Analysis method of pollutant microbial degradation and thallus metabolism network coupling mechanism based on multi-omics technology

Abstract

The invention discloses an analysis method of a pollutant microbial degradation and thallus metabolism network coupling mechanism based on a multi-omics technology, and particularly relates to the technical field of biology, and the analysis method comprises the steps of S1, sample collection, S2, multi-omics detection, S3, metabolism network model construction, S4, topological structure analysis, S5, coupling mechanism analysis, and S6, feedback regulation and optimization. According to the method, a multi-omics technology is integrated to deeply analyze a microbial pollutant degradation mechanism, the traditional limitation is overcome, scientific cognition is deepened, a coupling mechanism is analyzed to position key nodes, a regulation and control strategy is formulated and optimized, the degradation efficiency is improved, the environmental pollution problem is solved, meanwhile, microbial resources can be excavated, the application range can be expanded, an innovative thought is provided, and the method is worthy of popularization and application. The development of environmental biotechnology is promoted, and the method has great significance on ecological environment protection.

Description

Technical Field

[0001] The present invention relates to the field of biological technologies, and more specifically, to a method for analyzing the coupling mechanism between microbial degradation of pollutants and the cell body metabolism network based on multi-omics technologies. Background Art

[0002] With the rapid development of modern industry and the continuous expansion of the scope of human activities, various pollutants have been discharged into the environment in large quantities, causing serious damage to ecological environments such as soil, water bodies, and the atmosphere, threatening the balance of the ecosystem and the health and safety of humans. Common pollutants include organic pollutants such as petroleum hydrocarbons, polycyclic aromatic hydrocarbons, pesticides, etc., heavy metal pollutants such as mercury, cadmium, lead, etc., and radioactive pollutants.

[0003] Traditional physical and chemical remediation methods often have limitations such as high costs and easy secondary pollution. The use of microbial degradation of pollutants as an environmentally friendly and relatively economical and efficient bioremediation method has received increasing attention. Microorganisms, with their diverse species and metabolic functions, can convert pollutants into harmless substances through their own metabolic activities, showing great application potential in the field of environmental remediation.

[0004] At present, although certain achievements have been made in the research on microbial degradation of pollutants, there are still many deficiencies. For example:

[0005] 1. Most studies often focus on one or several isolated aspects of the microbial degradation process. For example, only the ability of microorganisms to degrade a certain pollutant is concerned, and traditional microbial cultivation and simple biochemical detection methods are used to determine the degradation rate and analyze the degradation pathway, etc. There is a lack of a systematic understanding of the overall physiological state and internal complex regulatory mechanisms of microorganisms during the degradation process. This one-sided research method is difficult to comprehensively reveal the internal principle of microbial degradation of pollutants and cannot accurately grasp how microorganisms coordinate their own metabolic activities to achieve efficient degradation under different environmental conditions, different pollutant concentrations, and other complex situations.

[0006] 2. The research on the association between microbial degradation and the cell body's own metabolism network is not deep enough. The metabolism of microorganisms is a highly complex and interconnected network system, and the degradation of pollutants is only one part of its many metabolic functions. However, previous studies rarely integrate the pollutant degradation process into the entire cell body metabolism network for comprehensive consideration, and it is not clear how microorganisms redistribute metabolic resources, regulate the flux of metabolic pathways, and gene expression regulation during the degradation of pollutants. Therefore, it is difficult to formulate effective strategies to further optimize the degradation performance of microorganisms, limiting the application efficiency and effect of microorganisms in actual environmental remediation.

[0007] In view of the above situation, the present invention provides an analysis method for the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, the present invention provides an analysis method for the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: An analysis method for the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology, specifically including the following steps:

[0010] S1. Sample collection, collecting samples of microorganisms under the condition of the presence of pollutants, and the samples include microbial cells and metabolites;

[0011] S2. Multi-omics detection, using genomics, transcriptomics, proteomics and metabolomics technologies to detect the samples to obtain genomic information, transcriptomic information, proteomic information and metabolomic information of the microorganisms;

[0012] S3. Construction of metabolic network model, based on the genomic information, transcriptomic information, proteomic information and metabolomic information, constructing a comprehensive metabolic network model for the degradation of pollutants by microorganisms, and in the construction process, using systems biology modeling software to associate and integrate each information according to established data integration rules and logic, and the systems biology modeling software includes COPASI or CellDesigner;

[0013] S4. Topological structure analysis, performing topological structure analysis on the comprehensive metabolic network model to identify key nodes and key metabolic pathways, and the key nodes include key genes, key proteins and key metabolites. When performing topological structure analysis, network analysis algorithms are used, including but not limited to the degree centrality calculation formula and the betweenness centrality calculation formula to calculate the relevant topological parameters of each node, and then key nodes and key metabolic pathways that play important roles in the network are identified based on the parameter values;

[0014] The degree centrality calculation formula is: Node degree centrality C d (v) = the number of edges connected to node v;

[0015] The betweenness centrality calculation formula: Betweenness centrality where σ st represents the number of shortest paths from node s to node t , and σ st (v) represents from node s to nodet and the number of the shortest paths passing through the node v;

[0016] S5. Coupling mechanism analysis: By analyzing the variation rules of the key nodes and key metabolic pathways during the process of microbial degradation of pollutants, the coupling mechanism between microbial degradation and the cell metabolic network is analyzed;

[0017] S6. Feedback regulation and optimization: According to the coupling mechanism and key node information, a feedback regulation strategy is formulated, and real-time monitoring and optimization adjustment are carried out during the process of microbial degradation of pollutants.

[0018] Preferably, in step S1, the pollutants include, but are not limited to, one or more of organic pollutants, heavy metal pollutants, and radioactive pollutants, and the microorganisms are bacteria, fungi, or algae capable of degrading the pollutants.

[0019] Preferably, in step S2, when detecting the sample by using genomics technology, it includes sequencing the whole genome of the microorganism to obtain gene sequence information, analyzing the structure and function of genes, and determining the genes related to pollutant degradation;

[0020] When detecting the sample by using transcriptomics technology, it includes extracting the total RNA of the microorganism, performing reverse transcription and sequencing, analyzing the gene expression level, and determining the genes with differential expression during the process of pollutant degradation;

[0021] When detecting the sample by using proteomics technology, it includes extracting the proteins of the microorganism, performing separation and identification, analyzing the protein expression level, modification state, and interaction relationship, and determining the key proteins during the process of pollutant degradation;

[0022] When detecting the sample by using metabolomics technology, it includes extracting the metabolites of the microorganism, performing separation and identification, analyzing the types and content changes of metabolites, and determining the key metabolites during the process of pollutant degradation.

[0023] Preferably, in step S3, when constructing a comprehensive metabolic network model for microbial degradation of pollutants, a systems biology method is adopted to integrate genomic information, transcriptomic information, proteomic information, and metabolomic information, and a comprehensive metabolic network model including a gene regulatory network, a protein interaction network, and a metabolic pathway network is constructed. Specifically, logical connections are made according to the interaction relationships between the biological entities represented by each information to form a network model with a hierarchical structure and dynamic feedback.

[0024] Preferably, in step S4, when performing topological structure analysis on the comprehensive metabolic network model, a systems biology modeling software is used, and the degree centrality calculation formula, betweenness centrality calculation formula, and topological parameter calculation method of clustering coefficient are utilized to identify key nodes and key metabolic pathways that play important roles in the network. Among them, the clustering coefficient is used to measure the local connection tightness of nodes, and the calculation formula is: where e i represents the actual number of edges existing between the neighbor nodes of node i, and k i represents the number of neighbor nodes of node i.

[0025] Preferably, in step S5, when analyzing the coupling mechanism between microbial degradation and the cell metabolism network, by analyzing the expression changes, interaction relationships, and regulatory mechanisms of key nodes and key metabolic pathways during the process of microbial degradation of pollutants, it is revealed how microorganisms achieve the degradation of pollutants by regulating their own metabolic networks. In the analysis process, statistical methods can be used to compare the changes in relevant data of key nodes and metabolic pathways at different stages and under different conditions. For example, statistical means such as variance analysis and correlation analysis can be used to judge the degree of association and significant differences between various factors.

[0026] Preferably, in step S6, according to the analysis results of key nodes and the coupling mechanism, the key factors affecting the microbial degradation efficiency are determined, and corresponding regulation strategies are formulated, including but not limited to adjusting the growth environment of microorganisms, adding inducers, and optimizing the microbial community structure. When formulating the strategies, relevant microbial growth kinetics models and enzyme kinetics models are used to evaluate the impact of environmental factor changes on microbial growth and subsequent degradation efficiency;

[0027] For example, for the microbial growth process, the Monod equation can be referred to: where μ is the specific growth rate of microorganisms, μ max is the maximum specific growth rate, S is the substrate concentration, and K S is the half-saturation constant.

[0028] The technical effects and advantages of the present invention:

[0029] 1. By integrating multi-omics technologies such as genomics, transcriptomics, proteomics, and metabolomics, the present invention can comprehensively and deeply analyze the detailed mechanism of microorganisms in the process of degrading pollutants from multiple dimensions, including the gene level, gene expression regulation level, protein function and interaction level, and metabolite change level. It can not only accurately determine the genes directly related to pollutant degradation and the genes whose expression changes at different degradation stages, but also clarify the expression, modification status of key proteins and their cooperative effects, as well as the dynamic changes of key metabolites and the flow adjustment of metabolic pathways. Compared with traditional single-dimensional research methods, this comprehensive analysis helps to more thoroughly understand how microorganisms mobilize their various physiological functions to respond to and degrade pollutants, providing a solid theoretical basis for more accurate intervention and regulation of the microbial degradation process in the future;

[0030] 2. By analyzing the coupling mechanism between microbial degradation and the microbial metabolic network and identifying key nodes such as key genes, key proteins, and key metabolites, the present invention can accurately locate the core factors affecting the efficiency of microbial pollutant degradation. The feedback regulation strategies formulated based on these key information, whether it is adjusting the microbial growth environment, adding inducers, or optimizing the microbial community structure, etc., are highly targeted. And by real-time monitoring the microbial degradation process, the regulation strategies can be optimized and adjusted in a timely manner according to the actual situation, forming a dynamic and effective closed-loop management, so that the efficiency of microbial pollutant degradation can be effectively improved, which helps to more efficiently address the problems of pollutant treatment in various environments and reduce the negative impact of pollutants on the ecological environment;

[0031] 3. The method of the present invention helps to discover more microbial resources with potential pollutant degradation ability and their unique degradation mechanisms. Through multi-omics analysis of different microorganisms in degrading different pollutants, some microbial strains and their metabolic pathways that have not been fully understood but have significant degradation effects can be excavated, opening up new directions for the application of microbial resources in the field of environmental remediation. At the same time, the revealed coupling mechanism between microbial degradation and the microbial metabolic network also provides innovative ideas for developing new bioremediation technologies and designing more reasonable microbial enhanced remediation schemes, promoting the entire environmental biotechnology field to develop in a more scientific and efficient direction, and having more technical means to choose from when dealing with complex environmental pollution situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the overall flow chart of the present invention;

[0033] Figure 2 is the flow chart of genomic detection in step S2 of the present invention;

[0034] Figure 3It is the flow chart of transcriptomics detection in step S2 of the present invention;

[0035] Figure 4 It is the flow chart of proteomics detection in step S2 of the present invention;

[0036] Figure 5 It is the flow chart of metabolomics detection in step S2 of the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] The present invention provides an analysis method for coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology. Taking polycyclic aromatic hydrocarbon (PAHs) organic pollutants existing in a chemical industrial wastewater discharge area and bacterial flora in the soil of this area that can degrade such pollutants as an example, it specifically includes the following steps;

[0039] S1. Sample collection: Samples are collected from the polluted soil near the chemical industrial wastewater discharge outlet. The sampling depth is set at the surface layer of 0-20 cm. The multi-point sampling and mixing method is used to ensure the representativeness of the samples. A total of 5 soil samples are collected, and the weight of each sample is about 500 grams. The collected soil samples are placed in a sterile sealed bag and quickly taken back to the laboratory for storage at 4°C, and subsequent processing is carried out as soon as possible to obtain samples containing bacterial microorganisms and metabolites that can degrade polycyclic aromatic hydrocarbon pollutants;

[0040] S2. Multi-omics detection,

[0041] 1. Genomics detection

[0042] 1.1. Extract the genomic DNA of bacterial microorganisms from the soil samples, and use the FastDNA Spin Kit for Soil kit to extract according to its instruction operation process. The concentration and purity of the extracted DNA are detected by a Nanodrop nucleic acid quantifier to ensure that the A260 / A280 ratio is between 1.8 and 2.0, the A260 / A230 ratio is greater than 2.0, and the integrity of the DNA is verified by agarose gel electrophoresis. Subsequently, use the Illumina NovaSeq 6000 sequencing platform for whole-genome sequencing. Before sequencing, construct a library according to the platform standard process and add sequencing adapters, etc.;

[0043] 1.2. Use the FastQC software to perform quality assessment on the raw data obtained by sequencing, and use the Trimmomatic software to remove low-quality reads (reads with a quality value lower than Q30), sequencing adapters, and reads containing excessive Ns to obtain clean data. Then, use the SPAdes software to splice and assemble the clean data to obtain a draft genome of the bacterial flora;

[0044] 1.3. Predict open reading frames (ORFs) in the genome through the Glimmer software to determine the gene coding regions. Compare the predicted gene sequences with the GenBank database of NCBI by BLAST to search for homologous genes and speculate their functions. At the same time, combine the MEME software to analyze gene structure characteristics, such as promoter regions, intron-exon structures, etc., to assist in judging the role of genes in microbial physiology and polycyclic aromatic hydrocarbon degradation. Finally, screen out genes directly related to polycyclic aromatic hydrocarbon degradation, such as those encoding polycyclic aromatic hydrocarbon dioxygenase and dehydrogenase, as well as transcription factor genes that regulate the expression of these degradation enzyme genes, and construct an evolutionary tree through the MEGA software to analyze the conservation and specificity of these genes in different strains.

[0045] 2. Transcriptomics detection

[0046] 2.1. Use TRIzol reagent combined with column purification method to extract total RNA from bacterial samples at different stages of polycyclic aromatic hydrocarbon degradation (initial degradation stage, mid-degradation stage, nearly completed degradation stage). Use a Nanodrop nucleic acid quantifier to detect the concentration and purity of RNA. The A260 / A280 ratio is controlled between 1.8 - 2.0, the A260 / A230 ratio is greater than 2.0, and after confirming good RNA integrity by agarose gel electrophoresis, use M-MLV reverse transcriptase and the supporting reaction system to perform reverse transcription with an oligo(dT) primer to convert RNA into cDNA;

[0047] 2.2. Construct a library for the cDNA. Fragment it to a length of about 300 - 500 bp, add sequencing adapters, perform end repair and A-tail addition operations, etc. Then, use quantitative PCR to measure the library concentration. After passing the test, sequence it on the Illumina HiSeq X Ten sequencing platform to obtain transcriptome sequencing read data;

[0048] 2.3. Apply the bioinformatics analysis process. First, use the FastQC software to evaluate the data quality and remove low-quality data. Then, use the HISAT2 software to align the clean read data with the reference genome obtained from the previous genomics analysis to determine the location of the reads on the genome. Then, count the number of reads corresponding to each gene, and quantitatively analyze the gene expression level by calculating the RPKM value;

[0049] 2.4. Use the DESeq2 software to perform differential expression analysis based on the transcriptome data of different degradation stages. Set the screening threshold as P value less than 0.05 and FoldChange (fold change) greater than 2 or less than 0.5. Screen out the genes with significantly up-regulated or down-regulated expression levels during the polycyclic aromatic hydrocarbon degradation process. Use the DAVID tool to perform Gene Ontology (GO) enrichment analysis and KEGG pathway enrichment analysis on these differentially expressed genes to explore their biological significance and regulatory mechanisms during the degradation process.

[0050] 3. Proteomics detection

[0051] 3.1. For the collected bacterial samples, use the method of ultrasonic disruption combined with a lysis buffer containing protease inhibitors and phosphatase inhibitors to break the cells and release intracellular proteins. Use the BCA method to quantify the extracted protein solution to ensure consistent loading amounts for each sample.

[0052] 3.2. Use two-dimensional electrophoresis technology (2-DE) to separate proteins. In the first dimension, isoelectric focusing uses a linear gel strip with pH 3-10 to focus proteins to their isoelectric point positions under appropriate voltage and time conditions. In the second dimension, SDS-polyacrylamide gel electrophoresis (using a 12% polyacrylamide gel) further separates proteins according to their molecular weight sizes, forming different protein spots. At the same time, some samples are separated by high-performance liquid chromatography-reverse phase chromatography (HPLC-RP), and separation is achieved in the chromatographic column based on the hydrophobicity differences of proteins. Monitor and collect protein components through an ultraviolet detector.

[0053] 3.3. Cut protein spots from the two-dimensional electrophoresis gel or collect protein components after liquid chromatography separation, perform trypsin digestion to break down proteins into peptide segments, use matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOFMS) and electrospray ionization mass spectrometry (ESI-MS) to analyze the peptide segments, obtain mass spectrometry information such as the mass-to-charge ratio (m / z) of the peptide segments, compare the mass spectrometry data with the UniProt database, perform protein identification through the Mascot search engine, determine the protein types corresponding to each spot or component, and analyze the changes in the expression levels of each protein at different degradation stages based on information such as the mass spectrometry peak intensity and combined with a quantitative algorithm based on peak area.

[0054] 3.4. For the analysis of protein modification status, a phosphorylation protein enrichment kit (based on the principle of immunoaffinity) was used to enrich phosphorylation proteins. Phosphorylation modification sites and corresponding proteins were identified by mass spectrometry analysis. After enriching glycosylation-modified proteins by lectin affinity chromatography, mass spectrometry identification was carried out. The yeast two-hybrid system and co-immunoprecipitation technology (Co-IP) were used to determine the interaction relationships between key proteins during the degradation process of polycyclic aromatic hydrocarbons, and a protein interaction network was constructed.

[0055] 4. Metabolomics detection

[0056] 4.1. For intracellular metabolites, after collecting bacterial cells, a mixed solution of methanol: chloroform: water = 2.5:1:1 was used as the extractant. After vortex oscillation and ultrasonic treatment, centrifugation was carried out for layering, and the organic phase and aqueous phase were collected, and the phases containing metabolites were combined; for extracellular metabolites, the bacterial culture supernatant was directly collected. After ultrafiltration and concentration, acetonitrile was used for extraction to obtain the extract. The extracted metabolite samples were stored in a -80 °C refrigerator in the dark.

[0057] 4.2. Liquid chromatography-mass spectrometry (LC-MS) technology was used to separate and identify metabolites. Reverse-phase liquid chromatography (RP-LC) was used for liquid chromatography. Separation was achieved in a chromatographic column equipped with a C18 stationary phase according to the polarity differences of metabolites. The separated metabolites entered the mass spectrometer for detection. The mass spectrometer analyzed the ion fragment information (mass-to-charge ratio, ion abundance, etc.) generated after the ionization of metabolites, and combined with the METLIN database comparison and the retention time, mass spectrum and other characteristics of metabolite standards for matching to identify the specific types of metabolites and construct a metabolite qualitative spectrum.

[0058] 4.3. Based on the LC-MS data, the XCMS software was used to quantitatively analyze signal intensity indicators such as the peak area of metabolites, determine the relative content changes of each metabolite at different stages of polycyclic aromatic hydrocarbon degradation, and perform dimensionality reduction and visualization processing on the metabolite data through multivariate statistical analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Key metabolites with significant content changes were screened out, such as some organic acids and alcohols involved in the intermediate metabolic process of polycyclic aromatic hydrocarbon degradation. Then, the KEGG metabolic pathway database was used for metabolic pathway enrichment analysis to explore the metabolic pathways where these key metabolites are located and their impacts on the microbial metabolic network and the polycyclic aromatic hydrocarbon degradation process.

[0059] S3. Construction of metabolic network model: Integrate the information obtained from the above genomics, transcriptomics, proteomics, and metabolomics detections. Using systems biology modeling software, logically connect them according to the interaction relationships between the biological entities represented by each piece of information, and integrate the gene regulatory network, protein-protein interaction network, and metabolic pathway network to construct a comprehensive metabolic network model for the degradation of polycyclic aromatic hydrocarbon pollutants by this bacterial flora, making it have the characteristics of hierarchical structure and dynamic feedback. For example, associate and integrate the expression regulation of genes encoding degradation enzymes with the changes in the activities of corresponding proteins and the changes in the fluxes of substrates and products in the metabolic pathway, forming a network model that completely reflects the relationship between the polycyclic aromatic hydrocarbon degradation process and the bacterial metabolism;

[0060] S4. Topological structure analysis: Utilize the network analysis function built into the systems biology modeling software, and combine the degree centrality calculation formula (node degree centrality C d (v) = the number of edges connected to node v), betweenness centrality calculation formula (betweenness centrality where, σ st represents the number of shortest paths from node s to node t , and σ st (v) represents the number of shortest paths from node s to node t and passing through node v) and clustering coefficient calculation formula ([[]] where, e i represents the actual number of edges between the neighbor nodes of node i, and ki represents the number of neighbor nodes of node i)) to conduct topological structure analysis on the constructed comprehensive metabolic network model, calculate the relevant topological parameters of each node, and identify the key nodes and key metabolic pathways that play important roles in the network. For example, it is found that the node corresponding to the gene encoding polycyclic aromatic hydrocarbon dioxygenase has a relatively high degree centrality and betweenness centrality in the network, and at the same time, the clustering coefficient of the polycyclic aromatic hydrocarbon initial degradation metabolic pathway where it is located is relatively high, indicating that this gene and the corresponding metabolic pathway are in a key position in the entire polycyclic aromatic hydrocarbon degradation and bacterial metabolism network and have an important impact on aspects such as degradation efficiency;

[0061] S5. Analysis of coupling mechanism: By analyzing the expression changes, interaction relationships, and regulatory mechanisms of the above-identified key nodes (such as key genes, key proteins, and key metabolites) and key metabolic pathways during the degradation of polycyclic aromatic hydrocarbons by the bacterial flora, use statistical methods such as analysis of variance and correlation analysis to compare the changes in the data related to key nodes and metabolic pathways under different degradation stages and different environmental conditions (such as different initial concentrations of polycyclic aromatic hydrocarbons, different temperatures, etc.), and reveal how microorganisms achieve the degradation of polycyclic aromatic hydrocarbons by regulating their own metabolic networks;

[0062] For example, it was found that as the concentration of polycyclic aromatic hydrocarbons increased, the expression of key degradation enzyme genes was up-regulated. At the same time, the production of key metabolites in the corresponding metabolic pathways increased, and the fluxes of other related metabolic pathways also changed adaptively. In this way, the coupling mechanism between microbial degradation and the cell metabolism network was analyzed to clarify how microorganisms coordinated the allocation of their own metabolic resources when degrading polycyclic aromatic hydrocarbons;

[0063] S6. Feedback regulation and optimization: According to the analysis results of the coupling mechanism and key nodes, determine the key factors affecting the efficiency of microbial degradation of polycyclic aromatic hydrocarbons. For example, it was found that the expression levels of certain key degradation enzyme genes limited the overall degradation efficiency to a certain extent. Accordingly, corresponding regulation strategies were formulated. For example, adjust the growth environment of microorganisms, optimize the nutritional conditions of microorganisms by adding appropriate amounts of nitrogen sources (such as ammonium nitrate) and carbon sources (such as glucose), and refer to the Monod equation ( where μ is the specific growth rate of microorganisms, μ max is the maximum specific growth rate, S is the substrate concentration, and K S is the half-saturation constant) to evaluate the impact of adding nutrients on the growth of microorganisms and subsequent degradation efficiency. At the same time, add inducers (such as certain small molecule compounds similar in structure to polycyclic aromatic hydrocarbons) to induce the high expression of key degradation enzyme genes, optimize the microbial community structure, and enhance the synergistic degradation ability of the entire microbial community to polycyclic aromatic hydrocarbons by adding specific functional strains.

[0064] In the actual process of microbial degradation of polycyclic aromatic hydrocarbon pollutants, use on-line monitoring equipment to continuously monitor the biomass of microorganisms (monitored by a turbidimeter), the degradation of polycyclic aromatic hydrocarbons (detect the concentration change by high performance liquid chromatography), and the content change of key metabolites (regularly detected by LC-MS). Adjust the regulation strategy in a timely manner according to the monitoring results. For example, if it is found that the growth rate of microorganisms is too fast but the degradation efficiency has not increased significantly, appropriately reduce the amount of carbon source added to guide microorganisms to allocate more metabolic resources to the metabolic pathways related to polycyclic aromatic hydrocarbon degradation, and ensure that microorganisms can continuously and efficiently degrade polycyclic aromatic hydrocarbon pollutants.

[0065] Through this example, the complete process from sample collection to final feedback regulation and optimization was demonstrated, reflecting the feasibility and effectiveness of the analysis method of the coupling mechanism between pollutant microbial degradation and the cell metabolism network based on multi-omics technology in practical research and application, which helps to deeply understand the internal mechanism of microbial degradation of pollutants and provide strategic support for improving the degradation efficiency;

[0066] It should be noted that when actually applying this example of the present invention, the specific methods, technical parameters, etc. of each step can be appropriately adjusted and optimized according to factors such as the types of microorganisms, pollutant types, and laboratory conditions in specific research, and all are included in the examples of the present invention.

[0067] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for analyzing the coupling mechanism between pollutant microbial degradation and bacterial metabolic network based on multi-omics technology, characterized by: The specific steps include: S1. Sample collection: collecting samples of microorganisms in the presence of pollutants, wherein the samples include microbial cells and metabolites; S2. Multi-omics detection, using genomics, transcriptomics, proteomics and metabolomics technologies to detect the sample and obtain the genome information, transcriptome information, proteome information and metabolome information of the microorganism; S3, metabolic network model construction, based on the genome information, transcriptome information, proteome information and metabolome information, to construct a comprehensive metabolic network model of microorganisms for pollutant degradation, in the construction process, using system biology modeling software to correlate and integrate various information according to established data integration rules and logic, the system biology modeling software includes COPASI or CellDesigner; S4. Topological structure analysis: performing topological structure analysis on the comprehensive metabolic network model to identify key nodes and key metabolic pathways, wherein the key nodes include key genes, key proteins and key metabolites. In the topological structure analysis, a network analysis algorithm is used, including but not limited to a degree centrality calculation formula and a betweenness centrality calculation formula to calculate relevant topological parameters of each node, and then the key nodes and key metabolic pathways that play an important role in the network are identified based on the parameter values. The degree centrality calculation formula is: d (v) = the number of edges connected to node v; The betweenness centrality calculation formula is: Among them, σ st represents the number of shortest paths from node s to node t, σ st (v) represents the number of shortest paths from node s to node t that pass through node v; S5. Coupling mechanism analysis: by analyzing the changing rules of the key nodes and key metabolic pathways in the process of microbial degradation of pollutants, the coupling mechanism between microbial degradation and bacterial metabolic network is analyzed; S6. Feedback control and optimization: Based on the coupling mechanism and key node information, a feedback control strategy is formulated, and real-time monitoring and optimization adjustments are carried out during the microbial degradation of pollutants.

2. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S1, the pollutants include, but are not limited to, one or more of organic pollutants, heavy metal pollutants and radioactive pollutants, and the microorganisms are bacteria, fungi or algae that can degrade the pollutants.

3. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S2, when the sample is tested using genomic technology, the whole genome of the microorganism is sequenced to obtain gene sequence information, the structure and function of the gene are analyzed, and genes related to pollutant degradation are determined; When the sample is tested using transcriptomics technology, it includes extracting total RNA of the microorganism, performing reverse transcription and sequencing, analyzing the expression level of the gene, and determining the genes that are differentially expressed during the degradation of the pollutant; When the sample is tested using proteomics technology, it includes extracting the proteins of the microorganisms, separating and identifying them, analyzing the expression level, modification state and interaction relationship of the proteins, and determining the key proteins in the degradation process of pollutants; When the samples are tested using metabolomics technology, it includes extracting the metabolites of microorganisms, separating and identifying them, analyzing the types and content changes of the metabolites, and determining the key metabolites in the pollutant degradation process.

4. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S3, when constructing a comprehensive metabolic network model of microorganisms for pollutant degradation, a systems biology approach is used to integrate genomic information, transcriptomic information, proteomic information, and metabolomic information to construct a comprehensive metabolic network model including gene regulatory networks, protein interaction networks, and metabolic pathway networks. The interaction relationships between the biological entities represented by each piece of information are logically connected to form a network model with a hierarchical structure and dynamic feedback.

5. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S4, when performing topological structure analysis on the comprehensive metabolic network model, the system biology modeling software is used to identify the key nodes and key metabolic pathways that play an important role in the network using the degree centrality calculation formula and the betweenness centrality calculation formula as well as the topological parameter calculation method of the clustering coefficient, wherein the clustering coefficient is used to measure the local connection density of the node, and the calculation formula is: Among them, e i represents the number of edges actually existing between neighboring nodes of node i, k i Indicates the number of neighbor nodes of node i.

6. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S5, when analyzing the coupling mechanism between microbial degradation and bacterial metabolic network, the expression changes, interaction relationships and regulatory mechanisms of key nodes and key metabolic pathways in the process of microbial degradation of pollutants are analyzed to reveal how microorganisms achieve pollutant degradation by adjusting their own metabolic networks. During the analysis process, statistical methods can be used to compare the changes in key nodes and metabolic pathway related data at different stages and under different conditions, such as using statistical methods such as variance analysis and correlation analysis to determine the degree of correlation and significant differences between factors.

7. The method for analyzing the coupling mechanism of pollutant microbial degradation and bacterial metabolic network based on multi-omics technology according to claim 1, characterized in that: In step S6, based on the analysis results of key nodes and coupling mechanisms, key factors affecting microbial degradation efficiency are determined, and corresponding control strategies are formulated, including but not limited to adjusting the growth environment of microorganisms, adding inducers, and optimizing the community structure of microorganisms. When formulating strategies, relevant microbial growth kinetic models and enzyme kinetic models are used to evaluate the effects of environmental factor changes on microbial growth and subsequent degradation efficiency; For example, the Monod equation can be used for the microbial growth process: Where, μ is the specific growth rate of the microorganism, μ max is the maximum specific growth rate, S is the substrate concentration, K S is the half-saturation constant.

Images