A method for functional microorganism environmental application potential quantitative evaluation based on environmental genomic data

Abstract

The application discloses a kind of functional microorganism environmental application potential quantitative evaluation method based on environmental genomic data.The method is first obtained from target environment microbial genomic data, then constructs a quantitative evaluation model comprising four core dimensions, including: target function potential, environmental adaptability, resource competition ability and environmental application risk potential.Candidate microorganisms are scored and weighted integrated in the above dimensions, and the comprehensive application potential index is calculated, and the priority is sorted according to the index.Compared with the prior art, the application breaks through the limitation of traditional screening based on function or abundance only, realizes the systematization, multidimensional quantitative evaluation of microbial application potential, can significantly improve the accuracy and efficiency of functional microorganism screening, reduce environmental application risk, and provide reliable technical path for microbial inoculant development and environmental governance.

Description

Technical Field

[0001] This invention belongs to the field of environmental microbiology technology, specifically relating to a method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data. Background Technology

[0002] The rapid development of microbial technology has provided crucial support for fields such as medicine, agriculture, environmental governance, and ecological restoration. With the increasing severity of environmental pollution, the discovery of environmental microbial resources with functions of pollutant degradation, transformation, or ecological regulation has become an important research direction. Traditional microbial screening methods mainly rely on artificial isolation and culture, but most microorganisms in nature are difficult to culture under laboratory conditions, severely limiting the efficiency of functional microbial resource development. In recent years, the development of genome sequencing and related bioinformatics technologies has enabled researchers to directly analyze the potential functions of microorganisms from environmental sources using their genomic data, providing a new technical pathway for the discovery of functional microorganisms. However, existing research mainly focuses on the identification of functional genes or the analysis of single functional potential, lacking a systematic evaluation of the comprehensive performance of microorganisms in practical environmental applications. Especially under complex environmental conditions, the functional performance of microorganisms depends not only on their functional gene composition but also on their environmental adaptability, resource competitiveness, and potential ecological risks. Currently, there is a lack of a universal method based on genomic and functional annotation information that can quantitatively evaluate the environmental application potential of microorganisms from multiple dimensions. Therefore, it is necessary to develop a quantitative assessment method based on genomic data and incorporating multidimensional functional characteristics to achieve efficient screening and accurate evaluation of the application potential of functional microbial environments. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing a method for quantitatively assessing the environmental application potential of functional microorganisms based on environmental genomic data. This method can rapidly and accurately predict the environmental application potential of functional microorganisms, and it possesses universality and representativeness.

[0004] A method for quantitatively assessing the environmental application potential of functional microorganisms based on environmental genomic data includes the following steps: (1) Determine the target environment and obtain genomic data: Determine the target environment for the study, and obtain the microbial genomic data of the target environment by searching literature or online databases, or by metagenomic sequencing and assembly binning. The microbial genomic data includes metagenomic spliced ​​genomes (MAGs) and single-strain isolated genomes.

[0005] (2) Data processing and functional gene annotation: The acquired genome data is subjected to quality control and redundancy removal to obtain a non-redundant dataset; the non-redundant genome dataset is compared with the functional database to identify the microbial genome carrying the target functional gene; Quality control requires a genome integrity of greater than 50% and a contamination rate of less than 5%. Redundancy removal is performed based on the average nucleotide similarity (ANI), with the ANI threshold set at ≥95%.

[0006] (3) Calculate the quantitative indicators for the four core dimensions: 1) Functional potential (FP) i ): Calculate the number of genes encoding the target function in the microbial genome, and calculate the target function potential index using the following formula: , in This represents the original count of the target functional gene in the j-th genome; and These represent the minimum and maximum counts of the target functional genes in the genomes of all candidate microorganisms, respectively. is the normalized functional potential index, with a value range of [0,1]. 2) Environmental adaptability AC i The environmental adaptability index is calculated by counting the number of genes in the microbial genome related to abiotic stress responses and environmental sensing systems (genes related to pH homeostasis, osmotic pressure regulation, stress protein coding, biofilm formation, and environmental signal sensing systems), and then normalizing them using a formula. , in Let be the original gene count value of the j-th fitness index in the i-th genome. and These are the minimum and maximum values ​​of the gene count for the j-th fitness index in the genomes of all candidate microorganisms, respectively. This is the normalized environmental adaptability index, with a value range of [0,1]. 3) Resource competitiveness (CC) i Calculate the number of genes in a microbial genome that participate in the cycling of the five elements: carbon, nitrogen, phosphorus, sulfur, and potassium, and then calculate the resource competitiveness index using the following formula: , in This represents the original gene count value of the j-th element cycle module in the i-th genome; and These are the minimum and maximum gene counts of the j-th element cycling module in all candidate microbial genomes, respectively. This is the normalized resource competitiveness index, with a value range of [0,1]. 4) Application risk potential ER i The number of pathogenic factors, virulence factors, and antibiotic resistance genes in the microbial genome (identified and counted by comparing the CARD, VFDB, and PHI-base databases) is calculated, and the environmental application risk index is calculated using the following formula: , in This represents the raw count value of the j-th risk indicator in the i-th genome; and These are the minimum and maximum values ​​of the gene count for the j-th risk indicator in the genomes of all candidate microorganisms, respectively. The normalized application risk potential index has a value range of (0, 1]. (4) Environmental application potential PI i : to bring the aforementioned functional potential FP i Environmental adaptability AC i Resource competitiveness (CCi) and application risk potential (ER) i Substituting into the comprehensive evaluation model, we obtain the environmental application potential (PI) of the microorganism. i ; , (5) Potential Grading and Application Strategy Based on Machine Learning: This invention further introduces unsupervised machine learning methods to automatically grade candidate microorganisms. Z-score standardization is used to standardize the PI to eliminate the influence of differences in the scale of different indicators. The K-means clustering algorithm in scikit-learn is used to perform cluster analysis on all candidate microorganisms, setting the number of clusters K = 4, and dividing the microorganisms into four potential levels: 1) EXCELLENT level (preferred application): Microorganisms with high FP, high AC, high CC and low ER indicate that they have high plastic degradation capacity, good environmental adaptability and resource competitiveness, while having low ecological risk and can be directly used for environmental bioremediation.

[0007] 2) HIGH level (high potential): Microorganisms with high FP and certain environmental adaptability, but slightly lower resource competitiveness or stability, are suitable for application by constructing synthetic microbial communities or synergistic systems.

[0008] 3) MEDIUM level (medium potential): While possessing certain functional potential, these organisms may have shortcomings in environmental adaptability or competitiveness, or a high risk index, requiring genetic engineering modification or optimization of culture conditions before they can be applied.

[0009] 4) LOW level (low potential): Microorganisms with low functional potential or high environmental risk are not recommended for environmental applications.

[0010] Compared with the prior art, the beneficial effects of the present invention are: (1) The method of the present invention is no longer limited to the traditional evaluation method based solely on the presence or quantity of functional genes. Instead, it integrates four key dimensions—functional potential, environmental adaptability, resource competitiveness, and application risk potential—to construct a multi-dimensional quantitative evaluation system, which can more comprehensively and realistically reflect the application performance of microorganisms in the actual environment.

[0011] (2) The method of the present invention can identify microorganisms that have stable survival and functional expression ability under complex environmental conditions, avoid the screening bias of "strong function but unable to play a practical role" in traditional methods, and improve the environmental adaptability and application success rate of screening results.

[0012] (3) The present invention provides quantitative constraints on the ecological safety of candidate microorganisms, reduces potential environmental and health risks from the source, and provides theoretical basis and technical support for the safe application of functional microorganisms.

[0013] (4) This invention can not only be used to screen efficient degrading bacteria or functional bacteria resources, but also guide the optimization of functional microbial combinations and the design of application strategies, which plays an important supporting role in pollution control, ecological restoration and agricultural micro-ecological regulation. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the method of the invention is shown in the specific implementation.

[0015] Figure 2 A flowchart illustrating an application example of the specific implementation method. Detailed Implementation

[0016] The present invention will be further analyzed below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0017] like Figure 1 and Figure 2 As shown, a method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data includes the following steps: (1) Target environment identification and genomic data acquisition This embodiment selects a lake ecosystem as the target environment and collects bacterial genomes from lakes in different geographical regions from the NCBI Genome and Genome Taxonomy Database public databases, including metagenomic assembled genomes and lake-derived isolated genome data. CheckM is used to assess the quality of the obtained genomes, screening for medium-to-high-quality genomes with integrity greater than 50% and contamination less than 5%. Furthermore, dRep is used to remove redundancy from the genomes based on average nucleotide similarity (ANI ≥ 95%), ultimately constructing a non-redundant lake microbial genome dataset.

[0018] (2) Functional gene annotation and identification of plastic-degrading microorganisms The aforementioned non-redundant genomic data were compared with the PlasticDB database of functional genes related to plastic degradation, including key enzymes such as PET hydrolases, MHET enzymes, lipases, esterases, and oxidoreductases. Based on sequence similarity and structure-function prediction, microbial genomes carrying potential plastic degradation genes were screened to construct a candidate set of plastic-degrading microorganisms.

[0019] (3) Calculation of multidimensional indicators For the candidate microbial genomes obtained through screening, the following four core quantitative indicators were calculated based on the functional annotation results: 1) Plastic degradation potential (FP) p ): Count the number of plastic degradation-related functional genes in each genome, and normalize them to obtain the functional potential index.

[0020] 2) Environmental adaptability (AC) p Functional annotation of candidate microbial genomes was performed, preferably using the KEGG database, with KO (KEGG Orthology) annotation performed via DIAMOND. Functional genes related to lake environmental adaptation in the genome were statistically analyzed, including low-temperature adaptation proteins, osmotic pressure regulation-related genes, oxidative stress response genes, nutrient limitation response systems, and biofilm formation-related genes. Normalization calculations were then performed to obtain the environmental adaptability index.

[0021] 3) Resource competitiveness (CC) pFunctional annotation related to element cycling was performed on candidate microbial genomes. Functional alignment was performed using the following databases: CCycDB (carbon cycle), NCYcDB (nitrogen cycle), PCycDB (phosphorus cycle), KEGG (potassium cycle), and SCycDB (sulfur cycle), with DIAMOND being the preferred database for sequence alignment. The number of functional genes involved in the metabolism of key nutrients such as carbon, nitrogen, phosphorus, and sulfur in the genome was counted, including genes related to organic carbon utilization, nitrification and denitrification, phosphorus transport and mineralization, and sulfur oxidation and reduction. Different element cycling modules were normalized and weighted summed to obtain a resource competitiveness index.

[0022] 4) Application Risk Potential (ER) p The potential risk-related functional annotation of candidate microbial genomes was performed using databases including CARD (antibiotic resistance genes), VFDB (virulence factors), and PHI-base (pathogenicity-related genes), with DIAMOND being the preferred database for sequence alignment. The number of antibiotic resistance genes, virulence factors, and pathogenicity-related genes in each genome was counted, normalized, and summed to obtain an application risk potential index.

[0023] (4) Calculation of Comprehensive Potential Index The potential for plastic degradation (FP) p ), environmental adaptability (AC) p ), resource competitiveness (CC) p ) and application risk potential (ER) p Substitute these values ​​into the comprehensive model to calculate the environmental application potential index (PI) for each plastic-degrading microorganism. p ): PI p The higher the value, the greater the potential for plastic degradation applications of this microorganism in lake environments and the lower the risk.

[0024] , (5) Potential classification and application strategies based on machine learning This invention further introduces an unsupervised machine learning method for automatic classification of candidate microorganisms. Z-score normalization is used to classify the PI. p Standardization was performed to eliminate the influence of differences in the units of measurement of different indicators. The K-means clustering algorithm from scikit-learn was used to perform cluster analysis on all candidate microorganisms, setting the number of clusters K = 4, and classifying the microorganisms into four potential levels: 1) EXCELLENT (Preferred Application): High FP p High AC p High CC p And low ERp The presence of microorganisms indicates that they possess high plastic degradation capabilities, good environmental adaptability and resource competitiveness, while also having low ecological risk, making them suitable for direct use in environmental bioremediation.

[0025] 2) HIGH (High Potential): High FP p Microorganisms with a certain degree of environmental adaptability, but slightly lower resource competitiveness or stability, are suitable for application by constructing synthetic microbial communities or synergistic systems.

[0026] 3) MEDIUM (Medium potential): While possessing certain functional potential, these organisms may have shortcomings in environmental adaptability or competitiveness, or a high risk index, requiring genetic engineering modification or optimization of culture conditions before they can be applied.

[0027] 4) LOW (Low Potential): Microorganisms with low functional potential or high environmental risk are not recommended for environmental applications.

[0028] In summary, this invention, based on environmentally sourced microbial genomic big data, achieves efficient mining of functional microbial resources through systematic integration and functional annotation analysis of metagenomic splicing genomes and isolated bacterial genomes. Compared to traditional methods relying on single-culture experiments, this invention expands the research object from a limited number of culturable strains to a wide range of microbial genomic resources in the environment, significantly improving the identification efficiency and coverage of potentially high-efficiency functional microorganisms. Simultaneously, by integrating multi-source data from different geographical regions and various environmental conditions, this invention reduces the randomness and bias introduced by single data sources, enabling the identification of "core functional microorganisms" stably existing in different habitats, thereby improving the stability and generalization ability of prediction results. This big data-based analysis method has stronger representativeness and applicability. Furthermore, this invention constructs a multi-dimensional quantitative evaluation system integrating functional potential, environmental adaptability, resource competitiveness, and application risk potential, and quantitatively characterizes the environmental application potential of microorganisms through a comprehensive index model, assigning comparable functional niche indicators to each candidate microorganism. This indicator system can not only be used for the optimal screening of functional microorganisms but also provide a quantitative basis for subsequent microbial interaction network construction and synthetic microbial community design. Therefore, this invention enables high-throughput prediction and accurate identification of functional microbial resources in complex, variable environments where large numbers of microorganisms are difficult to culture, providing an efficient and reliable technical approach for environmental bioremediation and microbial resource development.

Claims

1. A method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data, characterized in that, Includes the following steps: (1) Determine the target environment and obtain genomic data: Determine the target environment for the study and obtain a non-redundant genomic dataset of microorganisms in the target environment; (2) Data processing and functional gene annotation: The non-redundant genome dataset is compared with the functional database to identify microbial genomes carrying target functional genes; (3) Calculate the quantitative indicators for the four core dimensions: 1) Functional potential FP i : Calculate the number of genes encoding the target function in the microbial genome, and calculate the target function potential index using the following formula: ， in This represents the original count of the target functional gene in the j-th genome; and These represent the minimum and maximum counts of the target functional genes in the genomes of all candidate microorganisms, respectively. is the normalized functional potential index, with a value range of [0,1]. 2) Environmental adaptability AC i The number of genes in the microbial genome associated with abiotic stress responses and environmental sensing systems is counted, and the environmental adaptability index is calculated using a normalized formula: ， in Let be the original gene count value of the j-th fitness index in the i-th genome. and These are the minimum and maximum values ​​of the gene count for the j-th fitness index in the genomes of all candidate microorganisms, respectively. This is the normalized environmental adaptability index, with a value range of [0,1]. 3) Resource competitiveness (CC) i Calculate the number of genes in a microbial genome that participate in the cycling of the five elements: carbon, nitrogen, phosphorus, sulfur, and potassium, and then calculate the resource competitiveness index using the following formula: ， in This represents the original gene count value of the j-th element cycle module in the i-th genome; and These are the minimum and maximum gene counts of the j-th element cycling module in all candidate microbial genomes, respectively. This is the normalized resource competitiveness index, with a value range of [0,1]. 4) Application risk potential ER i Calculate the number of pathogenic factors, virulence factors, and antibiotic resistance genes in the microbial genome, and calculate the environmental application risk index using the following formula: , in This represents the raw count value of the j-th risk indicator in the i-th genome; and These are the minimum and maximum values ​​of the gene count for the j-th risk indicator in the genomes of all candidate microorganisms, respectively. The normalized application risk potential index has a value range of (0, 1]. (4) Environmental application potential PI i : to bring the aforementioned functional potential FP i Environmental adaptability AC i Resource competitiveness (CCi) and application risk potential (ER) i Substituting into the comprehensive evaluation model, we obtain the environmental application potential (PI) of the microorganism. i ; ， (5) Potential classification and application strategy based on machine learning: Unsupervised machine learning methods are used to automatically classify candidate microorganisms and apply them according to their environmental application potential (PI). i Microorganisms are classified into at least three different potential levels, and differentiated environmental application strategies are developed for each level.

2. The method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data as described in claim 1, characterized in that, The microbial genome data in step (1) can be obtained by searching literature or online databases, or by metagenomic sequencing and assembly binning. The microbial genome data includes metagenomic spliced ​​genomes and single-strain isolated genomes. Before constructing the non-redundant dataset, the genomic data also undergoes quality control and redundancy removal. Quality control requires the genome to be more than 50% intact and less than 5% contamination. Redundancy removal is based on the average nucleotide similarity (ANI), with the ANI threshold set to ≥95%.

3. The method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data as described in claim 1, characterized in that, In step (3), the environmental adaptability AC is calculated. i At that time, genes related to abiotic stress response and environmental sensing systems specifically include: pH homeostasis-related genes, osmotic pressure regulation-related genes, stress protein coding genes, biofilm formation-related genes, and environmental signal sensing system genes.

4. The method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data as described in claim 1, characterized in that, In step (3), the application risk potential ER is calculated. i At the same time, pathogenic factors, virulence factors, and antibiotic resistance genes are identified and statistically analyzed by comparing the CARD database, VFDB database, and PHI-base database.

5. The method for quantitatively evaluating the environmental application potential of functional microorganisms based on environmental genome data as described in claim 1, characterized in that, The potential classification based on machine learning specifically includes: First, Z-score standardization is used to evaluate the potential PI for environmental applications. i Standardization was performed to eliminate the influence of differences in the dimensions of different indicators; then, the K-means clustering algorithm was used to perform comprehensive potential clustering analysis on all standardized candidate microorganisms, with the cluster number K=4, thereby automatically classifying the microorganisms into four potential levels: 1) EXCELLENT grade: Microorganisms with high functional potential, high environmental adaptability, high resource competitiveness and low application risk, which can be directly used for environmental bioremediation; 2) HIGH level: Microorganisms with high functional potential and certain environmental adaptability, but slightly lower resource competitiveness or stability, suitable for application by constructing synthetic microbial communities or synergistic systems; 3) MEDIUM grade: Microorganisms with certain functional potential, but insufficient in terms of environmental adaptability or competitiveness, or with a high risk index, which need to be genetically modified or have their culture conditions optimized before they can be used. 4) LOW grade: Microorganisms with low functional potential or high environmental risk are not recommended for environmental applications.

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