A method for evaluating river pollution types using bacterial community composition and environmental factors

Abstract

This invention provides a method for evaluating river pollution types based on bacterial community composition and environmental factors, belonging to the field of river pollution control technology. This invention uses high-throughput sequencing of microbial 16S-rRNA genes as targets, and reveals the relationship between changes in the microbial community structure of urban river sediments and environmental factors through specific methods. In particular, it relates to a method for evaluating river pollution types based on bacterial community composition and environmental factors. This addresses the problem of having few existing methods for river pollution assessment. This invention provides a scientific basis for the management and protection of biodiversity in urban river ecosystems. By detecting the microbial community structure of sediments, it is possible to determine which environmental factors affect a given location, providing technical support for the management and control of river ecosystem pollution.

Description

Technical Field

[0001] This invention relates to the field of river pollution control technology, and in particular to a method for evaluating the types of river pollution by means of bacterial community composition and environmental factors. Background Technology

[0002] Microorganisms are an essential component of ecosystems, playing a crucial role in sediment biogeochemical cycles. The characteristics of sediment microbial communities serve as biological indicators, providing insights into ecosystem biodiversity and health, and are significant for addressing aquatic environmental issues. The rapid development of high-throughput sequencing technology has provided important technical means for studying the complexity and diversity of microbial communities, particularly for difficult-to-culture and low-population microorganisms.

[0003] In recent years, with rapid economic development and the intensification of urban life and industrial production, high pollution loads have been generated and entered water bodies, making water pollution a highly concerning issue. For rivers, pollutants deposited in sediment will gradually be released into the water, leading to water quality deterioration. Sediment is an important component of aquatic ecosystems and contains valuable ecological information. Water pollutants accumulate in sediment through various pathways, and under certain conditions, sediment pollutants can be released back into the water, causing secondary pollution.

[0004] Currently, the potential patterns and mechanisms of change in urban river sediment microbial communities under the influence of human activities still require further research, which is of great significance for river pollution assessment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the types of river pollution by means of bacterial community composition and environmental factors, in order to solve the problem of the limited number of methods for river pollution assessment.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] This invention provides a method for evaluating river pollution types based on bacterial community composition and environmental factors, comprising the following steps:

[0008] S1. DNA was extracted from river sediments from different sampling points, amplified by PCR, purified, and then analyzed by high-throughput sequencing to obtain sequencing data.

[0009] S2. Use software to perform OTU clustering analysis on the sequencing data to obtain species classification information corresponding to each OTU;

[0010] S3. Perform species taxonomic analysis using Bayesian algorithm, and statistically analyze the bacterial community species composition of each sample at each taxonomic level.

[0011] S4. Measure environmental factor indicators of river water bodies;

[0012] S5. Principal component analysis was performed based on the bacterial community species composition and environmental factor indicators to obtain environmental factor indicators that were significantly positively correlated with the bacterial community composition.

[0013] S6. By analyzing the species composition of the bacterial community at the detection point, it can be determined what environmental factors affect the point and corresponding environmental pollution prevention and control measures can be taken.

[0014] Preferably, the software is QIIME 1.9.0.

[0015] Preferably, the similarity level used in the OTU clustering analysis is above 97%.

[0016] Preferably, the OTU clustering analysis includes filtering, splicing, and removing chimeras from the sequencing data.

[0017] Preferably, the environmental factors include pH, dissolved oxygen, temperature, redox potential, conductivity, turbidity, total dissolved solids, water content in sediments, organic matter, ammonia nitrogen, chemical oxygen demand, total nitrogen, and total phosphorus.

[0018] Preferably, the principal component analysis is performed using R language PCA statistical analysis software.

[0019] Preferably, the PCR amplification uses primers 338F_806R.

[0020] The technical effects and advantages of this invention are as follows:

[0021] This invention utilizes high-throughput sequencing targeting the 16S-rRNA gene of microorganisms to reveal the relationship between changes in the microbial community structure of urban river sediments and environmental factors through specific methods. This provides a scientific basis for the management and biodiversity protection of urban river ecosystems. The invention successfully demonstrated a significant positive correlation (p < 0.05) between pH, redox potential in water bodies, and water content, organic matter, and total phosphorus (TP) in sediments and microbial community composition. This indicates that these environmental factors are the main reasons affecting the differences in bacterial community structure in the Chanba River basin sediments, and corresponding environmental pollution prevention and control measures should be taken. This invention can determine which environmental factors influence a given location by detecting the microbial community structure of sediments, providing technical support for the management and control of pollution in river ecosystems. Attached Figure Description

[0022] Figure 1 Principal component analysis diagram of environmental factors and microbial community composition at the phylum level;

[0023] Figure 2Principal component analysis diagram of environmental factors and microbial community composition at the class classification level;

[0024] Figure 3 This is a principal component analysis diagram of environmental factors and microbial community composition at the order classification level;

[0025] Figure 4 Principal component analysis diagram of environmental factors and microbial community composition at the taxonomic level;

[0026] Figure 5 Principal component analysis diagram of environmental factors and microbial community composition at the genus taxonomic level;

[0027] Figure 6 This is a principal component analysis diagram of environmental factors and microbial community composition at the species classification level. Detailed Implementation

[0028] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0029] Example 1

[0030] Study area: Chan River (109°0′30″E~109°2′44″E, 33°11′41″N~34°20′24″N), originating in Shaanxi Province, China, at an altitude of over 2000m, with a river length of 63.5km and a drainage area of ​​760km². 2 Annual runoff is 235 million cubic meters. 3 The average annual temperature is 13.1℃~14.3℃, and the annual precipitation is 528.3mm~716.5mm. Sampling was conducted from September 24th to 29th, 2020. Based on the river's natural characteristics and water pollution distribution, 14 sampling points were collected, with 3 parallel samples taken from each point. The average total nitrogen (TN) in the sediments was 883 mg / kg, with a concentration range of 61~2478 mg / kg. According to the EPA's sediment classification and rating standards, the TN concentrations of C2, C10, and C13 in the Chan River were all above 2000 mg / kg, indicating severe pollution; the values ​​at the remaining sampling points were all below 1000 mg / kg, indicating mild pollution. The average total phosphorus (TP) concentration in the Chan River sediments was 1278 mg / kg, with a concentration range of 441~2786 mg / kg. According to the sediment classification and rating standards established by the EPA, except for point C1 where the TP content is between 420 and 650 mg / kg, which is considered moderately polluted, the TP content at all other points in the Chan River is higher than 650 mg / kg, which is considered heavily polluted.

[0031] The Ba River (E109°00′~109°47′, N33°50′~34°27′) is a major tributary of the Wei River in China. It flows from southeast to northwest, with a length of 107 km within the territory, a north-south length of approximately 78 km, an east-west width of 50 km, and a drainage area of ​​2581 km². 2 Annual runoff: 718 million m³ 3 The average annual temperature is 13.1℃~14.3℃, and the annual precipitation is 528.3mm~716.5mm. The Bahe River mainly serves functions such as farmland irrigation, rainwater drainage during the rainy season, and landscape irrigation. Sediment samples were collected from September 24th to 29th, 2020. Taking into account the natural attributes of the Bahe River, such as its width, depth, and riverbed soil and rock properties, as well as the distribution of water pollution, sampling points were set up at intervals of 1~1.5km, for a total of 18 points, including upstream points B1-B6, midstream points B7-B12, and downstream points B13-B18. Approximately 4L of water samples were collected and placed in sampling bottles; approximately 500g of surface sediment samples were collected, placed in sterile sealed plastic bags, and frozen in a freezer at -80℃. A portion of the sediment samples was subjected to high-throughput sequencing, while the other portion was cold-dried, ground, and sieved for later use.

[0032] Physicochemical property determination: Water temperature, pH, DO, ORP, EC, NTU, and TDS were measured in water samples using a Hach portable multi-parameter analyzer. TN in sediments was determined using alkaline potassium persulfate digestion ultraviolet spectrophotometry (HJ 636-2012), TP using ammonium molybdate spectrophotometry (GB11893-89), ammonia nitrogen using salicylic acid spectrophotometry (HJ 535-2009), CODcr using chemical oxygen demand determination (GB11914-89), water content (MC) using the gravimetric method, and organic matter (OM) using the loss on ignition method.

[0033] DNA extracted from sediments in the Chan River basin was detected by 1% agarose gel electrophoresis. PCR amplification and product purification were performed using primers 338F_806R. PCR products from the same sample were mixed and detected by 2% agarose gel electrophoresis. PCR products were recovered by gel cutting using an AxyPrep DNA gel extraction kit and eluted with Tris_HCl. Based on the electrophoresis quantification results, the PCR products were analyzed using QuantiFluor... TM The -ST blue fluorescence quantitative quantification system was used for detection, and the samples were mixed in appropriate proportions according to the sequencing volume of each sample. PE libraries were constructed, and Illumina sequencing was performed on 14 samples. The sampling sites in the Chanhe River were C1, C2, C3...C14. The average number of sequences sequenced was 43,772, the average number of basic sequences was 18,276,008, and the average sequence length was 417.58. Data analysis was completed using the Illumina MiSeq sequencing platform at Shanghai Meiji Biotechnology Testing Center.

[0034] The QIIME 1.9.0 analysis software filtered, assembled, and removed chimeras from the raw FASTQ files. Bioinformatics statistical analysis was performed on OTUs at a 97% similarity level. For each OTU, the species classification information was analyzed using the RDPclassifier Bayesian algorithm, and the community species composition of each sample was statistically analyzed at each taxonomic level: Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species. High-throughput sequencing technology detected 63 phyla, 201 classes, 464 orders, 770 families, 1590 genera, 3477 species, and 10127 OTUs of microorganisms in the Chan River basin sediments.

[0035] Principal Component Analysis (PCA) is a technique for simplifying data analysis. This method effectively identifies the most "primary" elements and structures in the data, removes noise and redundancy, reduces the dimensionality of complex data, and reveals the simple structure hidden behind the complexity. Its advantages include simplicity and no parameter limitations. PCA uses variance decomposition to reflect the differences between multiple sets of data on a two-dimensional coordinate graph. The coordinate axes are selected from the two characteristic values ​​that best reflect the differences between samples. For example, the more similar the species composition of the samples, the closer they are in the PCA graph.

[0036] pH, dissolved oxygen (DO), temperature (T), oxidation-reduction potential (ORP), conductivity (EC), turbidity (NTU), total dissolved solids (TDS) in the water bodies of the Chanba River Basin, and water content (MC), organic matter (OM), and ammonia nitrogen (NH4) in sediments. + The environmental factors include chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).

[0037] Principal component analysis (PCA) was performed on the sampling points and environmental factors in the Chanhe River Basin using R language PCA software. The OUT table was performed under flattened conditions. Principal component analysis was conducted at the phylum, class, order, family, genus, and species levels. Two groups were used: one group consisting of 14 sediment sampling points from the Chanhe River Basin and the other group consisting of 18 sediment sampling points from the Bahe River Basin. The distance algorithm used was Bray Curtis. Two groups of environmental factor data were also selected: one group consisting of 7 environmental factors in the water body and the other group consisting of 6 environmental factors in the sediments. The results are as follows: Figures 1-6 As shown: Figure 1 At the phylum classification level, the correlation between the Chanba River Basin and environmental factors explained 62.32% and 23.2% of the correlation on the PC1 and PC2 axes, respectively. Figure 2The correlation between the Chanba River Basin and environmental factors at the class classification level explained 38.91% and 21.05% of the correlation on the PC1 and PC2 axes, respectively. Figure 3 At the order classification level, the correlation between the Chanba River Basin and environmental factors explained 28.2% and 18.75% of the correlation on the PC1 and PC2 axes, respectively. Figure 4 At the taxonomic level, the correlation between the Chanba River Basin and environmental factors explained 28.21% and 16.79% of the correlation on the PC1 and PC2 axes, respectively. Figure 5 At the genus-level, the correlation between the Chanba River Basin and environmental factors explained 22.93% and 14.67% of the correlation on the PC1 and PC2 axes, respectively. Figure 6 At the species classification level, the correlation between the Chanba watershed and environmental factors explained 26.83% and 15.66% of the correlation on the PC1 and PC2 axes, respectively.

[0038] The results show:

[0039] from Figure 1 It can be observed that pH, ORP, MC, OM, TP, COD, NTU, and NH4... + -N showed a positive correlation between each pair of environmental factors, and these environmental factors were positively correlated with the microbial community composition at the phylum classification level at sites C2, C4, C9, C13, and C14 of the Chanhe River sediments and sites B3, B5, B8, B10, B17, and B18 of the Bahe River sediments, while they were negatively correlated with DO, T, EC, TDS, and TN.

[0040] from Figure 2 It can be concluded that pH, MC, OM, ORP, and TP show positive correlations with each other, and are also positively correlated with environmental factors COD, NTU, and NH4. + -N, T, EC, TDS, and TN were negatively correlated, and these environmental factors were correlated with the microbial community composition at the class and taxonomic level at sites C1 of Chanhe sediment and sites B1, B2, B3, B10, B11, B14, and B18 of Bahe sediment (p < 0.05).

[0041] Other Figures 3-6 Similar patterns can also be observed, namely, the environmental factors pH, ORP, TP, OM, and MC are positively correlated in pairs (p < 0.05), and are positively correlated with the microorganisms at sites B3, B17, and B18 in the Bahe River Basin at the order, family, genus, and species classification levels (p < 0.05).

[0042] The results show that, as demonstrated by this invention, pH, redox potential in water bodies, and water content, organic matter, and total phosphorus (TP) in sediments are significantly positively correlated with microbial community composition (p < 0.05). This indicates that these environmental factors are the main reasons for the differences in bacterial community structure in sediments in the Chanba River Basin, and corresponding environmental pollution prevention and control measures should be taken. This invention can determine which environmental factors affect a given location by detecting the microbial community structure of sediments, providing technical support for pollution management and control in river ecosystems.

[0043] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating river pollution types based on bacterial community composition and environmental factors, characterized in that, Includes the following steps: S1. DNA was extracted from river sediments from different sampling points, amplified by PCR, purified, and then subjected to high-throughput sequencing analysis to obtain sequencing data. The high-throughput sequencing analysis was performed using the microbial 16S-rRNA gene as the target, and the PCR amplification used 338F_806R primers. S2. Use QIIME 1.9.0 software to perform OTU cluster analysis on the sequencing data to obtain the species classification information corresponding to each OTU; S3. Perform species taxonomic analysis using Bayesian algorithm, and statistically analyze the bacterial community species composition of each sample at each taxonomic level. S4. Measure environmental factor indicators of river water bodies; S5. Principal component analysis was performed based on the bacterial community species composition and environmental factor indicators to obtain environmental factor indicators that were significantly positively correlated with the bacterial community composition. S6. By analyzing the species composition of the bacterial community at the detection point, it can be determined what environmental factors affect the point and corresponding environmental pollution prevention and control measures can be taken. The environmental factors include pH, dissolved oxygen, temperature, oxidation-reduction potential, conductivity, turbidity, total dissolved solids, water content in sediments, organic matter, ammonia nitrogen, chemical oxygen demand, total nitrogen, and total phosphorus.

2. The method for assessing river pollution risk based on bacterial community composition and environmental factors according to claim 1, characterized in that, The OTU clustering analysis used a similarity level of over 97%.

3. The method for assessing river pollution risk based on bacterial community composition and environmental factors according to claim 2, characterized in that, The OTU clustering analysis includes filtering, splicing, and removing chimeras from the sequencing data.

4. The method for assessing river pollution risk based on bacterial community composition and environmental factors according to claim 3, characterized in that, The principal component analysis was performed using R language PCA statistical analysis software.

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