Biological reagent research and development method based on microbial functional gene screening

Abstract

The invention relates to the technical field of microbiology, in particular to a biological reagent research and development method based on microbial functional gene screening. The method comprises the following steps: acquiring a microorganism sample, and performing genome sequencing extraction treatment and gene transcription analysis to generate transcriptome data of the microorganism sample; obtaining a biological reagent research and development target function, and performing target function gene screening treatment and gene targeted knockout treatment to generate a target function gene targeted knockout experimental group; carrying out gene cloning on the target functional gene targeted knockout experimental group, transferring into a preset host strain, carrying out function expression evaluation analysis to obtain a target functional expression level corresponding to each target functional targeted knockout gene, and carrying out excellent function screening and verification to generate a biological reagent excellent function verification gene, and the method is applied to a research and development process corresponding to a biological reagent so as to generate a target functional biological reagent product. The functional screening efficiency and the specificity of biological reagent products can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbiology, and in particular to a biological reagent research and development method based on microbial functional gene screening. Background Art

[0002] Microorganisms can produce a variety of natural products or enzyme substances with important biological activities through their unique metabolic pathways and biosynthetic capabilities. These substances are widely used in medicine, agriculture, environmental protection and other industries. In recent years, with the rapid development of genomics and molecular biology technologies, biological reagent research and development methods based on microbial functional gene screening have gradually emerged. Through large-scale sequencing, mining and analysis of microbial genomes, functional genes with specific biological activities can be systematically screened and identified. The enzymes, proteins or other bioactive molecules encoded by these functional genes can play an important role under specific conditions, and thus become a potential source for the development of efficient and specific biological reagents. However, although the existing microbial functional gene screening methods have made progress in many aspects, there are still some challenges. For example, there are problems such as low target gene expression levels, difficulty in functional verification, and low screening efficiency in the gene screening process. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a biological reagent development method based on microbial functional gene screening to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a biological reagent development method based on microbial functional gene screening comprises the following steps:

[0005] Step S1: obtaining a microbial sample, and performing genome sequencing and extraction processing on the microbial sample to obtain genome data of the microbial sample; performing gene transcription analysis on the genome data of the microbial sample to generate transcriptome data of the microbial sample;

[0006] Step S2: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions, and performing target function gene screening processing on microbial sample genome data and microbial sample transcriptome data based on the target functions of biological reagent research and development to generate biological reagent target function genome data;

[0007] Step S3: performing gene targeted knockout processing on the microbial target functional genes corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group; performing gene cloning on the target functional gene targeted knockout experimental group and transferring it into a preset host strain for functional expression evaluation and analysis to obtain the target functional expression level corresponding to each target functional targeted knockout gene;

[0008] Step S4: Based on the target function expression level corresponding to each target function targeted knockout gene, each target function targeted knockout gene in the target function gene targeted knockout experimental group is screened and verified for excellent function to generate a biological reagent excellent function verification gene; the biological reagent excellent function verification gene is applied to the corresponding research and development process of the biological reagent to generate a target function biological reagent product.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: obtaining original microbial samples from soil, ocean waters and extreme environment ecosystems;

[0011] Step S12: separating, purifying and culturing the original microbial sample to generate a microbial library;

[0012] Step S13: performing 16S rRNA gene identification and screening on the corresponding microbial samples in the microbial library to generate a representative gene sample set for preliminary screening of microorganisms;

[0013] Step S14: performing genome sequencing and extraction processing on the representative gene sample set of the initial screening of microorganisms to obtain genome data of the microorganism samples;

[0014] Step S15: performing gene transcription analysis on the microbial sample genome data to generate microbial sample transcriptome data.

[0015] Further, step S13 includes the following steps:

[0016] Step S131: pre-screening the sample diversity of the corresponding microbial samples in the microbial library to obtain a microbial diversity pre-selected sample set;

[0017] Step S132: extracting sample DNA from the microbial samples corresponding to the microbial diversity pre-selected sample set using DNA extraction technology to obtain a DNA sequence corresponding to each microbial pre-selected sample;

[0018] Step S133: performing a gene amplification reaction on the DNA sequence corresponding to each microbial preselected sample according to the preset 16S rRNA gene primers to obtain a 16S rRNA gene amplification product sequence fragment corresponding to each microbial preselected sample;

[0019] Step S134: Based on the sequence fragments of the 16S rRNA gene amplification products corresponding to each microbial pre-selected sample, sequence comparison, identification and screening are performed on the corresponding microbial samples in the microbial diversity pre-selected sample set to generate a representative gene sample set for microbial preliminary screening.

[0020] Further, step S15 includes the following steps:

[0021] Step S151: obtaining the corresponding RNA transcription start site and RNA intron splicing site from each microbial sample genome in the microbial sample genome data, and extracting the transcription region of the microbial sample RNA in the corresponding microbial sample genome based on the RNA transcription start site and RNA intron splicing site to obtain the microbial sample genome RNA transcription candidate region;

[0022] Step S152: performing a cDNA reverse transcription reaction on the candidate region of the microbial sample genomic RNA transcription to obtain a microbial sample reverse transcription cDNA library;

[0023] Step S153: performing tailing modification and library construction on the microbial sample reverse transcription cDNA library to add adapter sequences and tags to obtain a microbial sample transcriptome library construction product;

[0024] Step S154: performing high-throughput transcription sequencing processing on the microbial sample transcriptome library construction product to generate microbial sample transcriptome data.

[0025] Further, step S2 includes the following steps:

[0026] Step S21: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions;

[0027] Step S22: performing metabolic network analysis on each microbial sample functional gene corresponding to the microbial sample genome data and the microbial sample transcriptome data to generate a microbial metabolic network corresponding to each microbial sample functional gene;

[0028] Step S23: Obtain the corresponding microbial metabolic flux, microbial raw material utilization rate and metabolic byproduct generation efficiency through the microbial metabolic network corresponding to each microbial sample functional gene, and perform product synthesis evaluation analysis on the corresponding microbial sample functional gene based on the microbial raw material utilization rate and metabolic byproduct generation efficiency to generate the metabolic target product synthesis rate corresponding to each microbial sample functional gene;

[0029] Step S24: Based on the microbial metabolic flux and the metabolic target product synthesis rate corresponding to each microbial sample functional gene, a target functional gene matching calculation formula is used to perform a functional matching calculation on the target function of biological reagent research and development to obtain a microbial metabolic target function matching value corresponding to each microbial sample functional gene;

[0030] Step S25: Based on the microbial metabolism target function matching value corresponding to each microbial sample functional gene, target functional gene screening processing is performed on the microbial sample genome data and the corresponding microbial sample transcriptome data to generate biological reagent target functional genome data.

[0031] Furthermore, the target functional gene matching calculation formula in step S24 is specifically:

[0032]

[0033] In the formula, F i is the microbial metabolic target function matching value corresponding to the i-th microbial sample functional gene, N is the total number of microbial sample functional genes, i is the item index parameter of the microbial sample functional gene, T is the metabolic process observation time period, M is the total number of metabolic pathways, k is the item index parameter of the metabolic pathway, t is the time variable parameter, V ik (t) is the microbial metabolic flux of the functional gene of the i-th microbial sample at the corresponding time t in metabolic pathway k, Y ik (t) is the synthesis rate of the metabolic target product of the functional gene of the i-th microbial sample at the corresponding time t in the metabolic pathway k, K ik is the metabolic efficiency coefficient of the functional gene of the i-th microbial sample in the metabolic pathway k, S is the microbial synthesis contribution coefficient corresponding to the target function of biological reagent research and development, Q is the synthesis rate of the target metabolite corresponding to the target function of biological reagent research and development, R i (t) is the metabolic consumption efficiency of the functional gene of the i-th microbial sample at time t, v i (t) is the metabolic production rate of the functional gene of the i-th microbial sample at time t, and η is the correction coefficient of the matching value of the microbial metabolic target function.

[0034] Further, step S3 includes the following steps:

[0035] Step S31: performing gene targeted knockout processing on the microbial target functional gene corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group;

[0036] Step S32: performing gene cloning processing on the target functional gene targeted knockout experimental group to obtain a target functional gene knockout clone group;

[0037] Step S33: each target function knockout gene in the target function gene knockout clone group is transferred into a preset host strain for functional transformation and expression processing, so as to generate each target function knockout gene transformation strain;

[0038] Step S34: Cultivate each target function knockout gene transformation strain and obtain the corresponding target function knockout gene strain expression level and target function knockout gene enzyme activity level, and perform functional expression evaluation calculation on each target function knockout gene transformation strain based on the target function knockout gene strain expression level and target function knockout gene enzyme activity level using the functional expression evaluation calculation formula to obtain the target function expression level corresponding to each target function targeted knockout gene.

[0039] Further, step S31 includes the following steps:

[0040] Performing target function annotation processing on the microbial target function genes corresponding to the target function genome data of the biological reagent to obtain the annotated region sequence of the target function gene of the biological reagent;

[0041] Design gene targeted knockout primers according to the annotated region sequence of the target functional gene of the biological reagent to generate specific targeted knockout primers of the target functional gene of the biological reagent;

[0042] Based on the biological reagent target function gene-specific targeted knockout primers, the corresponding microbial target function genes in the biological reagent target function genomic data are subjected to gene targeted knockout processing to generate a target function gene targeted knockout experimental group.

[0043] Furthermore, the functional expression evaluation calculation formula in step S34 is specifically:

[0044]

[0045] In the formula, G j is the target function expression level corresponding to the jth target function targeted knockout gene, j is the item index parameter of the target function targeted knockout gene, t0 is the start time of the experiment, t1 is the end time of the experiment, τ is the experimental time variable parameter, E j (τ) is the expression level of the target function knockout gene strain of the jth target function targeted knockout gene at the experimental time τ, α j is the expression adjustment parameter corresponding to the jth target function targeted knockout gene, A j (τ) is the enzyme activity level of the target function knockout gene of the jth target function targeted knockout gene at the experimental time τ, β j is the enzyme activity regulation parameter corresponding to the jth target function targeted knockout gene, K j is the maximum enzyme activity corresponding to the jth target function targeted knockout gene, K0 is the maximum enzyme activity corresponding to the functional gene of the normal strain without knockout, γ j is the functional expression weight coefficient corresponding to the jth target function targeted knockout gene, λ jis the enzyme activity influence coefficient corresponding to the j-th target function targeted knockout gene, and ξ is the correction coefficient of the target function expression level.

[0046] Further, step S4 includes the following steps:

[0047] Step S41: performing target excellent function screening processing on the corresponding target function targeted knockout genes in the target function gene targeted knockout experimental group based on the target function expression level corresponding to each target function targeted knockout gene, and generating target excellent function genes of biological reagents;

[0048] Step S42: further verifying the target function of the biological reagent target excellent function gene by combining proteomics and metabolomics technology to generate the biological reagent excellent function verification gene;

[0049] Step S43: applying the biological reagent excellent function verification gene to the corresponding research and development process of the biological reagent to generate a target functional biological reagent product.

[0050] Beneficial effects of the present invention:

[0051] The biological reagent development method based on microbial functional gene screening proposed in the present invention, compared with the prior art, has the beneficial effect of ensuring the diversity and representativeness of the research samples by obtaining original microbial samples from soil, marine waters and extreme environment ecosystems. These environments contain a rich variety of microorganisms, which often have unique adaptability and biological functions, especially microorganisms in extreme environments, which can survive under harsh conditions such as high temperature, low temperature, strong acid, strong alkali, extremely low or extremely high salinity. Obtaining these microbial samples is helpful to explore the diversity of microorganisms and their ecological role in nature, and provides the possibility of discovering potential useful gene resources, providing valuable resources for subsequent genetic research and application development. By performing genome sequencing and extraction processing on microbial samples, the complete genome information of each microorganism can be obtained. The genome data not only contains the genetic background of the microorganism, but also reveals the metabolic pathways, stress response mechanisms, resistance genes and potential bioactive molecules of the microorganism. This process helps to provide basic data for in-depth research on microbial functions, and can also help researchers identify the genome characteristics of specific microorganisms. At the same time, by performing gene transcription analysis on the genome data of microbial samples, it can reflect the active genes and response mechanisms of microorganisms under specific environmental conditions, and can also obtain the transcription level of each gene at different growth stages and different environmental conditions, so as to understand the physiological state, metabolic pathways and response to environmental changes of microorganisms. This analysis helps to reveal the functional gene expression of microorganisms under specific conditions, and can more comprehensively understand the gene functions and biological characteristics of microorganisms. Secondly, by obtaining the target functions of biological reagent research and development, it can help the research and development team to clarify the research and development direction. Enzyme activity refers to the ability of enzymes produced by microorganisms to catalyze specific reactions. Antibiotic production refers to the production of antibacterial compounds through metabolic pathways by microorganisms during the production of antibiotics, which is of great significance to the spread and resistance of anti-resistant bacteria. Pollutant degradation refers to the ability of microorganisms to effectively decompose harmful substances in the environment through their metabolic pathways. By clarifying these target functions in the early stages of research and development, it can help researchers evaluate the potential application value of microorganisms from different angles and ensure a clear direction in the subsequent research process.Based on the target function of biological reagent development, the target function gene screening process is also carried out on the microbial sample genome data and the microbial sample transcriptome data, which can be used to screen out the genes most related to the target function. By screening and optimizing these functional genes, accurate genome data can be provided for the efficient development of biological reagents. This process includes the screening of target function-related genes, the optimization of metabolic pathways and the design of gene editing strategies, and further identification of key genes that play an important role in the target function, and functional enhancement or optimization through genetic engineering. These screened functional genes will serve as the core elements of biological reagent development, thereby improving the screening efficiency of functional genes. Then, the corresponding microbial target functional genes in the target functional genome data of biological reagents are subjected to gene targeted knockout treatment to ensure that the functions of these genes can be effectively studied. Gene targeted knockout technology uses specific tool molecules (such as CRISPR-Cas9 system, TALENs or ZFN, etc.) to accurately remove or inactivate the target gene, thereby eliminating the functional expression of the gene, and can also effectively reduce the non-specific effects of the target gene, ensuring the accuracy and reliability of subsequent experiments. The target functional gene targeted knockout experimental group is cloned and transferred into the preset host strain for functional expression evaluation and analysis. Gene cloning is to insert a specific gene fragment into a vector and introduce it into a suitable host cell for amplification, so as to evaluate the functional expression level of the target functional gene. By quantitatively analyzing the gene expression and enzyme activity level, we can effectively understand the actual performance of the target gene in the cell and judge its importance in a specific biological process. We can quantify the relationship between gene expression and enzyme activity to comprehensively evaluate the functional level of the targeted knockout gene, thereby providing an important basis for the subsequent screening and verification of target functional genes corresponding to higher gene expression levels.Finally, by screening the target excellent function of the corresponding target function targeted knockout genes in the target function gene targeted knockout experimental group based on the target function expression level corresponding to each target function targeted knockout gene, the target function gene can be deleted in a targeted manner, and those genes that can still maintain or enhance the target biological function after the corresponding functional gene is lost can be screened out, which is helpful to screen out the genes that perform best under different drug treatment conditions and conduct further target function verification. These technologies can deeply explore the functional effects of gene expression on the cell or tissue level, ensuring that the screened genes are not only effective at the gene level, but also can achieve the expected functions in the actual biological process. In this way, it can be directly verified whether the target gene exhibits excellent function at the protein level, thereby improving the accuracy of functional verification. In addition, by applying the biological reagent excellent function verification gene to the corresponding research and development process of the biological reagent, the function of the biological reagent can be better regulated and its performance can be improved, thereby providing more efficient and accurate products for various biotechnology applications, and the screened and verified excellent function genes can be directly applied to the production of biological reagents, such as enzymes, antibodies, vaccines or other biological products, so as to obtain biological reagent products with target functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0053] Figure 1 The figure is a schematic diagram of the steps of the biological reagent development method based on microbial functional gene screening of the present invention;

[0054] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0055] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0056] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0057] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for developing a biological reagent based on screening of microbial functional genes, the method comprising the following steps:

[0058] Step S1: obtaining a microbial sample, and performing genome sequencing and extraction processing on the microbial sample to obtain genome data of the microbial sample; performing gene transcription analysis on the genome data of the microbial sample to generate transcriptome data of the microbial sample;

[0059] Step S2: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions, and performing target function gene screening processing on microbial sample genome data and microbial sample transcriptome data based on the target functions of biological reagent research and development to generate biological reagent target function genome data;

[0060] Step S3: performing gene targeted knockout processing on the microbial target functional genes corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group; performing gene cloning on the target functional gene targeted knockout experimental group and transferring it into a preset host strain for functional expression evaluation and analysis to obtain the target functional expression level corresponding to each target functional targeted knockout gene;

[0061] Step S4: Based on the target function expression level corresponding to each target function targeted knockout gene, each target function targeted knockout gene in the target function gene targeted knockout experimental group is screened and verified for excellent function to generate a biological reagent excellent function verification gene; the biological reagent excellent function verification gene is applied to the corresponding research and development process of the biological reagent to generate a target function biological reagent product.

[0062] In the embodiment of the present invention, please refer to Figure 1 The figure is a schematic diagram of the steps of the biological reagent development method based on microbial functional gene screening of the present invention. In this example, the biological reagent development method based on microbial functional gene screening includes the following steps:

[0063] Step S1: obtaining a microbial sample, and performing genome sequencing and extraction processing on the microbial sample to obtain genome data of the microbial sample; performing gene transcription analysis on the genome data of the microbial sample to generate transcriptome data of the microbial sample;

[0064] In an embodiment of the present invention, by selecting several representative soils, marine waters and extreme environment ecosystems as sample collection sites, soil samples can be obtained by collecting surface soil in different geographical areas, and paying attention to avoiding pollution to ensure the representativeness of the samples. Marine water samples can be obtained by collecting plankton, sediments, etc. in water bodies in specific sea areas. Extreme environment samples such as microbial samples in high temperature, high salt and low temperature environments can be collected in special ecosystems such as hot springs, saline-alkali lands and polar regions. When sampling, sterile sampling instruments are used to avoid exogenous contamination, and detailed environmental parameters (such as temperature, humidity, salinity, etc.) are recorded for each collection point. After sample collection, the original microbial samples are obtained, and the obtained original microbial samples are separated, purified and cultured to establish a microbial library. First, different culture media are used for enrichment culture to separate different types of microorganisms from mixed samples, and aerobic, anaerobic or salt-adapted microorganisms are cultured separately. Each type of microorganism is cultured into a single colony by separation, thereby establishing a generated microbial library. After the establishment of the microbial library, gene identification is carried out to screen out representative and diverse microbial gene samples. At the same time, genome sequencing is performed based on the representative microbial gene samples previously screened to obtain the genomic data of the microorganisms. First, genomic DNA is extracted from each representative microbial sample. Common methods include using phenol-chloroform extraction, commercial DNA extraction kits, etc. The extracted DNA samples can be evaluated for purity by the A260 / A280 ratio. Genome sequencing is performed after passing the evaluation. High-throughput sequencing technology (such as Illumina NovaSeq, PacBio, etc.) is selected for sequencing technology. The genome sequence data of each microbial sample is obtained through whole genome sequencing, and low-quality sequences are eliminated to obtain the genome data of the microbial samples. Then, after completing genome sequencing and obtaining corresponding data, gene transcription analysis is performed to further study the gene expression of microorganisms. First, total RNA is extracted from microbial samples. High-quality total RNA is extracted using the TRIzol method or an RNA extraction kit to ensure the integrity and purity of the RNA. The concentration of RNA is measured using a spectrophotometer, and the integrity of RNA is detected by tools such as agarose gel electrophoresis or Bioanalyzer. The extracted RNA samples are reverse transcribed to synthesize cDNA, and then high-throughput sequencing technology is used to perform deep sequencing of the transcriptome (such as Illumina RNA-Seq). The sequencing data is quality controlled, low-quality reads are removed, and the data is compared and analyzed to further identify the expression differences of genes under different environmental conditions, thereby screening out important genes related to microbial functions and finally generating transcriptome data of microbial samples.

[0065] Step S2: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions, and performing target function gene screening processing on microbial sample genome data and microbial sample transcriptome data based on the target functions of biological reagent research and development to generate biological reagent target function genome data;

[0066] In an embodiment of the present invention, during the process of biological reagent development, it is necessary to determine the target functions, including enzyme activity, antibiotic production, and pollutant degradation, etc. These target functions can be determined by laboratory screening or literature research. The specific implementation method is to select microorganisms or their genes with potential functions according to the development needs. For the enzyme activity function, a standardized enzymatic activity detection method, such as a colorimetric method or a fluorescence method, is used to confirm the activity level of the enzyme by measuring the catalytic rate of the enzyme. For antibiotic production, a gene group known or assumed to have antibiotic synthesis ability in the microorganism is selected, and the relevant synthesis pathway is identified through genome data analysis. The antibiotic sensitivity test (such as the paper diffusion method) is used to verify the ability of the microorganism to produce antibiotics. The pollutant degradation function is to establish a pollutant degradation model, combine environmental monitoring data, select microbial populations with pollutant degradation potential as targets, ensure that the target function matches the metabolic capacity of the microorganism, and thus obtain the target function of biological reagent development. After obtaining the genomic data and transcriptome data of the microbial samples, metabolic network analysis is performed to explore each functional gene related to the target function. The specific operation is to use the genomic data for gene prediction, and combine the transcriptome data to analyze which genes are in an activated state under different conditions and which genes are related to the target metabolic process. The genomic data is first obtained through high-throughput sequencing technology (such as Illumina sequencing or PacBio sequencing), and the sequence splicing and gene prediction are completed using genome splicing software (such as SPAdes, Canu). The transcriptome data is obtained through RNA sequencing (RNA-Seq), and sequence alignment and quantitative analysis are performed using software such as TopHat and Cufflinks. These data are input into metabolic network analysis tools (such as KEGG, MetaCyc) to obtain the metabolic network of each microbial sample. At the same time, metabolic flux analysis is further performed through the metabolic network of each microbial sample to calculate the metabolic flux, raw material utilization rate and by-product generation efficiency of the microorganism. First, a metabolic flux model is established. The commonly used calculation method is the metabolic flux analysis (Flux Balance Analysis, FBA) based on the Stoichiometric Matrix. This method can calculate the various reaction rates (i.e. metabolic flux) in the microbial metabolic process under given raw materials and environmental conditions through a linear programming model. The raw material utilization rate calculates the proportion of raw materials that can be effectively utilized by microorganisms under specific metabolic conditions, and the by-product generation efficiency indicates the efficiency of generating by-products under these metabolic fluxes. By combining the microbial raw material utilization rate and the metabolic by-product generation efficiency with the metabolic pathways related to the synthesis of target products (such as antibiotics, pollutant degradation products), the synthesis rate of metabolites can be evaluated, that is, the microbial raw material utilization rate xe^(1-metabolic by-product generation efficiency), thereby obtaining the synthesis rate of metabolic target products.A suitable gene matching calculation formula is also formed by combining microbial metabolic flux, metabolic target product synthesis rate, target metabolite synthesis rate and related parameters to perform functional matching calculation on the target function of biological reagent research and development, so as to determine the degree of functional matching between each functional gene and the target function (such as enzyme activity, antibiotic production, and pollutant degradation). Next, the corresponding functional genes in the microbial sample genome data and transcriptome data are screened. The specific implementation method is to first set a threshold of functional matching value according to the preset target function requirements. The functional genes of the microbial samples below the threshold will be eliminated, and sorted according to the matching value. The functional genes that meet the target function requirements are extracted from the genome and transcriptome data of the microbial samples, and further experimental verification and functional evaluation are carried out to verify the actual role of each functional gene in target product synthesis, antibiotic production or pollutant degradation, so as to determine the target functional genome of biological reagents closely related to the target function, and finally generate the target functional genome data of biological reagents.

[0067] Step S3: performing gene targeted knockout processing on the microbial target functional genes corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group; performing gene cloning on the target functional gene targeted knockout experimental group and transferring it into a preset host strain for functional expression evaluation and analysis to obtain the target functional expression level corresponding to each target functional targeted knockout gene;

[0068] In an embodiment of the present invention, by selecting microbial genome data related to the target functional gene, and determining the location, sequence information and functional role of the gene in the microorganism based on the data, specific primers are designed based on the target functional gene sequence, and CRISPR-Cas9 or other gene editing tools (such as TALEN, ZFN, etc.) are used to perform targeted knockout of the target gene. A common method of CRISPR-Cas9 technology is to construct a vector containing a guide RNA (gRNA) sequence, and introduce the vector into the microbial cell by electroporation or chemical transformation. The targeted guide RNA will guide the Cas9 protein to recognize and cut the target gene position, and then use the microorganism's own DNA repair mechanism to cause insertion or deletion (Indel) mutations to achieve the purpose of gene knockout, thereby generating a target functional gene targeted knockout experimental group. After obtaining the target functional gene targeted knockout experimental group, the next step is gene cloning. First, PCR amplification is performed on the genomic DNA extracted from the target functional gene targeted knockout experimental group to verify whether the target gene has been successfully knocked out. In this process, specific primers are used for gene amplification to ensure that the amplified fragment only comes from the target functional gene. Then the cloning vector (such as pUC19, pET, etc.) is cut and connected to the PCR product. T4 DNA ligase is used for ligation reaction, and clones containing the target gene are screened after selective culture to confirm whether the target gene is correctly cloned, and each target function targeted knockout gene that has been previously cloned is transferred into a preset host strain (such as Escherichia coli, yeast or other industrial microbial strains), and the recombinant vector is transferred into the host strain by conventional transformation methods (such as heat shock method, electric shock method). After transformation, positive strains are screened by selecting a culture medium containing a resistance marker, and colony PCR detection, enzyme cutting verification and other methods are performed on the positive transformed strains to confirm that the target function gene has been successfully transferred into the host strain. After that, each target function gene is induced to express (such as by adding IPTG or other inducers) so that it is efficiently expressed in the host strain, and a transformation strain corresponding to the target function gene is generated, thereby generating each target function knockout gene transformation strain. Then, after the target functional gene transformation strain is obtained, the next step is to evaluate the target functional gene expression and enzyme activity level of each strain. First, the culture fluid or cell sample of each transformed strain is collected, and protein quantitative methods such as Western blot and ELISA are used to determine the expression level of the target functional gene. On this basis, the target gene enzyme activity is measured, and the enzyme activity of each transformed strain is evaluated using a suitable enzyme activity detection method (such as colorimetry, fluorescence, etc.) to ensure that the activity of the target enzyme is effectively transformed and expressed, and to obtain the corresponding gene enzyme activity level.In addition, by combining the target function gene expression level and gene enzyme activity level, the preset functional expression evaluation calculation formula was used to evaluate the functional expression of each target function gene transformation strain. The calculation formula took into account gene expression level, enzyme activity and other related factors to comprehensively evaluate the target function expression level corresponding to each target function targeted knockout gene, and finally obtained the target function expression level corresponding to each target function targeted knockout gene.

[0069] Step S4: Based on the target function expression level corresponding to each target function targeted knockout gene, each target function targeted knockout gene in the target function gene targeted knockout experimental group is screened and verified for excellent function to generate a biological reagent excellent function verification gene; the biological reagent excellent function verification gene is applied to the corresponding research and development process of the biological reagent to generate a target function biological reagent product.

[0070] In an embodiment of the present invention, the target function expression levels corresponding to the target function targeted knockout genes previously obtained by quantification are compared and judged according to a preset target function expression level threshold. If the target function expression level is greater than or equal to the preset target function expression level threshold, the corresponding target function targeted knockout gene in the target function gene targeted knockout experimental group is considered to be an excellent function gene and is screened out. If the target function expression level is less than the preset target function expression level threshold, it is eliminated. In order to further verify the excellent function of the target excellent function gene in the biological reagent, the screened target excellent function gene is first transferred into a wild-type microorganism or other cell system for recombinant expression, and through proteomics technology, For example, liquid chromatography-mass spectrometry (LC-MS / MS) is used to identify and quantify the expressed proteins, evaluate the protein expression of gene knockout or recombinant genes and their distribution in cells, and further, metabolomics analysis is performed using gas chromatography-mass spectrometry (GC-MS) or high performance liquid chromatography-mass spectrometry (HPLC-MS) to comprehensively detect changes in metabolites. By comparing the metabolite spectra of the recombinant strains with the target gene and the control group, the regulatory effect of the target gene on the microbial metabolic pathway is confirmed, and the functional characteristics of the target gene are evaluated. If the overexpression or knockout of the target gene can significantly improve the desired metabolite or enhance a certain biological function, the gene is further confirmed to be "successfully verified", thereby generating a gene for excellent functional verification of biological reagents. Then, the verified excellent functional genes are introduced into specific microbial hosts through genetic engineering technology. For example, the target genes are introduced into Escherichia coli, yeast or other industrial microorganisms for functional verification using gene transformation technology (such as electrotransformation, heat shock method, etc.), and the screening system is used to determine the recombinant strains in the transformed strains that can stably express the target functional genes. On this basis, fermentation culture is carried out, and culture conditions such as temperature, pH value, dissolved oxygen level, etc. are optimized to ensure that the recombinant strains can maintain stable expression levels and efficient target function performance in large-scale production. Through downstream extraction and purification technologies, such as affinity chromatography, ion exchange chromatography or gel filtration, biological molecules or biological products with target functions are extracted from the culture medium to generate target biological reagent products that meet the expected functions. For example, if the target function is the production of a certain enzyme, the generated biological reagent product will contain an efficient enzyme, and ultimately generate a target functional biological reagent product.

[0071] Further, step S1 includes the following steps:

[0072] Step S11: obtaining original microbial samples from soil, ocean waters and extreme environment ecosystems;

[0073] Step S12: separating, purifying and culturing the original microbial sample to generate a microbial library;

[0074] Step S13: performing 16S rRNA gene identification and screening on the corresponding microbial samples in the microbial library to generate a representative gene sample set for preliminary screening of microorganisms;

[0075] Step S14: performing genome sequencing and extraction processing on the representative gene sample set of the initial screening of microorganisms to obtain genome data of the microorganism samples;

[0076] Step S15: performing gene transcription analysis on the microbial sample genome data to generate microbial sample transcriptome data.

[0077] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0078] Step S11: obtaining original microbial samples from soil, ocean waters and extreme environment ecosystems;

[0079] In an embodiment of the present invention, by selecting several representative soils, marine waters and extreme environment ecosystems as sample collection sites, soil samples can be obtained by collecting surface soil in different geographical areas, and taking care to avoid contamination to ensure the representativeness of the samples. Marine water samples can be obtained by collecting plankton, sediments, etc. in water bodies in specific sea areas. Extreme environment samples such as microbial samples in high temperature, high salt and low temperature environments can be collected in special ecosystems such as hot springs, saline-alkali lands and polar regions. When sampling, sterile sampling instruments are used to avoid exogenous contamination, and detailed environmental parameters (such as temperature, humidity, salinity, etc.) are recorded for each collection point. After the samples are collected, they should be stored under low temperature conditions in time to prevent the growth and changes of the microbial samples, ensure their representativeness in subsequent experiments, and finally obtain the original microbial samples.

[0080] Step S12: separating, purifying and culturing the original microbial sample to generate a microbial library;

[0081] In an embodiment of the present invention, a microbial library is established by isolating, purifying and culturing original microbial samples obtained from soil, marine waters and extreme environments. First, different culture media are used for enrichment culture so as to separate different types of microorganisms from mixed samples. For soil and water samples, nutrient agar, agar plates, selective culture media, etc. are used for culture to culture aerobic, anaerobic or salt-adapted microorganisms, respectively. Each type of microorganism is isolated and cultured into a single colony for subsequent analysis. The morphology, color and other characteristics of each colony are preliminarily identified by using a microscope and morphological observation methods, and their biochemical characteristics are detected by a biochemical kit. After culture, all isolated strains should be stored separately in a freezing solution containing glycerol for long-term storage, and finally a microbial library is established.

[0082] Step S13: performing 16S rRNA gene identification and screening on the corresponding microbial samples in the microbial library to generate a representative gene sample set for preliminary screening of microorganisms;

[0083] In an embodiment of the present invention, after the microbial library is established, gene identification is performed to screen representative microorganisms. First, multiple single culture strains are randomly selected from the microbial library, and their genomic DNA is extracted. Then, the microbial 16S rRNA gene is amplified by PCR. This gene has high conservation and variability in bacteria and is suitable for species identification. Common 16S rRNA primers are selected, and the target gene is amplified by a standard PCR reaction system. The amplified PCR product is verified by gel electrophoresis to ensure the specificity and integrity of the amplified product, and the purified PCR product is sent to a gene sequencing platform for sequencing to obtain sequence data of the 16S rRNA gene. According to the comparison results, bioinformatics tools (such as BLAST) are used to compare with databases (such as Greengenes, RDP, etc.), so as to determine the type and classification information of each microorganism, screen out representative and diverse microbial gene samples, and finally generate a representative gene sample set for initial screening of microorganisms.

[0084] Step S14: performing genome sequencing and extraction processing on the representative gene sample set of the initial screening of microorganisms to obtain genome data of the microorganism samples;

[0085] In an embodiment of the present invention, a representative gene sample set of the previously screened microorganisms is screened, and genome sequencing is performed to obtain the genome data of the microorganisms. First, genomic DNA is extracted from each representative microorganism sample. Common methods include using phenol-chloroform extraction, commercial DNA extraction kits, etc. to ensure that the extracted DNA is of high quality and purity. The extracted DNA sample can be evaluated for purity by the A260 / A280 ratio. After passing the evaluation, genome sequencing is performed. The sequencing technology selects high-throughput sequencing technology (such as Illumina NovaSeq, PacBio, etc.). The genome sequence data of each microorganism sample is obtained by whole genome sequencing, and low-quality sequences are eliminated to improve the accuracy of the sequencing results, thereby obtaining the complete genome data of the microorganism, and finally obtaining the genome data of the microorganism sample.

[0086] Step S15: performing gene transcription analysis on the microbial sample genome data to generate microbial sample transcriptome data.

[0087] In an embodiment of the present invention, after completing genome sequencing and obtaining corresponding data, gene transcription analysis is performed to further study the gene expression of the microorganism. First, total RNA is extracted from the microbial sample, and high-quality total RNA is extracted using the TRIzol method or an RNA extraction kit to ensure the integrity and purity of the RNA. The concentration of the RNA is measured by using a spectrophotometer, and the integrity of the RNA is detected by tools such as agarose gel electrophoresis or Bioanalyzer. The extracted RNA sample is subjected to a reverse transcription reaction to synthesize cDNA, and then the transcriptome is deeply sequenced using high-throughput sequencing technology (such as Illumina RNA-Seq). The sequencing data is quality controlled, low-quality reads are removed, and the data is compared and analyzed. The transcription data is compared to the reference genome using tools such as TopHat and STAR. The gene expression is quantitatively analyzed by methods such as FPKM and TPM, and the expression differences of genes under different environmental conditions are further identified, thereby screening out important genes related to microbial functions, and finally generating microbial sample transcriptome data.

[0088] Further, step S13 includes the following steps:

[0089] Step S131: pre-screening the sample diversity of the corresponding microbial samples in the microbial library to obtain a microbial diversity pre-selected sample set;

[0090] Step S132: extracting sample DNA from the microbial samples corresponding to the microbial diversity pre-selected sample set using DNA extraction technology to obtain a DNA sequence corresponding to each microbial pre-selected sample;

[0091] Step S133: performing a gene amplification reaction on the DNA sequence corresponding to each microbial preselected sample according to the preset 16S rRNA gene primers to obtain a 16S rRNA gene amplification product sequence fragment corresponding to each microbial preselected sample;

[0092] Step S134: Based on the sequence fragments of the 16S rRNA gene amplification products corresponding to each microbial pre-selected sample, sequence comparison, identification and screening are performed on the corresponding microbial samples in the microbial diversity pre-selected sample set to generate a representative gene sample set for microbial preliminary screening.

[0093] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0094] Step S131: pre-screening the sample diversity of the corresponding microbial samples in the microbial library to obtain a microbial diversity pre-selected sample set;

[0095] In an embodiment of the present invention, microbial samples that meet the research objectives are selected from a microbial library, and the selection criteria are based on the ecological diversity, species representativeness and expected functions of the samples. Samples from different environments, different sources and different microbial populations are screened to ensure the diversity of the samples to cover more microbial species. In specific operations, multiple screening criteria are adopted, such as microbial genome data, species classification information, ecological environment categories, etc., and high-throughput sequencing technology is used to preliminarily analyze the species diversity of each microbial population in the microbial library samples, and based on the sequencing results, microbial samples with high representativeness and diversity are further screened out to finally obtain a microbial diversity pre-selected sample set.

[0096] Step S132: extracting sample DNA from the microbial samples corresponding to the microbial diversity pre-selected sample set using DNA extraction technology to obtain a DNA sequence corresponding to each microbial pre-selected sample;

[0097] In an embodiment of the present invention, for each microbial preselected sample, an efficient DNA extraction method is used to extract DNA from cells. For different types of microorganisms such as bacteria and fungi, extraction kits suitable for their characteristics are used. For example, for bacterial samples, special extraction kits for bacteria with strong cell walls or containing more lipid components are selected. During this step, various methods such as mechanical fragmentation, enzymatic hydrolysis, and ultrasonic fragmentation are used to ensure the thoroughness of the extraction process and avoid DNA degradation. After the sample is processed, the DNA quality and concentration are checked by gel electrophoresis or UV spectroscopy to ensure that the extracted DNA is suitable for subsequent PCR amplification. The DNA sequence of each microbial preselected sample is accurately extracted, and finally the DNA sequence corresponding to each microbial preselected sample is obtained.

[0098] Step S133: performing a gene amplification reaction on the DNA sequence corresponding to each microbial preselected sample according to the preset 16S rRNA gene primers to obtain a 16S rRNA gene amplification product sequence fragment corresponding to each microbial preselected sample;

[0099] In an embodiment of the present invention, a gene amplification reaction is performed on the DNA of each microbial sample using preset high-specificity 16S rRNA gene primers. The primers used are generally universal 16S rRNA gene primers, such as 27F and 1492R primers widely used in bacterial classification and species identification. These primers can recognize and amplify the 16S rRNA gene sequences of most bacteria. The amplification reaction is carried out in a standard PCR reaction system, and the reaction conditions are optimized according to the DNA properties of the target microorganism and the characteristics of the primers. For example, a hot-start Taq DNA polymerase is used, and a temperature gradient PCR program is set to ensure the specificity and yield of the PCR product. After the reaction is completed, the size, purity and concentration of the amplified product are analyzed by agarose gel electrophoresis to confirm the specificity of the 16S rRNA gene amplification product, and prepare for downstream sequencing analysis, and finally obtain the 16S rRNA gene amplification product sequence fragment corresponding to each microbial pre-selected sample.

[0100] Step S134: Based on the sequence fragments of the 16S rRNA gene amplification products corresponding to each microbial pre-selected sample, sequence comparison, identification and screening are performed on the corresponding microbial samples in the microbial diversity pre-selected sample set to generate a representative gene sample set for microbial preliminary screening.

[0101] In an embodiment of the present invention, sequence comparison and identification analysis are performed on the corresponding microbial samples in the microbial diversity pre-selected sample set by combining the 16S rRNA gene amplification product sequence fragments corresponding to each microbial pre-selected sample obtained after the previous amplification reaction, so as to submit the obtained 16S rRNA gene amplification product sequence fragments to a public database, such as NCBI's GenBank, and use the BLAST tool for comparison to determine the species information and classification level of the microorganisms, and based on the comparison results, the species classification and pedigree information of each sample can be determined, and further, based on the comparison results, each microbial sample can be functionally predicted and screened to screen out representative gene samples with high species diversity and functional potential, and through the comparison results, a group of representative microbial gene samples are selected to form a representative gene sample set for microbial preliminary screening, and finally a representative gene sample set for microbial preliminary screening is screened and generated.

[0102] Further, step S15 includes the following steps:

[0103] Step S151: obtaining the corresponding RNA transcription start site and RNA intron splicing site from each microbial sample genome in the microbial sample genome data, and extracting the transcription region of the microbial sample RNA in the corresponding microbial sample genome based on the RNA transcription start site and RNA intron splicing site to obtain the microbial sample genome RNA transcription candidate region;

[0104] In an embodiment of the present invention, the whole genome sequence is first analyzed in the genome data of the microbial sample, and the transcription start site (TSS) and intron splicing site are accurately located using bioinformatics tools. Specifically, high-quality genome annotation information, such as the microbial genome annotation file in the RefSeq or Ensembl database, is used in combination with RNA-seq experimental data for verification, and transcriptome analysis tools such as Cufflinks, Tophat2, STAR, etc. are used for mapping and alignment to detect the transcription start site and the corresponding intron-exon splicing site corresponding to each microbial sample genome. After obtaining these site information, the corresponding transcription region sequence is extracted based on the transcription start site and the splicing site. These sequences are candidate microbial RNA transcription regions. This process is crucial for the transcriptome study of microbial samples, because accurate start sites and splicing sites can accurately determine the transcription region, and ultimately obtain candidate regions for microbial sample genome RNA transcription.

[0105] Step S152: performing a cDNA reverse transcription reaction on the candidate region of the microbial sample genomic RNA transcription to obtain a microbial sample reverse transcription cDNA library;

[0106] In an embodiment of the present invention, a cDNA library is generated by reverse transcription of a previously extracted candidate region of microbial RNA transcription. First, total RNA of the microbial sample is extracted. TRIzol and other reagents can be used to extract and purify RNA to ensure that the integrity and quality of the RNA meet the requirements of the reverse transcription reaction. A reverse transcriptase (such as SuperScript IV) and random primers or specific primers are used for reverse transcription reaction. During this process, the reverse transcriptase will synthesize a complementary cDNA chain from an RNA template. The reaction temperature is usually set to 42°C to 50°C, and the reaction time is 1 hour. During the reverse transcription process, necessary reaction buffer, Mg2+, dNTP mixture, reverse transcriptase and other reagents are added to ensure the high efficiency of the reaction. After the reverse transcription is completed, single-stranded cDNA is obtained by removing the RNA template (such as using RNase H). These cDNA fragments contain complete information of the target RNA transcription region, and finally a reverse transcription cDNA library of the microbial sample is obtained.

[0107] Step S153: performing tailing modification and library construction on the microbial sample reverse transcription cDNA library to add adapter sequences and tags to obtain a microbial sample transcriptome library construction product;

[0108] In an embodiment of the present invention, after obtaining single-stranded cDNA, tailing modification and library construction are performed. First, the cDNA is end-modified to add an adapter sequence suitable for high-throughput sequencing. The adapter sequence is necessary for subsequent PCR amplification and sequencing platform recognition. For example, a commercially available library construction kit such as NEBNext Ultra II DNA Library Prep Kit can be selected for end modification. The specific operation includes first using T4 DNA polymerase to repair the 3' end of the cDNA, and then adding a primer with an adapter sequence, using a ligase (such as T4 DNA ligase) to connect the adapter to the end of the cDNA fragment, followed by PCR amplification, and enrichment using specific primers to ensure high abundance of target cDNA in the library. This process requires library quality detection. Commonly used methods include electrophoresis analysis and qPCR detection to ensure the quality of library construction and the efficiency of amplification. The obtained transcriptome library contains all transcription information of the microbial sample, and finally obtains the microbial sample transcriptome library construction product.

[0109] Step S154: performing high-throughput transcription sequencing processing on the microbial sample transcriptome library construction product to generate microbial sample transcriptome data.

[0110] In an embodiment of the present invention, after the library is constructed, high-throughput transcription sequencing is performed to obtain the transcriptome data of the microbial sample. The Illumina platform (such as NovaSeq or HiSeq series) is usually selected for high-throughput sequencing. In this process, the transcriptome library is first loaded onto a sequencing chip, and then a platform-specific sequencing reaction is used for data acquisition. The Illumina platform fixes the library fragments on the sequencing chip through bridge amplification technology, and uses a single-end or double-end sequencing mode to sequence each library fragment. The sequencing length can be set according to research requirements, such as selecting 100bp single-end or 150bp double-end sequencing. After sequencing is completed, the generated raw data (usually in FASTQ format) will contain sequence information from each transcription region, and the raw data is preprocessed using bioinformatics tools (such as FastQC for quality control, Trimmomatic for connector removal and low-quality sequence trimming) to obtain high-quality transcriptome data, and finally generate microbial sample transcriptome data.

[0111] Further, step S2 includes the following steps:

[0112] Step S21: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions;

[0113] In an embodiment of the present invention, during the development of biological reagents, it is necessary to determine target functions, including enzyme activity, antibiotic production, and pollutant degradation, etc. These target functions can be determined by laboratory screening or literature research. The specific implementation method is to select microorganisms or their genes with potential functions according to the development needs. For the enzyme activity function, a standardized enzymatic activity detection method, such as a colorimetric method or a fluorescence method, is used to confirm the activity level of the enzyme by measuring the catalytic rate of the enzyme. For antibiotic production, a gene group known or assumed to have antibiotic synthesis ability in the microorganism is selected, and the relevant synthesis pathway is identified through genome data analysis. The antibiotic sensitivity test (such as the paper diffusion method) is used to verify the ability of the microorganism to produce antibiotics. The pollutant degradation function is to establish a pollutant degradation model, combine environmental monitoring data, select microbial populations with pollutant degradation potential as targets, ensure that the target function matches the metabolic capacity of the microorganism, and finally obtain the target functions of biological reagent development, including enzyme activity, antibiotic production and pollutant degradation target functions.

[0114] Step S22: performing metabolic network analysis on each microbial sample functional gene corresponding to the microbial sample genome data and the microbial sample transcriptome data to generate a microbial metabolic network corresponding to each microbial sample functional gene;

[0115] In an embodiment of the present invention, after obtaining the genome data and transcriptome data of the microbial sample, a metabolic network analysis is performed to mine each functional gene related to the target function. The specific operation is to use the genome data for gene prediction, and combine the transcriptome data to analyze which genes are in an activated state under different conditions and which genes are related to the target metabolic process. The genome data is first obtained by high-throughput sequencing technology (such as Illumina sequencing or PacBio sequencing), and sequence splicing and gene prediction are completed using genome splicing software (such as SPAdes, Canu). The transcriptome data is obtained by RNA sequencing (RNA-Seq), and sequence alignment and quantitative analysis are performed using software such as TopHat and Cufflinks. These data are input into metabolic network analysis tools (such as KEGG, MetaCyc), and the metabolic pathway information of each microbial sample can be obtained. These metabolic pathways contain key enzymes or enzymes related to enzyme activity, antibiotic synthesis, pollutant degradation, etc., and finally generate a microbial metabolic network corresponding to the functional genes of each microbial sample.

[0116] Step S23: Obtain the corresponding microbial metabolic flux, microbial raw material utilization rate and metabolic byproduct generation efficiency through the microbial metabolic network corresponding to each microbial sample functional gene, and perform product synthesis evaluation analysis on the corresponding microbial sample functional gene based on the microbial raw material utilization rate and metabolic byproduct generation efficiency to generate the metabolic target product synthesis rate corresponding to each microbial sample functional gene;

[0117] In an embodiment of the present invention, a metabolic flux analysis is further performed through the metabolic network of each microbial sample to calculate the metabolic flux, raw material utilization rate and byproduct generation efficiency of the microorganism. First, a metabolic flux model is established. The commonly used calculation method is a metabolic flux analysis (Flux Balance Analysis, FBA) based on the Stoichiometric Matrix. This method can calculate the various reaction rates (i.e., metabolic flux) in the microbial metabolic process under given raw materials and environmental conditions through a linear programming model. The raw material utilization rate calculates the proportion of raw materials that can be effectively utilized by the microorganism under specific metabolic conditions, and the byproduct generation efficiency represents the efficiency of generating byproducts under these metabolic fluxes. By combining the microbial raw material utilization rate and the metabolic byproduct generation efficiency with the metabolic pathways related to the synthesis of target products (such as antibiotics, pollutant degradation products), the synthesis rate of metabolites can be evaluated, that is, the microbial raw material utilization rate xe^(1-metabolic byproduct generation efficiency), which functional genes can be confirmed to improve the metabolic efficiency and increase the synthesis rate of the target product, and finally the synthesis rate of the metabolic target product corresponding to the functional gene of each microbial sample is evaluated.

[0118] Step S24: Based on the microbial metabolic flux and the metabolic target product synthesis rate corresponding to each microbial sample functional gene, a target functional gene matching calculation formula is used to perform a functional matching calculation on the target function of biological reagent research and development to obtain a microbial metabolic target function matching value corresponding to each microbial sample functional gene;

[0119] In an embodiment of the present invention, a suitable target functional gene matching calculation formula is formed by combining the quantitative parameters of the functional genes of the microbial samples, the observation time period of the metabolic process, the quantitative parameters of the metabolic pathway, the time variable parameters, the microbial metabolic flux, the synthesis rate of the metabolic target product, the metabolic efficiency coefficient, the microbial synthesis contribution coefficient, the synthesis rate of the target metabolite, the metabolic consumption efficiency corresponding to the target function of the biological reagent research and development, the metabolic production rate and related parameters to perform a functional matching calculation on the target function of the biological reagent research and development to determine the functional matching degree between each functional gene and the target function (such as enzyme activity, antibiotic production, and pollutant degradation), and finally obtain the microbial metabolism target function matching value corresponding to each microbial sample functional gene.

[0120] Step S25: Based on the microbial metabolism target function matching value corresponding to each microbial sample functional gene, target functional gene screening processing is performed on the microbial sample genome data and the corresponding microbial sample transcriptome data to generate biological reagent target functional genome data.

[0121] In an embodiment of the present invention, after obtaining the metabolic target function matching value corresponding to each microbial sample functional gene, the next step is to screen the corresponding functional genes in the microbial sample genome data and transcriptome data. The specific implementation method is to first set a threshold of the function matching value according to the preset target function requirements, and the microbial sample functional genes below the threshold will be eliminated, and sorted according to the matching values, and the functional genes that meet the target function requirements are extracted from the genome and transcriptome data of the microbial samples for further experimental verification and functional evaluation. This screening process can verify the actual role of each functional gene in the synthesis of target products, the production of antibiotics or the degradation of pollutants through genome editing technology (such as CRISPR-Cas9). Through this screening process, the target functional genome of the biological reagent closely related to the target function is determined, and finally the target functional genome data of the biological reagent is generated.

[0122] Furthermore, the target functional gene matching calculation formula in step S24 is specifically:

[0123]

[0124] In the formula, F iis the microbial metabolic target function matching value corresponding to the i-th microbial sample functional gene, N is the total number of microbial sample functional genes, i is the item index parameter of the microbial sample functional gene, T is the metabolic process observation time period, M is the total number of metabolic pathways, k is the item index parameter of the metabolic pathway, t is the time variable parameter, V ik (t) is the microbial metabolic flux of the functional gene of the i-th microbial sample at the corresponding time t in metabolic pathway k, Y ik (t) is the synthesis rate of the metabolic target product of the functional gene of the i-th microbial sample at the corresponding time t in the metabolic pathway k, K ik is the metabolic efficiency coefficient of the functional gene of the i-th microbial sample in the metabolic pathway k, S is the microbial synthesis contribution coefficient corresponding to the target function of biological reagent research and development, Q is the synthesis rate of the target metabolite corresponding to the target function of biological reagent research and development, R i (t) is the metabolic consumption efficiency of the functional gene of the i-th microbial sample at time t, v i (t) is the metabolic production rate of the functional gene of the i-th microbial sample at time t, and η is the correction coefficient of the matching value of the microbial metabolic target function.

[0125] The present invention obtains a target function gene matching calculation formula by using a specific mathematical model and verifying it, which is used to perform functional matching calculation on the target function of biological reagent research and development. The target function gene matching calculation formula fully considers the key parameters in multiple metabolic processes such as microbial metabolic flux, metabolic target product synthesis rate, metabolic efficiency coefficient, etc. By integrating these parameters, it can fully reflect the metabolic dynamic behavior of microorganisms and changes in metabolic pathways at different time points. This comprehensive analysis makes the evaluation results not only accurate, but also covers the entire metabolic process, rather than a simple static analysis. The time variable and the number of metabolic pathways in the formula introduce dynamic factors of time and pathway, so that the instantaneous changes of the metabolic process can be taken into account. By integrating the changes in metabolic flux and synthesis rate over time, the formula can reflect the time dependence of metabolic activity and help predict the metabolic performance and functional contribution of microorganisms in different time periods. By calculating the metabolic flux, raw material utilization rate and byproduct generation efficiency of microbial samples, the formula can effectively evaluate the efficiency of microbial metabolism, especially the influence of raw material utilization rate and metabolic byproduct generation efficiency on the target function gene, which helps optimize the biosynthetic capacity of microbial samples, avoid waste of resources, and improve the generation efficiency of products. Secondly, the metabolic consumption efficiency and metabolic production rate in the formula are combined through the correction coefficient to ensure the balance of energy and resources in the metabolic process. This balance is crucial to the overall production capacity of microorganisms, avoiding the final synthesis of products affected by excessive energy consumption or inefficient production rate. The target functional gene matching value accurately evaluates the contribution of each microbial sample functional gene under a specific metabolic target through weighted calculation of all these variables. This evaluation can provide a scientific basis for microbial engineering and help screen out the most potential functional genes, thereby accelerating the development of biological reagents. Through the calculation of the formula, the metabolic capacity of different microbial samples can be quantitatively described, and on this basis, microorganisms with excellent metabolic performance can be selected for further research. This quantitative analysis method makes the development of biological reagents more accurate and helps to screen and optimize microbial strains more efficiently. Through this calculation formula, personalized optimization can be performed according to the matching degree of the metabolic behavior and target function of different microbial samples, and the microbial genome or transcriptome can be further adjusted according to the matching value of each microbial sample to achieve the purpose of optimizing product synthesis and improve the success rate of biological reagent development. In summary, the formula fully considers the microbial metabolic target function matching value F corresponding to the functional gene of the i-th microbial sample. i , the total number of microbial sample functional genes N, the item index parameter i of the microbial sample functional genes, the metabolic process observation time period T, the total number of metabolic pathways M, the item index parameter k of the metabolic pathway, the time variable parameter t, the microbial metabolic flux V of the i-th microbial sample functional gene at the corresponding time t in the metabolic pathway k ik(t), the synthesis rate Y of the metabolic target product of the functional gene of the i-th microbial sample at the corresponding time t in the metabolic pathway k ik (t), the metabolic efficiency coefficient K corresponding to the functional gene of the i-th microbial sample in the metabolic pathway k ik , the microbial synthesis contribution coefficient S corresponding to the target function of biological reagent research and development, the target metabolite synthesis rate Q corresponding to the target function of biological reagent research and development, and the metabolic consumption efficiency R of the functional gene of the i-th microbial sample at time t i (t), the metabolic production rate v of the functional gene of the i-th microbial sample at time t i (t), the correction coefficient η of the microbial metabolism target function matching value, according to the microbial metabolism target function matching value F corresponding to the functional gene of the i-th microbial sample i The correlation between the above parameters constitutes a functional relationship This formula can realize the functional matching calculation process of the target function of biological reagent research and development. At the same time, through the introduction of the correction coefficient η of the microbial metabolism target function matching value, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the target function gene matching calculation formula.

[0126] Further, step S3 includes the following steps:

[0127] Step S31: performing gene targeted knockout processing on the microbial target functional gene corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group;

[0128] In an embodiment of the present invention, microbial genome data related to the target functional gene is selected, and the location, sequence information and functional role of the gene in the microorganism are determined according to the data, specific primers are designed based on the target functional gene sequence, and CRISPR-Cas9 or other gene editing tools (such as TALEN, ZFN, etc.) are used to perform targeted knockout of the target gene. A common method of CRISPR-Cas9 technology is to construct a vector containing a guide RNA (gRNA) sequence, and introduce the vector into the microbial cell by electroporation or chemical transformation. The targeted guide RNA will guide the Cas9 protein to recognize and cut the target gene position, and then use the microorganism's own DNA repair mechanism to cause insertion or deletion (Indel) mutations, thereby achieving the purpose of gene knockout. By selecting a resistance marker gene for screening, it is ensured that only clones with successful knockout are obtained, and finally a target functional gene targeted knockout experimental group is generated.

[0129] Step S32: performing gene cloning processing on the target functional gene targeted knockout experimental group to obtain a target functional gene knockout clone group;

[0130] In an embodiment of the present invention, after obtaining the target functional gene targeted knockout experimental group, the next step is to perform a gene cloning operation. First, the genomic DNA extracted from the target functional gene targeted knockout experimental group is amplified by PCR to verify whether the target gene has been successfully knocked out. In this process, specific primers are used for gene amplification to ensure that the amplified fragment is derived only from the target functional gene. Subsequently, the cloning vector (such as pUC19, pET, etc.) is cut and connected to the PCR product, and T4 DNA ligase is used for ligation reaction. The connected vector is introduced into Escherichia coli or other appropriate host strains by electroporation or chemical transformation. After selective culture, clones containing the target gene are screened, and the cloned genomic DNA is sequenced to confirm whether the target gene is correctly cloned, and finally the target functional gene knockout clone group is obtained.

[0131] Step S33: each target function knockout gene in the target function gene knockout clone group is transferred into a preset host strain for functional transformation and expression processing, so as to generate each target function knockout gene transformation strain;

[0132] In an embodiment of the present invention, each target function targeted knockout gene in the previously cloned target function gene knockout clone group is transferred into a preset host strain (such as Escherichia coli, yeast or other industrial microbial strains), and a conventional transformation method (such as heat shock method, electric shock method) is used to transfer the recombinant vector into the host strain. After transformation, positive strains are screened by selecting a culture medium containing a resistance marker, and colony PCR detection, enzyme digestion verification and other methods are performed on the positive transformed strains to confirm that the target function gene has been successfully transferred into the host strain. After that, each target function gene is induced to express (such as by adding IPTG or other inducers) so that it is efficiently expressed in the host strain, and a transformed strain corresponding to the target function gene is generated. At this time, by culturing and observing different transformed strains, it is ensured that the target function gene in each transformed strain is correctly expressed, and finally each target function knockout gene transformed strain is generated.

[0133] Step S34: Cultivate each target function knockout gene transformation strain and obtain the corresponding target function knockout gene strain expression level and target function knockout gene enzyme activity level, and perform functional expression evaluation calculation on each target function knockout gene transformation strain based on the target function knockout gene strain expression level and target function knockout gene enzyme activity level using the functional expression evaluation calculation formula to obtain the target function expression level corresponding to each target function targeted knockout gene.

[0134] In an embodiment of the present invention, after the target function gene transformed strain is obtained, the next step is to evaluate the target function gene expression and enzyme activity level of each strain, first by collecting the culture fluid or cell sample of each transformed strain, using protein quantitative methods such as Western blot, ELISA, the expression of the target function gene is measured, so as to obtain the target function knockout gene strain expression. On this basis, the target gene enzyme activity is measured, and suitable enzyme activity detection methods (such as colorimetry, fluorescence method, etc.) are used to evaluate enzyme activity for each transformed strain, ensuring that the activity of the target enzyme is effectively converted and expressed, so as to obtain the target function knockout gene enzyme activity level. Meanwhile, by combining the target function knockout gene strain expression and the target function knockout gene enzyme activity level, the function expression evaluation calculation formula preset is used to evaluate the function expression of each target function gene transformed strain, and the calculation formula takes into account gene expression, enzyme activity and other related factors, with comprehensive evaluation of the target function expression level corresponding to each target function targeted knockout gene, and finally obtains the target function expression level corresponding to each target function targeted knockout gene.

[0135] Further, step S31 includes the following steps:

[0136] Performing target function annotation processing on the microbial target function genes corresponding to the target function genome data of the biological reagent to obtain the annotated region sequence of the target function gene of the biological reagent;

[0137] In an embodiment of the present invention, the target function region is annotated for the target function gene of the microorganism corresponding to the previously acquired target function genome data of the biological reagent. The genome of the microorganism is usually sequenced using a high-throughput sequencing technology (such as the Illumina sequencing platform). The obtained raw sequence data is subject to quality control and splicing to obtain a complete genome sequence. Then, a functional genomics database such as NCBI, KEGG or EggNOG is used to perform functional annotation on the genome data using professional gene annotation software tools (such as Prokka or RAST). These tools will automatically identify the gene region in the genome, predict the function of each gene, and map this information to known functional categories. Comparison tools such as BLAST are used to further confirm whether these genes match the known target function gene sequence, extract the annotation region related to the target function gene of the biological reagent, and finally obtain the annotated region sequence of the target function gene of the biological reagent.

[0138] Preferably, gene targeting knockout primers are designed according to the annotated region sequence of the target functional gene of the biological reagent to generate specific targeted knockout primers for the target functional gene of the biological reagent;

[0139] In an embodiment of the present invention, a targeted primer is designed by combining the previously obtained target functional gene annotation region sequence using a special primer design software (such as Primer3, Oligo7). During the design, the specific region of the target gene needs to be strictly selected to avoid cross-reaction with other genes in the microbial genome. The following aspects should be considered when selecting primers: 1) The length of the primer should be 18 to 25 bases to ensure specificity; 2) The GC content of the primer should be maintained between 40% and 60% to avoid primer stability problems caused by too low or too high GC content; 3) When designing primers, self-complementarity or dimerization should be avoided to ensure the specificity and efficiency of the primers; 4) The designed primers are verified using genome data or software (such as BLAST) to ensure that they have no non-specific binding regions in the microbial genome. During the primer design process, the annealing temperature (Tm) of the primer should also be considered at the same time, and it should be ensured that the designed primer can efficiently bind to the target gene, and finally the target functional gene-specific targeted knockout primers of the biological reagent are designed and generated.

[0140] Preferably, the target function gene of the microorganism corresponding to the target function genomic data of the biological reagent is subjected to gene targeted knockout treatment based on the biological reagent target function gene-specific targeted knockout primer to generate a target function gene targeted knockout experimental group.

[0141] In an embodiment of the present invention, the corresponding microbial target functional gene is targeted and knocked out by using the previously designed gene-specific targeted knockout primers. The specific operation is first to combine the designed targeting primers with the CRISPR-Cas9 system or homologous recombination technology to perform targeted gene knockout. When using the CRISPR-Cas9 technology, an expression vector containing the Cas9 gene and the targeting primer sequence is first constructed. The vector can be introduced into the target microorganism by electroporation or chemical transformation, and the Cas9 protein is used to cut DNA at a specific gene position, and the gene knockout is caused by the cell's own repair mechanism. Another commonly used technology is homologous recombination. By introducing a DNA fragment containing a target gene deletion or mutation, the homologous recombination mechanism of the microorganism is used to replace the target functional gene (replace one target gene, two or three), thereby achieving gene knockout. Regardless of which method is used, the key is to ensure that the target functional gene of the microorganism is inactivated after gene knockout, thereby verifying the role of the gene in the microbial function. After knockout, PCR amplification, gene sequencing and other means are used to verify whether the target gene is successfully knocked out, and finally a target functional gene targeted knockout experimental group is generated.

[0142] Furthermore, the functional expression evaluation calculation formula in step S34 is specifically:

[0143]

[0144] In the formula, G j is the target function expression level corresponding to the jth target function targeted knockout gene, j is the item index parameter of the target function targeted knockout gene, t0 is the start time of the experiment, t1 is the end time of the experiment, τ is the experimental time variable parameter, E j (τ) is the expression level of the target function knockout gene strain of the jth target function targeted knockout gene at the experimental time τ, α j is the expression adjustment parameter corresponding to the jth target function targeted knockout gene, A j (τ) is the enzyme activity level of the target function knockout gene of the jth target function targeted knockout gene at the experimental time τ, β j is the enzyme activity regulation parameter corresponding to the jth target function targeted knockout gene, K j is the maximum enzyme activity corresponding to the jth target function targeted knockout gene, K0 is the maximum enzyme activity corresponding to the functional gene of the normal strain without knockout, γ j is the functional expression weight coefficient corresponding to the jth target function targeted knockout gene, λ j is the enzyme activity influence coefficient corresponding to the j-th target function targeted knockout gene, and ξ is the correction coefficient of the target function expression level.

[0145] The present invention obtains a functional expression evaluation calculation formula by using a specific mathematical model and verifying it, which is used to perform functional expression evaluation calculations on each target functional knockout gene transformation strain. The functional expression evaluation calculation formula can provide a comprehensive functional expression level by integrating the gene expression and enzyme activity levels at different time points during the experiment. This quantitative method enables researchers to clearly identify and compare the functional performance of different target genes. The time variable τ in the formula allows for dynamic analysis of gene expression and enzyme activity. By integrating data at different time points, the trend of gene expression and enzyme activity over time can be revealed, thereby helping to understand the role of genes in specific biological processes. By introducing the parameter α j , β j , γ j and λ j, so that the formula takes into account the effects of different factors on gene expression and enzyme activity. In this way, by adjusting these parameters, the effects of various environmental or genetic factors on the function of the target gene can be evaluated, making the evaluation process more comprehensive and detailed. The calculation formula contains the maximum enzyme activity of the normal strain without knockout, which provides a benchmark for the comparison between different gene knockout groups and the normal control group. This comparative analysis can help researchers evaluate the specific effects of gene knockout on microbial function, and then guide subsequent experiments and research. In addition, by introducing a correction coefficient, researchers are allowed to correct systematic errors in experimental data. This flexibility makes expression evaluation more accurate and reliable, and can improve the credibility of the results. By comprehensively considering gene expression and enzyme activity, the formula can quantitatively evaluate the expression level of the target function, which is of great value for understanding gene function, optimizing metabolic engineering, and microbial fermentation processes. In summary, the formula fully considers the target function expression level G corresponding to the jth target function targeted knockout gene j , the item index parameter j of the target function targeted knockout gene, the experiment start time t0, the experiment end time t1, the experiment time variable parameter τ, the target function knockout gene strain expression E of the jth target function targeted knockout gene at the experiment time τ j (τ), the expression adjustment parameter α corresponding to the jth target function targeted knockout gene j , the target function knockout gene enzyme activity level A of the jth target function targeted knockout gene at the experimental time τ j (τ), the enzyme activity regulation parameter β corresponding to the jth target function targeted knockout gene j , the maximum enzyme activity K corresponding to the jth target function targeted knockout gene j , the maximum enzyme activity K0 corresponding to the functional gene of the normal strain without knockout, the functional expression weight coefficient γ corresponding to the jth target function targeted knockout gene j , the enzyme activity influence coefficient λ corresponding to the jth target function targeted knockout gene j , the correction coefficient ξ of the target function expression level, according to the target function expression level G corresponding to the jth target function targeted knockout gene j The correlation between the above parameters forms a functional relationship:

[0146]

[0147] This formula can realize the functional expression evaluation calculation process of each target function knockout gene transformation strain. At the same time, by introducing the correction coefficient ξ of the target function expression level, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the functional expression evaluation calculation formula.

[0148] Further, step S4 includes the following steps:

[0149] Step S41: performing target excellent function screening processing on the corresponding target function targeted knockout genes in the target function gene targeted knockout experimental group based on the target function expression level corresponding to each target function targeted knockout gene, and generating target excellent function genes of biological reagents;

[0150] In an embodiment of the present invention, the target function expression levels corresponding to each target function targeted knockout gene previously obtained by quantification are compared and judged according to a preset target function expression level threshold. If the target function expression level is greater than or equal to the preset target function expression level threshold, the corresponding target function targeted knockout gene in the target function gene targeted knockout experimental group is considered to be an excellent function gene and is screened out. If the target function expression level is less than the preset target function expression level threshold, it is eliminated, and finally the corresponding biological reagent target excellent function gene is screened and generated.

[0151] Step S42: further verifying the target function of the biological reagent target excellent function gene by combining proteomics and metabolomics technology to generate the biological reagent excellent function verification gene;

[0152] In an embodiment of the present invention, in order to further verify the excellent function of the target excellent functional gene in the biological reagent, the screened target excellent functional gene is first transferred into a wild-type microorganism or other cell system for recombinant expression, and the expressed protein is identified and quantified by proteomics technology, such as liquid chromatography-mass spectrometry (LC-MS / MS), to evaluate the protein expression amount of the gene knockout or recombinant gene and its distribution in the cell. Further, by metabolomics analysis, gas chromatography-mass spectrometry (GC-MS) or high performance liquid chromatography-mass spectrometry (HPLC-MS) is used to comprehensively detect the changes in metabolites, and the regulatory effect of the target gene on the microbial metabolic pathway is confirmed by comparing the metabolite spectrum of the recombinant strain into which the target gene is transferred with that of the control group. For example, by comparing the changes in polysaccharide synthesis, amino acid metabolism or other key metabolites, the functional characteristics of the target gene are evaluated. If the overexpression or knockout of the target gene can significantly improve the desired metabolite or enhance a certain biological function, the gene is further confirmed to be "successfully verified", and finally the generation of the biological reagent excellent function verification gene is verified.

[0153] Step S43: applying the biological reagent excellent function verification gene to the corresponding research and development process of the biological reagent to generate a target functional biological reagent product.

[0154] In an embodiment of the present invention, a verified excellent functional gene is introduced into a specific microbial host through genetic engineering technology, for example, a target gene is introduced into Escherichia coli, yeast or other industrial microorganisms for functional verification using a gene transformation technology (such as electrotransformation, heat shock method, etc.), and a screening system is used to determine a recombinant strain that can stably express the target functional gene in the transformed strain. On this basis, fermentation culture is carried out, and culture conditions such as temperature, pH value, dissolved oxygen level, etc. are optimized to ensure that the recombinant strain can maintain a stable expression level and efficient target function performance in large-scale production. Through downstream extraction and purification techniques, such as affinity chromatography, ion exchange chromatography or gel filtration, biological molecules or biological products with target functions are extracted from the culture medium. The obtained biological reagent product is subjected to quality control and functional testing to confirm that it meets the application standards of the biological reagent, and a target biological reagent product that meets the expected function is generated. For example, if the target function is the production of a certain enzyme, the final biological reagent product will contain an efficient enzyme, and a target functional biological reagent product is finally generated.

[0155] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for developing biological reagents based on screening of microbial functional genes, characterized in that: The following steps are involved: Step S1: obtaining a microbial sample, and performing genome sequencing and extraction processing on the microbial sample to obtain genome data of the microbial sample; performing gene transcription analysis on the genome data of the microbial sample to generate transcriptome data of the microbial sample; Step S2: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions, and performing target function gene screening processing on microbial sample genome data and microbial sample transcriptome data based on the target functions of biological reagent research and development to generate biological reagent target function genome data; Step S3: performing gene targeted knockout processing on the microbial target functional genes corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group; performing gene cloning on the target functional gene targeted knockout experimental group and transferring it into a preset host strain for functional expression evaluation and analysis to obtain the target functional expression level corresponding to each target functional targeted knockout gene; Step S4: Based on the target function expression level corresponding to each target function targeted knockout gene, each target function targeted knockout gene in the target function gene targeted knockout experimental group is screened and verified for excellent function to generate a biological reagent excellent function verification gene; the biological reagent excellent function verification gene is applied to the corresponding research and development process of the biological reagent to generate a target function biological reagent product.

2. The method for developing biological reagents based on screening of microbial functional genes according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining original microbial samples from soil, ocean waters and extreme environment ecosystems; Step S12: separating, purifying and culturing the original microbial sample to generate a microbial library; Step S13: performing 16S rRNA gene identification and screening on the corresponding microbial samples in the microbial library to generate a representative gene sample set for preliminary screening of microorganisms; Step S14: performing genome sequencing and extraction processing on the representative gene sample set of the initial screening of microorganisms to obtain genome data of the microorganism samples; Step S15: performing gene transcription analysis on the microbial sample genome data to generate microbial sample transcriptome data.

3. The method for developing biological reagents based on screening of microbial functional genes according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: pre-screening the sample diversity of the corresponding microbial samples in the microbial library to obtain a microbial diversity pre-selected sample set; Step S132: extracting sample DNA from the microbial samples corresponding to the microbial diversity pre-selected sample set using DNA extraction technology to obtain a DNA sequence corresponding to each microbial pre-selected sample; Step S133: performing a gene amplification reaction on the DNA sequence corresponding to each microbial preselected sample according to the preset 16S rRNA gene primers to obtain a 16S rRNA gene amplification product sequence fragment corresponding to each microbial preselected sample; Step S134: Based on the sequence fragments of the 16S rRNA gene amplification products corresponding to each microbial pre-selected sample, sequence comparison, identification and screening are performed on the corresponding microbial samples in the microbial diversity pre-selected sample set to generate a representative gene sample set for microbial preliminary screening.

4. The method for developing biological reagents based on screening of microbial functional genes according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: obtaining the corresponding RNA transcription start site and RNA intron splicing site from each microbial sample genome in the microbial sample genome data, and extracting the transcription region of the microbial sample RNA in the corresponding microbial sample genome based on the RNA transcription start site and RNA intron splicing site to obtain the microbial sample genome RNA transcription candidate region; Step S152: performing a cDNA reverse transcription reaction on the candidate region of the microbial sample genomic RNA transcription to obtain a microbial sample reverse transcription cDNA library; Step S153: performing tailing modification and library construction on the microbial sample reverse transcription cDNA library to add adapter sequences and tags to obtain a microbial sample transcriptome library construction product; Step S154: performing high-throughput transcription sequencing processing on the microbial sample transcriptome library construction product to generate microbial sample transcriptome data.

5. The method for developing biological reagents based on screening of microbial functional genes according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining target functions of biological reagent research and development, including enzyme activity, antibiotic production, and pollutant degradation target functions; Step S22: performing metabolic network analysis on each microbial sample functional gene corresponding to the microbial sample genome data and the microbial sample transcriptome data to generate a microbial metabolic network corresponding to each microbial sample functional gene; Step S23: Obtain the corresponding microbial metabolic flux, microbial raw material utilization rate and metabolic byproduct generation efficiency through the microbial metabolic network corresponding to each microbial sample functional gene, and perform product synthesis evaluation analysis on the corresponding microbial sample functional gene based on the microbial raw material utilization rate and metabolic byproduct generation efficiency to generate the metabolic target product synthesis rate corresponding to each microbial sample functional gene; Step S24: Based on the microbial metabolic flux and the metabolic target product synthesis rate corresponding to each microbial sample functional gene, a target functional gene matching calculation formula is used to perform a functional matching calculation on the target function of biological reagent research and development to obtain a microbial metabolic target function matching value corresponding to each microbial sample functional gene; Step S25: Based on the microbial metabolism target function matching value corresponding to each microbial sample functional gene, target functional gene screening processing is performed on the microbial sample genome data and the corresponding microbial sample transcriptome data to generate biological reagent target functional genome data.

6. The method for developing biological reagents based on screening of microbial functional genes according to claim 5, characterized in that: The target functional gene matching calculation formula in step S24 is specifically: In the formula, F i is the microbial metabolic target function matching value corresponding to the i-th microbial sample functional gene, N is the total number of microbial sample functional genes, i is the item index parameter of the microbial sample functional gene, T is the metabolic process observation time period, M is the total number of metabolic pathways, k is the item index parameter of the metabolic pathway, t is the time variable parameter, V ik (t) is the microbial metabolic flux of the functional gene of the i-th microbial sample at the corresponding time t in metabolic pathway k, Y ik (t) is the synthesis rate of the metabolic target product of the functional gene of the i-th microbial sample at the corresponding time t in the metabolic pathway k, K ik is the metabolic efficiency coefficient of the functional gene of the i-th microbial sample in the metabolic pathway k, S is the microbial synthesis contribution coefficient corresponding to the target function of biological reagent research and development, Q is the synthesis rate of the target metabolite corresponding to the target function of biological reagent research and development, R i (t) is the metabolic consumption efficiency of the functional gene of the i-th microbial sample at time t, v i (t) is the metabolic production rate of the functional gene of the i-th microbial sample at time t, and η is the correction coefficient of the matching value of the microbial metabolic target function.

7. The method for developing biological reagents based on screening of microbial functional genes according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing gene targeted knockout processing on the microbial target functional gene corresponding to the target functional genome data of the biological reagent to generate a target functional gene targeted knockout experimental group; Step S32: performing gene cloning processing on the target functional gene targeted knockout experimental group to obtain a target functional gene knockout clone group; Step S33: each target function knockout gene in the target function gene knockout clone group is transferred into a preset host strain for functional transformation and expression processing, so as to generate each target function knockout gene transformation strain; Step S34: Cultivate each target function knockout gene transformation strain and obtain the corresponding target function knockout gene strain expression level and target function knockout gene enzyme activity level, and perform functional expression evaluation calculation on each target function knockout gene transformation strain based on the target function knockout gene strain expression level and target function knockout gene enzyme activity level using the functional expression evaluation calculation formula to obtain the target function expression level corresponding to each target function targeted knockout gene.

8. The method for developing biological reagents based on screening of microbial functional genes according to claim 7, characterized in that: Step S31 includes the following steps: Performing target function annotation processing on the microbial target function genes corresponding to the target function genome data of the biological reagent to obtain the annotated region sequence of the target function gene of the biological reagent; Design gene targeted knockout primers according to the annotated region sequence of the target functional gene of the biological reagent to generate specific targeted knockout primers of the target functional gene of the biological reagent; Based on the biological reagent target function gene-specific targeted knockout primers, the corresponding microbial target function genes in the biological reagent target function genomic data are subjected to gene targeted knockout processing to generate a target function gene targeted knockout experimental group.

9. The method for developing biological reagents based on screening of microbial functional genes according to claim 7, characterized in that: The functional expression evaluation calculation formula in step S34 is specifically: In the formula, G j is the target function expression level corresponding to the jth target function targeted knockout gene, j is the item index parameter of the target function targeted knockout gene, t0 is the start time of the experiment, t1 is the end time of the experiment, τ is the experimental time variable parameter, E j (τ) is the expression level of the target function knockout gene strain of the jth target function targeted knockout gene at the experimental time τ, α j is the expression adjustment parameter corresponding to the jth target function targeted knockout gene, A j (τ) is the enzyme activity level of the target function knockout gene of the jth target function targeted knockout gene at the experimental time τ, β j is the enzyme activity regulation parameter corresponding to the jth target function targeted knockout gene, K j is the maximum enzyme activity corresponding to the jth target function targeted knockout gene, K0 is the maximum enzyme activity corresponding to the functional gene of the normal strain without knockout, γ j is the functional expression weight coefficient corresponding to the jth target function targeted knockout gene, λ j is the enzyme activity influence coefficient corresponding to the j-th target function targeted knockout gene, and ξ is the correction coefficient of the target function expression level.

10. The method for developing biological reagents based on screening of microbial functional genes according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing target excellent function screening processing on the corresponding target function targeted knockout genes in the target function gene targeted knockout experimental group based on the target function expression level corresponding to each target function targeted knockout gene, and generating target excellent function genes of biological reagents; Step S42: further verifying the target function of the biological reagent target excellent function gene by combining proteomics and metabolomics technology to generate the biological reagent excellent function verification gene; Step S43: applying the biological reagent excellent function verification gene to the corresponding research and development process of the biological reagent to generate a target functional biological reagent product.

Images