A method for assessing the health of river ecosystems based on specific responses of microbial communities

Abstract

The present invention discloses a method for evaluating the soundness of a river ecosystem based on the specific response of a microbial community. This method includes the steps of: a: setting sampling points in rural, suburban, and urban central areas along the river respectively; b: measuring 12 indicators including pH, water temperature (WT), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total organic carbon, nitrate nitrogen, ammonium nitrogen, nitrite nitrogen, total phosphorus, sulfide, and fluoride, and calculating the water quality index WQI; c: extracting the DNA of aquatic microorganisms and performing bacterial 16s rRNA amplicon sequencing; d: obtaining a genus-level information table and screening effective genus information; e: performing sensitivity classification of microorganisms based on an ecological niche model; f: analyzing the reliability of the results. This method can quickly, accurately, and objectively reflect the health status of the urban river ecosystem.

Description

[Technical Field]

[0001] The present invention relates to the technical field of aquatic environment monitoring and protection, and in particular to a method for assessing the health of river ecosystems based on the specific responses of microbial communities. [Background technology]

[0002] River ecosystems provide important services to humankind and urban development, including water resources, food, and navigation. With intensifying human interference, pollution and degradation of the water environment are becoming more frequent, seriously threatening the health of river ecosystems. Research and establishment of methods for assessing the health of river ecosystems provides important fundamental support for the long-term monitoring and utilization of river ecosystems, as well as pollution treatment and restoration.

[0003] Microorganisms are decomposers and key producers in ecosystems, playing a crucial role in the treatment and remediation of environmental pollution. They are sensitive to changes in environmental conditions, and their diversity and community structure are closely related to ecosystem health. Microbial communities as indicators of ecosystem health have several advantages: First, different types of microorganisms have different tolerances and resistance to pollutants and environmental changes, so changes in the abundance and functional activity of specific microorganisms can provide important information for ecosystem health assessment. Second, because ecosystem microorganisms function in communities, changes in microbial community structure and function can more accurately reflect ecosystem health. However, our understanding of the specific response characteristics and associated mechanisms of microbial communities in river ecosystems is still quite limited. Therefore, classification criteria for microbial indicators and methods for assigning health indices for waterbody health assessments primarily rely on the knowledge and experience of decision makers and experts, resulting in bias and randomness in the assessment results.

[0004] The rapid development of microbial analysis and detection technologies and high-throughput sequencing technologies has further elucidated the mechanisms by which microbial communities respond to ecosystem changes, providing important theoretical and technological support for assessing the health of river ecosystems. This invention utilizes the specific response characteristics of sensitive microorganisms to environmental changes and combines the unique characteristics of microbial ecological niches to establish a scientific and rational indicator system, ensuring the objectivity and practicality of indicator weights. Summary of the Invention

[0005] The objective of the present invention is to provide a method for assessing the health of river ecosystems based on the specific responses of microbial communities. The method is highly directed and utilizes changes in the structure and function of specific microbial taxa to indicate characteristic contaminants.

[0006] The present invention provides a method for assessing the health of a river ecosystem based on the specific response of a microbial community, the method comprising: a: setting sampling points in rural areas, suburban areas and urban areas along the river respectively; b: A step of measuring the water quality indexes of the water body, such as pH, water temperature (WT), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total organic carbon, nitrate nitrogen, ammonium nitrogen, nitrite nitrogen, total phosphorus, sulfide, and fluoride, and calculating the water quality index (IWQ); c: Extracting aquatic microbial DNA and performing bacterial 16S rRNA gene amplicon sequencing; d: obtaining a bacterial genus level information table and screening valid bacterial genus information; e: performing a sensitivity classification of microorganisms based on an ecological niche model; f: analyzing the reliability of the results; Includes:

[0007] Preferably, step b comprises: Using the water quality calculation formula, five parameters are selected to evaluate the water quality including pH value, dissolved oxygen (DO), chemical oxygen demand (COD), phosphate nitrogen, and nitrate nitrogen, and the factor scores are as follows: pH: When 4.5 ≤ pH < 7.0, SI , , 3- , 3- , 3- , , , , NO3- , NO3- , , = 1.9EXP((pH - 1)*0.66) When 7.0 ≤ pH ≤ 7.6, SI pH = 100 When 7.6 < pH ≤ 10.5, SI pH = 100exp((pH - 7.65)* - 0.528) When 10.5 < pH < 4.5, SI pH = 10 DO:[[ID=二十一]] When DO < 3.3, SI DO = 10 When DO > 12.5, SI DO = 100 When 3.3 ≤ DO ≤ 12.5, SI DO = - 59.6 + 24.9*DO - 0.98*DO^2 COD: SI COD = 100*0.86^COD<000故0083>TP: When TP < 0.03, SI TP = 100 When TP > 1.2, SI<00000l0>= 10 When 0.03 < TP < 1.2, SI TP <-> = 99.5*0.17^(PO4 3- ) NO[[ID=5十七]] 3- : NO 3- ≤ 时 SI NO3- = 100 [[ID=故6]]NO 3- > 1 SI[[ID=六十]] NO3- = 102*0.8887^(NO 3- ) WQI calculation formula: n is the number of selected factors, and SIi is the factor score,

Number

[0008] Preferably, the susceptibility of the microorganisms is classified as follows: A. The relative abundance of genus-level microorganisms was statistically analyzed, and genera with a relative abundance of more than 1% in at least one sample site were considered effective for sensitive microorganism screening. B. The response curves of various genera of microorganisms to environmental factors were fitted based on the generalized additive model, and the curves with significance lower than 0.01 were selected as valid fitting curves. C, An Extension of Generalized Additive Models to Generalized Linear Models, For example, if we take two environmental factors, we can calculate it as follows: log(E(yi)=a+s1(x1i)+s2(x2i) where yi is the relative abundance of the microorganism, xi is the environmental parameter, s1(x1), s2(x2) are nonparametric smoothing functions, and Log() is an example of a related function.

[0009] D. According to the fitting curve type of the generalized additive model, the ecological niches of various genera are estimated respectively: for a fitting curve with a single peak, the environmental parameter value corresponding to the peak is the optimal ecological niche of this microorganism; E, in the case of a monotonically increasing or decreasing fitting curve, the microorganism with the largest correlation coefficient is selected as the sensitive microorganism, and the microorganism with a sharp increase or decrease in the fitting curve is selected as the sensitive microorganism to this environmental factor; F. Using the bacterial genus information table effective for screening sensitive microorganisms, the response curves of various genera of microorganisms to environmental factors are fitted based on the generalized additive model, and those with significance lower than 0.01 are selected as effective fitting curves. The ecological niche sensitivity of various microorganisms (genus level) is estimated respectively according to the fitting curve type of the generalized additive model.

[0010] A second object of the present invention is to provide an application of g_Jeotgalicoccus increase in indicating water quality deterioration.

[0011] The third object of the present invention is to provide an application of g_Sphingobium reduction in indication of water pollution and dissolved oxygen reduction.

[0012] The fourth objective of the present invention is to provide an application of g_Treponma reduction in reflecting COD reduction in water bodies.

[0013] The fifth objective of the present invention is to provide an application of g_Yersinia increase in indications that reflect total phosphorus increase in water bodies.

[0014] A sixth object of the present invention is to provide an application of g_Pleomorphomomas increase in indicating water quality deterioration. [Effects of the Invention]

[0015] Compared with the prior art, the present invention has the following technical advantages:

[0016] 1. This method is highly directional and utilizes changes in the structure and function of specific microbial taxa to identify characteristic contaminants. 2. By using microbial ecological niche screening, sensitive microbial taxa can be obtained scientifically and rationally. 3. This method can reflect the change in water quality through microbial indicators and indicate characteristic pollutants at the same time. 4. This method is not limited to specific environmental indicators and has good applicability to urban river ecosystems. 5. This method can quickly, accurately and objectively reflect the health status of urban river ecosystems. [Brief explanation of the drawings]

[0017] [Figure 1] Sensitive microorganism type 1 - environmental factor water temperature (WT) is shown. [Figure 2] Sensitive microorganism type 2 - indicates environmental factor nitrate nitrogen (NO3-N). [Figure 3] Sensitive microorganism type 3 - Environmental factor pH. [Figure 4] The relative abundance of sensitive microorganisms in different water quality classes is shown. DETAILED DESCRIPTION OF THE INVENTION

[0018] The following examples further illustrate the present invention, but are not intended to be limiting thereof.

[0019] Example 1 Sampling point selection: The sampling points in this study were Guangzhou city and surrounding rivers, mainly concentrated in the Pearl River Delta, which is a typical river city.

[0020] 1. Sampling site: Guangzhou river ecosystem.

[0021] Based on the river flow direction, eight sampling points were selected, with three duplicate samples taken each time. A total of 240 samples were collected 10 times, and repeated sampling was performed to ensure the scientific validity of the data. The distance between urban, suburban, and rural areas was approximately 100 km, for a total distance of 200 km, intended to cover Guangzhou's urban and surrounding river sampling sites.

[0022] 2. Water sample collection and water quality analysis: Water samples were collected monthly for a total of 10 times over the course of one year, in April, May, June, July, September, October, November, and December 2018, and January and February 2019. A total of 12 water quality indicators were measured: pH, water temperature (WT), dissolved oxygen (DO), COD, TN, total organic carbon, nitrate nitrogen, ammonium nitrogen, nitrite nitrogen, total phosphorus, sulfide, and fluoride.

[0023] 3. Major types of water pollution and WQI classification: Using typical water quality calculation formulas, five parameters are selected to evaluate the water quality including pH value, dissolved oxygen (DO), chemical oxygen demand (COD), phosphate nitrogen, and nitrate nitrogen. The factor scores are as follows: pH: When 4.5 ≤ pH < 7.0, SI pH = 1.9EXP((pH - 1)*0.66) When 7.0 ≤ pH ≤ 7.6, SI pH = 100 When 7.6 < pH ≤ 10.5, SI pH = 100exp((pH - 7.65)* - 0.528) When 10.5 < pH < 4.5, SI pH = 10 DO: When DO < 3.3, SI DO = 10 When DO > 12.5, SI DO = 100 When 3.3 ≤ DO ≤ 12.5, SI DO = - 59.6 + 24.9*DO - 0.98*DO^2 COD: SI COD = 100*0.86^COD TP: When TP < 0.03, SI TP = 100 When TP > 1.2, SI TP = 10 When 0.03 < TP < 1.2, SI TP <-> = 99.5*0.17^(PO4 3- ) NO 3- : NO 3- ≤ SI NO3- = 100 NO 3- > SI NO3- = 102*0.8887^(NO 3- ) WQI calculation formula: n is the number of selected factors, and SIi is the factor score.

Equation

[0024] Through calculation and statistics, V and VI were 55 and 76 times, respectively, in 240 detections (Table 1), where the main participation frequencies of characteristic pollutants were: pH 0 times in total, DO 42 times in total, COD 45 times in total, TP 38 times in total, NO 3- A total of 39 times (Table 2).

[0025] Table 1. Statistical table of river water quality classification in Guangzhou City [Table 1]

[0026] Table 2. Statistics of characteristic pollutants in river water quality in Guangzhou [Table 2]

[0027] 4. Microbial DNA extraction and high-throughput sequencing of water samples: Two liters of water samples were filtered through a 0.22 μm membrane filter, and microbial DNA was extracted from the filter membrane using the PowerWater DNA Isolation Kit (PowerWater® DNA Isolation Kit). DNA was extracted according to the kit's instructions, and the DNA concentration and purity were measured using an ultra-microspectrophotometer. DNA samples that passed the test were sent to Baiyoke Biotechnology Co., Ltd. for sequencing. Microbial 16S rRNA fragment PCR amplification was performed using the bacterial universal primer 338F / 806R, which amplifies bacterial V3-V4 hypervariable regions. The primer sequences used were 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-GGACTACHVGGGATCTWTCTAAT-3'). Sequencing was performed using a double-end sequencing method on an Illumina HiSeq 2500 System (Illumina, United States) benchtop sequencer.

[0028] 5. Microbial community information analysis: High-quality 16S rRNA gene amplicon sequences were analyzed using open-source quantitative analysis software for microbial ecology (QIIME2). Noise reduction was performed using DATA2 sequence reads in the QIIME2 system. The denoised sequences were then grouped into OTU classes with a similarity of 100%. Finally, a systematic taxonomic classification analysis was performed on the OTU sequences using SILVA 132 (http: / / www.mothur.org / wiki / Taxonnomy_outline). For bacterial diversity analysis statistics, the number of amplicon sequences measured was subjected to equal draw level processing, i.e., the lowest number of sequences in a sample was used as the basis. The same number of sequences was randomly selected for all samples (12,365 sequences were obtained after draw level processing), and a genus-level information table was obtained.

[0029] 6. Valid bacterial genus information table: In the genus-level information table, statistics are performed according to the relative abundance at the genus level being greater than 1%, greater than 0.5%, and greater than 0.01%, and if the sum of the total relative abundance is greater than 90%, it is classified into an independent appendix table. Then, unnamed OTUs at the genus level are removed or renamed to the species level (either order or phylum) to obtain a valid bacterial genus information table.

[0030] 7. Screening for sensitive microorganisms: Microbial susceptibility is classified as follows: A. The relative abundance of genus-level microorganisms was statistically analyzed, and genera with a relative abundance of more than 1% in at least one sample site were considered effective for sensitive microorganism screening. B. The response curves of various genera of microorganisms to environmental factors were fitted based on the generalized additive model, and the curves with significance lower than 0.01 were selected as valid fitting curves. C, An Extension of Generalized Additive Models to Generalized Linear Models, For example, if we take two environmental factors, we can calculate it as follows: log(E(yi)=a+s1(x1i)+s2(x2i) where yi is the relative abundance of microorganisms, xi is an environmental parameter, s1(x1) and s2(x2) are nonparametric smoothing functions, and Log() is an example of a related function. D. According to the fitting curve type of the generalized additive model, the ecological niches of various genera are estimated respectively: for a fitting curve with a single peak, the environmental parameter value corresponding to the peak is the optimal ecological niche of this microorganism; E, in the case of a monotonically increasing or decreasing fitting curve, the microorganism with the largest correlation coefficient is selected as the sensitive microorganism, and the microorganism with a sharp increase or decrease in the fitting curve is selected as the sensitive microorganism to this environmental factor; F. Using the bacterial genus information table effective for screening sensitive microorganisms, the response curves of various genera of microorganisms to environmental factors are fitted based on the generalized additive model, and those with significance less than 0.01 are selected as effective fitting curves. According to the fitting curve type of the generalized additive model, the ecological niche sensitivity of various microorganisms (genus level) is estimated respectively: For fitting curves with a single peak, the environmental parameter value corresponding to the peak represents the optimal ecological niche for this microorganism. The results of the data fitting for the environmental factor water temperature (TW) screening were g_Altererythrobacter and g_Facklamia, which were fitted to a single-peak model (Figure 2a, b). When multiple species exist in similar ecological niches, the species with the narrowest ecological niche was identified as the sensitive microorganism. For example, the TW results show that the peak for g_Altererythrobacter is 8.85 and the peak for g_Facklamia is 7.98. Altererythrobacter showed a stronger response than Facklamia, indicating that its ecological niche is narrower and that Altererythrobacter is more suitable as a sensitive microorganism for TW than Facklamia.

[0031] For monotonically increasing or decreasing fitting curves, the microorganism with the highest correlation coefficient was selected as the sensitive microorganism. Using the results of the screening for the environmental factor nitrate nitrogen as an example, g_Nitrospira and g_Trichococcus were selected for one-way linear fitting (Figure 3a, b). The fitting r value for g_Nitrospira was 0.153, with a significance level of <0.05, while the fitting r value for g_Trichococcus was 0.173, with a significance level of <0.05, indicating that Trichococcus responded more strongly than Nitrospira.

[0032] For fitting curves that show a flat trend but show a sudden increase or decrease in a certain range, this microorganism was determined to be sensitive to the environmental factor in the variation range. Taking the screening results for the environmental factor pH as an example, g_Jeotgalicoccus increased rapidly at pH > 8.2 (Figure 4), so factor g_Jeotgalicoccus can be considered a pH-sensitive microorganism.

[0033] 8. Statistics of sensitive microorganisms: Using an effective bacterial genus information table for sensitive microorganism screening, sensitive microorganisms for 12 environmental factors (including characteristic pollutants in water quality calculations) were determined, and sensitive microbial species for each factor were obtained through sensitive microorganism screening. The optimal sensitive microorganisms were obtained by comparison, and the results are shown in Table 3: a total of 11 sensitive microorganisms were obtained, of which 11 species for total nitrogen, only one species for dissolved oxygen and COD, and no effective sensitive microorganisms for fluoride, because the fluoride content in the 10-month sampling was low and the microorganisms did not respond to it.

[0034] Table 3. Statistical table of sensitive microorganisms for various environmental factors [Table 3]

[0035] 9. Reliability analysis of evaluation results To verify the effectiveness of this method for water quality assessment, the relative abundance of sensitive microorganisms and water quality IWQ obtained in this study were classified and statistically analyzed. The results are shown in Figure 5. The pH-sensitive microorganism (g_Jeotgalicoccus) increased sharply in water area VI, increasing by 1190% compared to the other water qualities. The sharp increase in g_Jeotgalicoccus indicates a deterioration in water quality (Figure 5a). The dissolved oxygen-sensitive microorganism (g_Sphingobium) had low relative abundance in water areas V and VI, decreasing by an average of 73% and 77% compared to the other water qualities. The sharp decrease in g_Sphingobium indicates a deterioration in the water area. This not only reflected a decrease in dissolved oxygen, but also a decrease in dissolved oxygen (Figure 5b). The relative abundance of a typical COD-sensitive microorganism (g_Treponma) was low in water bodies V and VI, and the decrease in g_Treponma reflected a decrease in COD (Figure 5c). The detection value of a typical total phosphorus (TP)-sensitive microorganism (g_Yersinia) was extremely low in water bodies IV, V, and VI, indicating an increase in TP (Figure 5d). The relative abundance of a typical nitrate-nitrogen-sensitive microorganism (g_Pleomorphomomas) increased sharply in water body VI, and an increase in nitrate nitrogen contributed to its proliferation. Changes in its abundance can characterize changes in water quality (Figure 5e).

[0036] The health of urban aquatic ecosystems is affected by many factors, including urban development, domestic wastewater discharge, and land use and management. As a result, the characteristic pollutants in urban water bodies become more complex, leading to a deterioration of the entire water environment. Therefore, an evaluation system that can quickly, accurately, and objectively grasp the deterioration of water quality and characteristic pollutants is needed. Therefore, the present invention can screen sensitive microbial taxa to environmental factors (including characteristic pollutants in water bodies) and quickly determine the water quality status and indicate the type of pollution based on changes in their relative abundance.

Claims

[Claim 1] a) setting sampling points along a river in rural areas, suburban areas, and urban areas; b: measuring water quality indicators including pH, water temperature (WT), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total organic carbon, nitrate nitrogen, ammonium nitrogen, nitrite nitrogen, total phosphorus (TP), sulfide and fluoride in the water area at each of the sampling points, and calculating a water quality index (WQI); c) extracting microbial DNA from the water body at each of the sampling points and analyzing bacterial 16S rRNA gene amplicon sequences; d. obtaining a bacterial genus level information table through the analysis and screening valid bacterial genus information; e. Performing a susceptibility classification of microorganisms based on an ecological niche model; f) Analyzing the reliability of the water quality assessment results by classifying the relative abundance of sensitive microorganisms and WQI; The step b Using the water quality calculation formula, five parameters, pH value, dissolved oxygen (DO), chemical oxygen demand (COD), total phosphorus (TP), and nitrate nitrogen, were selected to evaluate the water quality, and the factor scores were as follows: pH: 4.5≦pH<7.0 SI ｐＨ =1.9exp((pH-1)*0.66) SI when 7.0≦pH≦7.6 ｐＨ =100 7.6<pH≦10.5 SI ｐＨ =100exp((pH-7.65)*-(0.528)) 10.5<pH<4.5 SI ｐＨ =10 DO: If DO<3.3, SI ＤＯ =10 If DO>12.5, SI ＤＯ =100 3.3≦DO≦12.5 SI ＤＯ =-59.6+24.9*DO-0.98*DO^2 COD: S I ＣＯＤ =100*0.86^CODD TP: SI when TP<0.03 ＴＰ =100 When TP>1.2, SI ＴＰ =10 0.03<TP<1.2 SI ＴＰ =99.5*0.17^(TP) NO ３－ : NO ３－ ≦1 SI ＮＯ３－ =100 NO ３－ >1 SI ＮＯ３－ =102*0.8887^(NO ３－ ( WQI calculation formula: n is the number of selected factors, SIi is the factor score, [Equation 1] In step e, the susceptibility of the microorganism is classified as follows: A. The relative abundance of genus-level microorganisms is calculated, and those with a relative abundance of more than 1% at at least one sampling point are considered to be effective genera for sensitive microorganism screening; B. Fitting the response curves of various genera of microorganisms to environmental factors based on the generalized additive model, and selecting the curves with significance lower than 0.01 as the effective fitting curves; C. Extending the generalized linear model to the generalized additive model For n environmental factors, it is calculated as follows: F(yi)=a+Σj n sj(xji) where yi is the relative abundance of the microorganism, xi is the environmental parameter, sj(xji) is a nonparametric smooth function, and F() is an arbitrary function. D. According to the fitting curve type of the generalized additive model, the ecological niches of various genera are estimated respectively: for a fitting curve with a single peak, the environmental parameter value corresponding to the peak is the optimal ecological niche of this microorganism; E. In the case of a monotonically increasing or monotonically decreasing fitting curve, the microorganism with the largest correlation coefficient is selected as the sensitive microorganism, and the microorganism with a sharp increase or decrease in the fitting curve is selected as the sensitive microorganism to this environmental factor; F. A method for evaluating the health of river ecosystems based on the specific responses of microbial communities, characterized by using effective bacterial genus information for screening sensitive microorganisms, fitting response curves of microorganisms of various genera to environmental factors based on a generalized additive model, selecting those with significance lower than 0.01 as effective fitting curves, and respectively estimating the ecological niche sensitivity of various microorganisms (genus level) according to the fitting curve type of the generalized additive model.

Images