CN-118866100-B - Method for predicting interaction of rhizosphere beneficial bacteria and pathogenic bacteria based on genome metabolic model

Abstract

The invention discloses a method for predicting interaction of rhizosphere beneficial bacteria and pathogenic bacteria based on a genome metabolism model, which comprises the steps of A, carrying out whole genome sequencing on separated strains to obtain whole genome data of the strains, B, constructing an initial single genome metabolism model by utilizing the whole genome data of the strains by utilizing gapseq software, predicting available resources, C, detecting actual resource utilization characteristics of the strains, optimizing the single genome metabolism model by utilizing gapseq software through missing metabolic pathways of the strains, improving metabolism prediction capacity of the model, obtaining a corrected single genome metabolism model, D, constructing the genome metabolism model of the beneficial bacteria according to a principle of diversity full coverage based on the corrected single genome metabolism model, and E, predicting interaction characteristics of the beneficial bacteria and the pathogenic bacteria through the genome metabolism model of the beneficial bacteria. The invention can greatly shorten the time and develop large-scale co-culture interaction prediction.

Inventors

• YANG TIANJIE
• WEI ZHONG
• Yang Xinrun
• WANG XIAOFANG
• XU YANGCHUN
• SHEN QIRONG

Assignees

• 南京农业大学

Dates

Publication Date

20260512

Application Date

20240613

Claims (2)

1. 1. A method for predicting interaction of a rhizosphere beneficial flora with a pathogenic bacterium based on a genome metabolic model, comprising the steps of: A. sequencing the whole genome of the separated strain to obtain strain whole genome data; B. utilizing gapseq software, constructing an initial single-bacterium genome metabolism model by utilizing whole genome data of a strain, and predicting available resources, wherein the initial single-bacterium genome metabolism model is constructed by the following steps: Constructing an initial single-bacterium genome metabolism model for the separated genome sequences of beneficial bacteria and pathogenic bacteria by gapseq software, automatically comparing and predicting corresponding metabolism pathways after inputting the genome sequences, wherein the initial single-bacterium genome metabolism model comprises all known metabolism reactions and genes for encoding each enzyme in a living body; The predicted available resources include: calculating flux of metabolites generated or utilized by beneficial bacteria and pathogenic bacteria through an initial single-bacterial genome metabolism model of the beneficial bacteria and the pathogenic bacteria by adopting flux balance analysis in COBRApy bags of Python, and predicting available resources of the strain according to the fact that if the flux is smaller than 0, the substances can be utilized; C. detecting the actual resource utilization characteristic of the strain by utilizing a microplate test, optimizing an initial single-genome metabolic model by utilizing gapseq software for a metabolic pathway deleted by the strain, improving the metabolic prediction capacity of the model, and obtaining a corrected single-genome metabolic model, wherein the corrected single-genome metabolic model is obtained by the following steps: comparing the predicted available resources of the initial model with the microplate resource utilization test data, filling gaps of the initial single-fungus genome metabolism model by gapseq software, adding the related generation and transportation reactions which are predicted to be unavailable resources in the model into the model, so that the resource metabolism flux is less than 0, and indicating that the resources can be utilized to obtain a corrected single-fungus genome metabolism model; D. constructing a genome metabolism model of a beneficial bacterial group according to the principle of full diversity coverage based on the corrected single bacterial genome metabolism model, wherein the genome metabolism model of the beneficial bacterial group is obtained by constructing the genome metabolism model of the beneficial bacterial group by utilizing COBRApy bags of Python based on the corrected single bacterial genome metabolism model; E. the interaction characteristics of beneficial flora and pathogenic bacteria are predicted by flux balance analysis of COBRApy packages in Python, and specifically include: 1) For each flora, firstly simulating the biomass flux of pathogenic bacteria when the pathogenic bacteria grow alone, and then simulating the biomass flux of pathogenic bacteria when beneficial flora and pathogenic bacteria are co-cultured, wherein if the biomass flux of pathogenic bacteria is lower than the biomass flux of pathogenic bacteria when the pathogenic bacteria grow alone in the co-culture, the flora is considered to be effective in inhibiting the pathogenic bacteria, otherwise, if the biomass of pathogenic bacteria is higher than the biomass of pathogenic bacteria when the pathogenic bacteria grow alone in the co-culture, the flora is considered to be effective in promoting the pathogenic bacteria to grow; 2) The ratio of the biomass flux of the pathogenic bacteria in co-cultivation to log2 of the biomass flux of the pathogenic bacteria when cultivated alone is defined as the interaction score, and if the absolute value of the interaction score exceeds 1, it is considered as a strong inhibitory or strong promoting effect; 3) If the biomass flux of pathogenic bacteria is lower than 90% of the biomass flux of pathogenic bacteria when the single bacteria and pathogenic bacteria interact when the flora and pathogenic bacteria are co-cultured, the flora is considered to have a stronger inhibiting effect than the single bacteria.
2. 2. A method for selecting a bacterial colony for inhibiting diseases, characterized in that a method for predicting interaction between beneficial rhizosphere bacterial colony and pathogenic bacteria based on a genome metabolic model according to claim 1 is adopted, and bacterial colony with the strongest antibacterial effect is screened and used as a candidate representative for inhibiting diseases.

Description

Method for predicting interaction of rhizosphere beneficial bacteria and pathogenic bacteria based on genome metabolic model Technical Field The invention relates to the fields of soil biology and bioinformatics, in particular to a method for constructing a metabolism model of beneficial bacteria and pathogenic bacteria through genome data and realizing the prediction of interaction between rhizosphere beneficial bacteria and pathogenic bacteria based on flux balance analysis. Background Soil-borne diseases caused by soil-borne pathogenic bacteria severely restrict the grain safety and the soil productivity of China. In view of this problem, the application of beneficial bacteria is widely regarded as an efficient and environmentally friendly method capable of precisely inhibiting pathogenic bacteria. However, the interaction of beneficial bacteria with pathogenic bacteria is affected by complex species of microorganisms in the rhizosphere and root exudates, thereby affecting its ability to inhibit the invasion of soil pathogenic bacteria. The current research on flora metabolism interaction and inhibition of soil-borne pathogenic bacteria invasion mainly uses test means, the workload is heavy and time-consuming, and the number of detectable resources and microorganisms is limited. Therefore, how to rapidly and effectively predict the effect of flora in inhibiting pathogenic bacteria in complex rhizosphere environments is a major technical bottleneck currently faced. Disclosure of Invention Aiming at the problems in the background technology, the invention can rapidly, efficiently and accurately predict the effect of beneficial bacteria on inhibiting pathogenic bacteria based on metabolic interaction by constructing a new method for predicting interaction between beneficial bacteria on the root and pathogenic bacteria based on genome metabolic model, and provides an important method technology for constructing functional bacteria on inhibiting diseases and reducing soil-borne diseases in different complex root environments. The technical scheme provided by the invention comprises the following specific steps: A. And (3) performing whole genome sequencing on the separated strain to obtain whole genome data after the machine is started. B. Using gapseq software, an initial single genome metabolic model was constructed using strain whole genome data and available resources were predicted. C. Detecting the actual resource utilization characteristic of the strain by using a microplate test, optimizing a model by using gapseq on the missing metabolic pathway of the strain, improving the metabolism prediction capability of the model, and obtaining a corrected single-bacterial genome metabolism model. D. based on the corrected single-bacterium genome metabolism model, constructing a genome metabolism model of beneficial bacteria according to the principle of full diversity coverage. E. The interaction characteristics of the beneficial flora with pathogenic bacteria were predicted by flux balance analysis of the COBRApy package in Python. The invention also provides a selection method of the flora for inhibiting the diseases, which is based on screening the flora with the strongest antibacterial effect and is used as a candidate representative for inhibiting the diseases. 1) Construction of genome metabolism models of beneficial bacteria and pathogenic bacteria And performing whole genome sequencing on the isolated beneficial bacteria and pathogenic bacteria, and then constructing an initial single-bacteria genome metabolism model on genome sequences of the beneficial bacteria and the pathogenic bacteria through gapseq software. The software self-builds a large and complete database, and can automatically compare and predict corresponding metabolic pathways after inputting genome sequences, including pathway structure, pathway key enzymes, reaction chemometry and other information. Thus, the metabolic model comprises all known metabolic reactions in the organism and the genes encoding each enzyme. Subsequently, the flux of the metabolites was calculated by initial metabolic models of the beneficial and pathogenic bacteria using flux balance analysis in the COBRApy package of Python as an objective function of the flux of the resource metabolism. If the resource metabolic flux (flux) is less than 0, this means that the substance can be utilized, and the resources that the strain can utilize are predicted accordingly. 2) Optimization of genome metabolic models of beneficial and pathogenic bacteria And detecting the resource utilization characteristics of beneficial bacteria and pathogenic bacteria by utilizing a microplate system. Comparing the predicted available resources of the initial model with the strain resource utilization test data, filling gaps of the metabolic model by gapseq software, adding the related generation and transportation reactions which are predicted to be unavailable resources in the model int