug in a newer chloroplast M-model, which contains a more up-to-date understanding of chloroplast metabolism. We merged M-model iCre1355 (cellular model) and iGR774 (chloroplast model) by first deleting all chloroplast metabolites and reactions in the cellular model. The chloroplast model was slightly modified  for the TAG case study were downloaded as FASTQ files . We down505 for tOur PC-model formulation is shown below.P) in mg/gDW.The total amount of metabolic proteome may not exceed a weight fraction of the dry weight, which is further referred to as the ‘proteome budget’ and denoted by a scalar  in the later section.Enzyme concentration upper bounds but not forces the respective reaction flux. Enzymes are currently not compartmentalized.The protein constraints were implemented in the M-model by adding four sets of variables and four sets of constraints. Variables are defined as follows: Protein dilution: protein concentrations (or abundances) in nmol/gDW. Proteins are uniquely defined for each gene in the M-model.Complex formation: complex concentrations in nmol/gDW. We define ‘complex’ as a unique protein combination that can sufficiently catalyze any single reaction. The list of complexes is obtained by parsing rules in M-model (parseGeneRule.m).Enzyme formation: enzyme concentrations in nmol/gDW. We define ‘enzyme’ as a collection of indifferent complexes that can catalyze a certain reaction. A pair of forwarding and reverse enzymes are added for each enzymatic reaction, and no enzyme is added for spontaneous reactions.Enzyme dilution: One dilution reaction for each forward or reverse enzyme.Extra constraints were added to the model as follows: C : The sum of forward and reverse enzyme equals the total complex, according to r and B . B is Bo studies , 31.Enzymatic reaction fluxes are restrained by respective forward and reverse enzyme levels through the average rate constant of P, according to protein molecular weight vector d in mg/nmol. We assumed P being a constant across all growth conditions, and the weight fraction of total proteome to be t, as well as molecular mass vector for complete transcript d. Thus, P is approximated as below. P are shown by Additional file Protein concentrations are collectively constrained by the proteome budget We collected a complete protein sequence FASTA file using NCBI genome assembly Chlamydomonas reinhardtii v5.5 (GCF_000002595.2.gbff), Chlamydomonas reinhardtii chloroplast reference genome (NC_005353.1), and Chlamydomonas reinhardtii mitochondrial reference genome (NC_001638.1) [By adopting this formulation, we assumed the following: ome NC_0053.1, andt denotes the modelled transcript abundance vector, and w is a weighting vector for each transcript. This finds the proteome vector closest to the measured transcriptome while maintaining underlying metabolic feasibility. We definedt was scaled to satisfyt and p into the same magnitude. This guaranteed the possibility that objective function (We proposed a methodology to interpret the underlying cellular metabolism for a given RNA-seq data using the PC-model (overlayMultiomicsData.m). Assuming the proteome vector is similar to the mRNA vector, we formulated a quadratic objective function, subject to constraints –8):11\d:11\d8):1s within a reasonable range would not significantly affect the result.This is a convex QP problem and can be solved using commercial LP solvers such as Gurobi Optimizer or IBM ILOG CPLEX , 58. Thenstraint  with connstraint  for the Q set of samples of the same strain under different conditions, we rewrote objective function :E reached a constant slope . This was done by adding a variable term to the constraint , which bmentclass2pt{minimv was done to find To further understand the metabolic capabilities under each expression data, we used PC-FVA for an unbiased analysis. For each data-specific model, FVA of all metabolic reactions Additional file 1: Fig. S1. The complete record of consistency of simulated proteomes to transcriptomics before and after nonconvex QP by OVERLAY. This is an extended version of Fig. Additional file 2: Fig. S2. Hierarchical clustering result of 16 time-course RNA-seq sample.Additional file 3: Fig. S3. Histogram of Spearman’s ranking coefficient for all metabolic reactions. This supplements Fig. Additional file 4: Fig. S4. PC-FVA prediction results for starch synthesis reaction, phospholipase A2 reaction, and fatty acid CoA ligase reaction. This figure can be interpreted using the caption of Fig. Additional file 5: Fig. S5. Bar plot of proteome budget estimation using dataset.Additional file 6: Tables S1 to S3 for results section.Additional file 7: Tables S4 to S9 for methods section.