Conglomerate desert soil microbial diversity analysis method based on high-throughput sequencing
Through high-throughput sequencing technology and multi-dimensional data analysis, the problem of insufficient sampling strategies and data analysis in the study of gravel soil microbial microbial communities was solved, and accurate spatiotemporal dynamic characterization and ecological correlation analysis of gravel soil microbial communities were realized, which improved DNA extraction efficiency and data analysis depth.
Patent Information
- Application Number
- CN202510541647.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-28
- Publication Date
- 2025-08-01
AI Technical Summary
The research on gravel soil microbials in the prior art has problems such as limitations in sampling strategies and incomplete data analysis, and it is difficult to capture the seasonal dynamic characteristics of microbial communities and the differentiated analysis of critical periods.
High-throughput sequencing technology is used to extract DNA, PCR amplification and library construction through single-month time-interval sampling and protease K cleavage. Combined with multi-dimensional data analysis strategies, the sampling cycle and data processing flow are optimized, and OTU clustering and differential species analysis are performed.
Accurate spatial and temporal dynamic characterization and ecological correlation analysis of microbial communities in gravel desert soils have been realized, which has improved DNA extraction efficiency and data analysis depth, and has supported the mining of microbial resources and research on environmental response mechanisms in gravel desert ecosystems.
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Figure CN120400378A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of biological genetic engineering, and specifically discloses a method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing. Background Technique
[0002] Gravel desert is a type of desert mainly covered by gravel on the surface, commonly found in arid or semi-arid regions. The surface of this desert type is mainly composed of gravel and sand grains, lacking fine-grained substances such as clay, with poor water retention capacity and poor nutrients. The vegetation coverage rate in this type of desert area is low, with only a small number of drought-tolerant plants. The soil microorganisms in the gravel desert are an important part of this ecosystem. These microorganisms adapt to the environment through biofilm formation, water storage, and metabolic regulation, and play an important role in maintaining the ecological environment, mainly including participating in nutrient cycling, improving soil structure, enhancing the water retention capacity of the soil, and improving the disease resistance of plants by inhibiting plant pathogens, etc.
[0003] At present, the research methods of soil microorganisms have the following deficiencies: 1. The sampling strategy is limited. Most studies use non-periodic or non-equidistant sampling, resulting in poor comparability of time series data and difficulty in capturing the seasonal dynamic characteristics of microbial communities.
[0004] 2. The data analysis is not comprehensive. The existing processes have insufficient standardization for chimera filtering, sequence rarefaction, and diversity analysis, and mostly rely on single database annotation, affecting the classification accuracy; at the same time, there is a lack of differential analysis of microbial communities in the critical periods of the plant growth season.
[0005] Therefore, there is an urgent need to establish a method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing. Summary of the Invention
[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing. Through the following solutions, the problems of limited sampling strategy and incomplete data analysis mentioned in the above background technique are solved.
[0007] To achieve the above object, the present invention provides the following technical solution: A method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing. The method for analyzing the microbial diversity of gravel desert soil includes: sample collection and processing, data processing and analysis, specifically including the following steps: S1. Taking gravel desert soil samples at equal intervals with a single-month time period as the cycle, and removing impurities to obtain equal amounts of pretreated soil;
[0008] S2. Obtaining a soil suspension by high-throughput grinding of the pretreated soil, and obtaining DNA by protease k digestion method for the soil suspension;
[0009] S3. Perform PCR amplification on the obtained DNA and construct a sequencing library;
[0010] S4. Perform high-throughput sequencing on the constructed sequencing library on a high-throughput sequencing platform. Remove low-quality sequences and sequence adapters through quality control and splice them. Perform operational taxonomic unit (OTU) clustering on the spliced sequences and remove chimeras. After rarefying to the minimum sample sequence, obtain the OTU sequence set;
[0011] S5. Compare the sequence set with the reference sequences in the public database. Classify according to the comparison results and perform analysis on the data in different directions;
[0012] S6. Obtain the composition ratio of microorganisms in the gravel desert soil at different times based on the data analysis, and analyze the relationship between the composition ratio of gravel desert soil microorganisms and the changes in the ecological environment.
[0013] Furthermore, in S1, during the critical period of the plant growth season, sample the gravel desert soil at the sampling site in a monthly cycle, mix it with 5 ml of sterile water, and remove large particle impurities through grinding and filtration to obtain pretreated soil.
[0014] Furthermore, in S2, take 1 ml of pretreated soil, add 99 ml of sterile water and stir evenly to obtain a soil suspension. Perform 10 -6 gradient dilutions on the soil suspension and complete sample collection. Grind the collected samples evenly in a high-throughput tissue grinder and extract DNA using the proteinase K lysis method.
[0015] Furthermore, in S3, use polymerase chain reaction to amplify the target region of the 16S rRNA gene for the obtained DNA, fragment the amplification product, and add adapters to prepare a sequencing library.
[0016] Furthermore, in S4, sequence the constructed sequencing library on a high-throughput sequencing platform. Remove low-quality sequences and sequence adapters through quality control, then splice the remaining sequences to obtain the complete 16S rRNA amplicon sequence. Then perform operational taxonomic unit (OTU) clustering on the amplicon sequence according to 97% similarity and remove chimeras. After rarefying to the minimum sample sequence, obtain the OTU sequence set.
[0017] Furthermore, in S5, classify the OTU sequence set at different levels and perform data analysis on the diversity, compositional differences, and differential species of the gravel desert soil microbial community during the critical period of the plant growth season in three directions.
[0018] Combining all the above technical solutions, the positive effects of the present invention are:
[0019] 1. The present invention optimizes the sampling period, improves the DNA extraction technology, and integrates multi-dimensional data analysis strategies to accurately characterize the spatio-temporal dynamics of microbial communities and their ecological associations.
[0020] 2. The present invention improves the DNA extraction efficiency through periodic sampling, high-throughput grinding combined with proteinase K lysis technology, and realizes in-depth analysis of differential species based on PCoA visualization and LEfSe algorithm, providing technical support for the excavation of microbial resources and the study of environmental response mechanisms in the gravel desert ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 It is a diagram of the specific implementation steps of the present invention.
[0022] Figure 2 It is the data of α-diversity analysis of microbial communities in gravel desert soil at different times.
[0023] Figure 3 It is the data of PCoA analysis and sample hierarchical clustering analysis of microbial communities in gravel desert soil at different times.
[0024] Figure 4 It is the data of the composition of microbial communities in gravel desert soil.
[0025] Figure 5 It is the data of the number of common and unique microorganisms in gravel desert soil at different times.
[0026] Figure 6 It is the data of significantly different microorganisms in gravel desert soil at different times, where A represents the Class level; B represents the genus level.
[0027] Figure 7 It is the LDA value of different differential species in the LEfSe multi-level species difference discrimination analysis. The larger the LDA score, the greater the impact of species abundance on the differential effect. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0029] See Figure 1 As shown, the present invention proposes a method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing, which is characterized by including the following steps: S1. Taking gravel desert soil samples at a single-month time interval and removing impurities to obtain an equal amount of pretreated soil.
[0030] In the above step S1, the specific implementation method is as follows: the sampling location coordinates are (89.082322E, 45.246486N), and 10 g of gravel desert soil sample is collected from the gravel desert soil 10 cm below the surface of the sampling site using a sterile sampler in May, June, and July of each year. The gravel desert soil sample is mixed with 5 ml of sterile water under a sterile environment, ground into a fine slurry, and filtered with a 2 mm sieve to remove large particles of impurities to obtain pretreated soil;
[0031] S2. The pretreated soil is subjected to high-throughput grinding to obtain a soil suspension, and the soil suspension is decomposed by proteinase K to obtain DNA.
[0032] In the above step S2, the specific implementation method is as follows: 1 ml of pre-treated soil is added to 99 ml of sterile water and stirred evenly to obtain a soil suspension; the soil suspension is subjected to 10 -6 After gradient dilution and sample collection, the collected samples were ground evenly in a high-throughput tissue grinder. The lysis temperature was set to 56°C ± 1°C and the lysis time was 45 min. Proteinase K lysis method was used to destroy the cell protein structure, and DNA was purified and extracted.
[0033] S3. Perform PCR amplification on the obtained DNA and construct a sequencing library.
[0034] In the above step S3, the specific implementation method is: constructing a bacterial 16SrRNA hypervariable region V3-V4 region sequencing library by a two-step PCR method.
[0035] S4. The constructed sequencing library is sequenced on a high-throughput sequencing platform. Low-quality sequences and sequence adapters are removed through quality control and spliced. The spliced sequences are subjected to operational taxonomic unit classification (OTU) clustering and chimeras are removed. The OTU sequence set is obtained after leveling according to the minimum sample sequence.
[0036] In the above-mentioned step S4, the specific implementation method is as follows: the constructed library is subjected to high-throughput sequencing on a high-throughput sequencing platform, low-quality sequences and sequence adapters are removed by quality control, and the remaining sequences are spliced to obtain a complete 16SrRNA amplicon sequence. The quality-controlled spliced sequences are then subjected to operational taxonomic unit (OTU) clustering based on a similarity of 97% and chimeras are removed. The sequence is leveled according to the minimum sample sequence, and a total of 9233 OTU sequences are obtained.
[0037] S5. Compare the sequence set with the reference sequences in the public database, classify them according to the comparison results, and analyze the data in different directions.
[0038] In the specific implementation of step S5 above: The obtained OTU sequence set is compared with the reference data in the public database, and the comparison results are classified at different levels. A total of 38 phyla, 107 classes, 235 orders, 366 families, and 683 genera are obtained. Data analysis is carried out in three directions: the diversity, compositional differences, and differential species of the microbial community in the gravel desert soil during the critical period of the plant growth season based on the obtained data.
[0039] In step S5 above, for the analysis of the diversity of the microbial community in the gravel desert soil during the critical period of the plant growth season based on the obtained data, it should be noted that during the critical period of the plant growth season, the overall change trend of the sampling site environment is that the temperature rises month by month, and the precipitation and vegetation coverage first increase and then decrease. The specific analysis methods and analysis results are as follows: Analysis method: Perform α-diversity analysis and β-diversity analysis on the data at the OTU level.
[0040] Analysis result: For the α-diversity analysis at the OTU level, as Figure 2 shown, the results of the shannon and simpson indices show that the diversity of the microbial community in the gravel desert soil in May and July is significantly lower than that in June (shannon: p = 0.037, simpson: p = 0.034). The Chao index shows that the richness of the microbial community in the gravel desert soil in June is significantly higher than that in May and July (p = 0.034).
[0041] For the β-diversity analysis, as Figure 3 shown, the sample hierarchical clustering analysis and PCoA analysis show that the similarity of the bacterial diversity in the gravel desert soil in May and June is relatively high, and there are significant differences in the bacterial composition of the gravel desert soil in July.
[0042] In step S5 above, for the analysis of the compositional differences of the microbial community in the gravel desert soil during the critical period of the plant growth season based on the obtained data, the specific analysis results are as follows: As Figure 4As shown, from the perspective of the phylum level, the main components of the microbial community in the gravel desert soil during the critical period of the plant growth season are Actinobacteriota, Chloroflexi, Proteobacteria, Acidobacteriota, and Firmicutes. However, the difference is that the abundance of Actinobacteriota in the gravel desert soil in June is 44.5%, which is significantly higher than that in May (38.3%) and July (38.2%). Chloroflexi also shows a similar trend. On the contrary, the abundance of Proteobacteria in June (9.9%) is significantly lower than that in May (15.2%) and July (16.83%). With the changes in the environment in June and July, the abundance of Acidobacteriota in the gravel desert soil is significantly lower than that in May. From the perspective of the order level, Rubrobacterales, Vicinamibacterales, Nitrosococcales, Gaiellales, 0319-6L14, Solirubrobacterales, norank_c__TK10, Bacillales, Thermomicrobiales, Gemmatimonadales, norank_c__MB-A2-108 are the main dominant species. Among them, the abundances of Rubrobacterales and Nitrosococcales show a trend of first decreasing and then increasing with time, while Gaiellales, Solirubrobacterales, and norank_c__TK10 show a trend of first increasing and then decreasing. From the perspective of the genus level, Rubrobacter, norank_o__0319-7L14, norank_o__Gaiellales, unclassified_f__Nitrosococcaceae, norank_o__Vicinamibacterales, norank_c__TK10, RB41, Bacillus, etc. are the main dominant genera. However, with the changes in factors such as climate and soil moisture in the desert area, the abundance of Rubrobacter in June is significantly reduced (7.87%) and returns to the level in May (15.1%) in July (16.07%). In addition, unclassified_f__Nitrosococcaceae also shows the same trend. The difference is that the abundance of norank_o__Gaiellales is significantly increased in June, and there is no significant difference between May and July. The genus norank_c__TK10 has a relatively high abundance in June and July.Analysis at the subordinate level of Actinobacteriota revealed that Rubrobacter was a dominant genus in all three periods, but the abundance differed significantly among different periods, with the abundance in June being significantly lower than that in May and July. However, in June, the abundances of norank_o__Gaiellales, norank_c__MB-A2-108, and norank_f__67-14 in this phylum increased significantly.
[0043] In step S5 above, based on the obtained data, the differential species of gravel desert soil microorganisms during the critical periods of the plant growth season were analyzed, and the specific analysis results are as follows: As Figure 5 shown, the Venn diagram was used to count the number of soil bacteria in different periods during the critical periods of the plant growth season. It was found that the number of soil OTUs in June was 7,750, significantly higher than that in May and July. The total number of common OTUs in the three time periods was 2,195, and the number of unique OTUs in June was 4,136, which was significantly higher than that in May and July. Taxonomic analysis at the subordinate level showed that there were 610 genera of soil bacteria in June, 511 genera in May, and 484 genera in July. The total number of common genera in the three time periods was 403, and the number of unique genera in June was 103, which was significantly higher than that in May and July.
[0044] As Figure 6 shown, the significant differential species were analyzed at the Class level. The results showed that the abundances of Actinobacteria, Thermoleophilia, TK10, Acidimicrobiia, KD4-96, and Planctomycetes in the soil in June were significantly higher than those in May and July, becoming the main differential dominant species, while the abundances of Rubrobacteria, Holophagae, Nitrospiria, and Entotheonellia were just the opposite, being significantly lower in June than in May and July. The analysis results at the Genus level showed that the abundances of norank_o__Gaiellales, norank_c__TK10, norank_f__67-14, norank_f__JG30-KF-CM45, norank_c__KD4-96, Solirubrobacter, and norank_f__Gemmataceae in June were significantly higher than those in May and July, becoming the main differential dominant species, while Thermobaculum was the main dominant species in May and July, with its abundance being significantly higher than that in June.
[0045] As Figure 7As shown, environmental changes have a great impact on soil microorganisms. In this experiment, the LEfSe multi-level species difference discriminant analysis showed that with the passage of time, the changes in the abundances of genera such as RB41, Thermobaculum, norank_o__Subgroup_7, Candidatus_Entotheonella, and Sumerlaea in May were the main reasons for the differential composition of bacteria; the changes in the abundances of g__norank_f__JG30-KF-CM45, g__norank_o__Gaiellales, g__norank_c__TK10, etc. were the main reasons for the bacterial composition differences in June; the changes in the abundances of g__Nocardioides, g__Rubrobacter, g__Nitrospira, etc. in July were the main reasons for the differences in species composition diversity.
[0046] S6. According to the data analysis, obtain the composition ratio of microorganisms in the gravel desert soil at different times, and analyze the relationship between the composition ratio of the gravel desert soil microorganisms and the changes in the ecological environment.
[0047] In this embodiment, the present invention investigated and analyzed the changes in the diversity of gravel desert soil microorganisms during the critical periods of the plant growth season, combined with the climate changes during this period, analyzed the composition ratio and the change trend of the number of dominant species of the gravel desert soil microorganisms at different times during the critical periods of the plant growth season, and found that the numbers of Acidobacteriota, Proteobacteria, and Rubrobacterales were negatively correlated with the precipitation and vegetation coverage rate as a whole, while the numbers of Chloroflexi, Gaiellales, and Actinobacteria were positively correlated with the precipitation and vegetation coverage rate as a whole. When the temperature rises, the increase in precipitation can maintain the ecological balance by artificially increasing the numbers of Acidobacteriota, Proteobacteria, and Rubrobacterales in the ecological environment; when the precipitation decreases, the ecological balance can be maintained by artificially increasing the numbers of Chloroflexi, Gaiellales, and Actinobacteria in the ecological environment; when the temperature drops, the increase in precipitation can maintain the ecological balance by artificially increasing the numbers of Acidobacteriota, Proteobacteria, and Rubrobacterales in the ecological environment; when the precipitation decreases, the ecological balance can be maintained by artificially increasing the numbers of Chloroflexi, Gaiellales, and Actinobacteria in the ecological environment.
[0048] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to replace the specific embodiments described. As long as they do not deviate from the concept of the invention or exceed the scope defined by this claim book, they shall fall within the protection scope of the present invention.
Claims
1. A method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing, characterized in that, It includes the following steps: S1: Taking gravel desert soil samples at equal intervals with a single-month time period as a cycle, and removing impurities to obtain equal amounts of pretreated soil; S2: Obtaining a soil suspension by high-throughput grinding of the pretreated soil, and obtaining DNA by using the protease k decomposition method for the soil suspension; S3: Amplifying the obtained DNA by PCR and constructing a sequencing library; S4: Performing high-throughput sequencing on the constructed sequencing library on a high-throughput sequencing platform, removing low-quality sequences and sequence adapters through quality control and splicing, performing operational taxonomic unit (OTU) clustering on the spliced sequences and removing chimeras, and obtaining an OTU sequence set after rarefying according to the minimum sample sequence; S5: Comparing the sequence set with the reference sequences in the public database, classifying according to the comparison results, and analyzing the data in different directions; S6: Obtaining the composition ratio of microorganisms in the gravel desert soil at different times based on the data analysis, and analyzing the relationship between the composition ratio of the gravel desert soil microorganisms and the changes in the ecological environment.
2. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 1, characterized in that: The impurity removal in S1 specifically means mixing the gravel desert soil with sterile water for the sample, and then removing large particle impurities through grinding and filtration to obtain the pretreated soil.
3. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 1, characterized in that: The specific method for obtaining DNA in S2 is to perform gradient dilution on the suspension and complete sample collection to obtain soil collection samples, grind the soil collection samples evenly in a high-throughput tissue grinder, and extract DNA using the protease K lysis method.
4. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 1, wherein: The specific method for constructing the sequencing library in S3 is to use PCR to amplify the target region of the 16S rRNA gene for the DNA, fragment the amplification product, and add adapters to prepare the sequencing library.
5. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 1, characterized in that: The specific method for obtaining the OTU sequence set in S4 is to sequence the constructed sequencing library through a high-throughput sequencing platform, remove low-quality sequences through quality control, then perform operational taxonomic unit (OTU) clustering on the sequences according to 97% similarity and remove chimeras, and obtain the OTU sequence set after rarefying according to the minimum sample sequence.
6. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 1, characterized in that: The specific different-direction analysis specifically includes: diversity analysis of the gravel desert soil microbial community at the key periods of the plant growth season, compositional difference analysis of the gravel desert soil microbial community at the key periods of the plant growth season, and differential species analysis of the gravel desert soil microbial community at the key periods of the plant growth season.
7. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 6, wherein: In the diversity analysis of the gravel desert soil microbial community at the key periods of the plant growth season, the α-diversity index is used to characterize the community richness, and the β-diversity is visualized by PCoA through the Bray-Curtis distance matrix.
8. The method for analyzing the microbial diversity of gravel desert soil based on high-throughput sequencing according to claim 6, wherein: In the differential species analysis of the gravel desert soil microbial community at the key periods of the plant growth season, the LEfSe algorithm is used to screen for signature species.