Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for  Corynebacterium glutamicum is widely known as an excellent producer of amino acids [C. glutamicum, but it remains difficult to synthesize these compounds on an industrially relevant scale [C. glutamicum have been developed  [Escherichia coli, which only introduced one pseudo-reaction and pseudo-metabolite into the stoichiometric matrix. Different from GECKO and AutoPACMEN, ECMpy simply adds a constraint on the total amount of enzymes and does not require modification of the stoichiometric matrix, while providing higher prediction accuracy for the simulation of the E. coli growth rate [E. coli [S. cerevisiae [Yarrowia lipolytica [Aspergillus niger [Bacillus subtilis [With the accumulation of enzyme kinetic data and the availability of high-throughput omics data, it has become possible to integrate these data into models to add constraints on individual reactions or aggregate constraints on enzyme resources ,12. In 2nalysis) . Inspirenalysis) , which cnalysis)  and SABInalysis)  databasewth rate . Current[E. coli ,22, S. crevisiae , Yarrowipolytica , Aspergius niger , and Bacsubtilis .kcat and molecular weight (MW) of an enzyme set constraints on the fluxes of the reactions catalyzed by that enzyme. Previous studies have made efforts to automatically acquire kcat values from databases and fill missing values using methods like machine learning [Cgl1452 is a homodimer and, therefore, the MW is 105.2 kDa instead of 52.6.2 kDa for the monomer. There are also many enzymes consisting of subunits encoded by different genes which are represented as ‘and’ GPR relationship in GEMs. However, what is missing in the GPR relationships is the number of each subunit in the protein complex. For example, Succinyl-CoA synthetase is a heterotetramer containing two alpha subunits (encoded by Cgl2565 in C. glutamicum with an MW of 30.26 kDa) and two beta subunits (encoded by Cgl2566 with an MW of 41.76 kDa). Therefore, the MW of this enzyme complex should be 144.04 kDa (2 × 30.26 + 2 × 41.76) instead of 72.02 kDa. Such quantitative information on enzyme subunit composition is difficult to obtain from databases and often missing in the published GEMs, leading to incorrect MW values which affect the prediction accuracy of enzyme-constrained models.In the enzyme-constrained models, learning . In contC. glutamicum using AutoPACMEN and constructed the enzyme-constrained model ecCGL1 based on the ECMpy workflow. We further comprehensively evaluated ecCGL1 and confirmed that it had a better prediction accuracy of phenotypes than iCW773R (Revised iCW773) and could simulate a variety of biological phenomena. Finally, we applied ecCGL1 to metabolic engineering and discovered potential targets for increasing the production of l-lysine.In this study, we first systematically corrected the GPR relationships in the iCW773 model based on the GPRuler tool  and prot(1)Metabolite correction: ‘(e)’ to ‘_e’, ‘-D’ to ‘__D’, ‘-L’ to ‘__L’, ‘-R’ to ‘__R’ and other ‘-’ to ‘_’.(2)Reaction correction: ‘-’ to ‘__’ in reactions beginning with ‘EX’ and ‘-’ to ‘__’ in other reactions.(3)Adding UniProt ID information to the annotation, which is the basis for obtaining kinetic parameters.We obtained the iCW773 model, which has 773 genes, 1207 reactions, and 950 metabolites, from the supplemental data of Zhang et al. [C. glutamicum protein complexes. For example, the protein name of P06557 (encoding by Cgl3029) is Anthranilate synthase component 1, which will not be identified as a subunit forming an ‘and‘ relationship with another subunit using the original terms. Therefore, we updated the terms by carefully checking the words used in UniProt to describe possible protein complex formation ) using published RNA-Seq data of wild-type C. glutamicum ATCC 13032 grown on glucose [After model correction, we split the reversible reactions in the model into two irreversible reactions because of different  glucose ,32. Fina enzymes ; Ptotal al cells ; f is thkcat values, like GECKO, AutoPACMEN and ECMpy. Because no suitable 13C data were found, the correction of ecCGL1 was performed using only method 1 proposed by ECMpy, which is based on enzyme usage. For enzymes that require calibration, the EC number was obtained and its corresponding kcat value was substituted by the highest value in the BRENDA and SABIO-RK databases for the given enzyme class. This iterative correction process was continued until the experimental value or the predefined number of iterations was reached, as described in GECKO 2.0 (2.0 version) [Generally, the initial enzyme-constrained model was unable to accurately predict the experimental value of the maximal growth rate, requiring an adjustment of the original version) .R and ecCGL1 for experimental measurements of μmax , using R and ecCGL1 to predict the biomass-specific growth rates by varying the glucose and oxygen uptake rates, as described in the literature [The PhPP analysis is a powerful tool that provides a global view of how optimal growth rates are affected by changes in two environmental variables such as the carbon and oxygen uptake rate ,35. We pterature . Therefoterature .C. glutamicum using ecCGL1 by varying the substrate uptake rate from 1 to 6.3 mmol/gDCW/h (the model reaches its maximum growth rate at 0.479 mmol/gDCW/h) and solving for the pFBA to maximize the biomass. To observe the trade-off phenomenon in unrestrained growth, we set glucose as the carbon source, and varied the substrate uptake rate from 1 to 6.3 mmol/gDCW/h to obtain the trade-off between the biomass yield (Equation (8)) and enzyme usage efficiency (Equation (9)). The We explored the overflow metabolism of of ECMpy .(8)bioml-lysine biosynthesis by fixing a low biomass growth rate (0.1 h−1) using the enzyme-constrained model of E.coli and improved l-lysine production by optimizing the expression of the proteins in the top 20 of the enzyme cost [−1) and low growth high product generation . Subsequently, we calculated the cost of each reaction (Equation (10)) for both pathways [Enzyme cost can be used to identify key enzymes in the product synthesis pathway. For example, Ye et al. calculated the enzyme cost for yme cost . Becausepathways . FinallyCgl2565 or Cgl2566’ in the model. However, in UniProt, Succinyl-CoA synthetase is described as a heterotetramer containing two alpha and two beta subunits. The beta subunit (Cgl2566) provides nucleotide specificity of the enzyme and binds the substrate succinate, while the binding sites for coenzyme A and phosphate are found in the alpha subunit (Cgl2565). Third, both the prediction results of the GPRuler tool and the GPR relationship in the model are wrong and should be manual corrected using Uniprot, BioCyc, KEGG or the literature (62 reactions). For example, the gene composition of the Adenosylcobalamin 5′-phosphate synthase (ADOCBLS) is given as ‘Cgl0245 and Cgl2201’ in the model, while from the GPRuler tool, the GPR relationship were obtained as ‘Cgl0245 and Cgl0246′. Whereas Cgl0245 and Cgl0246 do form a protein complex, its function is Lipid II isoglutaminyl synthase (glutamine-hydrolyzing) rather than Adenosylcobalamin 5′-phosphate synthase. Therefore, we corrected the GPR relationship for this reaction to ‘Cgl2201′ as Cgl2201 is the true adenosylcobalamin-5′-phosphate synthase based on information from UniProt.There are 1207 reactions in the iCW773 model, 96 of which are “and” relationships. We obtained protein composition information for a total of 112 reactions using the GPRuler tool, of which 24 had GPR relationships consistent with the model . The 88 Cgl0658 and Cgl0695’, which has 81% sequence similarity. By further searching the KEGG database for verification, we found that Cgl0695 and Cgl0658 have the same protein name, catalyse the same reaction and map the same EC number, but not any information implying they are subunits of a protein complex. They are more likely to be two isoenzymes and, therefore, the correct GPR relationship for Methylisocitrate lyase should be ‘Cgl0658 or Cgl0695’. After manual investigation, we modified ‘and’ relationships to ‘or’ relationships for 17 reactions in the model (R (Revised iCW773).In addition, we observed that 58 “and” relationships in the model were not identified by the GPRuler tool. To determine the correctness of these relationships, we calculated the protein sequence similarity for all gene pairs which have ‘and’ relationships in the model. The results showed no similarity between protein pairs in the 41 reactions . Howeverhe model . Finally−1 vs. 0.45 h−1) [−1 was close to the experimental value , and a total of 202 reactions were split into 571 reactions. After this step, the number of reactions in the model was expanded to 1850. The growth rate predicted by the initial ecModel at a glucose uptake rate of 5.05 mmol/gDCW/h was low compared to the experimental value  were more favorable for l-lysine production (with increased flux) and some others  are in contrast  supply has been considered a bottleneck for l-lysine production, while overexpression and point mutation of phosphoenolpyruvate carboxylase (PPC) has been applied to increase OAA availability [We compared the pathway characteristics at LGHP and HGLP to analyze the differences in the product synthesis pathways and found 23 reactions in which the change of enzyme cost was higher . We firsynthesis . Therefolability ,43,44.l-lysine production. For example, decreased flux via succinyl-CoA synthetase (SUCOAS) was reported to increase the l-lysine yield and maintain optimal cell growth at the same time [l-lysine production.At the same time, some reactions were more amenable for high-level growth and should be downregulated for ame time . In addiame time . Consequl-lysine synthetic pathway calculated from the enzyme-constrained model. To explore the variability of enzyme costs, we calculated the log2-fold changes of enzyme costs between LGHP and HGLP, as shown in l-lysine synthesis from aspartate  and the pentose phosphate (PP) pathway , which was in agreement with the literature [l-lysine [After analyzing these pathway changes, we further analyzed the enzyme costs of the terature ,46,47. Iterature ,49,50. FC. glutamicum using the upgraded workflow  directly populate unknown reactions with mean or median values of enzyme kinetic parameters from other reactions [C. glutamicum. Finally, although ecGEM has improved predictive power compared to traditional GEMs, biological systems are also subject to other constraints in addition to enzyme resources, and the construction of multi-constraint models  will ceC. glutamicum (ecCGL1) by integrating various enzymatic parameters at the entire network level. The results show that constraints on enzyme resources can simulate strain growth limitations and recapitulate metabolic overflow phenomena, resulting in more realistic pathway predictions, which can be used to identify key enzymes to provide metabolic engineering targets for creating cell factories to produce valuable chemicals.In this study, we constructed an enzyme-constrained genome-scale metabolic model of


The genome-scale metabolic model can predict the growth ability and metabolic flux distribution by genetic disturbances, which provides a strategy to construct a microbial cell factory for GABA and butyrolactam biosynthesis. Here, we performed model-guided metabolic engineering of Corynebacterium glutamicum ATCC13032 for GABA and butyrolactam fermentation from glucose. The biosynthetic pathways of GABA and butyrolactam were constructed by overexpressing the heterologous genes using a bi-cistronic expression cassette. The genetic modifications of the metabolic network cooperatively forced the carbon flux toward GABA and butyrolactam synthesis. This study provides new insights into engineering industrial microorganisms to produce target chemicals from renewable carbon sources.The fermentative production of desired chemicals from renewable resources is one of the promising biosynthetic routes to replace the petrochemical-based process. Gamma-aminobutyric acid (GABA) can be synthesized from Corynebacterium glutamicum for GABA and butyrolactam fermentation. A GABA biosynthetic pathway was constructed using a bi-cistronic expression cassette containing mutant glutamate decarboxylase. An in silico simulation showed that the increase in the flux from acetyl-CoA to α-ketoglutarate and the decrease in the flux from α-ketoglutarate to succinate drove more flux toward GABA biosynthesis. The TCA cycle was reconstructed by increasing the expression of acn and icd genes and deleting the sucCD gene. Blocking GABA catabolism and rewiring the transport system of GABA further improved GABA production. An acetyl-CoA-dependent pathway for in vivo butyrolactam biosynthesis was constructed by overexpressing act-encoding ß-alanine CoA transferase. In fed-batch fermentation, the engineered strains produced 23.07 g/L of GABA with a yield of 0.52 mol/mol from glucose and 4.58 g/L of butyrolactam. The metabolic engineering strategies can be used for genetic modification of industrial strains to produce target chemicals from α-ketoglutarate as a precursor, and the engineered strains will be useful to synthesize the bio-based monomer of polyamide 4 from renewable resources.Gamma-aminobutyric acid (GABA) can be used as a bioactive component in the pharmaceutical industry and a precursor for the synthesis of butyrolactam, which functions as a monomer for the synthesis of polyamide 4 (nylon 4) with improved thermal stability and high biodegradability. The bio-based fermentation production of chemicals using microbes as a cell factory provides an alternative to replace petrochemical-based processes. Here, we performed model-guided metabolic engineering of  Corynebacterium glutamicum is an important industrial microbial species traditionally used for amino acid production . T. T44]. T control B, indicaact and gadM genes in WT-tufP-acn-tufP-icdΔsucCD, WT-tufP-acn-tufP-icdΔsucCDΔgabDT, and WT-tufP-acn-tufP-icdΔsucCDΔgabDTΔgabP::potE, resulting in BLM-1, BLM-2, and BLM-3 strains. As expected, butyrolactam could be detected in the flask cultivation using HPLC–MS (Considering that GABA is the precursor for butyrolactam, engineered GABA strains were used as chassis cells for butyrolactam biosynthesis. Therefore, we constructed an acetyl-CoA-dependent pathway for butyrolactam biosynthesis in vivo by co-overexpressing  HPLC–MS . The BLM HPLC–MS D. HoweveThe production performance of the GABA-6 and BLM-2 strains was investigated in fed-batch fermentation with minimal medium. As shown in gad gene, GABA production is mostly dependent on the abundance of GAD; therefore, many efforts have been adopted to increase the expression level of GAD [tufP promoter was used to mediate the constitutive expression of gadM, resulting in a 16% increase in GABA production. The mRNA structure determines the accessibility of ribosome to translation initiation; thus, a high degree of mRNA structure at the N-terminus can be unavailable for ribosome binding to initiate translation [The overexpression of heterologous genes in chassis cells is commonly used to construct a new synthetic pathway or redirect metabolic flux into the synthetic pathway of desired metabolites. The expression levels of heterologous genes are determined by multiple factors, such as gene dosage, promoter strength, secondary structure of mRNA, RBS, and translation initiation efficiency ,66. For l of GAD ,30,32. Cnslation . Using aodhA, sucB, and lpdA genes, and its activity is regulated by PknG and OdhI [odhA/pknG or overexpressing odhI genes for GABA production [acn and icd genes, and then redirected α-ketoglutarate toward GABA synthesis by deleting the sucCD gene rather than inhibiting AKGDH activity. SucCD deficiency increased GABA production, demonstrating that the blocking of the conversion of succinyl-CoA to succinate led to the increase in α-ketoglutarate pool for GABA synthesis. There was no obvious effect of sucCD deficiency on the cell growth of engineered C. glutamicum, which might attribute to two aspects. Unlike the inhibition of α-ketoglutarate dehydrogenase activity, the deficiency of sucCD does not significantly impact the synthesis of succinyl-CoA, which is available for peptidoglycan and wall biosynthesis to support cell growth [aceA and aceB genes indicated that the deficiency of sucCD activated the glyoxylate shunt as a partial by-pass route for succinate formation to support cell growth; however, the glyoxylate shunt pathway was off in the C. glutamicum grown on glucose [sucCD in 4-hydroxyproline-producing C. glutamicum drove the redirection of carbon flux partitioning at the α-ketoglutarate node and had no significant impact on the cellular growth [sucCD deletion in C. glutamicum indicated that the TCA cycle could normally operate as the inactivation of succinyl-CoA synthetase [pknG or odhA, deleting mdh, and overexpressing ppc had a positive effect on GABA biosynthesis via the GAD-based route, and GABA production improved to 31.1 g/L, 29.5 g/L, and 26.3 g/L, respectively [Metabolic reprogramming has been performed by taking advantage of biotechnologies to overcome the metabolic bottleneck in product synthesis. Guided by in silico model-based simulation, we reprogrammed the metabolic network for GABA production by increasing the α-ketoglutarate supply, blocking GABA catabolism, and rewiring GABA transport. Precursor α-ketoglutarate is crucial to GABA production; however, most α-ketoglutarate is converted to succinyl-CoA for peptidoglycan biosynthesis by AKGDH and succinate by SUCOAS for the operation of the TCA cycle. AKGDH is composed of three subunits encoded by and OdhI . Metaboloduction ,35. Unlil growth . On the  glucose . Similarr growth . In addinthetase . Other rectively . The GABectively . In contectively ,29, the ectively . TherefoC. glutamicum, only butyrolactam was detected and none of valerolactam and caprolactam were co-accumulated owing to the incapacity of the engineered strain to synthesize 5-aminovaleric acid and 6-aminocaproic acid. However, GABA production was higher than butyrolactam production in all engineered BLM strains and in fed-batch fermentation, indicating that the activity of ß-alanine CoA transferase might be a limiting factor for acetyl-CoA-dependent butyrolactam biosynthesis. Screening new lactam-synthesizing enzymes with a high catalytic efficiency can provide the biological element with a high catalytic efficiency for metabolic engineering of lactam bio-production.The microbial fermentation production of lactams is relatively difficult due to the lack of natural biosynthetic pathways and catalytic enzymes. Despite an Act-mediated synthetic pathway being constructed, it showed a low efficiency for butyrolactam production ,45. Due C. glutamicum for GABA and butyrolactam fermentation from glucose guided by in silico simulation. The biosynthetic pathways of GABA and butyrolactam were constructed by overexpressing heterologous genes using a bi-cistronic expression cassette. The genetic modifications of genes cooperatively forced the carbon flux toward GABA and butyrolactam synthesis. This work provides new insights into engineering industrial microorganisms for the bio-based production of chemicals from renewable carbon sources.In this study, we constructed engineered


E. coli is modeled, simulated and optimized in order to find the maximum productivity in a steady-state continuous culture. The predicted computational results were consistent with the wet lab experiments.The current production of a number of commodity chemicals relies on the exploitation of fossil fuels and hence has an irreversible impact on the environment. Biotechnological processes offer an attractive alternative by enabling the manufacturing of chemicals by genetically modified microorganisms. However, this alternative approach poses some important technical challenges that must be tackled to make it competitive. On the one hand, the design of biotechnological processes is based on trial-and-error approaches, which are not only costly in terms of time and money, but also result in suboptimal designs. On the other hand, the manufacturing of chemicals by biological processes is almost exclusively carried out by batch or fed-batch cultures. Given that batch cultures are expensive and not easy to scale, technical means must be developed to make continuous cultures feasible and efficient. In order to address these challenges, we have developed a mathematical model able to integrate in a single model both the genome-scale metabolic model for the organism synthesizing the chemical of interest and the dynamics of the bioreactor in which the organism is cultured. Such a model is based on the use of Flexible Nets, a modeling formalism for dynamical systems. The integration of a microscopic (organism) and a macroscopic (bioreactor) model in a single net provides an overall view of the whole system and opens the door to global optimizations. As a case study, the production of citramalate with respect to the substrate consumed by  Methyl methacrylate (MMA) is a volatile synthetic chemical used mainly in the preparation of acrylic emulsion and extrusion resins. Polymers and co-polymers containing methyl methacrylate are used as solvents, adhesives, sealants, leather and paper coatings, inks, textiles, dental prothesis, etc.There are 17 different routes widely used in industry that end up synthesizing MMA. The main problem with these routes is that all the precursor molecules  have their origin in non-renewable sources such as petroleum and natural gas whose extraction is highly damaging for ecosystems .Methanocaldococcus jannaschii. The production of citramalate in M. jannaschii is due to the presence of the gene cimA which encodes the enzyme citramalate synthase (EC: 2.3.1.182). This enzyme catalyses the reaction in which one molecule of acetyl-CoA, one molecule of pyruvate and one molecule of water react to produce one molecule of (3 R)-citramalate, one molecule of CoA and liberating a proton  Dynamic Flux Balance Analysis (DFBA)  , can be nditions ,2016 andE. coli and triacetic acid lactone in mutant S. cerevisae were performed in Other methods not based on DFBA, such as k-OptForce, have been used to integrate kinetics in constraint-based models. For instance, the optimization of the production of L-serine in mutant E. coli culture. The optimization of the model obtained with respect to citramalate productivity provides the optimal settings, i.e., intracellular fluxes and bioreactor parameters, that maximize the productivity of citramalate in a steady-state continuous culture.In contrast to the previous works, we propose the use of Flexible Nets (FNs) Flexible Nets (FNs) is a modeling formalism for dynamic systems inspired by Petri Nets, see A, B, C, D}, two reactions {R1, R2} and two event handlers {v1, v2}. Such a net models the following reactions:As an example, the event net in a = x and c = 2x of v1 imply that each occurrence of reaction R1 consumes one unit of metabolite A and produces two units of metabolite C . On the other hand, the equalities a = 2x, b = x, and d = x of v2 mean that each occurrence of R2 consumes 2 units of A, 1 unit of B and produces 1 unit of D.The stoichiometry of the reactions is modeled by the equalities associated with the event handlers. In particular, the equalities R1 as twice that of the concentration of A, see equation r = 2a associated with the intensity handler s1. In addition to equalities, intensity handlers can be associated with inequalites to model uncertainty, e.g., if 1.8a ≤ r ≤ 2.2a was associated with s1 then the speed of R1 could be any value in the interval [1.8 [A], 2.2 [A]] where [A] is the concentration of A.The event net in s2 which imply that the speed of R2 is 0 if [A] is below 10, 2 [B] if [A] is between 10 and 30, and 4 [B] otherwise. Intensity handler s2 is said to be guarded, it has three guards (or regions) that can determine the speed of R2. Guarded handlers can be exploited to approximate non-linear kinetics of reactions.Moreover, several sets of equalities and inequalities can be associated with the same intensity handler. If this is the case, the set of inequalities that rules the reaction dynamics is determined by the concentrations of the system. The intensity net in The event net  can be cconstraint-based model  FNs can be analyzed by building a set of mathematical constraints that the state of the system necessarily satisfies . The assEscherichia coli strain K-12 MG1655  is the cell density in the tank, μ is the effective cell growth rate (h−1), and D is the dilution rate (h−1), which is the rate at which culture fluid is replaced divided by the culture volume.The dynamics of the bioreactor variables, which are named macroscopic variables, are determined by differential equations . The equi, in the tank is given by:ci is the concentration of the metabolite s in the medium (mM), si is the concentration of the metabolite in the tank (mM), D is the dilution rate (h−1), ui is the specific uptake rate of the metabolite by the cells (mmol gDW−1h−1), and X is the cell density in the tank (gDW L−1). If ui > 0 the metabolite is consumed by the cell, otherwise (ui < 0) the metabolite is secreted from the cell.The evolution of the concentration of a given metabolite, g, is consumed by an E. coli culture, cg is the concentration of glucose in the supply medium, sg is the concentration of glucose in the tank and, ug is the glucose uptake flux by the cell .For the particular case of a system in which glucose, denoted as metabolite c, is derived from sc is the citramalate concentration in the tank, and uc is the citramalate secretion flux. Notice that, since citramalate is secreted from the cell, uc is negative, and hence, − ucX is a positive contribution of citramalate to the tank.The variation of a given product, e.g., citramalate, denoted as G accounts of the concentration of glucose in the tank. As established by G has one input flux and two output fluxes. The input flux comes from the reservoir, it is modeled by transition tgin, and it is equal to D ⋅ cg. As this is a constant amount, no intensity handlers are needed, and the flux is modeled by the λ0 associated with tgin. The output flux modeled by tgfromtank represents the uptake rate of glucose by the cell culture, and it is equal to ugX where ug is the specific uptake rate and X is the cell biomass. This flux is produced by the intensity handler sug. Such an intensity handler scales by X the amount of glucose that is consumed by the cells, see equation ut = uX associated with sug. The output flux modeled by tgout represents the glucose that leaves the tank without being captured by the cells. Such an output flux is equal to the dilution rate times the concentration of glucose in the tank, see equation Dsg associated with sgout: r = Dsg.The above differential equations can be modeled by FNs.C and the elements connected to it in tct and it is equal to ucX, see equation associated with hc. The output flux corresponds to the amount of citramalate in the tank, once it has been released by the cell, that forms part of the effluent. Such a flux is modeled by tcout and it is equal to scD, see equation associated with scout.X and the net elements connected to it. The input flux of X, i.e. the rate at which X increases, is modeled by txt and it is equal to the specific growth rate of the culture times the cell density, rX, see equation associated with hr. The output flux of X, i.e. the rate at which X decreases, corresponds to the cells that are cleared away in the continuous culture, it is modeled by txout and is equal to Dx, see equation associated with sgout.Finally, tgin accounts for the presence of glucose in the medium, and transition tsin represents the presence of a given metabolite s in the medium.The merger of the FNs that model the dynamics of metabolite concentrations in the bioreactor, see S represents a generic nutrient, i.e., it is provided by the medium, it is consumed by the culture (see arrow going from S to vst) and forms part of the effluent. On the other hand, place P represents a generic product, i.e., it is not provided by the medium, it is produced by the culture (see arrow going from S to vst) and forms part of the effluent. Although the cell density, X, is not a metabolite, its evolution, see X and the net elements connected to it (the increase of X is due to the biomass production which is determined by the growth rate).The concentrations of metabolites in the bioreactor are modeled by the places in the tank compartment. In a general system, there will be as many places in the tank compartment as there are metabolites being tracked. In E. coli culture, glucose-limited conditions are assumed. Thus, in addition to the cell density modeled by place X with units gDWL−1, the focus will be on the concentration of one nutrient (glucose which is modeled by place G with units mM), and the concentration of one product . Recall that the dynamics of X is ruled by G and C are ruled by E. coli strain K-12 (iJO1366), MODEL1108160000 fnyzer tool  combined with the reactions that allow citramalate production, tGlucose_in represented the glucose exchange reaction, tgrowth is used for the biomass reaction and tExCit defined the citramalate exchange reaction.The aforementioned intensity handlers are graphically located at the interface between the cell and tank compartments, and each one acts as a bridge between two transitions: This section discusses the approximations that must be applied to the FN in hu is ut = ugX where both ug (the uptake rate of glucose) and X (the cell density) are real variables. The optimization of a non-linear system is, in general, very demanding from a computational point of view. To overcome such a computational burden, non-linear equations can be approximated by piece-wise linear inequalities that are associated with intensity handlers. This approximation results in a guarded FN .A non-linear equation such as n regions, the first region is active if the cell density X is in the interval  . The values X1, … , Xn−1 do not need to be evenly separated, the only condition they must satisfy is Xmin < X1 < …, < Xn−1 < Xmax. This way, one and only one region is active at any particular time. The region that is active determines the linear inequality that is used to produce intensity, i.e., if region i is active then the intensity produced by hu can be any value in the interval . Clearly, the higher the number of regions , the better the approximation to the original non-linear equation. Since a higher number of regions involves a longer run time, there is a trade-off between accuracy and computational cost. As discussed below, the number of regions was determined experimentally so that both the computational burden and the obtained precision are acceptable.The above approximation considers X in their equations. Given that the number of regions has a direct impact on the complexity of the programming problem which needs to be solved (the number of binary variables is linear in the number of regions), partitioning X instead of ug is advantageous from a computational point of view.Notice that the previously defined regions can also be used to approximate the non-linear equations of the other handlers in the interface between the macroscopic and microscopic models because all include Python tool fnyzer  and the inequalities X2 ⋅ ug ≤ ut ≤ X3 ⋅ ug are associated with hu . The programming problem associated with each of these nets is linear, and hence, can be solved very efficiently. The optimum solution of the original guarded FN can be obtained straightforwardly by taking the maximum of all the computed objective values of the particular non-guarded FNs.In order to speed up the model optimization, a non-guarded FN has been defined for each of the above regions, and each of these nets has been solved separately. For instance, region X in an appropriate number of regions, the productivity on substrate (PS), see gL−1, and dilution rate, 0.23 h−1. The obtained maximum PS are shown in In order to partition the cell concentration Among the different objective functions that can be considered, we focus on two measures for the productivity of a culture in continuous mode: 1) volumetric productivity, and 2) productivity on substrate.VP and is expressed in grams of product per liter per hour, i.e., g ⋅ L−1 ⋅ h−1. In terms of the FN in flux(P), i.e. intensity of transition tpout, expressed in g ⋅ L−1 ⋅ h−1. The volumetric productivity would be the primary concern of a chemical engineer designing an industrial process, which needs to be both feasible and economically viable.The volumetric productivity, a. k.a. space-time yield, accounts for the amount of product produced per liter and per hour, it will be denoted μ (units h−1), and the product yield coefficient, YP/S, where YP/S denotes the number of grams of product that are produced per Gram of substrate fed into the tank, i.e. YP/S is unitless. In particular, the productivity on substrate, which will be denoted PS, for a given net YP/S is unitless, h−1. Notice that The productivity on substrate takes into account both the specific growth rate of the culture, YP/S is equal to the grams of product produced per Gram of substrate per time unit, i.e. it can be expressed as flux(P) is the flux of produced product in g ⋅ L−1 ⋅ h−1, and flux(S) is the flux of provided substrate in g ⋅ L−1 ⋅ h−1. Notice that flux(S) is equal to Dcs where D is the dilution rate in h−1 and cs is the concentration of the substrate in the fresh medium in g ⋅ L−1. This way, the yield can be expressed as:In the following, the mathematical relation between volumetric productivity and productivity on substrate is explored. In a continuous culture, the yield μ = D. Thus, On the other hand, the cell density is assumed to be constant in a continuous culture, i.e., cs, optimizing the model to maximize productivity on substrate is equivalent to optimizing the model to maximize volumetric productivity. Moreover, for a given cs, the linear objective function flux(P) can be used to perform such an optimization. For the particular case of citramalate production, flux(P) is given by the value of λ[tcout] . The E. coli strain used for all fermentation experiments was BW25113 ΔldhA pET29a-Cer-BBaJ23119-RFP-cimA3.7. The E. coli ldhA deletion prevents lactate formation, improving flux towards citramalate . Biomass concentrations were determined by centrifuging measured samples from the fermenter into pre-weighed tubes, washing the pellets, and drying to constant weight at 100°C for 48 h. The supernatants from these samples were used to measure glucose, citramalate, and acetate concentrations. These analyses were performed using an UltiMate 3000 HPLC system  equipped with an Aminex HPX-87H ion-exclusion column  and a RefractoMax520 RI detector . The mobile phase used was 0.1% (v/v) trifluoroacetic acid (TFA) in Milli-Q water.Continuous cultures were grown in a DASboxramalate . The plaregation . Glucoseified MS  medium (gL−1), and different dilution rates.The validation of the model was carried out by comparing several simulation runs of the designed FN, see in vivo, and in silico by the FN model. The columns of the table are divided in three parts: the first part sets the experimental parameters, i.e., the glucose concentration in the medium and the dilution rate for the glucose-limited continuous cultures; the second part reports the in vivo experimental results; and the third part reports the in silico results. For each pair of experimental parameters (glucose in medium and dilution rate), the columns corresponding to the “In vivo results” report the cell density (column “Biomass”), the concentration of citramalate , and the concentration of glucose  measured in the tank. In order to validate the model, the concentrations of citramalate and glucose in the tank are computed for each set of experimental parameters (glucose in medium and dilution rate) and measured biomass. The columns corresponding to the “In silico results” report the computed concentration of citramalate in the tank , the relative error of such predicted concentrations with respect to the measured in vivo concentrations , and the computed concentration of glucose in the tank .in vivo” of For a specific concentration of glucose and dilution rate, the biomass and the citramalate concentration reached the values showed in columns “Biomass” and “Citramalate in tank in vivo”) are consistent with the results obtained by the simulation of the FN model  for the production of citramalate. The most similar outcome occurs when the glucose concentration was 10 gL−1 as shown in the “Citramalate relative error” column in The results obtained for a set of experiments ”). This fact is correctly predicted by the FN model ”).Notice that, in all cases, the supplied glucose is used up by the culture, i.e. the concentration of residual glucose is 0 Once the model was validated, it was exploited to estimate the theoretical maximum productivity see  of citraVP) and productivity on substrate (PS) of citramalate are reported in the heatmaps in VP was reached when the glucose concentration was 11.0 gL−1 and the dilution rate was 0.51 h−1, such a productivity is obtained with a biomass of 3.14 gDW L−1. With respect to PS, the highest value was obtained for a glucose concentration of 1.0 gL−1 and a dilution rate of 0.51 h−1, such a productivity is obtained with a biomass of 0.275 gDW L−1. It is important to note that the maximum productivities for all concentrations of glucose are obtained when the dilution rate is 0.51 h−1. As expected, the amount of biomass necessary to maximize the productivity increases as the dilution rate and the glucose concentration increase, The theoretical maximum volumetric productivity (in silico results were obtained by fnyzer (The reported y fnyzer  which try fnyzer  to compuThe mass production of commodity chemicals from fossil fuels can entail a serious negative impact in the environment. As a consequence, alternative approaches are being designed in order to redirect chemical production to more sustainable methods. However, the implementation of these novel approaches at an industrial scale requires optimization before they can replace traditional methods. To achieve this, biotechnology needs to exploit the advantages offered by computational models. Models can provide guidance for the design of experiments, give insights about the underlying mechanisms of the system, perform predictions and rule out infeasible hypotheses. Given the speed at which models can be simulated and optimized, they can save significant amounts of time, effort, and money in the wet lab.In the last 2 decades, different modeling approaches have been developed, and particular attention has been paid to models of metabolism. In this work, it has been proven that the modeling formalism of FNs can integrate genome-scale constraint-based models, which lack detailed kinetic information, and kinetic models, which account for the concentration of the compounds of the system and are expressed as differential equations. Furthermore, FNs can also accommodate uncertainties inherent to the model, for example, partially unknown parameters.An FN is represented as the combination of two nets: the event net and the intensity net. The event net models the stoichiometry, whilst the intensity net models the system dynamics. Such a graphical representation produces an overall view of the whole system. The analysis of an FN relies on the solution of a programming problem derived from the FN. If all the reaction rates are linear, i.e., the FN does not have guards, then the resulting programming problem includes only real variables with linear and quadratic constraints that can be solved very efficiently. As a consequence, FNs can handle efficiently genome-scale metabolic networks whose kinetic information is given by flux bounds and linear expressions that define the quantity of metabolites.In contrast, if the reaction rates are not linear, they need to be approximated by piecewise linear functions, i.e. a guarded FN, which results in programming problems with real and binary variables. The complexity of the algorithms to solve mixed-integer programming problems is exponential in the number of binary variables. In order to obtain a balance between computational burden and accuracy of the model, the number of regions, and hence the number of binary variables, can be modified.in vivo experiments, especially, the ones in which the glucose concentration in the medium was 10 gL−1.In Although the simulations were highly predictive, the model could be improved further by adding some additional information, such as more components in the culture medium, or constraining the uptake of glucose depending on the glucose import rate, and similarly with the citramalate export rate. Both rates depend on protein transporters that can be saturated. Furthermore, the implementation of omics data could improve the model as well .in vivo experiments, but it can also help guide these experiments and optimize the conditions without wasting time and resources. The optimization performed in in vivo the conditions that maximized the productivity in the computational simulations.Not only was this method useful to reproduce the results of the


Bactrocera dorsalis as the study model, we investigated how protein feeding regulated sex pheromone synthesis. We show that protein ingestion is essential for sex pheromone synthesis in male. While protein feeding or deprivation did not affect Bacillus abundance, transcriptome analysis revealed that sarcosine dehydrogenase (Sardh) in protein-fed males regulates the biosynthesis of sex pheromones by increasing glycine and threonine (sex pheromone precursors) contents. RNAi-mediated loss-of-function of Sardh decreases glycine, threonine, and sex pheromone contents and results in decreased mating ability in males. The study links male feeding behavior with discrete patterns of gene expression that plays role in sex pheromone synthesis, which in turn translates to successful copulatory behavior of the males.Protein feeding is critical for male reproductive success in many insect species. However, how protein affects the reproduction remains largely unknown. Using  Females in many animal species are ‘investment breeders’, foraging for reproductive resources during adulthood, which are directed into offspring production . IntriguBactrocera dorsalis males can significantly enlarge after protein consumption  phe release . B. tryo protein  (3) mati protein ; (4) spe protein  and the  protein ; (5) inh protein . Convers protein .B. dorsalis, one of the most economically important tephritid species, attract females by emitting a pheromone produced by bacteria in the rectal glands  rectum microbial communities were inferred by 16S rRNA gene quantification and amplicon sequencing . The resBacillus, glucose, and threonine/glycine are three essential factors for sex pheromone synthesis, we further measured glucose and threonine/glycine contents to confirm whether protein supplementation regulates sex pheromone synthesis by influencing rectal glucose or threonine/glycine contents. The results showed that YH feeding only increased glucose contents in males in the sucrose and fructose groups . Principal component analysis (PCA) using the expression profiles of the identified genes indicated that YH-deprived males were significantly different from the YH-fed ones . PearsonB. dorsalis, sex pheromones can only be produced 9 d after emergence  male mergence . Thus, wmergence . RNA-seqmergence . DEG anamergence . KEGG anmergence . Consistmergence . The higB. dorsalis.We next focused on genetically testing whether Sardh was necessary for sex pheromone biosynthesis. To this end, we first measured the relative expression level of Sardh in the head, thorax, and rectum of mature males (12 d old) fed with YH and sucrose to further confirm that Sardh plays a role in sex pheromone biosynthesis in tissue. The qPCR results showed that Sardh was indeed highly expressed in the rectum . We thenB. dorsalis was indicated. Highly expressed Sardh can convert protein into threonine and glycine, which can be used by the rectum Bacillus to synthesize the sex pheromone  was upregulated and ltaE (that converts glycine to threonine) was not differentially expressed. Does YH provide threonine directly to the flies, or are there other ways to synthesize threonine? These questions also need to be confirmed by further experiments. Moreover, we found that Sardh knockdown males showed significantly decreased rectal threonine and glycine contents, but TTMP level was not significantly reduced  can be converted into TMP  orchard in Guangzhou, Guangdong Province, was reared under laboratory conditions ). A maize-based artificial diet containing 150 g of corn flour, 150 g of banana, 0.6 g of sodium benzoate, 30 g of yeast, 30 g of sucrose, 30 g of paper towel, 1.2 ml of hydrochloric acid, and 300 ml of water was used to feed the larvae. Adults were fed a solid diet (consisting of 50 g YH [protein food] and 50 g sugar) and 50 ml sterile water. Approximately, 200 adults were held in a 35 cm × 35 cm × 35 cm wooden cage. To test the effect of protein supplementation on sex pheromone synthesis, flies fed only sugar and sterile water were also prepared. To test the effect of different type of sugar on sex pheromone synthesis, flies fed with sucrose, glucose, or fructose as sugar were also prepared.The 60 rectums of 12 d old males fed different types of diets were dissected at 20:00 P.M. Sex pheromones in the rectum were extracted with 500 µl n-hexane by shaking (180 rpm) in a 30°C incubator for 24 hr. Then, GC‒MS with an Agilent 7890B Series GC system coupled to a quadrupole-type-mass-selective detector  was used to identify sex pheromones in the rectum extraction according to our previous method .Flies that were fed with and without YH were prepared to determine the effect of protein on biological parameters . To study survival, the studies were initiated with six groups of newly emerged males . Each group was maintained separately and was provided different types of food . The mortality of the males was recorded each day until the males matured (12 d later). The rectum width of the mature males was measured.The glucose content in the rectum of the mature males was measured with a glucometer. To determine glucose content, the rectums of 12 mature males were collected and placed in a 1.5 ml microcentrifuge tube containing 10 μl of sterile Milli-Q water. Then, the samples were ground with a grinding machine. The samples were centrifuged for 15 min at 12,000 rpm. Then, the supernatants were collected and analyzed with a glucometer . Then, glucose contents were normalized to rectum weight and compared between different treatments.For threonine and glycine identification, sample preparation for free amino acid analysis was performed as described by Mating competition between YH-deprived males and control males was performed in a 35 cm × 35 cm × 35 cm wooden cage. Briefly, 60 mature males with colored pronota  were placed in one cage, and then 30 mature unmated females were placed in the cage at 8:00 P.M. Mating behavior was observed for 2 hr, and the number of mated males was recorded and compared. Five replicates were conducted for each diet pair.To analyze bacterial diversity in the male rectum, the rectums of five males fed different foods were collected (five replicate samples were prepared). Then, bacterial DNA was extracted from the rectum samples using the Bacterial Genomic DNA Extraction Kit  according to the manufacturer’s protocol. qPCR (16S-338F and 16S-518R primers were used ) was useB. dorsalis was built, and paired-end clean reads were mapped to the reference genome using HISAT2. 2.4 with ‘-rna-strandness RF’ and other parameters set as a default  were compared. For each group, five rectums were dissected for RNA extraction. In addition, five replicates were included for each group. In the next step, paired-end RNA-seq libraries were prepared by following Illumina’s library construction protocol. The libraries were sequenced on an Illumina HiSeq2000 platform . FASTQ files of raw reads were produced and sorted by barcodes for further analysis. Prior to assembly, paired-end raw reads from each cDNA library were processed to remove adaptors, low-quality sequences (Q<20), and reads contaminated with microbes. The clean reads were de novo assembled to produce contigs. An index of the reference genome of  default . To eval default . Then, g default . Genes/tα-tubulin and actin genes were used as reference genes  using the extracted RNA. Then, a PerfectStarTM Green qPCR SuperMix Kit  was used to perform quantitative real-time PCR to compare the gene expression levels. Gene-specific primers  were desce genes . The PCRdsRNA primers  tailed whttps://www.graphpad.com/.Statistical analysis methods used in the study were indicated in the figure legends. Differences were considered significant when p<0.05. All data were analyzed using the GraphPad Prism version 8, GraphPad Software, La Jolla, CA, USA,  The manuscript describes the effects of dietary yeast and sugars on male Bactrocera dorsalis sex pheromone biosynthesis and other mating-related traits. This is an important study showing that yeast feeding stimulates the production of specific sex pheromones and promotes fly mating ability. The data are solid and will be of interest to the fields of chemical ecology and pest management. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, including Sonia Sen as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by K VijayRaghavan as the Senior Editor.Thank you for submitting your article "Protein feeding mediates sex pheromone biosynthesis in an insect" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. To strengthen the claim that rectal bacilli use the fly's dietary amino acid to make TMP/TTMP, we recommend that the authors demonstrate that in the absence of bacilli, the dietary amino acids do not make a difference to TMP/TTMP level or mating success.2. Increase in levels of glycine and threonine: There is a possibility that these levels are elevated because the flies fed on YH and not because a specific threonine and glycine biosynthesis pathway was elevated. Could the authors please explicitly discuss this?3. Figures: The figures could use considerable improvement. Please see the detailed reviews below for specifics.4. Text: We request that the authors explain their experimental setups in more detail. Could they also please describe their results before they infer from them?5. Please normalise the glycine/threonine content to tissue weight or total protein content rather than the sample volume.In addition to this, please read the detailed reviews provided below and address them wherever possible.Reviewer #1 (Recommendations for the authors):The link between dietary protein and reproductive success is well documented in females of various species. Here, the authors investigate this link in male Oriental Fruitflies, which are a major agricultural pest.In their previous work, the authors demonstrated that bacteria in the male rectum produce sex pheromones to attract females. In this study, the authors demonstrate that dietary protein triggers the expression of the gene, sarcosine dehydrogenase (sardh) in the male rectum. This, in turn, results in an increase in the levels of glycine and threonine, which are the precursors used by bacteria to produce the sex pheromones TMP and TTMP. Consequently, they demonstrate that protein feeding in males is necessary for mating success.We found the manuscript interesting, and the claims generally well supported, particularly in the context of their previous work. Our recommendations are largely for clarity and presentation of data.1. Figures: The figures could use considerable improvement. In some cases the graphs are too small to decipher the various conditions, occasionally the resolution is low, and 3D graphs are not ideal. In general, we recommend the use of schematics depicting the experiments and their conditions, and a final schematic to demonstrate their working model will be nice.2. Text: It would help to have the experimental conditions better explained. For example, the considerations of the various sugar conditions are not really addressed in the text except in a single sentence. The interpretations from these different sugar conditions are also not really discussed, leaving one wondering why they were introduced in the first place. It would also help to have the experimental design briefly discussed in the Results section so that the results are easily interpretable by the reader without having to go to the methods.3. The link between rectal microbial communities and TMP: The authors make the claim that yeast feeding does not affect bacterial communities. I'm not sure if this claim can be made based on the data, or at least this level of analysis of the data. I am not very familiar with community ecology analyses, but the indices used here are relative abundances of bacterial classes and overall richness. It's suggestive of their claim, but there is a possibility that species composition might be different in these conditions. If the authors agree, perhaps they could bring this point out and temper their claims.+ and YH- in each sugar group. The assessment to be made is whether the amount of glucose is affected by yeast feeding or not. So, should the comparison not be made across sugar groups? Glucose will naturally be higher in a glucose-fed fly, hence the apparent lack of difference between YH- and YH+.4. The sentence in 107-108: "Yeast feeding only positively influenced glucose contents in males in the sucrose and fructose groups ." The comparison seems to be being made between YH5. It wasn't clear to me whether the authors were making the claim that Sardh levels were increased with age irrespective of the protein feeding. This would be interesting! The experimental setup of this section was particularly unclear to me.6. The methods, for example, the bioinformatics pipelines, could be described better, particularly since this is a non-traditional model organism. Was the functional enrichment analysis done keeping the whole genome as the background? This won't help in determining the enrichment of genes within a specific tissue.Reviewer #2 (Recommendations for the authors):Gui et al. investigated the effects of dietary yeast and sugars on male Bactrocera dorsalis sex pheromone biosynthesis and other mating-related traits . The authors first compared male B. dorsalis fed on diets with or without yeast hydrolase ("YH-supplemented" vs. "YH-deprived") and on three different sugars , showing that the sex pheromone compounds (particularly TMP) were detected in YH-supplemented male fly rectums but are absent in YH-deprived counterparts. The mating ability was also significantly lower in the YH-deprived males. Following the initial observations, the team conducted a series of experiments, including 16S amplicon seq, RNAseq, and gene knockdown by RNAi to identify the mechanism underlying these effects.The manuscript is overall well-written, and the figures are nicely presented. Results demonstrating the importance of the sarcosine dehydrogenase (Sardh) gene in sex pheromone biosynthesis and mating are novel. However, I have reservations about some of the statements and data interpretation.1. Previous work from the group suggests that the rectal bacteria can produce sex pheromones in male B. dorsalis . However, the involvement of bacteria in the dietary yeast effects on sex pheromone biosynthesis and mating is not clear in this study. The microbiome data suggest that the dietary yeast treatments did not affect the dominant rectal bacterium Bacillus abundance. The only indirect evidence was that rectal threonine and glycine appeared to be elevated in YH-fed flies, and Bacillus can use these amino acids to produce sex pheromones. In my opinion, the data present are not sufficient to support statements like "We show that male flies rely on rectal Bacillus to produce a complex sex pheromone. (L11-12)", "This study clearly links male feeding behavior with discrete patterns of gene expression that lead to pheromone production by rectal Bacillus… (L18-20)", "This is the first report that clarifies the molecular mechanism by which host feeding and rectal microbes co-regulate sex pheromone biosynthesis. (L173-174)". For these statements to be valid, one would need to use flies deprived of Bacillus or the microbiome to confirm that there is no difference between YH-fed and YH-deprived flies.2. Some sections of the manuscript would benefit from clarification, for instance,a. The authors reasoned that YH feeding would stimulate threonine and glycine synthesis pathways (L116-117) when they justified focusing on glycine/threonine metabolism DEGs. This is an interesting prediction but presumably, yeast can provide these amino acids directly as a protein source. Knowing how much threonine and glycine are available in the YH might be helpful.b. The RNAseq data identified two DEGs, Sardh and AGXT2, associated with glycine or threonine synthesis. As shown in Figure 3E, Sardh can convert Sarcosine to glycine, but not much information was provided regarding where Sarcosine may come from. Does the YH provision Sarcosine to the fly? What is the alternative hypothesis for the Sardh upregulation by YH feeding if not?3. Based on the fold changes and p values, the positive effect of YH feeding on rectal threonine level is more significant than on glycine . Sardh upregulation can explain the elevated glycine level in YH-fed flies, but ltaE (that converts glycine to threonine) was not differentially expressed. The genetic basis for the elevated threonine seems unresolved and is worthy of more in-depth discussion.4. Figure 1C shows that TTMP (blue arrow) is only detected in the glucose + YH treatment but not in the sucrose + YH or fructose + YH treatments. What are the possible explanations?5. In Figure 5, Sardh knockdown males showed significantly decreased rectal threonine and glycine contents, but TTMP level was not significantly reduced. What are the possible explanations?6. Please indicate the sample time (day 12?) of rectal glycine/threonine for figures 2 and 5.7. For RNAseq analysis, the FPKM normalization method is generally not recommended for between-sample comparisons for DE analysis. Suggest using the median of ratios method in DESeq2 or TMM in EdgeR.8. L203: "Our results indicate that the amount of sex pheromone produced is significantly affected by the type of sugar ." – I think the authors were referring to Figure 1c, not 2c.9. To measure rectal glycine/threonine content, the team collected 15 male recta per replicate of each treatment group. While rectal width did not differ between the YH-supplemented and YH-deprived flies, it's unclear whether the total protein content or mass was different between the groups. My concern is that the lower glycine/threonine level could be due to the lower amount of tissue collected from the YH-deprived flies. I also noticed that the scale of glycine content varied quite a bit among experiments . Normalizing the glycine/threonine content to tissue weight or total protein content will be more appropriate than the sample volume. Essential revisions:1. To strengthen the claim that rectal bacilli use the fly's dietary amino acid to make TMP/TTMP, we recommend that the authors demonstrate that in the absence of bacilli, the dietary amino acids do not make a difference to TMP/TTMP level or mating success.Thanks for your recommendation. In previous study, we have found that flies can't synthesize sex pheromones when recta bacilli were removed by antibiotics treatment . Therefore, it is difficult to assess the effect of dietary amino acids on sex pheromones in the absence of bacilli . To make the claim clearer, we have mentioned this in the manuscript. See line 79-84. We hope the explanation will meet with approval.2. Increase in levels of glycine and threonine: There is a possibility that these levels are elevated because the flies fed on YH and not because a specific threonine and glycine biosynthesis pathway was elevated. Could the authors please explicitly discuss this?Thanks for your comments. We agree with your opinion that glycine and threonine levels may be elevated because the flies fed on YH. We have discussed such possibility and the ways to make it clear in discussion. See line 196-199. We hope the correction will meet with approval.3. Figures: The figures could use considerable improvement. Please see the detailed reviews below for specifics.Thank you very much for you valuable suggestions. We have made corrections to the figures.4. Text: We request that the authors explain their experimental setups in more detail. Could they also please describe their results before they infer from them?Thanks for your suggestion. Experimental setups were described in more detail with more statements and schematics. And we have described the results in the result part. We hope the corrections will meet with approval.5. Please normalise the glycine/threonine content to tissue weight or total protein content rather than the sample volume.We have done this for both glycine/threonine contents and glucose contents.In addition to this, please read the detailed reviews provided below and address them wherever possible.Reviewer #1 (Recommendations for the authors):The link between dietary protein and reproductive success is well documented in females of various species. Here, the authors investigate this link in male Oriental Fruitflies, which are a major agricultural pest.In their previous work, the authors demonstrated that bacteria in the male rectum produce sex pheromones to attract females. In this study, the authors demonstrate that dietary protein triggers the expression of the gene, sarcosine dehydrogenase (sardh) in the male rectum. This, in turn, results in an increase in the levels of glycine and threonine, which are the precursors used by bacteria to produce the sex pheromones TMP and TTMP. Consequently, they demonstrate that protein feeding in males is necessary for mating success.We found the manuscript interesting, and the claims generally well supported, particularly in the context of their previous work. Our recommendations are largely for clarity and presentation of data.Thank you very much for your positive comments.1. Figures: The figures could use considerable improvement. In some cases the graphs are too small to decipher the various conditions, occasionally the resolution is low, and 3D graphs are not ideal. In general, we recommend the use of schematics depicting the experiments and their conditions, and a final schematic to demonstrate their working model will be nice.Thanks for your suggestion. We have improved the figures by increasing the resolution. And we have added schematics to demonstrate the working model. We hope the corrections will meet with approval.2. Text: It would help to have the experimental conditions better explained. For example, the considerations of the various sugar conditions are not really addressed in the text except in a single sentence. The interpretations from these different sugar conditions are also not really discussed, leaving one wondering why they were introduced in the first place. It would also help to have the experimental design briefly discussed in the Results section so that the results are easily interpretable by the reader without having to go to the methods.We are sorry for the confusion caused. We have described the experimental conditions in detail in the result and method part. See line 68-72, line 242-243. We hope the corrections will meet with approval.3. The link between rectal microbial communities and TMP: The authors make the claim that yeast feeding does not affect bacterial communities. I'm not sure if this claim can be made based on the data, or at least this level of analysis of the data. I am not very familiar with community ecology analyses, but the indices used here are relative abundances of bacterial classes and overall richness. It's suggestive of their claim, but there is a possibility that species composition might be different in these conditions. If the authors agree, perhaps they could bring this point out and temper their claims.We agree with you very much. In order to clarify this issue, we have rewritten this part. We have only highlighted the effect of protein supplementation on microbes at the class level. And we have tempered our claims by saying that the results indicate that protein intake may not affect the abundance of Bacillus synthesizing pheromones in the rectum and sex pheromone loss in YH-deprived males may not be associated with Bacillus. See line 88-97. Thanks again for your important suggestions. We hope the revision will meet with approval.+ and YH- in each sugar group. The assessment to be made is whether the amount of glucose is affected by yeast feeding or not. So, should the comparison not be made across sugar groups? Glucose will naturally be higher in a glucose-fed fly, hence the apparent lack of difference between YH- and YH+.4. The sentence in 107-108: "Yeast feeding only positively influenced glucose contents in males in the sucrose and fructose groups ." The comparison seems to be being made between YHThanks for your comments. In our study, we found that protein supplementation can significantly affect the synthesis of sex pheromones. Therefore, we wanted to further confirm which part  in sex pheromone synthesis was affected by protein supplementation. In the issue you have mentioned, we want to clarify whether protein supplementation affects sex pheromone synthesis by influencing the rectal glucose contents. We are sorry that we didn't make this issue clear. We have restated the purpose of such experiments in the manuscript. See line 98-104. We hope the explanation will meet with approval.5. It wasn't clear to me whether the authors were making the claim that Sardh levels were increased with age irrespective of the protein feeding. This would be interesting! The experimental setup of this section was particularly unclear to me.We are sorry for the unclear description of the experimental setup. In our previous study, we have found that sex pheromones can only be produced 9 days after emergence in normally reared (sugar and protein were both provided) male B. dorsalis. Thus, we infer the reason may be that Sardh levels were increased in mature stage to synthesize amino acid for sex pheromone synthesis. And we can set such experiment to verify the potential role of Sardh in influencing sex pheromone synthesis. We have made correction in the results and method part. Sorry again for the confusion caused. We hope the corrections and explanation will meet with approval.6. The methods, for example, the bioinformatics pipelines, could be described better, particularly since this is a non-traditional model organism. Was the functional enrichment analysis done keeping the whole genome as the background? This won't help in determining the enrichment of genes within a specific tissue.Thanks for your comments. Before we did pathway enrichment, we already knew that threonine/glycine contents were affected by protein supplementation. Therefore, the purpose of pathway enrichment analysis is to further screen genes involved in synthesizing threonine/glycine. Doing functional enrichment analysis with the whole genome as the background is commonly used in many literatures. We certainly agree with you that using genes expressed in the rectum as background will make the results more credible. And we re-did the enrichment analysis with the method you recommended, and the results also showed that the glycine and threonine metabolism pathway was significantly enriched. Thanks again for your valuable comments. We hope our explanation will meet with approval.Reviewer #2 (Recommendations for the authors):Gui et al. investigated the effects of dietary yeast and sugars on male Bactrocera dorsalis sex pheromone biosynthesis and other mating-related traits . The authors first compared male B. dorsalis fed on diets with or without yeast hydrolase ("YH-supplemented" vs. "YH-deprived") and on three different sugars , showing that the sex pheromone compounds (particularly TMP) were detected in YH-supplemented male fly rectums but are absent in YH-deprived counterparts. The mating ability was also significantly lower in the YH-deprived males. Following the initial observations, the team conducted a series of experiments, including 16S amplicon seq, RNAseq, and gene knockdown by RNAi to identify the mechanism underlying these effects.The manuscript is overall well-written, and the figures are nicely presented. Results demonstrating the importance of the sarcosine dehydrogenase (Sardh) gene in sex pheromone biosynthesis and mating are novel. However, I have reservations about some of the statements and data interpretation.Thanks for your positive comments.1. Previous work from the group suggests that the rectal bacteria can produce sex pheromones in male B. dorsalis . However, the involvement of bacteria in the dietary yeast effects on sex pheromone biosynthesis and mating is not clear in this study. The microbiome data suggest that the dietary yeast treatments did not affect the dominant rectal bacterium Bacillus abundance. The only indirect evidence was that rectal threonine and glycine appeared to be elevated in YH-fed flies, and Bacillus can use these amino acids to produce sex pheromones. In my opinion, the data present are not sufficient to support statements like "We show that male flies rely on rectal Bacillus to produce a complex sex pheromone. (L11-12)", "This study clearly links male feeding behavior with discrete patterns of gene expression that lead to pheromone production by rectal Bacillus… (L18-20)", "This is the first report that clarifies the molecular mechanism by which host feeding and rectal microbes co-regulate sex pheromone biosynthesis. (L173-174)". For these statements to be valid, one would need to use flies deprived of Bacillus or the microbiome to confirm that there is no difference between YH-fed and YH-deprived flies.Thanks for your important comments. Although we have shown in previous studies that Bacillus can synthesize sex pheromones, these conclusions are not part of this study. In order to be more precise, we have rephrased or deleted the statements in the manuscript. Thanks again for your valuable comments. We hope the corrections will meet with approval.2. Some sections of the manuscript would benefit from clarification, for instance,a. The authors reasoned that YH feeding would stimulate threonine and glycine synthesis pathways (L116-117) when they justified focusing on glycine/threonine metabolism DEGs. This is an interesting prediction but presumably, yeast can provide these amino acids directly as a protein source. Knowing how much threonine and glycine are available in the YH might be helpful.Thanks for your important comments. We agree with your opinion that glycine and threonine levels may be elevated because the flies fed on YH. And the editor also points out that we need to discuss such possibility. However, our results at least indicate that glycine/threonine metabolism pathway is one of the factors regulating glycine/threonine and sex pheromone synthesis in rectum. Taking the editor's advice into consideration, we have discussed such possibility and the ways to make it clear in discussion. See line 196-199. We hope the corrections will meet with approval.b. The RNAseq data identified two DEGs, Sardh and AGXT2, associated with glycine or threonine synthesis. As shown in Figure 3E, Sardh can convert Sarcosine to glycine, but not much information was provided regarding where Sarcosine may come from. Does the YH provision Sarcosine to the fly? What is the alternative hypothesis for the Sardh upregulation by YH feeding if not?Thank you very much for your important comments. Indeed, the issues you have raised are critical for understanding the mechanism that protein supplementary regulated sex pheromone synthesis. We need a series of experiments to clarify these issues, which is almost a whole other topic. However, we agree with you very much. Considering the suggestions of the editor, we have carried out an in-depth discussion in the discussion part, and proposed further research directions. See line 216-230. Thanks again for your comments. We hope our corrections will meet with approval.3. Based on the fold changes and p values, the positive effect of YH feeding on rectal threonine level is more significant than on glycine . Sardh upregulation can explain the elevated glycine level in YH-fed flies, but ltaE (that converts glycine to threonine) was not differentially expressed. The genetic basis for the elevated threonine seems unresolved and is worthy of more in-depth discussion.We agree with your opinion. We inferred elevated threonine may come from two sources. One is provided directly by YH, another one is converted by other genes. However, we need more date to confirm such speculation. According to your suggestions and the editor’s, we have mentioned this in the discussion part. See line 216-230. We hope the correction will meet with approval.4. Figure 1C shows that TTMP (blue arrow) is only detected in the glucose + YH treatment but not in the sucrose + YH or fructose + YH treatments. What are the possible explanations?We are very sorry for our unclear description. TTMP has been detected in the glucose + YH, sucrose + YH and fructose + YH treatments. In order to show the results clearly, we added some extra marks in the Figure. Sorry again for our negligence. We hope the correction will meet with approval.5. In Figure 5, Sardh knockdown males showed significantly decreased rectal threonine and glycine contents, but TTMP level was not significantly reduced. What are the possible explanations?Thanks for your important comments. In the pyrazine synthesis pathway of Bacillus, two molecules of glucose can be converted into TTMP, while one molecule of glucose and one molecule of threonine (or glycine) can be converted into TMP . Therefore, we speculate that the reason why TTMP level is not affected is that glucose content in the rectum is not regulated by Sardh. Thanks for your question. We are sorry that we didn’t discuss this in the manuscript. In the revised manuscript, we have added the explanations in discussion. See line 216-230. We hope the revisions will meet with approval.6. Please indicate the sample time (day 12?) of rectal glycine/threonine for figures 2 and 5.Thank you very much for your reminding. The flies used were mature males (12-day-old). We have mentioned this in the manuscript.7. For RNAseq analysis, the FPKM normalization method is generally not recommended for between-sample comparisons for DE analysis. Suggest using the median of ratios method in DESeq2 or TMM in EdgeR.Thanks for your suggestions. We have found that both FPKM and TPM are used for quantitative analysis in transcriptome in many literatures. Although the quantitative methods are different, the results obtained are highly consistent. And FPKM is still the main reference transcriptome quantitative method at present. We analyzed the date again with the method you recommended and found it was highly consistent with our previous results. So we think there is no problem in using FPKM normalization method. Thanks again for your recommendation. We hope our explanation will meet with approval.8. L203: "Our results indicate that the amount of sex pheromone produced is significantly affected by the type of sugar ." – I think the authors were referring to Figure 1c, not 2c.We are sorry for our mistake. We have corrected it.9. To measure rectal glycine/threonine content, the team collected 15 male recta per replicate of each treatment group. While rectal width did not differ between the YH-supplemented and YH-deprived flies, it's unclear whether the total protein content or mass was different between the groups. My concern is that the lower glycine/threonine level could be due to the lower amount of tissue collected from the YH-deprived flies. I also noticed that the scale of glycine content varied quite a bit among experiments . Normalizing the glycine/threonine content to tissue weight or total protein content will be more appropriate than the sample volume.Thank you very much for your valuable suggestions. We have normalized both glycine/threonine content and glucose content to tissue weight in the revised manuscript.