Bacillus amyloliquefaciens genome-scale metabolic network model, construction method and application

Abstract

The application discloses a bacillus amyloliquefaciens genome-scale metabolic network model, a construction method and application, and belongs to the field of system biology. The method comprises the following steps: firstly, a rough model of bacillus amyloliquefaciens is automatically constructed according to protein sequencing results of bacillus amyloliquefaciens in a Uniport database; secondly, the rough model is manually refined and the synthesis reaction of acetoin is added according to a literature database, a metabolic pathway diagram and a biochemical database, so as to construct a genome-scale metabolic network model; thirdly, the genome-scale metabolic network model is converted into a computer-readable mathematical model; and finally, the mathematical model is analyzed by using a flux balance analysis method, and compared with experimental values in the literature. The method has the advantages of short construction time, simple operation and high accuracy, can systematically predict the nutritional conditions for the growth of bacillus amyloliquefaciens, and provides a feasible basis for improving the yield of acetoin and industrial production.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of systems biology, and particularly relates to a Bacillus amyloliquefaciens genome-scale metabolic network model, a construction method and application. BACKGROUND

[0002] Acetoin (3-hydroxybutanone) can be used as a food additive in various condiments, and also as a platform compound in the fields of functional materials, pharmaceutical production and chemical synthesis, etc., with a huge market demand. Bacillus amyloliquefaciens is an advantageous strain for the fermentation production of acetoin, and has the advantages of not producing exotoxin and endotoxin, and thus has great potential for the fermentation production of acetoin. However, due to the complex metabolic network, the un-discovered part of key genes and other problems, the yield of metabolites is low, which greatly limits the industrialization of various products. Therefore, it is necessary to systematically explore the physiological metabolic characteristics of Bacillus amyloliquefaciens to provide a basis for the optimization of fermentation conditions and the improvement of metabolite yield.

[0003] The genome-scale metabolic network model contains most of the biochemical reactions occurring inside the given microorganism, which can provide an efficient platform to globally explore the physiological metabolic functions of the microorganism, and can be used to predict and analyze the growth of the microorganism, thereby providing an effective basis for improving the fermentation production of metabolites by the microorganism. At present, the construction methods of the genome-scale metabolic network model include manual construction and automatic construction, wherein the accuracy of the manual construction is relatively high, but a large amount of database queries, simplification and other operations are required in the specific construction, which has the defects of long time consumption and complicated operation; the automatic construction can realize rapid and batch construction of the model, but the accuracy of the constructed model is low. In addition, as of now, there is no related report on the Bacillus amyloliquefaciens genome-scale metabolic network model and the use thereof to predict and improve the yield of acetoin. SUMMARY

[0004] The application aims to provide a Bacillus amyloliquefaciens genome-scale metabolic network model, a construction method and application, which can systematically predict and analyze the nutritional conditions required for the growth of Bacillus amyloliquefaciens, thereby providing a feasible basis for improving the yield of acetoin and industrial production.

[0005] In order to achieve the above-mentioned purpose, the technical scheme of the application is as follows:

[0006] The first aspect of the application provides a construction method of a Bacillus amyloliquefaciens genome-scale metabolic network model, which comprises the following steps:

[0007] Constructing a coarse model: obtaining the proteome sequencing results of Bacillus amyloliquefaciens from the Uniport database, and automatically constructing a coarse model of Bacillus amyloliquefaciens in the Model SEED database;

[0008] Manual refinement: according to the literature database, metabolic pathway map and KEGG, BiGGModels, MetaCyc database, the missing metabolic reactions in the coarse model are filled and the invalid metabolic reactions are deleted, and then the synthesis reaction of acetoin is added, to construct a genome-scale metabolic network model with “gene-protein-reaction correlation” relationship;

[0009] Mathematical model transformation: importing the genome-scale metabolic network model into the Matlab platform with Cobra toolbox, and converting it into a computer-readable mathematical model;

[0010] Model verification and analysis: using flux balance analysis method to analyze the mathematical model, and comparing with the experimental values in the literature.

[0011] In an optional implementation of the first aspect, the Bacillus amyloliquefaciens is Bacillus amyloliquefaciens FMME044.

[0012] The second aspect of the present application provides a Bacillus amyloliquefaciens genome-scale metabolic network model constructed by the method of the first aspect.

[0013] The third aspect of the present application provides the application of the Bacillus amyloliquefaciens genome-scale metabolic network model constructed by the method of the first aspect in predicting and improving the yield of acetoin.

[0014] In an optional implementation of the third aspect, the application method of predicting and improving the yield of acetoin comprises:

[0015] In the analysis of the Bacillus amyloliquefaciens genome-scale metabolic network model, the influence of metal ion and oxygen absorption rate on acetoin synthesis is analyzed by using robustness analysis method.

[0016] In an optional implementation of the third aspect, the analysis of the Bacillus amyloliquefaciens genome-scale metabolic network model further comprises:

[0017] The target for improving the yield of acetoin is predicted by using MOMA algorithm.

[0018] In an optional implementation of the third aspect, the target comprises 36 up-regulated targets and 25 knockout targets.

[0019] The fourth aspect of the present application provides a method for fermenting Bacillus amyloliquefaciens to produce acetoin, which comprises the following steps:

[0020] Initial culture: Bacillus amyloliquefaciens is inoculated into the initial culture medium, and initial culture is carried out at a temperature of 37℃, wherein the fermentation capacity is 50mL / 500mL, and the rotation speed is 200rpm; the fermentation capacity is 4L / 7L, and the rotation speed is 300-500rpm;

[0021] Fermentation culture: the initial culture medium is inoculated into the fermentation culture medium at a inoculation amount of 10v / v%, and fermentation culture is carried out at a temperature of 37℃, and the fermentation time is 60h, wherein the fermentation capacity is 50mL / 500mL, and the rotation speed is 200rpm.

[0022] In the fourth aspect, the fermentation culture further comprises: controlling the oxygen absorption rate to be 6-13mmol·gDW -1 ·h -1 .

[0023] In the fourth aspect, the initial culture medium comprises, in g / L: 120 parts of glucose, 10 parts of yeast powder, 10 parts of proteose peptone and 5 parts of K2HPO4.

[0024] The fermentation culture medium comprises, in g / L: 120 parts of glucose, 10 parts of soybean peptone, 10 parts of yeast powder, 3 parts of K2HPO4, 3 parts of KH2PO4, 5 parts of NaCl and 0.2 parts of Mg SO4·7H2O.

[0025] Compared with the prior art, the advantages or beneficial effects of the present application at least include:

[0026] The present application automatically constructs a Bacillus amyloliquefaciens rough model through the Model SEED database, manually simplifies the Bacillus amyloliquefaciens rough model, constructs a genome-scale metabolic network model with a "gene-protein-reaction correlation" relationship, and then uses the Matlab platform with the Cobra toolbox to perform mathematical simulation and verification analysis on the model, thereby realizing the rapid and batch construction of the Bacillus amyloliquefaciens genome-scale metabolic network model, greatly shortening the construction time and simplifying the specific operation, while making the model accurate up to 89.6%. At the same time, the model can be used to systematically predict and determine the nutritional conditions required for the growth of Bacillus amyloliquefaciens, and to provide a feasible basis for improving the yield of acetoin and industrial production. The examples demonstrate that the growth of Bacillus amyloliquefaciens can be significantly improved by predicting and analyzing the growth of Bacillus amyloliquefaciens through the model. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0028] Figure 1 The flowchart for constructing the genome-scale metabolic network model of Bacillus amyloliquefaciens provided by the embodiments of the present application;

[0029] Figure 2 The reaction distribution of the metabolic subsystems provided by the embodiments of the present application;

[0030] Figure 3 The Venn diagram for comparing the reactions in iJYQ746, iJA1121 and iYO844 provided by the embodiments of the present application;

[0031] Figure 4 The part of the reactions related to the genes in the metabolic subsystems provided by the embodiments of the present application;

[0032] Figure 5 The influence of metal ions Mn 2+ , Fe 2+ , Zn 2+ on the accumulation of acetoin;

[0033] Figure 6 The influence of the oxygen uptake rate on the acetoin synthesis rate predicted by the iJYQ749 model provided by the embodiments of the present application;

[0034] Figure 7 The acetoin yield in the fermenter at 300, 350, 400 and 500 rpm provided by the embodiments of the present application;

[0035] Figure 8 The acetoin flux and fPH value of the potential target after up-regulation provided by the embodiments of the present application;

[0036] Figure 9 The acetoin flux and fPH value of the potential target after gene knockout provided by the embodiments of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] In the following description of the embodiments, the term "and / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the cases of A alone, B alone and A and B existing at the same time. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after it are in an "or" relationship.

[0039] In the following description of the embodiments, the term "at least one" means one or more, and "multiple" means two or more. "At least one of the following (one)" or similar expressions means any combination of these items, including any combination of single (one) or multiple items. For example, "at least one of a, b or c", or "at least one of a, b and c", can represent a, b, c, a-b (i.e. a and b), a-c, b-c, or a-b-c, wherein a, b and c can be single or multiple.

[0040] Those skilled in the art should understand that in the following description of the embodiments of the present application, the order of the serial numbers does not mean the order of execution, and some or all steps can be executed in parallel or in sequence, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0041] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0042] In a first aspect, the embodiments of the present application provide a construction method of Bacillus amyloliquefaciens genome-scale metabolic network model, which comprises the following steps S101-S104.

[0043] S101-Construction of a rough model: Obtain the proteome sequencing results of Bacillus amyloliquefaciens from Uniport database, and automatically construct a rough model of Bacillus amyloliquefaciens in Model SEED database. The specific operation is to download the proteome sequence of Bacillus amyloliquefaciens in fasta format from Uniport database, and upload the proteome sequence to Model SEED platform to automatically construct Bacillus amyloliquefaciens model. Then the initial reaction list is obtained by homologous alignment method to integrate the proteome sequence, so as to construct a rough model of Bacillus amyloliquefaciens genome-scale metabolic network.

[0044] S102-manual refining: according to the metabolic pathway map, KEGG, BiGGModels, MetaCyc database and literature database and Web of Science and PubMed and the like literature database, first, the missing metabolic reactions in the rough model are filled and the repeated and wrong metabolic reactions are deleted, and then the synthesis reaction of acetoin is added to establish a perfect biomass equation, and a genome-scale metabolic network model with "gene-protein-reaction correlation" relationship is constructed;

[0045] S103-transformation of mathematical model: the basic information of the model is imported into the Matlab platform through the xls2model.m instruction of the Cobra 3.0 toolbox, and is converted into a computer-readable mathematical model;

[0046] S104-verification and analysis of the model: the mathematical model is analyzed by using the flux balance analysis method, and is compared with the experimental values in the literature. The specific operation is to simulate the metabolic condition of the microorganism under specific conditions by using the Matlab platform with Cobra 3.0 toolbox, and to compare with the experimental values in the literature.

[0047] The embodiment of the present application provides a construction method of Bacillus amyloliquefaciens genome-scale metabolic network model, so that the physiological metabolic characteristics of Bacillus amyloliquefaciens can be systematically explored, thereby providing a basis for optimization of fermentation conditions and improvement of metabolite yield. Specifically, the construction method of the embodiment of the present application comprises: first, automatically constructing a Bacillus amyloliquefaciens rough model through the Model SEED database, second, manually refining the Bacillus amyloliquefaciens rough model to construct a genome-scale metabolic network model with "gene-protein-reaction correlation" relationship, and finally, using the Matlab platform with Cobra toolbox to transform and verify the analysis of the model, so as to realize the rapid and batch construction of the Bacillus amyloliquefaciens genome-scale metabolic network model, greatly shorten the construction time and simplify the specific operation, and the model accuracy can be as high as 89.6%.

[0048] In specific embodiments of the first aspect, the Bacillus amyloliquefaciens is preferably Bacillus amyloliquefaciens FMME044.

[0049] In specific embodiments of the first aspect, the Bacillus amyloliquefaciens is preferably Bacillus amyloliquefaciens FMME044.

[0050] In a third aspect, the embodiments of the present application further provide an application of the Bacillus amyloliquefaciens genome-scale metabolic network model constructed by the method of the first aspect in predicting and improving the production of acetoin.

[0051] The Bacillus amyloliquefaciens genome-scale metabolic network model can be used to systematically explore the physiological metabolic characteristics of Bacillus amyloliquefaciens, and can be used in predicting and improving the production of acetoin, so as to effectively determine the influence of metal ion concentration, dissolved oxygen amount, up-regulated target points and knockout target points on the production of acetoin, thereby improving the production of acetoin by controlling the metal ion concentration, dissolved oxygen amount, up-regulated target points and knockout target points.

[0052] In specific embodiments of the third aspect, the application method of predicting and improving the production of acetoin preferably comprises: in the analysis of the Bacillus amyloliquefaciens genome-scale metabolic network model, using a robustness analysis method to analyze the influence of metal ion and oxygen absorption rate on the synthesis of acetoin.

[0053] Through the analysis of metal ions, it can be determined that metal ions Mn 2+ , Fe 2+ and Zn 2+ will inhibit the synthesis of acetoin, and the influence of Fe 2+ is the smallest, and the addition amount of metal ions Mn 2+ , Fe 2+ and Zn 2+ can be strictly limited in fermentation production; through the analysis of oxygen absorption rate, it can be determined that an oxygen absorption rate of 6-13 mmol·gDW -1 ·h -1 is helpful to significantly improve the production of acetoin.

[0054] In specific embodiments of the third aspect, the analysis of the Bacillus amyloliquefaciens genome-scale metabolic network model further comprises: using a MOMA algorithm to predict target points for improving the production of acetoin.

[0055] In specific embodiments of the third aspect, the target points are preferably 36 up-regulated target points and 25 knockout target points.

[0056] In a fourth aspect, the embodiments of the present application further provide a method for Bacillus amyloliquefaciens to ferment and produce acetoin, which comprises the following steps:

[0057] Initial culture: inoculate Bacillus amyloliquefaciens into an initial culture medium, and perform initial culture at a temperature of 37°C, wherein when the fermentation capacity is 50 mL / 500 mL, the rotation speed is 200 rpm; and when the fermentation capacity is 4 L / 7 L, the rotation speed is 300-500 rpm.

[0058] Fermentation culture: the initial culture medium is inoculated into the fermentation culture medium at a 10 v / v% inoculation amount, and the fermentation culture is carried out at a temperature of 37℃, and the fermentation time is 60h, wherein the fermentation volume is 50mL / 500mL, and the rotation speed is 200rpm.

[0059] In the embodiment of the present application, the fermentation is produced in two stages, and the fermentation temperature and the fermentation volume, rotation speed and other parameters are controlled in each stage of the fermentation production, so that the fermentation production process can be kept at a very high activity level, thereby significantly improving the yield of acetoin.

[0060] In the specific embodiments of the fourth aspect, preferably in the fermentation culture, the oxygen absorption rate is controlled to be 6-13mmol·gDW -1 ·h -1 . By controlling the oxygen absorption rate to be 6-13mmol·gDW -1 ·h -1 , the synthesis rate of acetoin can be kept at a high level, thereby accelerating the output of acetoin to improve the production efficiency.

[0061] In the specific embodiments of the fourth aspect, the components of the initial culture medium are preferably included in g / L:

[0062] 120 parts of glucose, 10 parts of yeast powder, 10 parts of peptone and 5 parts of K2HPO4;

[0063] The components of the fermentation culture medium are preferably included in g / L:

[0064] 120 parts of glucose, 10 parts of soybean peptone, 10 parts of yeast powder, 3 parts of K2HPO4, 3 parts of KH2PO4, 5 parts of NaCl and 0.2 parts of MgSO4·7H2O.

[0065] By effectively controlling the components of the culture medium, not only the nutritional needs of Bacillus amyloliquefaciens fermentation are met, but also the addition of metal ions Mn 2+ , Fe 2+ and Zn 2+ is limited to reduce the influence of metal ions on the synthesis of acetoin, so as to realize the rapid and large synthesis of acetoin.

[0066] The present application will be further described in detail in conjunction with specific embodiments.

[0067] Embodiment 1

[0068] The present embodiment provides a method for constructing a Bacillus amyloliquefaciens genome-scale metabolic network model. The related programs used in the present embodiment are described in Table 1 below; the database and software are described in Table 2 below.

[0069] Table 1 - Programs related to constructing the genome-scale metabolic network model of Bacillus amyloliquefaciens

[0070]

[0071] Table 2 - Database and software for constructing the genome-scale metabolic network model of Bacillus amyloliquefaciens

[0072]

[0073] Please refer to Figure 1 As shown in the figure, the method comprises the following steps S101-S104.

[0074] S101 - Constructing a rough model: After downloading the proteome sequence of Bacillus amyloliquefaciens in fasta format from the Uniport database, the Bacillus amyloliquefaciens model is automatically constructed in the ModelSEED platform by setting the required parameters. Then the proteome sequence is homologously compared with the proteome sequences of Bacillus subtilis 168 and Bacillus megatherium DSM319 to integrate the proteome sequence and obtain an initial reaction list, thereby constructing a rough model of the genome-scale metabolic network of Bacillus amyloliquefaciens. The rough model of the genome-scale metabolic network of Bacillus amyloliquefaciens consists of 1438 reactions, 1461 metabolites and 796 genes.

[0075] S102 - Manual refinement: According to the metabolic pathway map, KEGG, BiGG Models, MetaCyc database and literature database, and Web of Science and PubMed and other literature databases, first, the missing metabolic reactions in the rough model are filled and the repeated and incorrect metabolic reactions are deleted, and then the synthesis reaction of acetoin is added to establish a perfect biomass equation, thereby constructing a genome-scale metabolic network model with "gene-protein-reaction correlation" relationship. The genome-scale metabolic network model consists of 746 genes, 1736 reactions and 1611 metabolites, and is named iJYQ746.

[0076] The correlated reactions in the iJYQ746 include intracellular reactions, extracellular reactions, conventional biochemical reactions, transport reactions and exchange reactions. Among them, there are 137 transport reactions; there are 134 exchange reactions.

[0077] Please refer to Figure 2As shown, according to the KEGG pathway map, the above reactions are divided into 14 metabolic sub-systems, and the main metabolic pathways are lipid metabolism, amino acid metabolism, carbon metabolism, and various cofactor metabolisms, etc. Among them, the three metabolic sub-systems with the most reactions are lipid metabolism, with a total of 301, accounting for 17.35% of all metabolic reactions; amino acid metabolic reactions have 287, accounting for 16.54% of the total reactions; carbohydrate metabolic reactions have 232, accounting for 13.37% of the total reactions.

[0078] This example compares iJYQ746 with Bacillus subtilis (iYO844) and Bacillus megaterium (iJA1121), and the specific details are shown in Table 3 below.

[0079] Table 3 - Comparison between model iJYQ746, iJA1121 and iYO844

[0080]

[0081] According to Table 3, iJYQ746 has 302 common metabolic reactions with iYO844 and iJA1121, mainly involving amino acid metabolism, carbohydrate metabolism, energy metabolism and nucleotide metabolism, such as various amino acid metabolic pathways, glycolysis, tricarboxylic acid cycle and pentose phosphate pathway, etc.

[0082] See Table 3 Figure 3 As shown, iJYQ746 has 353 unique reactions, mainly distributed in amino acid metabolism, sugar metabolism and various cofactor metabolic pathways. For example, the cofactor Adenosyl cobinamide has its unique synthesis pathway, which can be accumulated by (R)-1-Aminopropan-2-ol to provide the possibility of synthesizing more vitamin B12.

[0083] See Table 3 Figure 4 As shown, the gene coverage of model iJYQ746 is 17.6%, and the gene-related reactions account for 86.16% of the total reactions (excluding exchange reactions).

[0084] In this example, single gene knockout is used to identify the genes of Bacillus amyloliquefaciens, and according to the simulation results, these genes can be divided into three categories: essential genes (15.42%), partial essential genes (7.24%), and non-essential genes (76.68%). Among them, there are 115 essential genes, mainly involved in amino acid metabolism, cofactor metabolism and nucleotide metabolism.

[0085] To verify the accuracy of essential genes, the simulation results were compared with all essential genes in DEG database (identity≥30%, e-value≤1E-6), and it was found that 103 genes could be matched (accuracy 89.6%).

[0086] S103-Transformation of mathematical model: the basic information of the model was imported into the Matlab platform through the xls2model.m instruction of the Cobra 3.0 toolbox, and converted into a computer-readable mathematical model.

[0087] S104-Verification and analysis of the model: the mathematical model was analyzed by using the flux balance analysis method, and compared with the experimental values in the literature. The specific operation is: using the Matlab platform with Cobra 3.0 toolbox to simulate the metabolic conditions of microorganisms under specific conditions, and compared with the experimental values in the literature.

[0088] Among them, the verification of the model described in this embodiment is based on the prediction of the growth ability of different carbon sources and nitrogen sources under the simplest culture conditions. Specifically:

[0089] The condition for analyzing the utilization ability of carbon sources is: using NH 4+ as nitrogen source and glucose, sucrose, fructose, maltose, mannose, lactose, xylose, starch and sorbitol as the only carbon source, respectively, and simulating the growth of the bacterial cells under aerobic conditions, and setting the carbon source absorption rate to 11.585 mmol·gDW -1 ·h -1 .

[0090] The effect is judged as follows: if the predicted growth rate is greater than zero, it means that this culture condition can support the growth of the bacterial cells; otherwise, it means that the predicted bacterial cells cannot adapt to the given culture condition.

[0091] In addition, when analyzing the utilization ability of nitrogen sources, in addition to 20 kinds of amino acids, the nitrogen sources also include urea, ammonium nitrate and NH 4+ ; and the nitrogen source absorption rate is set to 1000 mmol·gDW -1 ·h -1 .

[0092] This embodiment uses the above-mentioned model to simulate the growth of Bacillus amyloliquefaciens on 9 kinds of carbon sources and 23 kinds of nitrogen sources, and compares with the experimental results, which are described in detail in Table 4 below.

[0093] Table 4-Growth simulation of Bacillus amyloliquefaciens on different carbon sources and nitrogen sources

[0094]

[0095] According to Table 4, there are 9 carbon sources and 21 nitrogen sources that can support the growth of cells. Among them, for carbon sources, the model can grow on all these substances, and the simulation results are consistent with the experimental results; for nitrogen sources, except that urea and glutamine cannot be utilized, others can be utilized, which may be caused by the lack of some metabolic reactions or the lack of gene annotation of related enzymes in the metabolic pathway. For example, even if the corresponding reaction about urea is added to the model, it cannot be utilized as a nitrogen source, which may be because the strain lacks allantoicase, making Allantoate unable to generate Urea.

[0096] By searching literature, the growth rate of the bacterial cells under different glucose absorption rates was collected, and the results are shown in Table 5.

[0097] Table 5- Quantitative simulation of different substrate absorption rates

[0098]

[0099] According to Table 5, the simulation results differ from the experimental results by 14.03% and 7.42%, indicating that the model iJYQ746 of this embodiment has the metabolic ability to predict various carbon sources and nitrogen sources, and the model has high accuracy.

[0100] Example 2

[0101] This embodiment provides a strategy for improving the fermentation level of acetoin, which specifically includes:

[0102] 2.1 This embodiment runs the RobustnessAnalysis.m program based on Matlab to simulate the influence of metal ions Mn 2+ , Fe 2+ , and Zn 2+ on the synthesis rate of acetoin, and the results are shown in Table 6. Figure 5

[0103] According to Table 6, with the increase of the absorption rate of metal ions Mn 2+ , Fe 2+ , and Zn 2+ , the synthesis rate of acetoin first slowly decreases and then presents a cliff-like decrease. Specifically: Figure 5

[0104] With the increase of the concentration of metal ions, the yield of acetoin gradually decreases, and when the concentration of metal ions is 0.07 gL -1 , Bacillus amyloliquefaciens hardly produces acetoin. It can be seen that the prediction results of this embodiment are consistent with the experimental results and literature reports, thereby confirming that excessive concentration of metal ions will inhibit the synthesis of acetoin, and compared with the other two metal ions, the influence of Fe 2+ is the smallest.​​

[0105] 2.2 This embodiment is based on Matlab to analyze the effect of dissolved oxygen level on acetoin fermentation, and the results are shown in Figure 6 .

[0106] According to Figure 6 , the oxygen uptake rate increased from 0 mmol-gDW -1 ·h -1 to 5.892 mmol-gDW -1 ·h -1 , and the acetoin synthesis rate also increased, with the maximum being 11.585 mmol-gDW -1 ·h -1 . When the oxygen uptake rate continued to increase, the acetoin yield did not change significantly. When the oxygen uptake rate was greater than 11.783 mmol-gDW -1 ·h -1 , the acetoin synthesis rate gradually decreased, and when the uptake rate was 22.977 mmol-gDW -1 ·h -1 , the acetoin synthesis rate decreased to 0 mmol-gDW -1 ·h -1 .

[0107] It should be noted that, in this embodiment, the dissolved oxygen was controlled by adjusting the rotation speed of the fermenter, and the results of the acetoin fermentation experiment are shown in Figure 7 .

[0108] According to Figure 7 , when the rotation speed of the fermenter was 400 rpm, the acetoin yield reached a maximum of 49.8 g / L, and when the rotation speed increased, the acetoin yield decreased. It can be seen that the simulation values are basically consistent with the experimental results, indicating that a relatively gentle dissolved oxygen level is more conducive to the synthesis of acetoin.

[0109] Combined with flux balance analysis (FBA), when the oxygen uptake rate is too low, a large amount of pyruvate in the EMP pathway is decomposed to form phosphoenolpyruvate, and when the oxygen is sufficient, pyruvate can be well accumulated.

[0110] 2.3 Prediction of target points for improving acetoin yield

[0111] To further improve the yield of acetoin, this embodiment predicts potential modification targets based on the MOMA algorithm. The prediction targets can be divided into up-regulation targets and knockout targets.

[0112] By analyzing these potential reactions, it is found that the reactions affecting acetoin synthesis can be divided into two categories: one is related to the synthesis of acetoin itself, and the other is related to the growth of the strain. Specifically:

[0113] ①Please refer to Figure 8 As shown in Table 1, 36 genes were identified in the up-regulated target points, and these genes were mainly involved in amino acid metabolism. Accumulation of pyruvate is crucial in the synthesis of acetoin.

[0114] Through prediction, it was found that the up-regulated gene serC (2-oxoglutarate aminotransferase, EC 2.6.1.52) could increase the production of serine, thereby more pyruvate was synthesized. Through FBA, it was found that the rate of serine synthesizing pyruvate increased by 0.13 mmol·gDW -1 ·h -1 -1. Meanwhile, the generation rate of acetoin increased from 0.01 mmol·gDW -1 ·h -1 -1 to 0.071 mmol·gDW -1 ·h -1 -1, an increase of 610%. The overexpressed gene yrhA (L-serine hydro-lyase, EC 4.2.1.22) is an enzyme related to serine hydrolysis; overexpression of this gene resulted in a 2.1% decrease in specific growth rate, but the generation rate of acetoin increased by 100%.

[0115] ②Please refer to Figure 9 , 25 genes were identified in the knockout target points, and these genes were mainly involved in amino acid metabolism and nucleotide metabolism.

[0116] Through prediction, it was found that knockout of the gene yjcl (Cystathionine gamma-synthase, EC 2.5.1.48) increased the generation rate of acetoin by 19%, but the growth rate only decreased by 0.7%; knockout of the gene pgi (Glucose-6-phosphate isomerase, EC 5.3.1.9) could increase the generation rate of acetoin from 0.01 mmol·gDW -1 ·h -1 -1 to 0.011 mmol·gDW -1 ·h -1 -1, an increase of 10%, which is consistent with the report by Gao et al. that weakening the pgi gene can increase the content of 6-phosphogluconate. The increase of 6-phosphogluconate content can make the EMP pathway accumulate more pyruvate, thereby providing the possibility of synthesizing more acetoin.

[0117] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly explains the difference from other embodiments.

[0118] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or some or all of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for constructing a genome-scale metabolic network model of Bacillus amyloliquefaciens, characterized in that, The method includes: Constructing a coarse model: Obtain the proteomic sequencing results of Bacillus amyloliquefaciens from the Uniport database and automatically construct a coarse model of Bacillus amyloliquefaciens in the ModelSEED database; Manual simplification: Based on literature databases, metabolic pathway maps, and KEGG, BiGGModels, and MetaCyc databases, missing metabolic reactions in the coarse model were filled in and invalid metabolic reactions were deleted. Then, the synthesis reaction of acetoin was added to construct a genome-scale metabolic network model with "gene-protein-reaction association" relationships. Transformation of mathematical model: The genome-scale metabolic network model was imported into the Matlab platform equipped with the Cobra toolbox and transformed into a computer-readable mathematical model; Model validation and analysis: The mathematical model was analyzed using flux balance analysis and compared with experimental values ​​in the literature.

2. The construction method according to claim 1, characterized in that, The amyloliquefaciens mentioned is Bacillus amyloliquefaciens FMME044.

3. A genome-scale metabolic network model of Bacillus amyloliquefaciens constructed according to the method described in claim 2.

4. The application of the Bacillus amyloliquefaciens genome-scale metabolic network model according to claim 3 in predicting and improving acetoin production.

5. The application according to claim 4, characterized in that, include: In the analysis of the genome-scale metabolic network model of Bacillus amyloliquefaciens, the effects of metal ion and oxygen uptake rates on acetoin synthesis were analyzed using robustness analysis methods.

6. The application according to claim 5, characterized in that, The analysis of the Bacillus amyloliquefaciens genome-scale metabolic network model also includes: The MOMA algorithm was used to predict targets for increasing acetoin production.

7. The application according to claim 6, characterized in that, The targets include 36 upregulation targets and 25 knockout targets.

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