Target bacterial flora construction method

Abstract

To provide a method for constructing a target bacterial flora.SOLUTION: By adjusting the content of target population-sensing signal molecules in the wastewater treatment plant and constructing a target bacterial flora to decompose target pollutants, specifically sulfamethoxazole, roxithromycin, azithromycin, or triclosan, the wastewater treatment efficiency of the plant is enhanced. The method for constructing the target bacterial flora involves screening a target bacterial flora including keystone microorganisms, hub microorganisms, and functional microorganisms. Through the correlation between each population-sensing signal molecule in the wastewater treatment plant and the target bacterial flora, the target population-sensing signal molecules are screened, thereby achieving the construction of the target bacterial flora in the wastewater treatment plant.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to the field of wastewater treatment, and specifically to a method for establishing a target bacterial flora. [Background technology]

[0002] A wastewater treatment plant is a wastewater treatment facility whose main function is to treat wastewater through a series of physical, chemical and microbiological processes. Through physical treatment, harmful substances in wastewater are removed, and the treated wastewater meets discharge standards or is reused. The goal is to ensure that water quality standards are met as much as possible. Wastewater treatment plants are used to treat wastewater with microorganisms, The presence of some contaminants that are not included in the wastewater treatment plant (i.e., target contaminants) The effectiveness of the microorganisms in treating wastewater will be reduced. Summary of the Invention

[0003] The present invention provides a method for establishing a target microbiota, comprising: Adjusting the content of sensing signal molecules to regulate the target bacterial flora for controlling the target pollutants This can improve the treatment efficiency of sewage or wastewater in sewage treatment plants. In order to achieve the above objectives, the present invention adopts the following technical solutions: The method for constructing a target flora includes: The target flora for decomposing the target pollutant is determined, and the target flora is composed of cornerstone microorganisms, hub microorganisms, and and functional microorganisms, with cornerstone microorganisms being Hirschia, Methylotenera and and Lactobacillus, and the hub microorganisms are Terrimonas, Denis tratisoma and Bradyrhizobium, and functional microorganisms include Ott owia, Dechloromonas and Zoogloea, Here, the target contaminants are sulfamethoxazole, roxithromycin, and azithromycin. or triclosan, Based on the correlation between each quorum-sensing signal molecule and the target flora in the wastewater treatment plant, Determine the target population sensing signal molecule of Here, the quorum sensing signal molecule is generated by microorganisms in the wastewater treatment plant during the quorum sensing process. and released chemical signal molecules, and quorum sensing is a communication mechanism between microorganisms. Coordinating microbial behavior by secreting and sensing chemical signal molecules, The wastewater treatment plant applies a target community-sensing signal molecule to detect the presence of a target flora in the wastewater treatment plant. Promote proliferation, adjust the content of target flora, and establish target flora. In one embodiment of the present invention, the correlation between each quorum-sensing signal molecule and the target flora in a wastewater treatment plant is Determining a target community-sensing signal molecule of the target microbiota based on the correlation includes: Measure the content of each swarm-sensing signal molecule in the wastewater treatment plant; We performed a correlation analysis between the content of each quorum-sensing signal molecule and the relative abundance of various microorganisms in the target flora. First, we will build a signal molecular biological related network. Here, the first signal molecular biological association network is composed of multiple points and edges connecting multiple points. Each point in the first signal molecular biological network corresponds to each microorganism and each cluster in the target flora. The first signal molecule corresponds to the first edge of the biologically related network. This shows the correlation analysis result between two connected points, and the correlation analysis result is connected by an edge. It includes correlation data and significance data between two connected points. Correlation data is positive correlation, negative correlation Includes correlation and non-correlation, and significance data includes significant and non-significant. The first signal is to retain the edges with significant data in the molecular biological association network, and the second signal is to retain the edges with significant data in the molecular biological association network. Obtain a signal molecular biological association network, The second signaling molecular biological association network has the highest degree of connectivity and betweenness centrality. determining the ensemble sensing signal molecule corresponding to the point as the target ensemble sensing signal molecule; Here, connectivity means the number of edges that connect a point, and betweenness centrality means the number of edges that connect a point to the network. It indicates how often a point is traversed by the shortest path between any two points in the work. In one embodiment of the present invention, the method for determining cornerstone microorganisms is as follows: Obtain microbial data from wastewater treatment plants, and the microbial data is information on bacterial strains in wastewater treatment plants. and strain abundance information, Here, the strain information includes the names of various bacteria among the microorganisms contained in the wastewater treatment plant. Strain abundance information contains the relative abundance of various strains of microorganisms contained in the wastewater treatment plant. , Regarding the various types of microorganisms contained in sewage treatment plants, the strains of sewage treatment plants Through the mapping relationship between the information and the bacterial strain abundance information, the wastewater treatment plan after removing the bacteria The strain abundance information of the wastewater treatment plant when bacteria are not removed is obtained. The difference between the information on bacterial abundance in the wastewater treatment plant and the information on bacterial abundance in the wastewater treatment plant after bacteria removal is called the bacterial abundance difference. Calculated as minutes, Here, the abundance difference value is calculated using the Euclidean distance formula or the Manhattan distance formula. , The bacteria with abundance difference values ​​in the top 10-15% of all bacteria are determined as cornerstone microorganisms. do. In one embodiment of the present invention, a mapping between strain information and strain abundance information of a wastewater treatment plant is performed. The relationship is determined by learning using a deep learning model. The model is a neural ordinary differential equation. In one embodiment of the present invention, the method for determining hub microorganisms is as follows: Correlation analysis of bacterial strain abundance information in wastewater treatment plants was performed to establish the first biological association network. Build Here, the first biological association network consists of a plurality of vertices and edges connecting the plurality of vertices, 1 Each point in the biological network is connected to each of the microorganisms contained in the wastewater treatment plant. In one-to-one correspondence, each edge in the first biological association network is the number of points connected by the edge. The correlation analysis result shows the correlation between two points connected by an edge. Correlation data includes positive correlation, negative correlation, and no correlation. Significance data includes significant and non-significant data. The edges in the first biological association network where the significance data is significant are retained, and the edges in the second biological association network where the significance data is significant are retained. Gain relevant networks, For the correlation data represented by each edge of the second biological association network, A sequential multivariate analysis was performed to compare the non-microbiological data with the data from the wastewater treatment plant. The correlation data was obtained after removing the influence of the data. Here, non-microbiological data include information on the microhabitat of the wastewater treatment plant, process parameters, etc. data and geographic information, The correlation data, each represented by an edge in the second biological association network, is then analyzed using non-microbes. The correlation data is updated after removing the influence of the biological data, and the third biological association network is created. Get the In the third biological association network, the connectivity is in the top 2-5% of the connectivity of all points. The bacteria whose betweenness centrality is in the top 2-5% of the betweenness centrality of all points are called hub microorganisms. It was decided that Here, connectivity means the number of edges that connect a point, and betweenness centrality means the number of edges that connect a point to the network. It indicates how often a point is traversed by the shortest path between any two points in the work. In one embodiment of the present invention, the method for determining functional microorganisms is as follows: Measuring the content of target pollutants in wastewater treatment plants; Correlation analysis was performed between the bacterial strain abundance information of the wastewater treatment plant and the content of the target pollutants. The correlation analysis results include correlation data and significance data, and the correlation data is positive. Correlation includes correlation, negative correlation, and no correlation, and significance data includes significant and non-significant. The correlation data from the correlation analysis results for microorganisms contained in sewage treatment plants showed a positive correlation. Bacteria for which the significance data is significant are determined to be functional microorganisms. In one embodiment of the present invention: When the target contaminant was sulfamethoxazole, the target population sensing signal of the determined target flora was The signal molecule is C8-HSL, and the dose of the target population sensing signal molecule is 5-15 nmol / L. can be, When the target contaminant was roxithromycin, the target population sensing signal component of the determined target bacterial flora was The target population-sensing signal molecule was C8-HSL, and the dose was 5-15 nmol / L. , When the target contaminant is azithromycin, the target population-sensing signal molecule of the determined target bacterial flora is C8-HSL, and the dose of the target population sensing signal molecule is 5-15 nmol / L. When the target contaminant was triclosan, the target population-sensing signal molecule of the determined target bacterial flora was C The target population-sensing signal molecule is 10-HSL, and the dosage is 5 to 15 nmol / L. [Effects of the Invention]

[0004] The present invention has the following beneficial effects. The method for constructing a target bacterial flora provided by the present invention includes screening the target bacterial flora; The target flora includes cornerstone microorganisms, hub microorganisms and functional microorganisms. After obtaining the target flora, Based on the correlation between each com- munication signal molecule and the target flora in the treatment plant, the target com- munication signal Screening molecules and tailoring target population-sensing signal molecules in wastewater treatment plants This promotes the growth and colonization of target flora among the microorganisms contained in wastewater treatment plants. This allows the construction of a target bacterial flora in a wastewater treatment plant. The bacterial flora can control target pollutants in wastewater treatment plants, This can promote the sewage treatment effect. [Brief explanation of the drawings]

[0005] [Figure 1] FIG. 1 is a schematic diagram showing the method for constructing a target bacterial flora provided by an example of the present application. [Figure 2] FIG. 1 is a schematic diagram showing a second signal molecular biological association network provided by an embodiment of the present application. [Figure 3] FIG. 1 is a schematic diagram showing the connectivity and betweenness centrality corresponding to points in a second signal molecular biological association network provided by an embodiment of the present application. [Figure 4] FIG. 1 is a schematic diagram showing the removal rate of the target contaminant SMX by the target bacterial flora provided by the examples of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0006] The methods and apparatus provided by the embodiments of the present application relate to the control of pollutants in wastewater treatment plants. The content of target population-sensing signal molecules in the wastewater treatment plant was adjusted to detect the presence of resident colonies. This promotes the growth of target flora within the soil, thereby achieving the establishment of target flora for controlling target pollutants. To solve the problem of low sewage treatment efficiency of sewage treatment plants in the background art The examples of this application provide a method for constructing a target flora, and extract cornerstone microorganisms, Screening of target microbiota including pathogenic and functional microorganisms in wastewater treatment plants Based on the correlation between each quorum-sensing signal molecule and the target flora, the target quorum-sensing signal molecules were screened. This allows the establishment of a target bacterial flora in the wastewater treatment plant. The constructed target bacterial flora can control the target pollutants in the wastewater treatment plant, The wastewater treatment efficiency of water treatment plants can be improved. For example, as shown in FIG. 1, the method for constructing a target bacterial flora provided by the examples of the present application is as follows: Includes S101 to S103. S101, determining the target bacterial flora for degrading the target pollutant. In the examples of this application, the target contaminants were sulfamethoxazole, roxithromycin, azide, The target microbiota is composed of cornerstone microorganisms, hub microorganisms, and functional Cornerstone microorganisms include Hirschia, Methylotenera and L Actobacillus, and the hub microorganisms are Terrimonas and Denitra tisoma and Bradyrhizobium, and functional microorganisms include Ottowi The target flora includes Dechloromonas and Zoogloea. It is placed in the activated sludge treatment plant or biofilm treatment plant in the sewage to target contaminants. Used to process things. It should be noted that the target contaminants may include other contaminants known in the prior art, and the target bacteria may It is understood that the flora may include other known strains and is not limited to these in the examples of this application. I want to be done that. In one embodiment, the target bacterial flora specifically includes strain information shown in Table 1. Table 1: Information on strains included in the target bacterial flora in one embodiment JPEG2025174841000001.jpg238168 In another embodiment, the strain information specifically included in the target bacterial flora is shown in Table 2. Table 2: Information on strains included in the target bacterial flora in another embodiment JPEG2025174841000002.jpg238168Optionally, the method for determining the cornerstone microorganisms includes S1011A to S1013A. S1011A, Obtaining Microbial Data from Wastewater Treatment Plants. Here, the strain information includes the names of various bacteria among the microorganisms contained in the wastewater treatment plant. Strain abundance information contains the relative abundance of various strains of microorganisms contained in the wastewater treatment plant. , the sum of the relative abundances of all bacteria in the microbial data is 1. In one embodiment, 1068 sampling points were collected from 187 wastewater treatment plants. Selected and sampled to obtain 1068 sludge samples. The sequencing results of 1068 sludge samples are obtained. The sequencing results of the sludge samples include the number of strains of each strain and the total number of strains. The sequencing results of 068 sludge samples were collected from a total of 187 wastewater treatment plants. Obtain microbial data. Specifically, for one sludge sample, the sequencing process is as follows: Sludge samples obtained by filtration were subjected to 16S rRNA amplicon sequencing. Polymerase chain reaction (PC) of bacterial 16S rRNA V3-V4 region in mud samples R) Amplification was performed to obtain the DNA sequence present in the V3-V4 region of the bacteria in the sludge sample. The amplified DNA sequences can be subjected to high-throughput sequencing techniques (e.g., Illu The sludge was subjected to bipartite sequencing using the mina MiSeq platform. Obtain sequence data for bacteria (also called microorganisms) in the sample. The 16S rRNA primer used for 16S rRNA amplicon sequencing was 341 Includes F and 806R. 341F:CCTAYGGGRBGCASCAG, shown in SEQ ID NO:1. 806R:GGACTACNNGGGTATCTAAT, as shown in SEQ ID NO:2 can be. The V3-V4 region of bacterial 16S rRNA consists of two highly These are important variable regions, and by determining the sequences of these two regions, bacterial strain information can be obtained. can be obtained. Furthermore, we will classify the bacterial sequence data in the sludge samples and analyze the bacterial species composition to evaluate the effectiveness of the sludge treatment. Names of various microorganisms contained in the plant (corresponding to bacterial strain information for wastewater treatment plants) The number of occurrences of each bacterium (i.e., the abundance of each bacterium) was calculated, and the number of occurrences of each bacterium was calculated based on the number of occurrences of all bacteria. The relative abundance of various microorganisms in the wastewater treatment plant is calculated based on the value divided by the actual number. The abundance (corresponding to the strain abundance information of the wastewater treatment plant) is calculated. Obtain microbial information from treatment plants. Specifically, the above classification is performed by clustering sequence data to classify similar sequences into a single classification unit. The above bacterial species composition analysis means that the sequence data and the The taxonomic information of each OTU was determined by comparing it with known 16S rRNA databases and the samples This means understanding the type and composition of bacteria inside. S1012A, Regarding various types of bacteria contained in sewage treatment plants, Through the mapping relationship between the plant's bacterial strain information and bacterial strain abundance information, Obtaining bacterial strain abundance information for wastewater treatment plants and determining the bacterial abundance of the wastewater treatment plants when bacteria are not removed The difference between the strain abundance information of the wastewater treatment plant and that of the plant after removing bacteria is calculated as , calculated as the difference in bacterial abundance. The above abundance difference value is calculated using the Euclidean distance formula or can be calculated using the Manhattan Distance formula Optionally, the abundance difference value is calculated as the Jaccard distance (Jaccard Di It can also be calculated using the formula (stance). Specifically, the number of resident colonies before removing the bacteria is The strain abundance information of the colony and the strain abundance information of the resident colony after removing the bacteria are substituted into the distance function. The difference in abundance between the two is calculated. Optionally, the mapping relationship between the strain information and the strain abundance information of the wastewater treatment plant is This is obtained by learning a deep learning model. Specifically, the above deep learning model is based on neural ordinary differential equations (cNODE, co mposition Neural Ordinary Differential E quation), and the equation for the above deep learning model is as follows: JPEG2025174841000003.jpg2681where, JPEG2025174841000004.jpg713 shows the output result of the deep learning model, JPEG2025174841000005.jpg44 indicates the number of model iterations, JPEG2025174841000006.jpg797, JPEG2025174841000007.jpg929 shows the results of the previous iteration of the model, and is selectable. JPEG2025174841000008.jpg57 may be 100, the step size of each iteration may be 0.001, JPEG2025174841000009.jpg1151, JPEG2025174841000010.jpg1247, JPEG2025174841000011.jpg44 shows bacterial strain information from a wastewater treatment plant. JPEG2025174841000012.jpg1136, if the bacterial species is present it is set to 1, if it is not present it is set to 0, JPEG2025174841000013.jpg56 shows the number of microbial species, and the match between the strain information and the strain abundance information of the above wastewater treatment plant is The relationship between JPEG2025174841000014.jpg1177, JPEG2025174841000015.jpg1228 JPEG2025174841000016.jpg713 and JPEG2025174841000017.jpg929, where the prediction error is obtained by calculating six intermediate points; JPEG2025174841000018.jpg76 indicates the weighting coefficient of the i-th intermediate point, JPEG2025174841000019.jpg87 indicates the rate of change of the i-th midpoint. The midpoint is the rate of change of the i-th midpoint in the Runge-Kutta method. Calculating multiple intermediate increment points or step estimates involves calculating a series of weighted increment points. These results are not repeated in the examples of this application. The numerical integration method used in the above equation is the Dormand-Prince fifth-order Runge-Kutta method (D ormand-Prince 5th order Runge-Kutta meth od, DOPRI5), and the solution equation may be constructed using other existing methods. It will not be repeated in the examples. The training process of the above deep learning model is as follows: Step 1: Collect microbial data from multiple wastewater treatment plants to build a training dataset do. In one application scenario, 187 microorganisms from a wastewater treatment plant obtained in S1011A were The data is used to build a training dataset for deep learning models. Rarefaction of biological data is performed to identify the frequency of occurrence of microorganism data. Filter data for bacteria with a frequency of less than 20% and a relative abundance of less than 0.05%. Step 2: Build a deep learning model and train it on multiple wastewater treatment processes in the training dataset. The strain information in the plant's microbial data is used as input for the deep learning model, and multiple pollutants are identified. Predicting strain abundance information in microbial data from water treatment plants. Step 3: The deep learning model predicts the micro-scale of multiple wastewater treatment plants. The strain abundance information in the biological data and the microbial abundance of multiple wastewater treatment plants in the training dataset The deep learning model is adjusted and trained according to the strain abundance information error in the biological data. Obtain the resulting deep learning model. Selectively tune the deep learning model by minimizing a loss function. The minimization loss function satisfies the following equation: JPEG2025174841000020.jpg2089where, JPEG2025174841000021.jpg55 shows the training dataset, JPEG2025174841000022.jpg44 shows the strain information in the training dataset, JPEG2025174841000023.jpg55 shows the strain abundance information in the training dataset, JPEG2025174841000024.jpg714 denotes the distance function or dissimilarity measure function, JPEG2025174841000025.jpg933 was predicted by the strain abundance information in the training dataset and the deep learning model. Shows distance / dissimilarity between strain abundance information. S1013A: The bacteria with the highest abundance difference value of all bacteria are classified as cornerstone microorganisms. and decide. Optionally, the abundance difference value is used to determine the abundance of all microorganisms contained in the wastewater treatment plant. The bacteria with the highest abundance difference value of 15% were determined as cornerstone microorganisms. The selection criteria for the object are not further limited. The method for determining the hub microorganism includes steps S1011B to S1015B. In S1011B, correlation analysis of bacterial strain abundance information from wastewater treatment plants was performed. Build relevant networks. Here, the first biological association network consists of a plurality of vertices and edges connecting the plurality of vertices, 1 Each point in the biological network is connected to each of the microorganisms contained in the wastewater treatment plant. In one-to-one correspondence, each edge in the first biological association network is the number of points connected by the edge. The correlation analysis results show the correlation between two points connected by an edge. Includes correlation data and significance data. Correlation data includes positive correlation, negative correlation, and no correlation. Significant data includes significant and non-significant. In some application scenarios, correlation data may be non-linear. It also includes correlation and perfect correlation, and the significance data may further include highly significant data. Specifically, for correlation data, if the correlation data represented by one side is a positive correlation, In this case, the strain abundance information of the bacteria corresponding to the two points is positively correlated, i.e., The correlation data shown by one side indicates that the relative abundance of the bacteria increases or decreases simultaneously. If is negatively correlated, the strain abundance information of the bacteria corresponding to the two points is negatively correlated, i.e. , the relative abundance of bacteria corresponding to one point increases / decreases, and the relative abundance of bacteria corresponding to another point indicates a decrease / increase. If the correlation data represented by one side is uncorrelated, The strain abundance information of the bacteria corresponding to the two points is uncorrelated, i.e., the strain abundance information of the bacteria corresponding to the two points is uncorrelated. This shows that the relative abundances of the two are not correlated. For significant data, if the significant data represented by one side is significant, two points The correlation between the strain abundance information of the corresponding bacteria is statistically significant, i.e., the two points The correlation data between the relative abundances of corresponding bacteria is statistically significant. If the significance data represented by The correlation between the data was not statistically significant, i.e., the relative abundance of the bacteria corresponding to the two points was This indicates that the correlation data between the two is not statistically significant. The above correlation data is usually quantified by a correlation coefficient, and a general correlation coefficient is a peer-reviewed Pearson correlation coefficient, Spearman's rank correlation coefficient coefficient and Kendall's rank correlation coefficient For example, the Pearson correlation coefficient When quantifying correlation data by a correlation coefficient, a positive Pearson correlation coefficient indicates correlation. The correlation data is positively correlated, and if the Pearson correlation coefficient is negative, the correlation data is negatively correlated. If the Pearson correlation coefficient is zero, the correlation data are uncorrelated. The significance is usually statistical significance, i.e., the difference between the sample statistics and the overall parameters is The significance data is quantified by P-value. For example, if the significance level value is set to 0.05 and the P value is greater than 0.05, the significance is obtained. Data were significant if the P value was less than or equal to 0.05, and non-significant data were significant if the P value was less than or equal to 0.05. Optionally, the correlation analysis method is SPIEC-EASI (Sparse Inverse Equation 1). sE Covariance estimation for Ecological Association and Statistical Inference) and i DIRECT(Inference of Direct and Indirect Relationships with Effective Copula-based d Transitivity) combination method may be adopted, and the Pearson correlation coefficient Calculation method and Random Matrix Theory (RMT) It is also possible to combine the Spearman correlation coefficient calculation method with the random matrix theory (Ra The implementation of this application may also combine The examples are not particularly limited. In one application scenario, we combine the Pearson correlation coefficient calculation method with random matrix theory to It is used as a correlation analysis method. For one side connecting two points, calculate the Pearson correlation coefficient. The correlation data between two points is calculated by the calculation method, and the correlation data is calculated by the random matrix theory. By determining the significance of , we obtain the significance data between the two points. It should be noted that the above correlation data and significance data can be quantified using other methods in the prior art. The examples of the present application are not limited thereto. S1012B, the significance data in the first biological association network is retained for the significant edges. , to obtain a second biological association network. S1013B, the correlation data represented by each edge of the second biological association network A sequential multivariate analysis of correlation data and non-microbiological data from the wastewater treatment plant was performed. Correlation data is obtained after removing the influence of non-microbiological data. Non-microbiological data include information on the microhabitat of the wastewater treatment plant, information on process parameters, and Optionally, the microhabitat information includes the total nitrogen (T N), ammonia nitrogen (NH 4+ -N), nitrate nitrogen (NO3 - -N), nitrite nitrogen (NO2 - -N), water soluble organic nitrogen (DON), water temperature (T), pH, total phosphorus (TP) and The process parameter information includes information on the wastewater treatment plant's The geographical information can be acquired according to the actual situation and is not particularly limited. Includes longitude and latitude coordinates. In the examples of the present application, after sampling the wastewater from the wastewater treatment plant, It is understood that the sampled material will be measured to obtain non-microbiological data for the wastewater treatment plant. The above-mentioned sequential multivariate analysis is a statistical method that combines the advantages of sequential analysis and multivariate analysis. The steps of the sequential multivariate analysis described above are included in the examples of the present application. I won't explain any further. S1014B, correlation data represented by each edge in the second biological association network The correlation data was updated after removing the influence of non-microbiological data, and the third biological data was Gain relevant networks. S1015B, in the third biological association network, the connectivity of all points is The bacteria whose betweenness centrality is in the top 2% of all points are called hub microorganisms. Determine it as an object. Here, connectivity refers to the number of edges that connect a point, and betweenness centrality refers to the number of edges that connect a point. Ness Centrality (Ness Centrality) is the degree to which a point is located between any two points in the network. Indicates how often the route is traversed by the shortest path. Connectivity and betweenness centrality are both topological coefficients of the third biological association network Therefore, the correlation data represented by the edges in the third biological association network changed. If the third biological association network is changed, it means that the relationship between the points in the third biological association network has changed. The topological coefficients (e.g., connectivity and betweenness centrality) of the biological association network change. Optionally, a third biological association network with connectivity that is the highest connectivity of all points 5%, and the bacteria whose betweenness centrality corresponds to the top 5% of the betweenness centrality of all points are called hub microorganisms. The methods for determining hub microorganisms are not limited to those described in the examples of this application. The method for determining functional microorganisms includes S1011C to S1013C. S1011C, measuring the content of target pollutants in wastewater treatment plants. Optionally, the content of the target contaminant may be a concentration, and measuring the concentration of the target contaminant The determination method may be a liquid chromatography mass spectrometry method. S1012C, Correlation analysis between bacterial strain abundance information and target pollutant content in wastewater treatment plants The correlation analysis results include correlation data and significance data, and the correlation analysis results are obtained. The sex data includes positive correlation, negative correlation, and no correlation, and the significance data includes significant and non-significant. . Specifically, the interpretation of the above correlation data and significance data is explained in S1011B, Correlation Data. The above correlation analysis method can be performed in S101. Please refer to the correlation analysis method in 1B. S1013C, Correlation data of correlation analysis results for microorganisms contained in wastewater treatment plants Bacteria with positive correlation and significant significance data are determined as functional microorganisms. In one application scenario, sulfamethoxazole (SMX) was selected as the target contaminant, and the The method for measuring the concentration of SMX in a water treatment plant is as follows: Aglient RapidFire 400 High-throughput MS Contaminant measurements are performed using a high-throughput mass spectrometry system. dFire Cartridge C18 Type C chromatography column The mobile phase A is 10% methanol-water and the mobile phase B is acetonitrile. Fluid phase B was used, the flow rate was 0.8 mL / min, and sample loading was performed using fluid phase A. The flow rate was 1.5 mL / min, the sample injection volume was 10 μL, and the analysis time was 10 s. The parent ion is 251.1 and the daughter ion is 156 / 92, with a collision energy of 20 / 32 eV. SMX concentrations in each biological section of the wastewater treatment plant and in the water inlet and outlet, and bacteria in the wastewater treatment plant Correlation analysis of strain abundance information was performed to identify correlations among microorganisms contained in wastewater treatment plants. Bacteria with positive correlation data and significant significance data from the analysis results were selected as functional microorganisms. Make a decision. The examples of the present application relate to the method for determining the cornerstone microorganisms, the method for determining the hub microorganisms, and the above. The order of execution of the method for determining functional microorganisms is not limited. After carrying out the determination method, the hub microorganism determination method is carried out, and finally the functional microorganism determination method is carried out. The determination method of the cornerstone microorganism, the determination method of the hub microorganism, and The above-mentioned method for determining functional microorganisms may be carried out simultaneously. S102, based on the correlation between each quorum-sensing signal molecule and the target bacterial flora in a wastewater treatment plant , to determine the target population-sensing signal molecules of the target flora. Here, the Quorum Sensing Signal Molecule ules) are produced and released by microorganisms in wastewater treatment plants during the ensemble sensing process. When microbial quorum sensing molecules accumulate in the environment to a certain concentration, they are released into the atmosphere. These proteins are sensed by the environment, which control the population behavior and physiological state of microorganisms and are important for the environmental adaptability and stability of microorganisms. Quorum sensing (QS) is a mechanism for communication between microorganisms. mechanism that coordinates microbial behavior by secreting and sensing chemical signal molecules. To do this. Optionally, S102 includes S1021 to S1024. S1021, measuring the content of each swarm-sensing signal molecule in a wastewater treatment plant. In one application scenario, sulfamethoxazole (SMX) was selected as the target contaminant and Risk Quotient (RQ) model for SMX ecological risk Select 12 wastewater treatment plants, where the risk index is the actual concentration of SMX in the wastewater plants. It is obtained by dividing the concentration by the SMX environmental standard concentration. In one embodiment, the functional microorganisms are determined based on the target contaminant (SMX) to determine the target flora. After determining the number of AHL signal molecules, a plurality of AHL signal molecules are selected as the plurality of ensemble-sensing signal molecules. The correlation information of several swarm-sensing signal molecules is shown in Table 2. The full name of the AHL signaling molecule is N-acyl homoserine lactone (N-acyl ho AHLs (antioxidant hormones) are widely distributed among gram-negative bacteria. It should be understood that the phosphodiesterase is a group-sensing signal molecule. Table 3: Information table for multiple ensemble-sensing signaling molecules

[0007] In the above application scenario, each ensemble sensing signal molecule is extracted by solid-phase extraction. The extraction method was performed under the following conditions: 3 mL methanol activated extraction column, 3 mL pure water equilibration, water 30 mL of sample, 1 mL of solution A for washing, 1 mL of solution B for washing, 2 mL of solution C for elution, 30°C in nitrogen Blow down the column with nitrogen gas, then redissolve in 0.5 mL of pure chromatographic methanol. 5:95 methanol-water solution, with 2% (v / v) ammonium hydroxide added. Elution Solution B is a 5:95 methanol-water solution with 2% acetic acid (v / v). is methanol and 2% acetic acid (v / v) is added. In the above application scenario, each ensemble sensing signal molecule is UPLC system (Waters, Milford, Massachusetts, USA) The identification was performed using a VION IMS-QTOF mass spectrometer and the above identification method was used in the prior art. Since it is a technical matter, it will not be repeated here. S1022, the correlation between the content of each quorum-sensing signal molecule and the relative abundance of various microorganisms in the target flora. Conduct correlation analysis and construct the first signal molecular biological association network. Here, the first signal molecular biological association network is composed of multiple points and edges connecting multiple points. Each point in the first signal molecular biological network is connected to each microorganism and each cluster in the target flora. Each edge of the first signal molecule biological association network corresponds to a single edge. The correlation analysis results between two points connected by the edge are shown. It includes correlation data and significance data between two connected points. Correlation data is positive correlation, Includes negative correlation and no correlation, and significance data includes significant and non-significant. Specifically, the interpretation of the correlation data and significance data is explained in S1011B. Please refer to the interpretation explanation of the data and significance data. The above correlation analysis method is Please refer to the correlation analysis method. The points in the first signal molecular biological network above were types of bacteria in the target bacterial flora. It may be one of the ensemble sensing signal molecules among a plurality of ensemble sensing signal molecules. If point A in the first signal molecular biological network is a type of bacteria, point A is point B (Point B may be connected to any ensemble sensing signal molecule among the multiple ensemble sensing signal molecules), and point A may be connected to point C (point C is any bacterium other than A among the multiple bacterium). Then, the side AB of point A is the correlation analysis result between point A and point B, that is, the relative The correlation analysis results of the abundance and content of ensemble sensing signal molecules corresponding to point B are shown. Side A of point A C is the correlation analysis result between point A and point C, i.e., the relative abundance of bacteria corresponding to point A and the relative abundance of bacteria corresponding to point C. The results of a correlation analysis of the relative abundance of the corresponding bacteria are shown. Point A' in the first signal molecule biological association network is one of the ensemble-sensing signal molecules. In this case, point A' may be connected to point B' (point B' is any fungus of the multiple fungi), and point A A' may be connected to point C' (point C' is any one of the multiple ensemble sensing signal molecules other than A'). (These are the collective sensing signal molecules.) Then, the side A'B' of point A' is the relative position of point A' and point B'. The correlation analysis results, i.e., the content of the ensemble sensing signal molecule corresponding to point A' and the content of the ensemble sensing signal molecule corresponding to point B', The results of the correlation analysis of the relative abundance of bacteria are shown. The side A'C' of point A' is the correlation between points A' and C'. The correlation analysis results, i.e., the content of the ensemble sensing signal molecule corresponding to point A' and the content of the ensemble sensing signal molecule corresponding to point C', 1 shows the results of correlation analysis of the content of ensemble-sensing signal molecules. As can be seen from the above, the edges in the first signal molecular biological association network are The correlation analysis results between one bacterium and another bacterium may be shown, and one bacterium and one swarm-sensing signal may be shown. The correlation analysis results between molecules may be shown, and the correlation between one swarm sensing signal molecule and another swarm sensing signal molecule may be shown. The results of a correlation analysis between the signal molecules may also be shown. S1023, the first signal in the molecular biological association network, the significant data of the significant edge The second signal molecular biological association network is obtained. S1024, the second signal molecular biological related network with the highest degree of connectivity and betweenness centrality The ensemble sensing signal molecule corresponding to the point with the highest value is taken as the target ensemble sensing signal molecule. In the above step S1024, the ensemble sensing signal of the second signal molecular biological related network is Only the points corresponding to the molecules are determined, and the corresponding points correspond to the bacteria in the microorganisms contained in the wastewater treatment plant. The above explanation of connectivity and betweenness centrality is based on the connection and betweenness centrality in S1015B. Please refer to the interpretation explanation of centrality. In one embodiment of S1024 above, the second signal molecular biological association network is The connectivity and betweenness centrality corresponding to the points in the second signal molecular biological association network are shown in Figure 3. As can be seen from FIG. 3, N-octanoylhomoserine lactone (N octa Connectivity and betweenness centers of C8-HSL The target ensemble sensing signal molecule determined from the plurality of ensemble sensing signal molecules is It is C8-HSL. S103, applying the target population sensing signal molecule to the sewage treatment plant; Promote the growth of the target flora, adjust the content of the target flora, and establish the target flora. The above-mentioned wastewater treatment plants refer to those that need to strengthen their treatment effectiveness. It should be understood that the target flora may be present in activated sludge treatment equipment or bioreactors in wastewater treatment plants. The film is placed in the treatment device and targets the microorganisms contained in the wastewater treatment plant. It can be used to treat contaminants. For example, in the above case, seven sequential batch reactors are constructed and divided into CK and C8 groups. includes C8-5, C8-7, C8-9, C8-11, C8-13 and C8-15. Here, the CK group was a sludge sump to which only an equal amount of ethanol was added without inoculating C8-HSL. The influent pH is 7.0, and the control group is the same. The blood pressure was maintained at .0 to 7.1, with other conditions remaining the same, and one day constituted one cycle. When inoculating the sludge, 5 nmol / L of C8-HSL signaling molecule was injected into C8-5, and C8 Inject 7 nmol / L of C8-HSL signaling molecule into C-7 and 9 nmol / L of C8-HSL into C-9. Inject 8-HSL signaling molecules and inject 11 nmol / L of C8-HSL signaling molecules into C8-11. Inject 13 nmol / L of C8-HSL signaling molecule into C8-13 and 13 nmol / L of C8-HSL signaling molecule into C8-15. 15 nmol / L of C8-HSL signal molecule was injected. After the reactor was allowed to stabilize, CK, C8-5, C8-7, C8-9, C8-11, C8-13 and C8 -15 to 300 μg L -1 SMX mother liquor was injected into each of the tubes. The SMX removal rates of the CK and C8 groups are shown in Figure 4. As can be seen from Figure 4, The average SMX removal rate for the eight groups was 54%, and the C8-HSL dosage was 13 nmol / L. In this case, the SMX removal rate was the highest at 80%. Subsequently, 16S rRNA sequencing of activated sludge samples from C8-13 was performed. Among the bacterial species with significantly higher abundance in the samples, the percentage of identified functional microorganisms was 71.5%. Therefore, the addition of C8-HSL to the reactor enhanced the coupling and colonization of the target flora. The nitrification was promoted, and a stable and highly efficient sulfamethoxazole-degrading bacterial population was formed in the resident colony. was successfully constructed. When the target contaminant was roxithromycin, the target population sensing signal component of the determined target bacterial flora was The target cell was C8-HSL, and the dose of the target cell-sensing signal molecule was 10 nmol / L. Alternatively, the dose of the target population-sensing signal molecule is 5 nmol / L or 15 nmol / L. and is not further limited in the examples of this application. When the target contaminant is azithromycin, the target population-sensing signal molecule of the determined target bacterial flora The target population-sensing signal molecule was C8-HSL, and the dose was 10 nmol / L. Possibly, the dose of the target population sensing signal molecule is 5 nmol / L or 15 nmol / L. There may be, and the examples of this application are not further limited. When the target contaminant was triclosan, the target population-sensing signal molecule of the determined target bacterial flora was C 10-HSL, and the dose of the target population sensing signal molecule is 10 nmol / L. Preferably, the dose of the target population sensing signal molecule is 5 nmol / L or 15 nmol / L. The present application is not limited to the examples. In summary, in the method for constructing a target bacterial flora provided by the examples of the present application, cornerstone microorganisms Screening of target flora including microbial communities, hub microorganisms and functional microorganisms for wastewater treatment plans Based on the correlation between multiple quorum-sensing signal molecules in the target microbiota and the target quorum-sensing signal molecules, By screening and adjusting the above target population-sensing signal molecules in wastewater treatment plants, and target flora (i.e. cornerstone microorganisms, hub microorganisms and functional microorganisms) in wastewater treatment plants. It can promote the colonization of bacteria and establish the target flora in the wastewater treatment plant. Furthermore, the target bacterial flora constructed in this invention controls the target pollutants in wastewater treatment plants. This can promote the sewage treatment effect of the sewage treatment plant.

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[0009]

Claims

1. A method for constructing a target bacterial flora, comprising the steps of: A target flora for decomposing the target pollutant is determined, and the target flora includes cornerstone microorganisms, hub microorganisms, and the like. and functional microorganisms, wherein the cornerstone microorganisms are Hirschia, Methylotene era and Lactobacillus, and the hub microorganisms include Terrimona s, Denitratisoma and Bradyrhizobium, The bacterial species include Ottowia, Dechloromonas, and Zoogloea. 、 wherein the target contaminants are sulfamethoxazole, roxithromycin, azithromycin, Synthon or Triclosan, Based on the correlation between each quorum-sensing signal molecule and the target flora in the wastewater treatment plant, Determine the target population-sensing signal molecules of the target microbiota; Here, the quorum sensing signal molecule is generated by microorganisms in the sewage treatment plant during the quorum sensing process. The chemical signal molecules produced and released by microorganisms are known as quorum sensing, a communication mechanism between microorganisms. and coordinate the behavior of microorganisms by secreting and sensing chemical signal molecules. the law of nature, The target community-sensing signal molecule is applied to a wastewater treatment plant, and the target community-sensing signal molecule is applied to a wastewater treatment plant. and regulate the content of the target flora to establish the target flora. A method for constructing a target bacterial flora, characterized by:

2. Based on the correlation between each quorum-sensing signal molecule and the target flora in the wastewater treatment plant, Determining the target quorum-sensing signal molecule of the target microbiota comprises: Measure the content of each swarm-sensing signal molecule in the wastewater treatment plant; Correlation between the content of each quorum-sensing signal molecule and the relative abundance of various microorganisms in the target flora. Conduct analysis and construct the first signal molecular biological association network. Here, the first signal molecule biological association network includes a plurality of points and a plurality of connecting points. and each point of the first signal molecule biological association network and each point of the target bacterial flora are connected to each other. and each of the microorganisms and each of the quorum-sensing signal molecules has a one-to-one correspondence with the first signal molecule. Each edge of the network represents a correlation analysis result between the two points connected by said edge; The correlation analysis results are correlation data and significance data between the two points connected by the edge. Correlation data includes positive correlation, negative correlation, and no correlation, and significance data includes significant and and non-significant, Retaining edges in the first signal molecular biological association network for which significance data is significant; Obtaining a second signal molecular biological association network; In the second signal molecular biological association network, the signal molecular biological association network with the highest degree of connectivity and the highest betweenness centrality is Use the ensemble sensing signal molecule corresponding to the high point as the target ensemble sensing signal molecule; Here, connectivity means the number of edges that connect a point, and betweenness centrality means the number of edges that connect a point to the network. The frequency of passing through the shortest path between any two points in the work is shown. The method for constructing a target bacterial flora according to claim 1,

3. The method for determining the cornerstone microorganisms is as follows: Microbial data of the wastewater treatment plant is acquired, and the microbial data is a bacterial strain of the wastewater treatment plant. information and strain abundance information, Here, the strain information includes the names of various bacteria among the microorganisms contained in the wastewater treatment plant. The strain abundance information indicates the relative abundance of various bacteria among the microorganisms contained in the wastewater treatment plant. Including the amount Regarding the various types of microorganisms contained in sewage treatment plants, the strains of sewage treatment plants Through the mapping relationship between the information and the bacterial strain abundance information, the sewage treatment plant after removing the bacteria was The amount of bacterial strains present in the wastewater treatment plant when the bacteria are not removed is obtained. The difference between the information and the strain abundance information of the sewage treatment plant after removing the bacteria is calculated as the difference between the information and the strain abundance information of the sewage treatment plant after removing the bacteria. Calculated as abundance difference value, Here, the abundance difference value is calculated by the Euclidean distance formula or the Manhattan distance formula. And, The bacteria whose abundance difference value is in the top 10 to 15% of the abundance difference value of all bacteria are defined as the cornerstone microorganisms. The method for constructing a target bacterial flora according to claim 1, characterized in that the target bacterial flora is determined by:

4. The mapping relationship between the strain information and the strain abundance information of the wastewater treatment plant is The deep learning model is obtained by learning using neural networks. The method for constructing a target bacterial flora according to claim 3, characterized in that the method is a general ordinary differential equation.

5. The method for determining the hub microorganism is as follows: Correlation analysis of bacterial strain abundance information in wastewater treatment plants was performed to create the first biological association network. Build Here, the first biological association network includes a plurality of points and edges connecting the plurality of points. each point of the first biological association network is a microorganism contained in the wastewater treatment plant; Each edge of the first biological association network corresponds to each bacterium in the object in one-to-one correspondence. This shows the correlation analysis result between the two points connected by the edge. Therefore, it contains correlation data and significance data between two connected points, and the correlation data is positive. Includes correlation, negative correlation, and no correlation, and significance data includes significant and non-significant. The first biological association network is configured to retain edges for which the significance data is significant, and the second biological association network is configured to retain edges for which the significance data is significant. Gaining academic related networks, For correlation data represented by each edge of the second biological association network, Sequential multivariate analysis of correlation data and non-microbiological data from wastewater treatment plants was performed to identify non-microbiological Obtain correlation data after removing the influence of the statistical data, Here, non-microbiological data includes microhabitat information, process parameters, etc. of the wastewater treatment plant. including meter information and geographic information, Correlation data represented by each edge in the second biological association network, The correlation data after the removal of the non-microbiological data is updated to the third biological association network. network, In the third biological association network, the connectivity is in the top 2 to 5 of the connectivity of all points. %, and the bacteria whose betweenness centrality is in the top 2-5% of the betweenness centrality of all points are It was determined to be a microorganism, Here, connectivity refers to the number of edges that connect a point, and betweenness centrality refers to the number of edges that connect a point to a network. The frequency of a point being traversed by the shortest path between any two points in the network is indicated by the The method for constructing a target bacterial flora according to claim 3,

6. The method for determining the functional microorganisms is as follows: Measuring the content of target pollutants in wastewater treatment plants; Correlation analysis was performed between the bacterial strain abundance information of the wastewater treatment plant and the content of the target pollutants. The correlation analysis results include correlation data and significance data, and the correlation data The data includes positive correlation, negative correlation and no correlation, and the significance data includes significant and non-significant; The correlation data from the correlation analysis results for microorganisms contained in sewage treatment plants showed a positive correlation. A bacterium for which the significance data is significant is determined as the functional microorganism. Item 6. The method for constructing a target bacterial flora according to Item 5.

7. When the target contaminant is sulfamethoxazole, the target population of the target flora determined The target population sensing signal molecule is C8-HSL, and the dosage of the target population sensing signal molecule is 5-15n mol / L, When the target contaminant is roxithromycin, the target population sensitivity of the target flora determined The signal molecule is C8-HSL, and the dosage of the target population-sensing signal molecule is 5-15 nmo. 1 / L, When the target contaminant is azithromycin, the target population sensitivity of the target flora is determined. The signal molecule is C8-HSL, and the dose of the target population-sensing signal molecule is 5 to 15 nmol. / L, When the target contaminant is triclosan, a target population sensing signal of the target flora is determined. The molecule is C10-HSL, and the dosage of the target population-sensing signal molecule is 5-15 nmol / The method for constructing a target bacterial flora according to claim 1, characterized in that the bacterial flora is L.

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