Method and system for predicting metabolic capability of uncultured microorganism individual
By extracting the assembled genome of uncultured microorganisms from metagenomic sequences and establishing a metabolic network model, the problem that existing technology cannot predict the metabolic ability of uncultured microorganisms is solved, and effective prediction of the metabolic ability of uncultured microorganisms is achieved.
Patent Information
- Application Number
- CN202411169973.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-26
- Publication Date
- 2025-01-24
AI Technical Summary
Existing metabolic network models cannot predict individual metabolic abilities of uncultured microorganisms.
By obtaining the metagenomic sequence of the analyte, extract the assembled genome of uncultured microorganisms, conduct phylogenetic analysis, and build a metabolic network model based on the analysis results to predict the metabolic ability of microorganisms.
The forecast of individual metabolic capabilities that do not cultivate microorganisms have been implemented, and gaps that cannot predict this field cannot be predicted in this field.
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Figure CN119360965A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of bioinformatics, and in particular to a method and system for predicting the metabolic capacity of uncultured microorganism individuals. Background Art
[0002] In recent years, there have been more and more reports on analyzing sample metagenomes and studying microbial metabolic network models. As the computing power of computers continues to improve, the number of metabolic network models is increasing day by day, and they are constantly developing from the individual level to the community level, gradually realizing the prediction of individual metabolic capacity through genome-scale metabolic network models. However, existing metabolic network models cannot predict the individual metabolic capacity of uncultured microorganisms.
[0003] In view of this, this application is hereby filed. Summary of the invention
[0004] The technical problem to be solved by the present invention is that the existing metabolic network model is still unable to predict the individual metabolic capacity of uncultured microorganisms.
[0005] In order to solve the above technical problems, the present invention is implemented by the following technical solutions:
[0006] In a first aspect, a method for predicting the metabolic capacity of individual uncultured microorganisms is provided, comprising the following steps: obtaining a metagenomic sequence of an analyte; extracting an uncultured microorganism assembled genome from the metagenomic sequence; performing a phylogenetic analysis on the uncultured microorganism assembled genome to obtain an analysis result; establishing a metabolic network model based on the analysis result; and using the metabolic network model to predict the individual metabolic capacity of the uncultured microorganism assembled genome to obtain a prediction result.
[0007] Furthermore, after performing phylogenetic analysis on the uncultured microbial assembled genome, the method further includes the following steps: annotating the uncultured microbial assembled genome.
[0008] Furthermore, extracting an assembled genome of uncultured microorganisms from the metagenome sequence comprises the following steps: performing read length sequencing, read length evaluation and read length trimming on the metagenome sequence to obtain a clean metagenome sequence; assembling the clean metagenome sequence to obtain an assembled metagenome sequence; binning the assembled metagenome sequence to obtain multiple categories of metagenome sequences; performing genome quality evaluation on each category of metagenome sequences to obtain evaluation results; and extracting the assembled genome of uncultured microorganisms from multiple categories of metagenome sequences according to the evaluation results.
[0009] Furthermore, the contamination degree of the extracted uncultured microorganism assembled genome is 5.12%, and the integrity of the extracted uncultured microorganism assembled genome is 100%.
[0010] In a second aspect, a system for predicting the metabolic capacity of individual uncultured microorganisms is provided, comprising: a data acquisition module for acquiring the metagenomic sequence of an analyte; a data processing module for extracting an uncultured microorganism assembled genome from the metagenomic sequence; a data analysis module for performing phylogenetic analysis on the uncultured microorganism assembled genome to obtain an analysis result; a model building module for establishing a metabolic network model based on the analysis result; and a result prediction module for predicting the individual metabolic capacity of the uncultured microorganism assembled genome using the metabolic network model to obtain a prediction result.
[0011] Furthermore, it also includes: an annotation adding module, which is used to annotate the uncultured microbial assembled genome.
[0012] Furthermore, the data processing module includes: a data cleaning unit, which is used to perform read length sequencing, read length evaluation and read length pruning on the metagenome sequence to obtain a clean metagenome sequence; a data assembly unit, which is used to assemble the clean metagenome sequence to obtain an assembled metagenome sequence; a data classification unit, which is used to bin the assembled metagenome sequence to obtain multiple types of metagenome sequences; a data evaluation unit, which is used to perform genome quality evaluation on each type of metagenome sequence to obtain an evaluation result; and a data extraction unit, which is used to extract the uncultured microbial assembled genome from multiple types of metagenome sequences according to the evaluation result.
[0013] Compared with the prior art, the present invention has the following advantages and beneficial effects: taking sample microorganisms as the research object, extracting the assembled genome of uncultured microorganisms from the metagenome sequence, and establishing a metabolic network model based on the phylogenetic analysis results of the assembled genome of uncultured microorganisms, and using the metabolic network model to realize the study of the individual metabolic capacity of uncultured microorganisms. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0015] Figure 1 A flow chart of a method for predicting the metabolic capacity of uncultured microorganisms provided in an embodiment of the present invention;
[0016] Figure 2 A read length evaluation graph of the vinegar-fermented mash metagenome provided in an embodiment of the present invention;
[0017] Figure 3 A read length evaluation graph of the vinegar-fermented mash metagenome provided in an embodiment of the present invention;
[0018] Figure 4 A statistical analysis diagram of the vinegar-fermented mash metagenomic assembly provided by an embodiment of the present invention;
[0019] Figure 5 An evaluation diagram of the assembled sequence of the uncultured microorganism Limosilactobacillus metagenome provided in an embodiment of the present invention;
[0020] Figure 6 A diagram of the lactic acid and acetic acid metabolic pathways of the uncultured microorganism Limosilactobacillus using glucose as a carbon source provided in an embodiment of the present invention;
[0021] Figure 7 A diagram of the lactic acid and acetic acid metabolic pathways of the uncultured microorganism Limosilactobacillus using fructose as a carbon source provided in an embodiment of the present invention;
[0022] Figure 8 A diagram of the metabolic pathways of glutamate and aspartate of the uncultured microorganism Limosilactobacillus provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.
[0024] Embodiment: In view of the problem that it is difficult to predict the individual metabolic capacity of uncultured microorganisms in the prior art, the first aspect of this embodiment proposes a method for predicting the individual metabolic capacity of uncultured microorganisms.
[0025] In order to fully explain the specific implementation steps of the method, this embodiment takes the uncultured microorganism Limosilactobacillus in the fermentation process of vinegar mash as an example to illustrate the prediction process of the individual metabolic capacity of the uncultured microorganism Limosilactobacillus in vinegar mash. Of course, the method is not limited to predicting the individual metabolic capacity of the uncultured microorganism Limosilactobacillus in vinegar mash.
[0026] Fermentation is a complex biochemical process, in which the interaction and metabolic activities of various microorganisms play a vital role in fermentation. There are a large number of uncultured microorganisms in traditional fermentation, which have a great impact on the fermentation quality. Some uncultured microorganisms may produce unique metabolites, which will have a significant impact on the flavor, aroma and texture of fermented foods; some special aroma components are often produced by uncultured microorganisms, and these aroma components are difficult to produce alone by existing cultured strains; there may be complex mutualistic or inhibitory relationships between these uncultured microorganisms and cultured strains, which have a significant impact on the balance of the entire microbial community and the stability of the fermentation process. Limosilactobacillus occupies a large proportion in the fermentation process of vinegar mash, and can produce a series of volatile and non-volatile flavor compounds that have an important impact on the flavor, aroma and sensory properties of vinegar. It can also form complex interactive relationships with other microorganisms such as acetic acid bacteria. These interactive relationships help maintain the balance and dynamic stability of the microbial community. Although Limosilactobacillus is very common in vinegar mash, it is difficult to predict its related metabolic pathways and functions based on existing technologies.
[0027] This embodiment provides a method for predicting the metabolic capacity of uncultured microorganisms, which uses metagenomic assembly and analysis, constructs a metabolic network model, and uses sample microorganisms as research, using metagenomic sequencing, microbial genome assembly, and community-level metabolic network model construction to predict the reaction and metabolism of uncultured microorganisms. Specifically, this method includes Figure 1 The following implementation steps are shown:
[0028] Step 1: Obtain the metagenomic sequence of the analyte.
[0029] This method uses the genome of the population in the sample to be analyzed as the research object, and uses functional gene screening and / or sequencing analysis as the research means to obtain the microbial diversity, population structure, functional activity, mutual cooperation relationship and relationship with the environment in the sample to be analyzed. For the uncultured microorganism Limosilactobacillus in vinegar mash, it is necessary to detect the DNA sequence information of the vinegar mash metagenome, and perform analysis, modeling and prediction based on the metagenome sequence information.
[0030] In this embodiment, the obtained vinegar mash metagenome contains 3 parallel sub-samples, and the average total read length measured for each sub-sample is 74371257, the average total base length is 11230059857bp, the GC content is 43.91%, and the average Q30 value is 96.57%.
[0031] Step 2: Extract uncultured microbial assembled genomes from metagenomic sequences.
[0032] The following steps are involved:
[0033] Step 2.1: Perform read length sequencing, read length evaluation and read length trimming on the metagenomic sequence to obtain a clean metagenomic sequence.
[0034] First, the metagenome sequence to be analyzed is imported into the KBase web tool to merge the metagenome reads. Specifically, the three parallel subsamples described in step 1 are imported into the KBase web tool and merged into the same sequence library.
[0035] Then, FastQC was used to evaluate the read length of the macrogenomic sequence. Specifically, FastQC was used to analyze and evaluate the merged sequence library. In the evaluation results, the GC content was 44%, and the quality evaluation score of each sequencing base was 39. Further, the results of the vinegar mash metagenome read length evaluation refer to Figure 2 , base sequencing quality report reference Figure 3 .
[0036] Finally, Trimmomatic was used to remove all read adapters, contaminants, and low-quality reads in the merged reads to improve the quality and accuracy of the metagenomic sequences and obtain clean metagenomic sequences.
[0037] Step 2.2: Assemble the clean metagenome sequence to obtain the assembled metagenome sequence.
[0038] After obtaining the clean metagenome sequence, it needs to be assembled. In this step, metaSPAdes is used to assemble the clean metagenome sequence to obtain the assembled metagenome sequence. Specifically, the clean metagenome sequence is assembled into contigs. The statistical analysis results of the vinegar mash metagenome assembly are referenced. Figure 4 , among which N50 is 8331, GC content is 47.45%, and L50 is 1082.
[0039] Step 2.3: Bin the assembled metagenomic sequences to obtain multiple types of metagenomic sequences.
[0040] The metagenome sequence containing the population genome was obtained through step 1. In order to predict the metabolic capacity of the uncultured microorganism Limosilactobacillus in vinegar mash, it is necessary to bin the metagenome sequence, separate different microbial sequences from the metagenome sequence, and cluster sequences from the same species. In this step, maxbin2 is used to bin the assembled contigs into 30 metagenome assembled genomes.
[0041] Step 2.4: Perform genome quality assessment on each type of metagenomic sequence to obtain the assessment results.
[0042] For each type of metagenome sequence after binning, CheckM is used to perform quality assessment, and the assessment results include indicators such as the contamination and integrity of the metagenome sequence. Figure 5 .
[0043] Step 2.5: Extract uncultured microbial assembled genomes from multiple types of metagenomic sequences based on the evaluation results.
[0044] In this example, BinUtil was used to extract an assembled genome of an uncultured microorganism Limosilactobacillus with a contamination degree of 5.12%, a completeness of 100%, and a size of 1909603 bp.
[0045] Step 3: Perform phylogenetic analysis on the uncultured microbial assembled genome, and annotate the uncultured microbial assembled genome to obtain analysis results.
[0046] Specifically, the extracted uncultured microorganism Limosilactobacillus assembled genome was classified by taxonomy using GTDB-Tk software. The classification of the genome was obtained as follows:
[0047] d_Bacteria; p_Bacillota; c_Bacilli; o_Lactobacillales; f_Lactobacillaceae; g_Limosilactobacillus. Through phylogenetic analysis, it was found that the species most closely related to the uncultured microorganism Limosilactobacillus was Limosilactobacillus merdipullorum.
[0048] The assembled genome of the uncultured microorganism Limosilactobacillus was imported into the RAST annotation software to obtain a Genbank file containing sequence and annotation information, and the Genbank file was sorted for taxonomy and annotation information.
[0049] Step 4: Establish a metabolic network model based on the analysis results.
[0050] The sorted Genbank files were imported into the modeling tool pathway tools to automatically construct a metabolic network model. According to the metabolic capacity of the metabolic network model, the basic information of the metabolic network of the uncultured microorganism Limosilactobacillus was obtained.
[0051] Step 5: Use the metabolic network model to predict the individual metabolic capacity of the assembled genome of uncultured microorganisms and obtain the prediction results.
[0052] The systems biology markup language file of the assembled genome metabolic network model of the uncultured microorganism Limosilactobacillus was extracted and imported into the metage2metabo software to predict individual metabolic capacity.
[0053] Results Reference Figures 6 to 8 And Tables 1 to 2, wherein Table 1 is a statistical table of enzymes that play a role in multiple enzyme reactions in the uncultured microorganism Limosilactobacillus, and Table 2 is a statistical table of predicted metabolites of the uncultured microorganism Limosilactobacillus. The results show that the genome contains 1607 metabolism-related genes, 176 metabolic pathways, 1046 enzyme reactions, 557 enzymes, 1997 proteins, 822 compounds and 980 chemical reactions. Among the predicted compounds, the main flavor substances of vinegar, lactic acid, acetic acid, glutamic acid, alanine, aspartic acid, and gamma-aminobutyric acid that improves the activity of glucose phospholipase, promotes energy metabolism in the animal brain, activates cerebral blood flow, increases oxygen supply, and restores brain cell function, among which lactic acid, acetic acid, glutamic acid and aspartic acid can obtain complete metabolic pathways in the predicted 176 metabolic pathways. The software metage2metabo predicts a total of 110 individual metabolites of the genome assembled by the metagenomic assembly.
[0054]
[0055]
[0056] Table 1
[0057]
[0058]
[0059]
[0060] Corresponding to the method described in the first aspect above, the second aspect of this embodiment proposes a system for predicting the metabolic capacity of individual uncultured microorganisms, including: a data acquisition module, used to obtain the metagenomic sequence of the analyte; a data processing module, used to extract the uncultured microbial assembled genome from the metagenomic sequence; a data analysis module, used to perform phylogenetic analysis on the uncultured microbial assembled genome to obtain analysis results; a model building module, used to establish a metabolic network model based on the analysis results; and a result prediction module, used to use the metabolic network model to predict the individual metabolic capacity of the uncultured microbial assembled genome to obtain a prediction result.
[0061] Furthermore, it also includes: an annotation adding module, which is used to annotate the uncultured microbial assembled genome.
[0062] Furthermore, the data processing module includes: a data cleaning unit, which is used to perform read length sequencing, read length evaluation and read length pruning on the metagenome sequence to obtain a clean metagenome sequence; a data assembly unit, which is used to assemble the clean metagenome sequence to obtain an assembled metagenome sequence; a data classification unit, which is used to bin the assembled metagenome sequence to obtain multiple types of metagenome sequences; a data evaluation unit, which is used to perform genome quality evaluation on each type of metagenome sequence to obtain an evaluation result; and a data extraction unit, which is used to extract the uncultured microbial assembled genome from multiple types of metagenome sequences according to the evaluation result.
[0063] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for predicting the metabolic capacity of uncultured microorganisms, characterized in that: The following steps are involved: Obtaining a metagenomic sequence of an analyte; extracting an uncultured microbial assembled genome from the metagenomic sequence; Performing phylogenetic analysis on the assembled genome of the uncultured microorganism to obtain analysis results; Establishing a metabolic network model according to the analysis results; The metabolic network model is used to predict the individual metabolic capacity of the uncultured microbial assembled genome to obtain a prediction result.
2. A method for predicting the metabolic capacity of uncultured microorganisms according to claim 1, characterized in that: After performing phylogenetic analysis on the uncultured microorganism assembled genome, the method further includes the following steps: annotating the uncultured microorganism assembled genome.
3. A method for predicting the metabolic capacity of uncultured microorganisms according to claim 1 or 2, characterized in that: Extracting an uncultured microbial assembly genome from the metagenome sequence comprises the following steps: Performing read length sequencing, read length evaluation, and read length trimming on the metagenome sequence to obtain a clean metagenome sequence; Assembling the clean metagenome sequence to obtain an assembled metagenome sequence; Binning the assembled metagenome sequences to obtain multiple types of metagenome sequences; Perform genome quality assessment on each type of metagenomic sequence to obtain an assessment result; The uncultured microbial assembled genome is extracted from multiple types of metagenome sequences according to the evaluation results.
4. A method for predicting the metabolic capacity of uncultured microorganisms according to claim 3, characterized in that: The contamination degree of the extracted uncultured microorganism assembled genome is 5.12%, and the integrity of the extracted uncultured microorganism assembled genome is 100%.
5. A system for predicting the metabolic capacity of uncultured microorganisms, characterized in that: include: A data acquisition module, used to obtain the metagenomic sequence of the object to be analyzed; A data processing module, used for extracting an uncultured microbial assembled genome from the metagenome sequence; A data analysis module, used to perform phylogenetic analysis on the assembled genome of the uncultured microorganism to obtain analysis results; A model building module, used to build a metabolic network model according to the analysis results; The result prediction module is used to predict the individual metabolic capacity of the uncultured microbial assembly genome using the metabolic network model to obtain a prediction result.
6. A system for predicting the metabolic capacity of uncultured microorganisms according to claim 5, characterized in that: Also includes: The annotation adding module is used to annotate the uncultured microorganism assembled genome.
7. A system for predicting the metabolic capacity of uncultured microorganisms according to claim 5 or 6, characterized in that: The data processing module comprises: A data cleaning unit, used for performing read length sequencing, read length evaluation and read length trimming on the metagenome sequence to obtain a clean metagenome sequence; A data assembly unit, used for assembling the clean metagenome sequence to obtain an assembled metagenome sequence; A data classification unit, used for binning the assembled metagenome sequences to obtain multiple types of metagenome sequences; A data evaluation unit, used to evaluate the genome quality of each type of metagenome sequence and obtain an evaluation result; A data extraction unit is used to extract the uncultured microbial assembled genome from multiple types of metagenome sequences according to the evaluation results.
8. A method for predicting the metabolic capacity of uncultured microorganisms according to claim 7, characterized in that: The contamination degree of the extracted uncultured microorganism assembled genome is 5.12%, and the integrity of the extracted uncultured microorganism assembled genome is 100%.