CN116092572A - Bacillus amyloliquefaciens genome scale metabolic network model, construction method and application - Google Patents
Bacillus amyloliquefaciens genome scale metabolic network model, construction method and application Download PDFInfo
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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, automatically constructing a bacillus amyloliquefaciens crude model according to a proteome sequencing result of bacillus amyloliquefaciens in a Uniport database; secondly, according to a literature database, a metabolic pathway diagram and a biochemical database, manually refining the crude model and adding an acetoin synthesis reaction to construct a genome scale metabolic network model; again converting the genome-scale metabolic network model into a computer-readable mathematical model; finally, the mathematical model is analyzed by 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 nutrition conditions of the growth of the bacillus amyloliquefaciens, and provides a feasible basis for improving the yield of acetoin and industrialized production.
Description
Technical Field
The application belongs to the technical field of system biology, and particularly relates to a bacillus amyloliquefaciens genome scale metabolic network model, a construction method and application.
Background
Acetoin (3-hydroxy butanone) can be used as a food additive for various seasonings on one hand, and can be used as a platform compound for functional materials, medical production, chemical synthesis and other fields on the other hand, so that the market demand is extremely large. Bacillus amyloliquefaciens is used as a dominant strain for producing acetoin by fermentation, has the advantage of not producing exotoxin and endotoxin, and has great potential for producing acetoin by fermentation. However, due to the problems of complex metabolic network, undiscovered part of key genes and the like, the metabolite yield is low, and the industrialization of various products is greatly limited. Therefore, the physiological metabolic characteristics of the bacillus amyloliquefaciens need to be systematically explored, and a basis is provided for optimizing fermentation conditions and improving metabolite yield.
The genome scale metabolic network model comprises most of biochemical reactions occurring in given microorganisms, can provide a high-efficiency platform to globally explore physiological metabolic functions of the microorganisms, can be used for predicting and analyzing growth conditions of the microorganisms, and provides effective basis for improving fermentation production of metabolites by the microorganisms. At present, the genome large-scale metabolic network model construction method comprises a manual construction method and an automatic construction method, wherein the accuracy of the manual construction is relatively high, but a large number of database inquiry, simplification and other operations are needed in the specific construction, and the genome large-scale metabolic network model construction method has the defects of long time consumption and complex operation; the automatic construction can realize the rapid and batch construction of the model, but has the problem of low accuracy of the constructed model. Furthermore, up to now there has been no report on the prediction and improvement of acetoin production by means of a genome-scale metabolic network model of Bacillus amyloliquefaciens.
Disclosure of Invention
The invention aims to provide a bacillus amyloliquefaciens genome scale metabolic network model, a construction method and application, wherein the model can systematically predict and analyze nutritional conditions required by the growth of bacillus amyloliquefaciens, thereby providing a feasible basis for improving the yield of acetoin and industrialized production.
In order to achieve the above purpose, the technical scheme of the application is as follows:
a first aspect of the present application provides a method for constructing a genome-scale metabolic network model of bacillus amyloliquefaciens, the method comprising the steps of:
building a coarse model: obtaining a proteome sequencing result of the bacillus amyloliquefaciens from a Uniport database, and automatically constructing a bacillus amyloliquefaciens crude Model in a Model SEED database;
manual simplification: filling the missing metabolic reaction in the crude model and deleting the invalid metabolic reaction according to a literature database, a metabolic pathway diagram and a KEGG, biGGModels, metaCyc database, and then adding the synthesis reaction of acetoin to construct a genome scale metabolic network model with a gene-protein-reaction association relationship;
conversion of mathematical model: importing the genome-scale metabolic network model into a Matlab platform with a Cobra toolbox, and converting the genome-scale metabolic network model into a computer-readable mathematical model;
model verification and analysis: the mathematical model was analyzed using a flux balance analysis method and compared to experimental values in the literature.
In an alternative implementation of the first aspect, the bacillus amyloliquefaciens is Bacillus amyloliquefaciens FMME044.
A second aspect of the present application provides a bacillus amyloliquefaciens genome-scale metabolic network model constructed according to the method of the first aspect.
A third aspect of the present application provides the use of the genome-scale metabolic network model of bacillus amyloliquefaciens constructed by the method of the first aspect for predicting and increasing acetoin yield.
In an optional implementation manner of the third aspect, the application method in predicting and improving acetoin yield includes:
in the analysis of the bacillus amyloliquefaciens genome scale metabolic network model, the influence of metal ions and oxygen absorption rate on acetoin synthesis is analyzed by a robust analysis method.
In an optional implementation manner of the third aspect, the analyzing the genome-scale metabolic network model of bacillus amyloliquefaciens further includes:
and predicting a target point for improving the acetoin yield by using a MOMA algorithm.
In an alternative implementation of the third aspect, the targets comprise 36 up-regulated targets and 25 knockout targets.
In a fourth aspect, the present application provides a method for producing acetoin by fermentation of bacillus amyloliquefaciens, the method comprising the steps of:
initial culture: inoculating bacillus amyloliquefaciens into an initial culture medium, and performing initial culture at 37 ℃, wherein the rotating speed is 200rpm when the fermentation capacity is 50mL/500 mL; when the fermentation capacity is 4L/7L, the rotating speed is 300-500rpm;
fermentation culture: the initial medium was inoculated into a fermentation medium at an inoculum size of 10v/v%, and fermentation culture was performed at 37℃for 60 hours, wherein the fermentation capacity was 50mL/500mL and the rotation speed was 200rpm.
In an optional implementation manner of the fourth aspect, in the fermentation culture, further includes: controlling oxygen absorption rate to 6-13mmol gDW -1 ·h -1 。
In an alternative implementation manner of the fourth aspect, the initial culture medium comprises the following components in g/L: 120 parts of glucose, 10 parts of yeast powder, 10 parts of peptone and 5 parts of K 2 HPO 4 ；
The fermentation medium comprises the following components in g/L: 120 parts of glucose, 10 parts of soybean peptone, 10 parts of yeast powder and 3 parts of K 2 HPO 4 3 parts KH 2 PO 4 5 parts NaCl and 0.2 part Mg SO 4 ·7H 2 O。
Compared with the prior art, the advantage or beneficial effect of this application includes at least:
according to the method, the Model SEED database is used for automatically constructing the bacillus amyloliquefaciens crude Model, the bacillus amyloliquefaciens crude Model is manually simplified, after the genome scale metabolic network Model with the relation of 'gene-protein-reaction association' is constructed, the Matlab platform with the Cobra tool box is used for carrying out mathematical simulation and verification analysis on the Model, so that the bacillus amyloliquefaciens genome scale metabolic network Model is quickly constructed and molded in batches, the construction time is greatly shortened, the specific operation is simplified, and meanwhile, the Model is accurate to 89.6%. Meanwhile, the nutritional conditions required by the growth of the bacillus amyloliquefaciens can be systematically predicted and determined by using the model, and a feasible basis is provided for improving the yield of acetoin and industrial production. Wherein the examples demonstrate that: according to the method, the yield of acetoin can be remarkably improved by predicting and analyzing the growth condition of the bacillus amyloliquefaciens through the model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of constructing a genome-scale metabolic network model of Bacillus amyloliquefaciens according to the embodiment of the present application;
FIG. 2 is a graph showing the distribution of reactions involving metabolic subsystems provided in embodiments of the present application;
FIG. 3 is a Venn diagram comparing reactions in iJYQ746, iJA1121, and iYO844 provided in the examples of the present application;
FIG. 4 is a portion of a gene-related reaction in a metabolic subsystem provided in an embodiment of the present application;
FIG. 5 shows a metal ion Mn provided in an embodiment of the present application 2+ 、Fe 2+ 、Zn 2+ Effects on acetoin accumulation;
FIG. 6 is a graph showing the effect of the predicted oxygen absorption rate of the iJYQ749 model provided in the example of the present application on the acetoin synthesis rate;
FIG. 7 shows acetoin yields of 300, 350, 400 and 500rpm in fermentors provided in examples of the present application;
FIG. 8 is an illustration of acetoin flux and fPH values for potential targets after upregulation provided in the examples herein;
FIG. 9 shows acetoin flux and fPH values of potential targets after gene knockout as provided in the examples of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the following description of the present embodiment, the term "and/or" is used to describe an association relationship of association objects, which means that three relationships may exist, for example, a and/or B may mean: a alone, B alone and both a and B. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship.
In the following description of the present embodiments, the term "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, "at least one (individual) of a, b, or c," or "at least one (individual) of a, b, and c" may each represent: a, b, c, a-b (i.e., a and b), a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple, respectively.
It should be understood by those skilled in the art that, in the following description of the embodiments of the present application, the sequence number does not mean that the sequence of execution is not sequential, and some or all of the steps may be executed in parallel or sequentially, and the execution sequence of each process should be determined by its functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application in the examples and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
In a first aspect, embodiments of the present application provide a method for constructing a genome-scale metabolic network model of bacillus amyloliquefaciens, which includes the following steps S101 to S104.
S101, constructing a coarse model: and obtaining a proteome sequencing result of the bacillus amyloliquefaciens from a Uniport database, and automatically constructing a bacillus amyloliquefaciens crude Model in a Model SEED database. The specific operation is to download the proteome sequence of the fasta format bacillus amyloliquefaciens from the Uniport database, upload the proteome sequence to the ModelSEED platform and automatically construct the bacillus amyloliquefaciens model. And integrating the proteome sequences by a homology alignment method to obtain an initial reaction list, so as to construct a bacillus amyloliquefaciens genome scale metabolic network coarse model.
S102-manual refining: according to metabolic pathway diagrams, KEGG, biGGModels, metaCyc databases and literature databases and Web ofScience, pubMed and other literature databases, firstly filling missing metabolic reactions in a crude model, deleting repeated and wrong metabolic reactions, then adding acetoin synthetic reactions to establish a perfect biomass equation, and constructing a genome scale metabolic network model with a 'gene-protein-reaction association' relationship;
s103-conversion of mathematical model: importing basic information of the model into a Matlab platform through an xls2model.m instruction of a Cobra 3.0 toolbox, and converting the basic information into a computer-readable mathematical model;
s104, verification and analysis of a model: the mathematical model was analyzed using a flux balance analysis method and compared with experimental values in the literature. The specific procedure was to simulate the metabolism of microorganisms under specific conditions using a Matlab platform with Cobra 3.0 toolbox and compare with experimental values in literature.
According to the embodiment of the application, by providing the construction method of the genome-scale metabolic network model of the bacillus amyloliquefaciens, the physiological metabolic characteristics of the bacillus amyloliquefaciens can be systematically explored in the field, so that basis is provided for optimizing fermentation conditions and improving metabolite yield. Specifically, the construction method of the embodiment of the application comprises the following steps: firstly, automatically constructing a bacillus amyloliquefaciens crude Model through a Model SEED database, secondly, manually simplifying the bacillus amyloliquefaciens crude Model to construct a genome scale metabolic network Model with a 'gene-protein-reaction association' relation, and finally, using a Matlab platform with a Cobra tool box to carry out conversion and verification analysis on the Model so as to realize rapid and batch construction and shaping of the bacillus amyloliquefaciens genome scale metabolic network Model, greatly shorten construction time, simplify specific operation and enable the Model to be accurate up to 89.6%.
In a specific embodiment of the first aspect, the bacillus amyloliquefaciens is preferably Bacillus amyloliquefaciens FMME044.
In a second aspect, embodiments of the present application also provide a bacillus amyloliquefaciens genome-scale metabolic network model constructed by the method of the first aspect. The method based on the first aspect has the advantages of short model construction time, simple operation and high model accuracy, so that the bacillus amyloliquefaciens genome scale metabolic network model has the advantages of high accuracy, low cost and easy popularization and utilization.
In a third aspect, the embodiment of the application also provides an application of the bacillus amyloliquefaciens genome-scale metabolic network model constructed by the method in the first aspect in prediction and improvement of acetoin yield.
The genome-scale metabolic network model based on the bacillus amyloliquefaciens can systematically explore the physiological metabolic characteristics of the bacillus amyloliquefaciens, is used for predicting and improving the acetoin yield, and can effectively determine the relative influence of metal ion concentration, dissolved oxygen, an up-regulation target point and a knockout target point on the acetoin yield, so that the acetoin yield can be improved by controlling the metal ion concentration, the dissolved oxygen, the up-regulation target point and the knockout target point.
In a specific embodiment of the third aspect, the application method in predicting and improving acetoin yield preferably includes: in the analysis of the bacillus amyloliquefaciens genome scale metabolic network model, the influence of metal ions and oxygen absorption rate on acetoin synthesis is analyzed by a robust analysis method.
Wherein the metal ion Mn can be determined by analyzing the metal ion 2+ 、Fe 2+ And Zn 2+ Inhibit acetoin synthesis and Fe 2+ Can strictly limit metal ion Mn in fermentation production with minimum influence 2+ 、Fe 2+ And Zn 2+ An added amount of (2); the oxygen absorption rate can be determined to be 6-13mmol gDW by analyzing the oxygen absorption rate -1 ·h -1 Is helpful for obviously improving the yield of acetoin.
In a specific embodiment of the third aspect, the analyzing the genome-scale metabolic network model of bacillus amyloliquefaciens further includes: and predicting a target point for improving the acetoin yield by using a MOMA algorithm.
In a specific embodiment of the third aspect, the targets are preferably 36 up-regulated targets and 25 knockdown targets.
In a fourth aspect, embodiments of the present application also provide a method for producing acetoin by fermentation of bacillus amyloliquefaciens, the method comprising the steps of:
initial culture: inoculating bacillus amyloliquefaciens into an initial culture medium, and performing initial culture at 37 ℃, wherein the rotating speed is 200rpm when the fermentation capacity is 50mL/500 mL; when the fermentation capacity is 4L/7L, the rotating speed is 300-500rpm;
fermentation culture: the initial medium was inoculated into a fermentation medium at an inoculum size of 10v/v%, and fermentation culture was performed at 37℃for 60 hours, wherein the fermentation capacity was 50mL/500mL and the rotation speed was 200rpm.
According to the embodiment of the application, through fermentation production in two stages, and fermentation production in each stage controls parameters such as fermentation temperature, fermentation capacity, rotation speed and the like, the fermentation production process can be kept at a very high activity level, so that the yield of acetoin is obviously improved.
In a specific embodiment of the fourth aspect, the fermentation culture further preferably includes: controlling oxygen absorption rate to 6-13mmol gDW -1 ·h -1 . Wherein, by controlling the oxygen absorption rate to be 6-13 mmol/gDW -1 ·h -1 The synthesis rate of the acetoin can be kept at a higher level, so that the yield of the acetoin is accelerated, and the production efficiency is improved.
In a specific embodiment of the fourth aspect, the initial medium preferably comprises, in g/L:
120 parts of glucose, 10 parts of yeast powder, 10 parts of peptone and 5 parts of K 2 HPO 4 ；
The fermentation medium preferably comprises the following components in g/L:
120 parts of glucose, 10 parts of soybean peptone, 10 parts of yeast powder and 3 parts of K 2 HPO 4 3 parts KH 2 PO 4 5 parts NaCl and 0.2 part MgSO 4 ·7H 2 O。
Wherein, through the effective control of the components of the culture medium, not only the nutrition requirement of the fermentation of the bacillus amyloliquefaciens is met, but also the metal ion Mn is limited 2+ 、Fe 2+ And Zn 2+ The addition of the catalyst can reduce the influence of metal ions on the synthesis of acetoin and realize the rapid and massive synthesis of acetoin.
The present application is described in further detail below in connection with specific embodiments.
Example 1
The embodiment provides a construction method of a genome-scale metabolic network model of bacillus amyloliquefaciens. The relevant procedure used in this example is shown in table 1 below; the database and software are described in table 2 below.
TABLE 1 related procedure for constructing a genome-scale metabolic network model of Bacillus amyloliquefaciens
TABLE 2 construction of database and software for genome-scale Metabolic network models of Bacillus amyloliquefaciens
Referring to fig. 1, the method includes the following steps S101-S104.
S101, constructing a coarse model: after downloading the fasta format proteome sequence of bacillus amyloliquefaciens from the Uniport database, parameters are set in a model seed platform according to requirements to automatically construct a bacillus amyloliquefaciens model. And then carrying out homologous comparison on the protein group sequence and the protein group sequences of Bacillus subtilis, 168 and Bacillus megatheriumDSM319 so as to integrate the protein group sequences to obtain an initial reaction list, and constructing a bacillus amyloliquefaciens genome scale metabolic network coarse model. Wherein the bacillus amyloliquefaciens genome-scale metabolic network coarse model consists of 1438 reactions, 1461 metabolites and 796 genes.
S102-manual refining: according to metabolic pathway diagrams, KEGG, biGG Models, metaCyc databases and literature databases, web ofScience, pubMed and other literature databases, firstly filling missing metabolic reactions in a coarse model, deleting repeated and wrong metabolic reactions, and then adding acetoin synthesis reaction to establish a complete biomass equation, so as to construct a genome scale metabolic network model with a 'gene-protein-reaction association' relationship. Wherein the genome-scale metabolic network model consists of 746 genes, 1736 reactions and 1611 metabolites, designated iJYQ746.
The associated reactions in the iJYQ746 include intracellular reactions, extracellular reactions, conventional biochemical reactions, transport reactions and exchange reactions. Wherein 137 transport reactions are involved; there were 134 exchange reactions.
Referring to FIG. 2, the above reaction is divided into 14 metabolic subsystems according to KEGG pathway map, and major metabolic pathways are lipid metabolism, amino acid metabolism, carbon metabolism, and various cofactor metabolism, etc. Wherein, three metabolic subsystems with the highest reaction are respectively lipid metabolism, and the total number of the three metabolic subsystems is 301, which accounts for 17.35% of all metabolic reactions; 287 amino acid metabolic reactions account for 16.54% of the total reaction; the carbohydrate metabolism reactions were 232, accounting for 13.37% of the total reaction.
In this example, iJYQ746 was compared with Bacillus subtilis (iYO 844) and Bacillus maxima (iJA 1121), as shown in Table 3.
TABLE 3 comparison between models iJYQ746, iJA1121 and iYO844
As can be seen from 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 metabolic pathways of various amino acids, glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, etc.
Referring to FIG. 3, iJYQ746 has 353 unique responses, which are mainly distributed in the metabolic pathways such as amino acid metabolism, sugar metabolism and various cofactors. For example, cofactor Adenosyl cobinamide has a unique synthetic pathway that can be accumulated by (R) -1-Aminoppran-2-ol, providing the opportunity for the synthesis of more vitamin B12.
Referring to FIG. 4, the gene coverage of model iJYQ746 was 17.6% and the gene-related response accounted for 86.16% of the total response (except for the crossover reaction).
In the embodiment, the genes for identifying the bacillus amyloliquefaciens are knocked out by a single gene, and the genes can be divided into three types according to simulation results: essential genes (15.42%), partially essential genes (7.24%), non-essential genes (76.68%). Among them, there are 115 essential genes, mainly involved in amino acid metabolism, cofactor metabolism and nucleotide metabolism.
In order to verify the accuracy of the essential genes, the simulation results are compared with all essential genes recorded in the DEG database (identity is more than or equal to 30%, E-value is less than or equal to 1E-6), and 103 genes are found to be matched (accuracy is 89.6%).
S103-conversion of mathematical model: the basic information of the model is imported into a Matlab platform through an xls2model. M instruction of a Cobra 3.0 toolbox and converted into a computer-readable mathematical model.
S104, verification and analysis of a model: the mathematical model was analyzed using a flux balance analysis method and compared with experimental values in the literature. The specific operation is as follows: the Matlab platform with Cobra 3.0 toolbox was used to simulate the metabolism of microorganisms under specific conditions and compared with experimental values in literature.
The model of this example was validated to predict the growth capacity of different carbon and nitrogen sources based on the simplest culture conditions. Specifically:
the conditions for analyzing the availability of the carbon source were controlled as follows: with NH 4+ Is nitrogen source, glucose, sucrose, fructose, maltose, mannose, lactose, xylose, starch and sorbitol are respectively used as the sole carbon source, and the growth condition of the thallus is simulated under the aerobic condition, and the absorption rate of the carbon source is respectively set as 11.585 mmol.gDW -1 ·h -1 。
The effect judgment is as follows: if the predicted growth rate is greater than zero, it is indicated that the culture conditions are capable of supporting the growth of the bacterial cells; on the other hand, it was revealed that the predicted cells could not be adapted to the given culture conditions.
In addition, in analyzing the availability of nitrogen sources, the nitrogen sources include urea, ammonium nitrate and NH in addition to 20 amino acids 4+ The method comprises the steps of carrying out a first treatment on the surface of the The absorption rates of the nitrogen sources were set to 1000 mmol/gDW, respectively -1 ·h -1 。
The growth of bacillus amyloliquefaciens on 9 carbon sources and 23 nitrogen sources was simulated by using the model, and compared with experimental results, and the experimental results are shown in the following table 4.
TABLE 4 simulation of growth of Bacillus amyloliquefaciens on different carbon and nitrogen sources
As can be seen from Table 4, a total of 9 carbon sources and 21 nitrogen sources are capable of supporting cell growth. Wherein, for carbon sources, the model can grow on all the substances, and the simulation result is consistent with the experimental result; in the case of nitrogen sources, other than urea and glutamine, may be used, possibly due to a partial metabolic reaction deficiency or a deficiency in the annotation of genes for enzymes involved in the metabolic pathway. For example, even if a corresponding reaction with Urea is added to the model, it cannot be utilized as a nitrogen source, probably because of the lack of alactose in the strain, making alactoate unable to produce Urea.
The results of the search for literature data were shown in Table 5, and the growth rates of the cells at different glucose absorption rates were collected.
TABLE 5 quantitative simulation of the absorption rates of different substrates
From table 5, the simulation results are different from the experimental results by 14.03% and 7.42%, which indicates that the model iJYQ746 of this example has the ability to predict the metabolism of various carbon and nitrogen sources, and the model accuracy is high.
Example 2
The embodiment provides a strategy study for improving acetoin fermentation level, which specifically comprises the following steps:
2.1 this example simulates the metal ion Mn based on Matlab running the robustness analysis program RobustnessAnalysis m 2+ 、Fe 2+ 、Zn 2+ The effect on acetoin synthesis rate was shown in FIG. 5.
According to FIG. 5The illustration is: with metal ion Mn 2+ 、Fe 2+ And Zn 2+ The absorption rate is increased, the synthesis rate of acetoin is firstly reduced slowly, and then cliff-type reduction is shown. Specifically:
the yield of acetoin gradually decreases with increasing metal ion concentration, and the metal ion concentration is 0.07gL -1 When bacillus amyloliquefaciens produces little acetoin. It can be seen that the predicted results of this example are consistent with experimental results and literature reports, thereby confirming that too high a metal ion concentration inhibits acetoin synthesis and Fe compared with the other two metal ions 2+ The influence of (2) is minimal.
2.2 this example is based on Matlab with robust analysis of the effect of dissolved oxygen levels on acetoin fermentation, the results of which are shown in FIG. 6.
According to fig. 6: the oxygen absorption rate is 0mmol gDW -1 ·h -1 Increase to 5.892mmol gDW -1 ·h -1 The acetoin synthesis rate is also increased, and the maximum value is 11.585mmol gDW -1 ·h -1 And when the oxygen absorption rate is continuously increased, the yield change of acetoin is not obvious; when the oxygen absorption rate is greater than 11.783mmol gDW -1 ·h -1 The acetoin synthesis rate gradually decreases when the absorption rate is 22.977 mmol/gDW -1 ·h -1 At the time of acetoin synthesis, the rate of acetoin synthesis was reduced to 0mmol gDW -1 ·h -1 。
In this example, the fermentation experiment of acetoin was performed by controlling dissolved oxygen by adjusting the rotation speed of the fermenter, and the results are shown in fig. 7.
As can be seen from FIG. 7, the acetoin yield reached a maximum of 49.8g/L at a fermenter speed of 400 rpm; and the yield of acetoin is reduced when the rotating speed is increased. It can be seen that the simulated values substantially match the experimental results, thus indicating that a relatively gentle dissolved oxygen level is more favorable for acetoin synthesis.
In conjunction with flow equilibrium analysis (FBA), pyruvate is largely decomposed to form phosphoenolpyruvate in the EMP pathway when the oxygen uptake rate is too low, and can accumulate well when the oxygen is sufficient.
2.3 target prediction to increase acetoin yield
In order to further increase acetoin yield, the embodiment predicts potential reconstruction targets based on the MOMA algorithm. The predicted targets can be divided into up-regulation targets and knockout targets.
By analyzing these potential reactions, it was found that reactions affecting acetoin synthesis are mainly classified into two types, one of which is a reaction related to acetoin synthesis itself and the other of which is related to strain growth. Specifically:
(1) referring to FIG. 8, 36 genes were identified in the up-regulation target, and these genes are mainly involved in amino acid metabolism. The accumulation of pyruvate is of critical importance in the synthesis of acetoin.
It was found by prediction that up-regulating the gene serC (2-oxoglutarate aminotransferase, EC 2.6.1.52) increased serine production and thus increased synthesis of pyruvate. As a result of the FBA, the rate of serine synthesis into pyruvic acid was increased by 0.13mmol gDW -1 ·h -1 . At the same time, the production rate of acetoin is from 0.01mmol gDW -1 ·h -1 To 0.071 mmol/gDW -1 ·h -1 The increase is 610 percent. The overexpressed gene yrhA (L-serine hydro-lyase, EC 4.2.1.22) is an enzyme involved in serine hydrolysis; overexpression of this gene resulted in a 2.1% decrease in specific growth rate, but an increase in acetoin production rate of 100%.
(2) Referring to fig. 9, 25 genes are identified in the knockdown target, and these genes are mainly involved in amino acid metabolism and nucleotide metabolism.
It was found by prediction that the acetoin production rate was increased by 19% but the growth rate was reduced by only 0.7% in the knockout gene yjcl (Cystathionine gamma-synthase, EC 2.5.1.48); knocking out gene pgi (Glucose-6-phosphate isomerase, EC 5.3.1.9) can lead to acetoin production rate from 0.01 mmol.gDW -1 ·h -1 Increase to 0.011mmol gDW -1 ·h -1 10% increase, which is in contrast to the attenuation of the pgi gene reported by Gao et al, which increases the glucose 6-phosphate contentAnd accords with the requirement. An increase in glucose 6-phosphate content may allow the EMP pathway to accumulate more pyruvate, thereby providing the opportunity to synthesize more acetoin.
Various embodiments in this specification are described in an incremental manner, and identical or similar parts of the various embodiments are referred to each other, with each embodiment focusing on differences from the other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions.
Claims (10)
1. A method for constructing a genome-scale metabolic network model of bacillus amyloliquefaciens, comprising the steps of:
building a coarse model: obtaining a proteome sequencing result of the bacillus amyloliquefaciens from a Uniport database, and automatically constructing a bacillus amyloliquefaciens crude Model in a Model SEED database;
manual simplification: filling the missing metabolic reaction in the crude model and deleting the invalid metabolic reaction according to a literature database, a metabolic pathway diagram and a KEGG, biGGModels, metaCyc database, and then adding the synthesis reaction of acetoin to construct a genome scale metabolic network model with a gene-protein-reaction association relationship;
conversion of mathematical model: importing the genome-scale metabolic network model into a Matlab platform with a Cobra toolbox, and converting the genome-scale metabolic network model into a computer-readable mathematical model;
model verification and analysis: the mathematical model was analyzed using a flux balance analysis method and compared to experimental values in the literature.
2. The method according to claim 1, wherein the bacillus amyloliquefaciens is Bacillus amyloliquefaciens FMME044.
3. The bacillus amyloliquefaciens genome-scale metabolic network model constructed according to the method of claim 2.
4. Use of a bacillus amyloliquefaciens genome-scale metabolic network model according to claim 3 for predicting and increasing acetoin production.
5. The use according to claim 4, characterized in that it comprises:
in the analysis of the bacillus amyloliquefaciens genome scale metabolic network model, the influence of metal ions and oxygen absorption rate on acetoin synthesis is analyzed by a robust analysis method.
6. The use according to claim 5, wherein the analysis of the bacillus amyloliquefaciens genome-scale metabolic network model further comprises:
and predicting a target point for improving the acetoin yield by using a MOMA algorithm.
7. The use of claim 6, wherein the targets comprise 36 up-regulated targets and 25 knockdown targets.
8. A method for producing acetoin by fermenting bacillus amyloliquefaciens, which is characterized by comprising the following steps:
initial culture: inoculating bacillus amyloliquefaciens into an initial culture medium, and performing initial culture at 37 ℃, wherein the rotating speed is 200rpm when the fermentation capacity is 50mL/500 mL; when the fermentation capacity is 4L/7L, the rotating speed is 300-500rpm;
fermentation culture: the initial medium was inoculated into a fermentation medium at an inoculum size of 10v/v%, and fermentation culture was performed at 37℃for 60 hours, wherein the fermentation capacity was 50mL/500mL and the rotation speed was 200rpm.
9. The method of claim 8, further comprising, in the fermentation culture:
controlling oxygen absorption rate to 6-13mmol gDW -1 ·h -1 。
10. The method according to claim 8 or 9, wherein the initial medium comprises, in g/L: 120 parts of glucose, 10 parts of yeast powder, 10 parts of peptone and 5 parts of K 2 HPO 4 ；
The fermentation medium comprises the following components in g/L: 120 parts of glucose, 10 parts of soybean peptone, 10 parts of yeast powder and 3 parts of K 2 HPO 4 3 parts KH 2 PO 4 5 parts NaCl and 0.2 part Mg SO 4 ·7H 2 O。
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