A method and system for identifying stability and function of a wastewater treatment biofilm bacterial community

Abstract

The application discloses a wastewater treatment biofilm bacterial community stability and function identification method and system, which comprises the following steps: firstly, sampling the biofilm in a target reactor, and performing 16S rRNA high-throughput sequencing and species annotation to obtain sequencing data; then, dividing subgroups according to the phylogenetic nature of the sequencing data, calculating a subgroup evolution index in each subgroup according to a beta-NTI value and sample abundance, and calculating a subgroup diffusion index in each subgroup according to an RC value and sample abundance; finally, performing polar coordinate transformation on the two-dimensional scatter coordinates of the evolution index and the diffusion index, identifying the stability of the function of the subgroup through the polar radius, and identifying the type of the function of the subgroup through the polar angle. Bray‑Curtis The application firstly introduces community assembly analysis into the development of biofilm functional bacterial community identification technology, simplifies the analysis process, realizes unified evaluation of the function of the biofilm and the establishment process thereof, has wide applicability, and provides a scientific basis and a universal method for guiding the whole-process control of biofilm process ecological design.

Description

Technical Field

[0001] This invention relates to the field of biofilm wastewater treatment technology, and in particular to a method and system for identifying the stability and function of microbial communities in wastewater treatment biofilms. Background Technology

[0002] Biofilm is one of the mainstream technologies for biological treatment of wastewater. The colonization and stable development of functional microbial communities are core factors ensuring the stable and efficient operation of biofilm processes, and are often influenced by endogenous (stress, degradation capacity, etc.) and exogenous (interspecific competition, quorum sensing, eco-drift, etc.) factors. Quantitatively analyzing and evaluating the functionality and assembly pathways of core species in biofilm communities is an important theoretical foundation for optimizing, upgrading, and controlling the entire process of biofilm processes under the current pressure of upgrading and transforming wastewater treatment standards.

[0003] Currently, with the rapid development of high-throughput sequencing and bioinformatics analysis technologies, functional microbial community identification and analysis technologies have achieved relatively mature results in various bioremediation processes. Conventional methods generally involve calculating inter-species correlations and performing topological analysis of their spatial distribution characteristics to obtain information on correlation hubs within the community. This approach is computationally complex, lacks a unified evaluation system, and suffers from a lack of common understanding of the community's functional system and the colonization behavior of core species, limiting its application in complex systems involving biological attachment and growth. For example, Chinese patent CN 107533592A discloses a microbial community analysis system, a judgment system, a microbial community analysis method, and a judgment method. This community analysis system is based on the raw sequencing data of activated sludge samples contained in the input unit. It calculates the similarity between groups and forms the coordinates of each data group in a multidimensional space, thereby determining the operational status of different microorganisms. This method achieves a considerable degree of automation, but the evaluation system lacks uniformity and quantitative characterization of species correlations within the community, making it unsuitable for highly complex biofilm systems. Chinese patent CN108486238A discloses a method based on high-throughput sequencing... The proposed method for analyzing functional microbial communities involves high-throughput sequencing of suspended sludge samples acclimated in a membrane bioreactor. Extended local similarity analysis of the time series data and corresponding water quality monitoring data is then performed. Correlation coefficients are calculated based on the synchronization and lag of the normalized ordination data. Finally, functional microbial community information is obtained through topological analysis and bioinformatics analysis. While this method boasts high accuracy, it requires high sampling frequency, involves complex computational processes, and presents results as taxonomic rather than evolutionary information. Furthermore, it suffers from a large number of identified nodes and lacks an understanding of phylogenetic development, resulting in weak assessment capabilities for complex systems. Summary of the Invention

[0004] Purpose of the invention: The first purpose of this invention is to provide a rapid, efficient, and widely applicable method for identifying the stability and function of microbial communities in wastewater treatment biofilms. The second purpose of this invention is to provide a rapid, efficient, and widely applicable system for identifying the stability and function of microbial communities in wastewater treatment biofilms, in order to solve the problem of insufficient systematic identification of functional microbial communities in complex communities of wastewater treatment biofilm processes in the prior art.

[0005] Technical solution: The method for identifying the stability and function of wastewater treatment biofilm microbial communities according to the present invention includes the following steps:

[0006] (1) Sample the biofilm in the target reactor and perform 16S rRNA high-throughput sequencing and species annotation to obtain sequencing data;

[0007] (2) The sequencing data are divided into subpopulations according to their phylogenetic relationship. Within each subpopulation, the subpopulation evolution index is calculated based on the β-NTI value and sample abundance. Within each subpopulation, the RC index is calculated. Bray-Curtis The subpopulation diffusion index is calculated from the value and sample abundance.

[0008] (3) The two-dimensional scatter coordinates of the evolution index and the diffusion index are transformed into polar coordinates. The stability of the subgroup function is identified by the polar radius, and the type of the subgroup function is identified by the polar angle.

[0009] Furthermore, the method for calculating the evolutionary index X0 in step (2) is as follows:

[0010]

[0011] Where, m k f is the number of valid sample pairings in the k-th subgroup. i f j The relative abundances of samples i and j in the k-th subgroup are β-NTI, respectively. i，j Let β be the β-NTI value between samples i and j in the k-th subgroup.

[0012]

[0013]

[0014] Wherein, β-MNTD is the abundance-weighted average phylogenetic distance between the closest relatives in two different communities; β-NTI is a standardized measure of the phylogenetic distance from each taxonomic unit in a sample to its nearest taxonomic unit; n k f is the total number of OTUs in bin k; iA f iB OTUs in samples A and B respectively i Relative abundance; min(ΔiA j B ) represents the OTUs in community A. i With all OTUs in community B j The minimum phylogenetic distance between them, min(Δi) B j A Similarly; β-MNTD obs This represents the actual observed value of β-MNTD in the sample; These are the sample expected value and standard deviation of β-MNTD for randomly generated community null models. Preferably, 1000 null models are generated to calculate the sample expected value and standard deviation.

[0015] Furthermore, the diffusion index Y0 mentioned in step (2) is calculated as follows:

[0016]

[0017] in, For the RC between samples i and j in k subgroups Bray-Curtis value.

[0018]

[0019]

[0020]

[0021] Among them, D Bray-Curtis Bray-Curtis distance, used to compare differences in the microbial composition of two communities; RC Bray-Curtis It is the standardized deviation between the observed Bray-Curtis distance and the zero distribution; n k This represents the total number of OTUs in bin k; These are OTUs in bin k respectively i Counting in communities A and B; For bin k OTU i The minimum count in both community A and community B; These are the actual observed Bray-Curtis distances in the sample; Let R be the Bray-Curtis distance value in the x-th null model. The specific method for generating the null model is as follows: while ensuring that the richness of the sample points remains unchanged, select the species that appear according to the probability of the occupancy table and select the abundance of the species that appear according to the probability of the abundance table, thereby obtaining the model under the simulation conditions; x The count variable is used to record the number of zero models that are less than the actual observed value; N is the total number of zero models generated, preferably N = 1000.

[0022] Furthermore, the polar coordinate transformation of the two-dimensional scatter coordinates of the evolutionary index and the diffusion index in step (3) includes the following steps:

[0023] The standardized evolutionary index is obtained by dividing the evolutionary index by the significance criterion of the degree of evolution, and the standardized diffusion index is obtained by dividing the diffusion index by the significance criterion of the degree of diffusion.

[0024] Calculate the two-dimensional scatter coordinates of the normalized evolutionary index and the normalized diffusion index, and then perform a polar coordinate transformation.

[0025]

[0026]

[0027] Where X is the standardized evolutionary index; Y is the standardized diffusion index; a is the significance criterion for the degree of evolution, generally taking a value of 1.96-2.00; b is the significance criterion for the degree of diffusion, generally taking a value of 0.93-0.97.

[0028] Furthermore, the stability of the subgroup identification function via polar trajectories described in step (3) includes:

[0029] When ρ≥1, the subgroup is identified as having stable functional development; when 0.75≤ρ<1, the subgroup is identified as having a transitional functional development; when ρ<0.75, the subgroup is identified as having unstable functional development.

[0030] Where ρ is the polar radius.

[0031] Furthermore, the types of subgroup identification functions via polar angles described in step (3) include:

[0032] When -π / 8≤θ≤π / 8, the subgroup is identified as having pollutant degradation function; when 3π / 8≤θ≤5π / 8, the subgroup is identified as having assembly hub function; when 7π / 8≤θ≤9π / 8, the subgroup is identified as having environmental stress response function; when 11π / 8≤θ≤13π / 8, the subgroup is identified as having biomass accumulation function.

[0033] When π / 8 < θ < 3π / 8, the subgroup is identified as having stress-enhancing function; when 5π / 8 < θ < 7π / 8, the subgroup is identified as having interaction-enhancing function; when 9π / 8 < θ < 11π / 8, the subgroup is identified as having attachment-enhancing function; when 13π / 8 < θ < 15π / 8, the subgroup is identified as having degradation-enhancing function.

[0034] Where θ is the polar angle.

[0035] Furthermore, the biofilm samples mentioned in step (1) are derived from industrial wastewater, aerobic, anaerobic, and facultative biological treatment units of municipal sewage treatment plants, as well as environmental microbial communities attached to and growing in slightly polluted surface water and polluted groundwater. The number of biofilm samples from the same source is no less than 3 groups.

[0036] The specific methods for high-throughput sequencing and species annotation in step (1) are as follows: Total DNA was amplified using primers 338F_806R targeting the highly variable V3-V4 region of the 16S rRNA gene, and the sample pool was sequenced on MiSeq. After quality control, the optimized sequences were clustered into operational taxonomic units (OTUs) with a minimum similarity of 0.97. The OTUs were then classified with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0037] The wastewater treatment biofilm microbial community stability and function identification system of the present invention includes:

[0038] The sampling unit is used to sample the biofilm in the target reactor and perform 16S rRNA high-throughput sequencing and species annotation to obtain sequencing data.

[0039] The computational unit is used to divide the sequencing data into subpopulations according to their phylogenetic properties, calculate the subpopulation evolution index within each subpopulation based on the β-NTI value and sample abundance, and calculate the RC value within each subpopulation. Bray-Curtis The subpopulation diffusion index is calculated from the value and sample abundance.

[0040] The identification unit is used to perform polar coordinate transformation on the two-dimensional scatter coordinates of the evolution index and the diffusion index, identify the stability of the subgroup function by polar radius, and identify the type of subgroup function by polar angle.

[0041] Furthermore, the method for calculating the evolutionary index X0 in the computing unit is as follows:

[0042]

[0043] The diffusion index Y0 in the calculation unit is calculated as follows:

[0044]

[0045] Where, m k f is the number of valid sample pairings in the k-th subgroup. i f j The relative abundances of samples i and j in the k-th subgroup are β-NTI, respectively. i，j Let β-NTI be the value between samples i and j in the k-th subgroup. For the RC between samples i and j in k subgroups Bray-Curtis value.

[0046] Beneficial effects: Compared with the prior art, the advantages of the present invention are: (1) Introducing community assembly analysis into the development of biofilm functional microbial community identification technology, based on β-NTI and RC Bray-Curtis The analysis of the two parameters not only simplifies the analysis process, but also realizes the unified evaluation of biofilm function and its establishment process. The results are reliable and reproducible. In addition, the structural complexity and functional redundancy of biofilm also determine that, in engineering applications, the group identification effect based on phylogenetic level in this invention should be better than the screening of single strains, which has stronger practical significance. (2) The method described in this invention is applicable to wastewater of various material carriers and various types of pollution. It has wide applicability and rich information, and provides a scientific basis and general method for guiding the ecological design, optimization and upgrading and whole-process control of biofilm process. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention.

[0048] Figure 2 This is a two-dimensional scatter plot of the evolution and diffusion indices of Embodiment 1 of the present invention.

[0049] Figure 3 This is a two-dimensional scatter plot of the evolution and diffusion indices in Embodiment 2 of the present invention.

[0050] Figure 4 This is a two-dimensional scatter plot of the evolution and diffusion indices in Embodiment 3 of the present invention.

[0051] Figure 5 This is a two-dimensional scatter plot of the evolution and diffusion indices in Embodiment 4 of the present invention.

[0052] Figure 6 This is a two-dimensional scatter plot of the evolution and diffusion indices in Embodiment 5 of the present invention.

[0053] Figure 7 This is a two-dimensional scatter plot of the evolution and diffusion indices of Embodiment 6 of the present invention.

[0054] Figure 8 This is a two-dimensional scatter plot of the evolution and diffusion indices of Embodiment 7 of the present invention.

[0055] Figure 9 This is a two-dimensional scatter plot of the evolution and diffusion indices of Embodiment 8 of the present invention. Detailed Implementation

[0056] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0057] like Figure 1As shown, the steps of the method for identifying the stability and function of biofilm microbial communities in wastewater treatment according to the present invention are as follows:

[0058] S1. Sample the biofilm in the target reactor and perform 16S rRNA high-throughput sequencing and species annotation.

[0059] S2. Divide the sequencing data into subgroups according to their phylogenetic nature and examine their phylogenetic signals;

[0060] S3. Calculate β-NTI and RC within each subgroup after filtering based on abundance. Bray-Curtis Parameter values;

[0061] S4. Calculate the statistics for constructing the sample abundance, and after standardizing the statistics, calculate the two-dimensional scatter coordinates of the evolution and diffusion index of different phylogenetic subgroups of biofilms.

[0062] S5. Calculate the intensity and type of assembly characteristics of each subgroup through coordinate transformation, and identify functional microbial communities in the biofilm community.

[0063] Example 1

[0064] The target reactor uses inorganic ceramsite as a carrier. The main influent water quality indicators are COD 120 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, and pH 7.3. The reactor temperature is maintained at 25℃, and the hydraulic retention time (HRT) is 4 h. The analytical procedure is as follows:

[0065] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0066] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0067] S3. After filtering based on abundance, 67 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0068]

[0069]

[0070]

[0071] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 2 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0072] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0073]

[0074] The analysis results show that the microbial community in the reactor lacks stable microbial community retention related to attachment and biomass accumulation, and may face the problem of poor resistance to interference.

[0075] Example 2

[0076] The target reactor uses polyurethane (PU) packing as the carrier. The main influent water quality indicators are COD 120 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, and pH 7.3. The reactor temperature is maintained at 25℃, and the heating time (HRT) is 4 h. The analytical procedure is as follows:

[0077] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0078] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0079] S3. After filtering based on abundance, 66 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0080]

[0081]

[0082] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 3 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0083] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0084]

[0085]

[0086] The analysis results showed that a large number of bacteria related to attachment and biomass accumulation remained in the reactor, while relatively few bacteria were degrading. This indicates that the reactor takes a long time to reach stable operation under these operating conditions and the treatment efficiency may be low.

[0087] Example 3

[0088] The target reactor uses inorganic ceramsite as a carrier. The main influent water quality indicators are: COD 20 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, total sulfur (added as Na2S) 50 mg / L, and pH 7.3. The reactor temperature is maintained at 25℃, and the HRT is 4 h. The analytical procedure is as follows:

[0089] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0090] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0091] S3. After filtering based on abundance, 62 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0092]

[0093]

[0094]

[0095] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 4 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0096] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0097]

[0098]

[0099] The analysis results show that a large number of bacteria related to attachment and biomass accumulation remain in the reactor, and there is a lack of bacteria that enhance intra-community interactions, proving that the reactor takes a long time to reach stable operation under these operating conditions.

[0100] Example 4

[0101] The target reactor uses PU packing material as the carrier. The main influent water quality indicators are: COD 20 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, total sulfur (added as Na2S) 50 mg / L, and pH 7.3. The reactor temperature is maintained at 25℃, and the HRT is 4 h. The analytical procedure is as follows:

[0102] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0103] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0104] S3. After filtering based on abundance, 64 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0105]

[0106]

[0107] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 5 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0108] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0109]

[0110]

[0111] The analysis results show that a large number of bacteria related to attachment and biomass accumulation remain in the reactor, and there is a lack of bacteria that enhance intra-community interactions, proving that the reactor takes a long time to reach stable operation under these operating conditions.

[0112] Example 5

[0113] The target reactor used inorganic ceramsite as a carrier. The main influent water quality indicators were COD 120 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, and pH 7.3. Sulfamethoxazole (SMX), sulfadiazine (SDZ), trimethoprim (TMP), carbamazepine (CBZ), azithromycin (AZM), clarithromycin (CLR), and erythromycin (ERY) were added to the influent at 20 μg / L each. The reactor temperature was maintained at 25℃, and the hRT was 4 h. The analytical procedure is as follows:

[0114] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0115] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0116] S3. After filtering based on abundance, 68 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0117]

[0118]

[0119]

[0120] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 6 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0121] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0122]

[0123] The microbial community in this reactor lacks stable bacterial communities associated with attachment and biomass accumulation, and also lacks microbial communities that enhance intracommunal interactions. It may be more sensitive to water quality fluctuations and operational disturbances, but it has a certain degree of adaptability to physical operating environments.

[0124] Example 6

[0125] The target reactor uses PU packing material as the carrier. The main influent water quality indicators are COD 120 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, and pH 7.3. SMX, SDZ, TMP, CBZ, AZM, CLR, and ERY are each added at 20 μg / L to the influent. The reactor temperature is maintained at 25℃, and the HRT is 4 h. The analytical procedure is as follows:

[0126] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0127] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0128] S3. After filtering based on abundance, 67 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0129]

[0130]

[0131] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 7 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0132] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0133]

[0134] The reactor lacks microbial communities with enhanced intracommunal interactions and antibiotic stress resistance, making it potentially sensitive to water quality fluctuations and operational disturbances, but it exhibits a certain degree of adaptability to physical operating environments.

[0135] Example 7

[0136] The target reactor used inorganic ceramsite as a carrier. The main influent water quality indicators were: COD 20 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, total sulfur (added as Na₂S) 50 mg / L, and pH 7.3. SMX, SDZ, TMP, CBZ, AZM, CLR, and ERY were each added at 20 μg / L to the influent. The reactor temperature was maintained at 25℃, and the heating time (HRT) was 4 h. The analytical procedure is as follows:

[0137] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0138] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0139] S3. After filtering based on abundance, 56 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0140]

[0141]

[0142] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 8 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0143] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0144]

[0145]

[0146] The analysis results showed that a large number of bacteria related to attachment and biomass accumulation remained in the reactor, while relatively few bacteria were degrading. This indicates that the reactor takes a long time to reach stable operation under these operating conditions and the treatment efficiency may be low.

[0147] Example 8

[0148] The target reactor uses PU packing material as the carrier. The main influent water quality indicators are: COD 20 mg / L, total nitrogen 20 mg / L, total phosphorus 1 mg / L, total sulfur (added as Na2S) 50 mg / L, and pH 7.3. SMX, SDZ, TMP, CBZ, AZM, CLR, and ERY are each added at 20 μg / L to the influent. The reactor temperature is maintained at 25℃, and the HRT is 4 h. The analytical procedure is as follows:

[0149] S1. Take out some packing material from the bottom, middle and top of the target reactor to collect biofilm samples, which are numbered B, M and T respectively, and perform 16S rRNA high-throughput sequencing. After quality control optimization, the OTUs are annotated with a confidence level of 0.7 according to the silva138 / 16s_bacteria database.

[0150] S2. Based on the phylogenetic tree, the sequenced OTUs were divided into 70 subgroups with ds=0.2 and Mmin=24, and their phylogenetic signals were examined using the Mantel Test method.

[0151] S3. After filtering based on abundance, 57 subgroups remain. Calculate the β-NTI and RCBray-Curtis values ​​between paired samples within each subgroup, as shown in the table below:

[0152]

[0153]

[0154] S4. Calculate the standardized evolutionary index X and the diffusion index Y, and plot a two-dimensional scatter plot of the evolution and diffusion degree of different phylogenetic subgroups of biofilms, such as... Figure 9 As shown, in this embodiment, the significance criterion for the degree of evolution is a = 1.96, and the significance criterion for the degree of diffusion is b = 0.95;

[0155] S5. The intensity and type of assembly characteristics of each subgroup were calculated through coordinate transformation, and the stable functional phylogenetic subgroups were identified as shown in the table below:

[0156]

[0157]

[0158] The reactor contains relatively few bacteria that enhance intracommunal interactions and possess antibiotic stress resistance, making it potentially more sensitive to water quality fluctuations and operational disturbances.

Claims

1. A method for identifying the stability and function of microbial communities in wastewater treatment biofilms, characterized in that, Includes the following steps: (1) Sample the biofilm in the target reactor and perform 16S rRNA high-throughput sequencing and species annotation to obtain sequencing data; (2) The sequencing data are divided into subgroups according to their phylogenetic properties, and within each subgroup, according to... The value and sample abundance are used to calculate the subpopulation evolution index, and within each subpopulation, based on... The subpopulation diffusion index is calculated from the value and sample abundance. (3) Perform polar coordinate transformation on the two-dimensional scatter coordinates of the evolution index and the diffusion index, identify the stability of subgroup function by polar radius, and identify the type of subgroup function by polar angle; The evolutionary index mentioned in step (2) The calculation method is as follows: in, The number of valid sample pairings in the k-th subgroup. , Let represent the relative abundance of samples i and j in the k-th subgroup, respectively. For samples i and j in the k-th subgroup value; The diffusion index mentioned in step (2) The calculation method is as follows: in, The number of valid sample pairings in the k-th subgroup. , Let represent the relative abundance of samples i and j in the k-th subgroup, respectively. For samples i and j in k subgroups value; The stability of the subgroup identification function via polar radius described in step (3) includes: When ρ ≥ 1, the subgroup is identified as having stable functional development; when 0.75 ≤ ρ < 1, the subgroup is identified as having transitional functional development; when ρ < 0.75, the subgroup is identified as having unstable functional development. Where ρ is the polar radius; The types of subgroup identification functions via polar angle described in step (3) include: When -π / 8 ≤ θ ≤ π / 8, the subgroup is identified as having pollutant degradation function; when 3π / 8 ≤ θ ≤ 5π / 8, the subgroup is identified as having assembly hub function; when 7π / 8 ≤ θ ≤ 9π / 8, the subgroup is identified as having environmental stress response function; when 11π / 8 ≤ θ ≤ 13π / 8, the subgroup is identified as having biomass accumulation function. When π / 8 < θ < 3π / 8, the subgroup is identified as having stress-enhancing function; when 5π / 8 < θ < 7π / 8, the subgroup is identified as having interaction-enhancing function; when 9π / 8 < θ < 11π / 8, the subgroup is identified as having attachment-enhancing function; when 13π / 8 < θ < 15π / 8, the subgroup is identified as having degradation-enhancing function. Where θ is the polar angle.

2. The method for identifying the stability and function of wastewater treatment biofilm microbial communities according to claim 1, characterized in that, Step (3) involves performing a polar coordinate transformation on the two-dimensional scatter coordinates of the evolutionary index and the diffusion index, which includes the following steps: The standardized evolutionary index is obtained by dividing the evolutionary index by the significance criterion of the degree of evolution, and the standardized diffusion index is obtained by dividing the diffusion index by the significance criterion of the degree of diffusion. Calculate the two-dimensional scatter coordinates of the normalized evolutionary index and the normalized diffusion index, and then perform a polar coordinate transformation.

3. The method for identifying the stability and function of wastewater treatment biofilm microbial communities according to claim 1, characterized in that, The biofilm samples mentioned in step (1) are derived from aerobic, anaerobic and facultative biological treatment units of industrial wastewater and municipal sewage treatment units, as well as environmental microbial communities attached to and growing in slightly polluted surface water and polluted groundwater.

4. The method for identifying the stability and function of wastewater treatment biofilm microbial communities according to claim 1, characterized in that, The number of samples from the same source for the biofilm mentioned in step (1) shall not be less than 3 groups.

5. A system for identifying the stability and function of biofilm microbial communities in wastewater treatment based on the method described in any one of claims 1-4, characterized in that, include: The sampling unit is used to sample the biofilm in the target reactor and perform 16S rRNA high-throughput sequencing and species annotation to obtain sequencing data. A computing unit is used to divide the sequencing data into subgroups according to their phylogenetic properties, and within each subgroup, according to... The value and sample abundance are used to calculate the subpopulation evolution index, and within each subpopulation, based on... The subpopulation diffusion index is calculated from the value and sample abundance. The identification unit is used to perform polar coordinate transformation on the two-dimensional scatter coordinates of the evolution index and the diffusion index, identify the stability of the subgroup function by polar radius, and identify the type of subgroup function by polar angle.

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