Self-adaptive laboratory evolution starting strain design method aiming at independently relying on certain unique carbon source

Abstract

The invention provides a design method of an initial strain in a self-adaptive laboratory evolution process. According to the method disclosed by the invention, through a reasonable reaction knockout strategy, an initial microbial strain evolved by a self-adaptive laboratory is constructed, so that the initial microbial strain gradually adapts to and finally can independently depend on a certain carbon source which cannot be utilized before to grow. According to the method, a genome-scale metabolism model is utilized, and a double-layer mixed integer programming algorithm is adopted, so that an initial strain capable of enhancing the adaptability to a target carbon source is accurately designed, and clear guidance and reference are provided for strain optimization in experiments. The preparation of the strain capable of growing on the specified carbon source is of great significance for expanding the application of industrial microorganisms in renewable resource utilization, so that the strain has great practical value.

Description

Technical field:

[0001] The present invention belongs to the field of bioengineering and metabolic engineering, and relates to a method for designing a starting strain in an adaptive laboratory evolution (ALE) process, and in particular to how to design a starting strain so that it gradually adapts in the ALE process and eventually grows solely on a carbon source that was previously unavailable. Background technology:

[0002] With the development of industrial biotechnology, industrial microorganisms are facing more and more challenges in production. Traditionally used carbon sources such as glucose can no longer meet the needs of certain industrial applications. Therefore, the use of previously unavailable carbon sources, especially renewable resources, has become a hot topic of research. However, many industrial microorganisms lack the natural ability to utilize these carbon sources, which greatly limits their scope of application in industrial production.

[0003] Adaptive laboratory evolution (ALE) is an effective method to force microorganisms to adapt to and eventually utilize new carbon sources by gradually adjusting environmental conditions. However, existing ALE methods usually require co-substrate assistance, and it is difficult to achieve complete dependence of strains on a single target carbon source. To solve this problem, the present invention proposes the AdaptUC-B method, which enables strains to gradually adapt and eventually rely solely on a previously unavailable carbon source for growth during the ALE process through the rational design of a reaction knockout strategy. Summary of the invention:

[0004] The present invention proposes a method for designing starting strains in the ALE process, called AdaptUC-B. The method uses a genome-scale metabolic model and a mixed integer programming algorithm to predict and design a reaction knockout (e.g., by gene knockout) strategy that can gradually adapt to and eventually rely solely on a previously unavailable carbon source for growth. This method provides clear guidance for strain design and optimization in experiments, so that the designed strain can successfully adapt to and eventually independently utilize the target carbon source during the ALE process.

[0005] The present invention provides a method for designing a starting strain in an adaptive laboratory evolution process, characterized in that it comprises the following steps:

[0006] S1 provides the following data, including bacterial species, metabolic network model, substrate and strain culture conditions for maximum uptake rate, target carbon source, and co-substrate carbon source;

[0007] S2 determines the reaction knockout strategy through a two-level mixed integer programming algorithm based on a genome-scale metabolic model, which enables the microorganism to gradually adapt during the evolution process and eventually rely solely on a previously unavailable carbon source for growth;

[0008] S3 applies the above-mentioned reaction knockout target to a microorganism for use in the evolutionary starting strain of the adaptive laboratory evolution ALE process, so as to gradually enhance its ability to utilize the carbon source through the adaptive laboratory evolution ALE process, thereby ensuring that the microorganism can eventually grow independently relying on the carbon source in the absence of other carbon sources.

[0009] Furthermore, the two-level mixed integer programming algorithm further comprises the following steps:

[0010] a) In the inner optimization problem, the growth performance of the knockout strain under the target carbon source and other co-substrate conditions is simulated;

[0011] b) In the outer optimization problem, the utilization of the target carbon source is maximized and its necessity for strain growth is ensured.

[0012] Specifically, the reaction knockout strategy includes:

[0013] a) selecting a reaction knockout combination that can couple the target carbon source with growth;

[0014] b) Ensure that the knockout strain can grow in medium without other carbon sources.

[0015] 4. The method according to any one of claims 1 to 3, characterized in that the method for implementing the two-level mixed integer programming algorithm is as follows: minimizing ∑y j

[0016] Subject to:

[0017] Maximize v1 bio =cv1

[0018] Submission

[0019]

[0020] Maximize v2 bio =cv2

[0021] Submission

[0022]

[0023] Maximize v3 bio =cv3

[0024] Submission

[0025]

[0026] minimize

[0027] Submission

[0028]

[0029] v1 bio ≥0.5v0 bio_UC for AdaptUC B

[0030] v1 bio ≤0.1v0 bio_UC for AdaptUC A

[0031] v2 bio ≤0.1v0 bio_cosub

[0032] v3 bio ≥v0 bio_cosub

[0033] ∑y j <K

[0034] v1.v2, v2, v4∈R, y∈[0, 1]

[0035] in,

[0036] S ij represents the stoichiometric coefficient matrix of metabolite i in reaction j, v1 j is the flux variable for reaction j, LB1 j and UB1 j They are v1 j The lower and upper limits of the binary variable y j Indicates whether reaction j is deactivated y j =1 or not disabled j =0.

[0037] The present invention provides a design system for executing the method described above, which comprises the following modules:

[0038] a. Data entry module, used to enter relevant information, including bacterial species, metabolic network model, substrate and strain culture conditions of maximum uptake rate, target carbon source, and co-substrate carbon source;

[0039] b. Data processing module, used for calculation and processing;

[0040] c. Result output module.

[0041] The present invention further provides a bioinformatics tool based on the system, comprising:

[0042] a. User interface, allowing users to enter relevant information;

[0043] b. the system;

[0044] d. Result display module, used to display the prediction results to users;

[0045] Optionally, it also includes: e. a report generator for creating results.

[0046] The main features and advantages of the present invention include:

[0047] Mixed integer programming algorithm: This algorithm combines genome-scale metabolic models and optimization techniques to effectively handle complex metabolic networks and predict the optimal reaction knockout combination.

[0048] Prediction of reaction knockout targets: The present invention predicts and designs a reaction knockout strategy that can promote the dependence of microorganisms on the target carbon source during the ALE process by simulating the growth of the strain after reaction knockout under the conditions of the target carbon source and other co-substrates. The principle is that the main input is a whole genome metabolic network model, which contains thousands or even tens of thousands of major intracellular small molecule metabolic pathways of the target bacteria. Using the flux balance analysis algorithm, given the objective function and culture medium composition, the intracellular metabolic flow distribution can be predicted. Each sub-problem of the AdaptUC-B method is a flux balance analysis problem. All sub-problems constitute inner layer problems, and the inner layer problems and outer layer problems constitute the combination of AdaptUC-B.

[0049] As a case study, we present metabolic engineering strategies for ALE starting strains, both strategies to co-utilize methanol with other carbon sources and strategies to use methanol as the sole carbon source. In our case studies, important industrial species such as Escherichia coli and Corynebacterium glutamicum were considered. We evaluated these strategies based on the ease with which the starting strains grow on non-adapted carbon sources and the evolutionary driving force to assimilate more non-adapted carbon sources. To validate our approach, we compared the predicted results with experimentally effective strategies. New strategies with higher potential were discovered through in-depth metabolic level analysis. We anticipate that our work will accelerate the transformation of industrial biology, enabling more industrial microorganisms to utilize previously non-adapted but promising renewable substrates. Description of the drawings:

[0050] Figure 1 :Schematic diagram of AdaptUC-B concept Specific implementation method:

[0051] Example 1: Establishment of AdaptUC-B

[0052] The present invention proposes a computational framework named AdaptUC-B, which aims to design a reaction knockout (K0) strategy for the starting strain of the ALE process. AdaptUC-B completely couples the growth of the strain with the unadapted substrate, so that it can only grow in the presence of the unadapted substrate. AdaptUC-B retains the potential to use the unadapted substrate as the sole carbon source. AdaptUC-B includes three internal problems (design requirements), each of which has a goal and must meet the constraints of the external level and make the least modification, that is, the global optimization goal is to achieve the minimum number of knockout genes ( Figure 1 ). Each inner problem and its corresponding outer constraint represents a design requirement for the ALE starting strain and delineates the expectations for the strain at the end of the ALE process.

[0053] The inner key constraint of subproblem 1 is to only take in C1, and the outer constraint is to be able to grow. The inner key constraint of subproblem 2 is to only take in traditional substrates, and the outer constraint is to reduce the growth rate by more than 90%. The inner key constraint of subproblem 3 is to only take in C1, and the outer constraint is to not be lower than the growth rate of traditional substrates alone. Among them, the manipulated variable is reaction knockout, and the outer constraint is the total number of reaction knockouts.

[0054] Figure 1 A schematic diagram of the AdaptUC-B concept proposed in the present invention is shown. The first internal problem represents the necessity of the strain after ALE to grow well on a medium with an unadapted substrate as the sole carbon source (AdaptUC-B). The second requirement stipulates that the strain after ALE should not grow well without an unadapted carbon source even in the presence of a co-substrate. Combining these two requirements effectively couples the assimilation of the unadapted carbon source to growth, creating a strong driving force for the cell to uptake the unadapted substrate. In the final internal problem, when the unadapted substrate can be taken up (as opposed to the second internal problem), the goal is to ensure that the optimal growth of the strain after ALE is not inferior to the growth of the reference strain on the co-substrate. This design requirement ensures that the addition of conventional substrates to a medium containing an unadapted carbon source can help the survival and growth of the starting strain during the ALE process. We assume that the gene deletions of the strain after ALE and the ALE starting strain are similar, or that their differences have little effect on growth. AdaptUC is specifically used to construct the ALE starting strain.

[0055] Design of AdaptUC-B

[0056] AdaptUC-B contains three design requirements, which must be met simultaneously. The first design requirement is that the genetically modified strain has the potential to evolve to grow on unutilized carbon sources. The growth rate is simulated by flux balance analysis. v1, v2, and v3 are the intracellular metabolic flux distributions under the three design requirements (unit: mmol / g dry weight cell / hour), LB1, LB2, and LB3 are the lower limits of the reaction flux under the three design requirements, and UB1, UB2, and UB3 are the upper limits of the reaction flux under the three design requirements.

[0057] Maximize v1 bio

[0058] Submission

[0059]

[0060] Among them, S ij represents the stoichiometric coefficient matrix of metabolite i in reaction j, v1j is the flux variable of reaction j, LB1 j and UB1 j They are v1 j The lower and upper bounds of the reaction can be used to set the reversibility of the reaction according to thermodynamics. j Indicates whether reaction j is disabled (y j =1) or not disabled (y j = 0). The outer layer constraint for the first inner problem is that for AdaptUC-B to still be able to grow on non-adapted substrates after deletion, the reduction in growth caused by gene deletion must not exceed 50%:

[0061] v1 bio ≥0.5v0 bio_UC (6)

[0062] where v0 bio_UC is the growth rate of the reference strain on the unadapted carbon source and can be calculated by solving the following FBA problem:

[0063] Maximize v0 bio_UC =cv

[0064] Submission

[0065]

[0066] where v j is the flux variable for reaction j.

[0067] The second design requirement is that after gene deletion, the strain does not grow or its growth is severely restricted on the conventional co-substrate, forcing the strain to utilize the C1 substrate when it is present. Considering that a 90% reduction in growth satisfies this design requirement, this design requirement is achieved by the following constraints:

[0068] v1 bio ≤0.1v0 bio_UC (10)

[0069] Calculation of the growth rate of the reference strain on the co-substrate and v0 bio_UC Similar, except that the boundaries of the co-substrate exchange reaction are set. The growth rate v2 is calculated by the following inner problem bio :

[0070] Maximize v2 bio =cv2

[0071] Submission

[0072]

[0073] The third design requirement is that the addition of the unadapted substrate to the medium containing only the co-substrate can restore the growth rate before the gene deletion. This design requirement is achieved by the following outer layer constraints:

[0074] v3 bio ≥v0 bio_cosub (16)

[0075] Under this condition, the growth rate v3 bio Calculated via the third inner question:

[0076] Maximize v3 bio =cv3

[0077] Submission

[0078]

[0079] Minimum uptake rate of unadapted carbon source v UC This can be obtained by asking the following internal questions,

[0080] minimize

[0081] Submission

[0082]

[0083] cv4≥v0 bio_cosub (27)

[0084] Where: v4 is the reaction flux distribution under the minimum uptake rate calculation conditions of the unadapted carbon source (unit: mmol / g dry weight cell / hour), LB4 and UB4 are the lower and upper limits of the reaction flux under the minimum uptake rate calculation conditions of the unadapted carbon source, respectively.

[0085] The total number of reaction deletions K must be limited, and the default K is 5 to speed up the solution process:

[0086] ∑y j <K (29)

[0087] In summary, the formula of the AdaptUC algorithm is as follows. The optimization algorithm is a whole, an indivisible step that is executed simultaneously. The previous description is an explanation of the various parts of this indivisible step:

[0088] Minimize ∑y j

[0089] Subject to:

[0090] Maximize v1 bio =cv1

[0091] Submission

[0092]

[0093] Maximize v2 bio =cv2

[0094] Submission

[0095]

[0096] Maximize v3 bio =cv3

[0097] Submission

[0098]

[0099] minimize

[0100] Submission

[0101]

[0102] v1 bi o≥0.5v0 bio_UC for AdaptUC B

[0103] v1 bio ≤0.1v0 bio_UC for AdaptUC A

[0104] v2bio ≤0.1v0 bio_cosub

[0105] v3 bio ≥v0 bio_cosub

[0106] ∑y j <K

[0107] v1.v2, v2, v4∈R, y∈[0, 1]

[0108] Example 2: Verification of AdaptUC

[0109] Example 1: Design of Escherichia coli starting strain with methanol as target carbon source

[0110] Model construction:

[0111] First, the methanol assimilation pathway (including methanol dehydrogenase, 3-hexose-6-phosphate synthase and 6-phosphohexose isomerase) was integrated into the genome-scale metabolic model iML1515 of Escherichia coli (Monk, JM, Lloyd, CJ, Brunk, E., Mih, N., Sastry, A., King, Z., Takeuchi, R., Nomura, W., Zhang, Z., Mori, H., Feist, A. M., Palsson, B. O., 2017. iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 35.904-908. https: / / doi.org / 10.1038 / nbt.3956), and some unnecessary metabolic pathways in the model were removed.

[0112] E. coli: Three heterogeneous reactions encoded by the RuMP methanol assimilation pathway (medh: methanol dehydrogenase, hps: 3-hexanone-6-phosphate synthase, and phi: 3-hexanone-6-phosphate isomerase) were added to the E. coli model iML1515. The reactions of 'PFL' (catalyzed by pyruvate formate lyase) and 'OBTFL' (catalyzed by 2-oxobutyrate formate lyase) were deactivated in the model because they are only active under anaerobic conditions. In addition, the reactions 'DRPA' and 'PAI2T' were removed because they were reported by the prior art as unrealistic.

[0113] Determination of reaction knockout strategy:

[0114] Through the AdaptUC-B algorithm, multiple reaction knockout strategies were simulated and predicted to enhance the growth ability of E. coli under methanol conditions. The details are shown in the following table:

[0115]

[0116]

[0117]

[0118]

[0119] Finally, the reaction knockout combination of △tpi△rpe△f6pa was determined as the optimal strategy, which can gradually enhance the utilization efficiency of methanol by Escherichia coli during the ALE process and reduce its dependence on other co-substrates.

[0120] Implementation of the ALE process:

[0121] First, the modified Escherichia coli strain is cultured in a medium containing methanol, and the supply of other co-substrates in the medium is gradually reduced, forcing the strain to gradually increase its dependence on methanol. After multiple generations of passage, an evolved strain that can grow in a medium containing only methanol is obtained. Among them, the AdaptUC-B algorithm of the present invention is a transformation strategy for determining the starting strain of the adaptive evolution passage culture experiment, and thus the effectiveness of the transformation strategy predicted by AdaptUC-B is demonstrated by comparing successful cases in the literature. For example, the AdaptUC-B strategy using pyruvate as a co-substrate and deleting tpi has also been verified by the literature; in addition, the AdaptUC-B strategy using pyruvate as a co-substrate and deleting tpi has also been verified by the literature (Keller, P., Noor, E., Meyer, F., Reiter, MA, Anastassov, S., Kiefer, P., Vorholt, JA, 2020. Methanol-dependent Escherichia coli strains with a complete ribulosemonophosphate cycle. Nat Commun 11, 1-10. https: / / doi.org / 10.1038 / s41467-020- 19235-5; Keller, P., Reiter, MA, Kiefer, P., Gassler, T., Hemmerle, L., Christen, P., Noor, E., Vorholt, JA, 2022. Generation of an Escherichia coli strain growing onmethanol via the ribulose monophosphate cycle. Nat Commun 13.1-13. https: / / doi.org / 10.1038 / s41467-022-).

[0122] The results showed that the growth efficiency of the transformed Escherichia coli in methanol medium was significantly improved through the reaction knockout strategy predicted by AdaptUC-B, verifying the effectiveness and applicability of the algorithm.

[0123] The present invention provides an effective method for designing starting strains for adaptive laboratory evolution processes, called AdaptUC-B. This method enables microorganisms to gradually adapt and eventually grow solely on a previously unavailable carbon source by predicting reaction knockout strategies. This method can significantly improve the efficiency of industrial microorganisms in utilizing previously unused carbon sources, helping to promote innovation and development in biosynthesis.

Claims

1. A method for designing starting strains in an adaptive laboratory evolution process, characterized in that The following steps are involved: S1 provides the following data, including bacterial species, metabolic network model, substrate and strain culture conditions for maximum uptake rate, target carbon source, and co-substrate carbon source; S2 determines the reaction knockout strategy through a two-level mixed integer programming algorithm based on a genome-scale metabolic model, which enables the microorganism to gradually adapt during the evolution process and eventually rely solely on a previously unavailable carbon source for growth; S3 applies the above-mentioned reaction knockout target to a microorganism for use in the evolutionary starting strain of the adaptive laboratory evolution ALE process, so as to gradually enhance its ability to utilize the carbon source through the adaptive laboratory evolution ALE process, thereby ensuring that the microorganism can eventually grow independently relying on the carbon source in the absence of other carbon sources.

2. The method according to claim 1, characterized in that The two-level mixed integer programming algorithm further comprises the following steps: a) In the inner optimization problem, the growth performance of the knockout strain under the target carbon source and other co-substrate conditions is simulated; b) In the outer optimization problem, the utilization of the target carbon source is maximized and its necessity for strain growth is ensured.

3. The method according to claim 1 or 2, characterized in that: The reaction knockout strategy described herein includes: a) selecting a reaction knockout combination that can couple the target carbon source with growth; b) Ensure that the knockout strain can grow in medium without other carbon sources.

4. The method according to any one of claims 1 to 3, characterized in that: The method for implementing the two-level mixed integer programming algorithm is as follows: in, Minimize ∑y j Subject to: Maximize v1 bio =cv1 Submission Maximize v2 bio =cv2 Submission Maximize v3 bio =cv3 Submission Minimize v c1 =-v j|j∈J c1 Submission v1 bio ≥0.5v0 bio-UC for AdaptUC B v1 bio ≤0.1v0 bio_UC for AdaptUC A <h2 style=";text-align:left;direction:ltr">v2<h2 style=";text-align:left;direction:ltr"> bio <h2 style=";text-align:left;direction:ltr"> ≤0.1v0<h2 style=";text-align:left;direction:ltr"> bio_cosub v3 bio ≥v0 bio_cosub ∑y j <K v1.v2, v2, u4∈R, y∈[0,1] S i，j represents the stoichiometric coefficient matrix of metabolite i in reaction j, v1 j is the flux variable for reaction j, LB1 j and UB1 j They are v1 j The lower and upper limits of the binary variable y j Indicates whether reaction j is deactivated y j =1 or not disabled j =0.

5. A design system for executing the method according to any one of claims 1 to 4, characterized in that: Includes the following modules: a. Data entry module, used to enter relevant information, including bacterial species, metabolic network model, substrate and strain culture conditions of maximum uptake rate, target carbon source, and co-substrate carbon source; b. Data processing module, used for calculation and processing; c. Result output module.

6. A bioinformatics tool based on the system according to claim 5, comprising: a. User interface, allowing users to enter relevant information; b. A system as claimed in claim 5; d. Result display module, used to display the prediction results to users.

7. The bioinformatics tool according to claim 6, characterized in that Also includes: e. Report generator, used to create results.

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