A method for evaluating the influence of sewage input on water body carbon sink and microbial community based on multi-omics integration

Abstract

The application discloses a method for evaluating the influence of sewage input on water body carbon sink and microbial community based on multi-omics integration, determines water body basic parameters, constructs a microcosm culture system containing a control group and a sewage experimental group, divides a short-term stress period and a long-term stable period, takes carboxyl group alicyclic substances as an inert dissolved organic carbon marker, evaluates the chemical stability of the inert dissolved organic carbon marker by means of a modified aromaticity index and a carbon nominal oxidation state, simultaneously carries out 16S rRNA amplicon, metagenome and metatranscriptome sequencing, analyzes the changes of microbial community structure, functional gene abundance and transcriptional activity, screens different period difference microbial groups and functional genes, and locks the key microbial groups and genes influencing the stability of the inert dissolved organic carbon by correlation analysis. The application integrates organic matter molecular characterization and multi-omics technology, clarifies the action mechanism of the influence of sewage on the water body carbon sink from the gene expression level, and can be applied to various water body carbon sink evaluation, ecological risk early warning and repair effect evaluation.

Description

Technical Field

[0001] This invention relates to a method for assessing the impact of wastewater input on aquatic carbon sinks and microbial communities based on multi-omics integration. More specifically, it relates to a multi-omics approach integrating microcosmic simulation culture, biodegradable dissolved organic carbon method, Fourier transform ion cyclotron resonance mass spectrometry, 16S rRNA amplicon sequencing, metagenomics, and metagenomics. This approach is suitable for assessing the impact of wastewater input on aquatic microbial community succession, functional metabolic remodeling, and RDOC stability. This invention belongs to the fields of environmental microbiology, molecular ecology, and environmental monitoring technology. Background Technology

[0002] Estuaries, coastal waters, and inland waters are important sites for carbon cycling. Inert dissolved organic carbon (RDOC) in water bodies, due to its resistance to microbial degradation, can be long-term stored through the "microbial carbon pump" process, making it a core carrier of carbon sequestration in water bodies. Accurately assessing the dynamic changes and stability of RDOC is crucial for understanding the carbon sequestration potential of water bodies.

[0003] With rapid urbanization and industrialization, large amounts of domestic sewage are continuously entering water bodies. Sewage is rich in nutrients such as carbon, nitrogen, and phosphorus, which significantly alters the stoichiometry of water bodies (especially the carbon-to-nitrogen ratio and C / N ratio), thereby affecting the metabolic strategies and structural succession of microbial communities. Existing research indicates that sewage input can change the kinetics of organic carbon degradation in water bodies and may reduce the chemical stability of locally generated organic carbon (RDOCs), thus weakening the carbon sequestration potential of water bodies.

[0004] Currently, the main technologies for assessing the impact of wastewater on aquatic ecosystems include the following categories, but each has significant shortcomings: (1) Conventional water quality indicator monitoring methods (such as chemical oxygen demand, total nitrogen, total phosphorus, etc.): can only reflect macroscopic carbon and nitrogen concentration changes, cannot reveal the evolution of molecular composition and chemical stability of RDOC, and cannot distinguish the changes in the "quality" of RDOC (such as aromaticity and oxidation state).

[0005] (2) Three-dimensional fluorescence spectroscopy-parallel factor analysis (EEM-PARAFAC): This method can only detect organic molecules with fluorescent properties (such as humic substances) and cannot cover non-fluorescent RDOC components (such as saturated alicyclic compounds). In estuaries and other water bodies with high turbidity and high dynamics, its detection results are often inconsistent with the actual trend of RDOC changes, making it difficult to use as a characterization method of RDOC alone.

[0006] (3) Microbial diversity analysis based on 16S rRNA gene amplicon sequencing: Although this method can obtain changes in community composition, it only stays at the level of species classification and cannot reveal the functional potential of microbial communities, let alone the true transcriptional activity of genes. Therefore, it is difficult to establish a direct causal relationship between microbial metabolic activities and changes in RDOC molecular structure.

[0007] (4) Conventional metagenomic analysis: This method can reveal the abundance (genetic potential) of functional genes in a community at the DNA level, but the existence of genes is not the same as their transcriptional expression. Many key metabolic pathways may be in a state of transcriptional silencing due to environmental stress or substrate lack, and metagenomics alone cannot capture the community's immediate response to environmental disturbances.

[0008] Therefore, there is an urgent need to establish a systematic assessment method that can simultaneously analyze changes in the molecular composition of RDOCs and the transcriptional activity of microbial communities, revealing the intrinsic mechanism by which wastewater input affects water carbon sinks at the gene expression level, and providing more accurate scientific basis and detection tools for early warning of water carbon sink function, pollution control and ecological restoration. Summary of the Invention

[0009] This invention aims to address the problem that existing technologies cannot systematically assess the impact of wastewater input on the succession of aquatic microbial communities, transcriptional activity of functional genes, and stability of RDOCs. It provides a multi-omics integrated assessment method based on microcosmic simulation culture, combined BDOC and FT-ICRMS, 16S rRNA amplicon sequencing, metagenomic sequencing, and metagenomic transcriptome sequencing.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: This invention discloses a method for assessing the impact of wastewater input on aquatic carbon sinks and microbial communities based on multi-omics integration. The method includes the following steps: Step a) Determine the basic nutrient parameters of the target water body and determine its initial carbon-nitrogen (C / N) ratio; Step b) Based on the measurement results of step a), a micro-universe simulation culture system was constructed, and a control group without sewage and an experimental group with 5% sewage were set up. The simulation culture was carried out under constant temperature and dark conditions of 20±1℃, and water samples were collected at different culture stages. Step c) The dissolved organic carbon concentration in the control group and experimental group in step b) was determined by the biodegradable dissolved organic carbon method to obtain the degradation curve of dissolved organic carbon concentration over time; the end time of the fastest degradation rate stage in the experimental group was determined as the sampling time point of the short-term stress period, and the starting time of the degradation curve of the experimental group tending to flatten and the concentration stabilizing was determined as the sampling time point of the long-term stable period. Step d) Select water samples based on the sampling time points for the short-term stress period and long-term stable period determined in step c). Use solid phase extraction combined with Fourier transform ion cyclotron resonance mass spectrometry to analyze the molecular composition of dissolved organic matter. Use carboxyl-like alicyclic substances as molecular markers of inert dissolved organic carbon (RDOC) to obtain the abundance ratio and chemical stability index of inert dissolved organic carbon (RDOC). Step e) Take water samples at the sampling time points determined in step c) for the short-term stress period and the long-term stable period, extract total microbial DNA and total RNA, and perform 16S rRNA gene amplicon sequencing, metagenomic sequencing and metagenomic sequencing, respectively. Step f) Based on the 16S rRNA gene amplicon sequencing results in step e), analyze the diversity and composition changes of the microbial community in the control group and the experimental group, obtain the relative abundance of each microbial group, and identify the main microbial groups that have changed. Step g) Based on the metagenomic and metatranscriptomic sequencing results in step e), analyze the changes in the abundance and transcriptional activity of functional genes in the control group and the experimental group, and identify the key functional genes that have changed. Step h) combines the abundance percentage and chemical stability index of inert dissolved organic carbon (RDOC) obtained in step d), the major microbial groups that have changed obtained in step f), and the key functional genes that have changed obtained in step g) to identify the core microbial groups and their key functional genes that are significantly associated with changes in inert dissolved organic carbon (RDOC), thereby assessing the impact of wastewater input on water carbon sinks.

[0011] Preferably, the method for defining the carboxyl-like alicyclic substance in step d) is as follows: in the Van Krevelen diagram obtained by Fourier transform ion cyclotron resonance mass spectrometry analysis, the molecular region with a hydrogen-to-carbon ratio between 0.7 and 1.5 and an oxygen-to-carbon ratio between 0.1 and 0.67 is defined as a carboxyl-like alicyclic substance.

[0012] Preferably, the chemical stability indicators in step d) include the average modified aromaticity index and the average nominal carbon oxidation state of carboxyl-like alicyclic molecules; wherein, a decrease in the modified aromaticity index or an increase in the nominal carbon oxidation state characterizes a decrease in the chemical stability of inert dissolved organic carbon (RDOC).

[0013] Preferably, the microbial groups that change in step f) include: one or more microbial groups whose relative abundance in the experimental group increases significantly by more than 10% during the short-term stress period, compared with the control group, and one or more microbial groups whose relative abundance increases by more than 10% during the long-term stable period.

[0014] Preferably, the key functional genes that change in step g) are determined according to the following method steps: Step 1) During the short-term stress period, the expression levels of functional genes in the experimental group changed significantly compared to the initial point, which can be identified as key functional genes that have changed. Step 2) During the long-term stable period, the expression levels of functional genes in the experimental group changed significantly compared to the short-term stress period, which can be identified as key functional genes that have changed. Step 3) In the control group, the expression levels of the above genes did not show this temporal variation pattern; Step 4) The criteria for determining a significant up- or down-adjustment are: the absolute value of the change factor compared to the initial point or the previous period is greater than or equal to 2, and the p-value is less than 0.05 after statistical testing to be significant.

[0015] Preferably, the method for identifying significantly related major microbial taxa and their key functional genes in step h) is as follows: Spearman correlation analysis was performed on the relative abundance of each microbial group obtained in step f) and the abundance and expression level of key functional genes obtained in step g). The absolute value of the correlation coefficient was greater than 0.7 and the p value was less than 0.05 as the judgment criteria to identify the microbial groups and functional genes that were statistically significantly associated with the inert dissolved organic carbon (RDOC) index.

[0016] Preferably, the method further includes a comprehensive assessment, which includes: based on the results of steps d) to h), comprehensively determining whether the wastewater input leads to a decrease in the chemical stability of inert dissolved organic carbon (RDOC), and identifying the core microbial groups and functional genes driving this change.

[0017] This invention relates to an application of a method for assessing the impact of wastewater input on water carbon sinks and microbial communities based on multi-omics integration, including applications in carbon sink health assessment, wastewater ecological risk early warning, or water ecological restoration effect evaluation in estuaries, coastal areas, lakes, or rivers.

[0018] Compared with existing technologies, this invention has the following advantages: It integrates BDOC and FT-ICRMS with multi-omics analysis, simultaneously achieving RDOC molecular composition characterization, microbial community structure analysis, functional gene potential detection, and transcriptional activity verification, overcoming the technical problem that existing technologies cannot reveal the mechanism of carbon stability change at the gene expression level; it reveals for the first time the temporal succession law of microbial community function after sewage input at the gene expression level, providing a new theoretical framework for understanding the regulatory mechanism of microbial carbon pumps; and it can identify key responding microbial groups and functional genes driving changes in RDOC chemical stability, providing potential biological indicators for water carbon sink health assessment. Attached Figure Description

[0019] Figure 1 This is a graph showing the change in DOC concentration over culture time as determined by the BDOC method in the preliminary experiment of this invention.

[0020] Figure 2 This is a Van Krevelen diagram showing the change in the composition of soluble organic matter molecules of this invention over culture time.

[0021] Figure 3 This is a graph showing the change in CRAM-like fluorescence intensity over culture time according to the present invention.

[0022] Figure 4 This is a diagram showing the impact of wastewater input of the present invention on the DOC of the Qiantang River estuary.

[0023] Figure 5 This is a Van Krevelen diagram showing the composition of dissolved organic matter molecules under different C / N ratios according to the present invention.

[0024] Figure 6 This is a graph showing the changes in the number and abundance of CRAM-like molecules with culture time under different C / N ratios according to the present invention.

[0025] Figure 7 These are structural feature diagrams of CRAM-like substances under different C / N ratios according to the present invention.

[0026] Figure 8 This is a diagram showing the alpha diversity analysis of microorganisms under the wastewater input conditions of this invention.

[0027] Figure 9 This is a diagram showing the microbial Beta diversity analysis under the wastewater input conditions of this invention.

[0028] Figure 10 This is a diagram showing the family-level and genus-level community composition of microorganisms under the wastewater input conditions of this invention.

[0029] Figure 11 This invention provides a map of key functional genes that reveal the response of microbial functions to wastewater input through metagenomic analysis.

[0030] Figure 12 This invention provides a map of key functional genes that reveal the microbial functional response to wastewater input through metatranscriptomics.

[0031] Figure 13 This is a network diagram showing the correlation between the microbial community and differential metabolic pathways in this invention.

[0032] in, Figure 2In the figure, each dot represents a unique molecule identified by FT-ICRMS, with the x-axis representing the oxygen / carbon ratio (O / C) and the y-axis representing the hydrogen / carbon ratio (H / C). The color gradient of the dots represents the normalized mass spectrum peak intensity, decreasing from red through yellow to green, visually reflecting the distribution and relative abundance of different types of organic compounds in chemical space. Based on classic molecular region division, the figure marks the typical distribution regions of eight major categories of substances: I: Lipids; II: Proteins; III: Amino sugars; IV: Carbohydrates; V: Unsaturated hydrocarbons; VI: CRAM-like substances (carboxyl alicyclic compounds); VII: Tannins; VIII: Polycyclic aromatic hydrocarbons.

[0033] Figure 4 (a) Dynamic changes in DOC concentration during the 28-day culture period in the experimental group (Group S). The black dotted line represents the exponential fitting curve y = 35.31·exp(-x / 2.55) + 15.72, R 2 The value was 0.986; (b) Dynamic changes in DOC concentration during the 28-day culture period in the control group (Q group). The black dotted line represents the exponential fitting curve y=1.69·exp(-x / 0.69)+2.38, R 2 It is 0.970.

[0034] Figure 5 In the middle: (a) Distribution of soluble organic matter molecular composition in the experimental group (S group) and (b) the control group (Q group) on days 0, 2 and 28 (VanKrevelen figure).

[0035] Figure 6 In the middle: (a) the bar chart shows the total number of molecular formulas identified in each sample and the number of molecules classified as CRAM-like; (b) the bar chart shows the relative contribution of CRAM-like molecules to the total fluorescence intensity in each sample.

[0036] Figure 7 (a) Changes in the average aromaticity index (AImod) of CRAM-like molecules; (b) Changes in the average oxidation state number (NOSC) of CRAM-like molecules.

[0037] Figure 8 In the middle: Based on the Chao1 (a) and Shannon (b) indices, the comparison of microbial community alpha diversity between the control group (Q) and the experimental group (S) on days 0, 2, and 28 is presented.

[0038] Figure 9In this study, principal coordinate analysis (PCoA, a) and nonmetric multidimensional scaling (NMDS, b) based on Bray-Curtis distance revealed differences in community structure among different treatment groups. The PCoA explained 58% of the first two axes, and the NMDS stress value was 0.035.

[0039] Figure 10 In the middle: the bar chart shows the relative abundance of the microbial community at the family (a) and genus (b) levels at different culture time points (0, 2, 28 days) in the experimental group (S group) and the control group (Q group). Only the top 20 dominant families (a) and genera (b) in abundance are shown, and the rest are combined into "Others".

[0040] Figure 11 In the middle: (a) Gene abundance heatmap of COG functional categories in the experimental group (S) and the control group (Q) on days 0, 2 and 28, with key differences in COG functional categories marked in red; (b) Gene abundance heatmap of KEGG functional categories in the experimental group (S) and the control group (Q) on days 0, 2 and 28, with key differences in KEGG secondary expression pathways marked in red.

[0041] Figure 12 In the middle: (a) Heatmap of gene expression levels of COG functional categories in the experimental group (S) and the control group (Q) on days 0, 2 and 28, with key differences in COG functional categories highlighted in red; (b) Heatmap of gene expression levels of KEGG functional categories in the experimental group (S) and the control group (Q) on days 0, 2 and 28, with key differences in KEGG secondary expression pathways highlighted in red. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Technical solution / principle: A method for assessing the impact of wastewater input on aquatic carbon sinks and microbial communities based on multi-omics integration: Step 1: Determine the analysis method and key sampling time points The water samples were analyzed using both biodegradable dissolved organic carbon (BOC) and Fourier transform ion cyclotron resonance (FTIR) mass spectrometry (TCRMS). The concentration of dissolved organic carbon (DOC) over time was determined using BOC, and degradation curves were plotted (Figure 1). Based on the characteristics of the degradation curves, the endpoint of the fastest degradation phase was identified as the sampling point for the short-term stress period, and the starting point where the degradation curve flattened and the concentration stabilized was identified as the sampling point for the long-term stable period. Simultaneously, Fourier transform ion cyclotron resonance (FTIR) mass spectrometry was used to analyze the molecular composition of the dissolved organic matter.

[0044] Step Two: Constructing a Microcosm Simulation Cultivation System Surface water samples were collected from the water bodies to be tested and, after pretreatment, were divided into a control group and an experimental group. The experimental group was supplemented with 5% simulated sewage, while the control group was not supplemented with sewage. Simulated culture was carried out under constant temperature and dark conditions, and water samples were collected at different stages of culture.

[0045] Step 3: Analyze the dynamic changes of inert dissolved organic carbon Water samples were collected at multiple predetermined time points during the cultivation process. The concentration of dissolved organic carbon at each time point was determined using the biodegradable dissolved organic carbon method to obtain degradation characteristic parameters. Simultaneously, water samples from the initial point, short-term stress period, and long-term stable period were selected. Solid-phase extraction combined with Fourier transform ion cyclotron resonance mass spectrometry was used to analyze the molecular composition of dissolved organic matter. Carboxyl-like alicyclic substances were identified as molecular markers of inert dissolved organic carbon using Van Krevelen plots. Their abundance proportion was calculated, and the corrected aromaticity index and nominal oxidation state of carbon were calculated to assess the relative abundance and chemical stability changes of inert dissolved organic carbon.

[0046] Step 4: Multi-omics analysis of microbial community structure and function Water samples were selected at the same time points as in step three (initial point, short-term stress period, and long-term stable period), and total microbial DNA and total RNA were extracted. 16S rRNA gene amplicon sequencing analysis, metagenomic sequencing analysis, and metagenomic transcriptome sequencing analysis were performed, respectively.

[0047] Step 5: Identify key functional genes Based on the combined analysis of metagenomics and metatranscriptomics, the abundance and transcriptional activity of functional genes in the control and experimental groups were analyzed. Functional genes that showed significant changes during short-term stress or long-term stable periods were screened out and identified as the main functional genes in response to sewage input.

[0048] Step Six: Integration, Analysis, and Comprehensive Evaluation The microbial community composition, functional gene abundance, and expression levels obtained in step four were integrated and analyzed from multiple dimensions to identify microbial groups and their associated functional genes that underwent significant changes during short-term stress and long-term stable periods. Based on the above analysis results, the impact of wastewater input on water carbon sequestration was comprehensively assessed.

[0049] Example 1: Preliminary experiment to determine the method and key time points for the combined use of BDOC and FT-ICRMS This embodiment describes the preliminary experiments conducted during the development of this invention to determine the optimal analysis method and key time points. In practical application of the method of this invention, the following determined parameters can be directly used (BDOC and FT-ICRMS are used in combination, with the rate inflection point and stability start point of the BDOC degradation curve as the basis for determining the sampling time point), without needing to repeat the comparative verification process of this embodiment.

[0050] 1.1 Experimental Materials and Pretreatment Water samples were collected from the target water body (such as Zhutoujiao Dam at the mouth of the Qiantang River), with approximately 20L of surface water collected. All equipment in contact with the samples underwent carbon removal pretreatment: it was rinsed sequentially with tap water, reverse osmosis water, and ultrapure water, then immersed in a 2mol / L hydrochloric acid solution for 48 hours, and finally rinsed with ultrapure water until neutral. Glassware was then incinerated in a muffle furnace at 450℃ for 4 hours.

[0051] 1.2 Determination by BDOC method Water samples were filtered through a 0.7 μm glass fiber membrane and then dispensed into 4 L brown glass bottles. The bottles were incubated in the dark at 20 ± 1 °C for 28 days. Samples were taken at 13 time points: days 0, 0.25, 0.5, 1, 1.5, 2, 3, 4, 5, 7, 14, 21, and 28. The dissolved organic carbon concentration was determined using a total organic carbon analyzer.

[0052] The results are as follows Figure 1 As shown, DOC concentration decreased rapidly in the first two days of incubation, after which the degradation rate slowed significantly. Based on this, day 2 was determined to be the critical sampling point for the short-term stress period and day 28 for the long-term stable period.

[0053] 1.3 FT-ICRMS Method Water samples taken on days 0, 2, and 28 were subjected to solid-phase extraction and FT-ICRMS analysis. The results are as follows: Figure 2 , 3 As shown, as the culture time increased from 0 days to 28 days, the number of molecules in the CRAM-like region in the Van Krevelen plot gradually increased, and the abundance of CRAM-like molecules rose from 55% to 85%. This indicates that the FT-ICRMS method can effectively track the accumulation process of RDOC.

[0054] 1.4 Preliminary Experiment Conclusions The BDOC method can quantitatively reflect changes in the bioavailability of dissolved organic carbon and determine key degradation time points; the FT-ICRMS method can analyze the composition and structural evolution of inert dissolved organic carbon at the molecular level. Therefore, this invention adopts the combined BDOC and FT-ICRMS methods as the core analytical methods, which complement each other and are used for joint evaluation.

[0055] Example 2: BDOC and FT-ICRMS combined analysis of RDOC dynamics 2.1 Construction of a Microcosm Simulation Cultivation System Surface water samples were collected from the target water body and filtered through a 0.7 μm glass fiber membrane. A simulated wastewater stock solution was prepared (carbon source: sucrose, a mixture of volatile fatty acids, and humic acid; nitrogen source: ammonium chloride, urea, and peptone; phosphorus source: dipotassium hydrogen phosphate and potassium dihydrogen phosphate; surfactant: sodium dodecylbenzene sulfonate; buffer: sodium bicarbonate, pH 7.0±0.2). Based on the total organic carbon and total nitrogen measurements of the initial water samples, 5% by volume of simulated wastewater was added to the raw water as the experimental group (Group S), and the raw water without added wastewater served as the control group (Group Q). 24 L of water from each group was divided into six 4 L brown glass bottles and incubated in the dark at 20±1℃ for 28 days. The bottles were gently shaken and briefly opened for ventilation daily.

[0056] 2.2 Determination by BDOC method Following the BDOC method in Example 1, the DOC concentration of the water sample was measured on day 28, and the degradation rate was calculated: the DOC removal rate of group S on day 28 was 70%, and that of group Q was 44%.

[0057] 2.3 Solid-phase extraction and FT-ICRMS analysis Solid-phase extraction (SPE) was performed on water samples collected on days 0, 2, and 28 of culture. The water samples were adjusted to pH 2.0 ± 0.1, loaded onto an activated C18 SPE column, washed with acidified ultrapure water, dried under nitrogen, and eluted with high-purity methanol. The eluent (>1 mL) was collected and stored at -20°C in the dark. The extracted samples were then analyzed by FT-ICRMS.

[0058] 2.4 Calculation of RDOC Stability Index After mass spectrometry data were calibrated, Van Krevelen plots were generated in Origin software (O / C on the x-axis, H / C on the y-axis, and peak intensity as color mapping). Regions with H / C between 0.7 and 1.5 and O / C between 0.1 and 0.67 were defined as carboxyl-like alicyclic compounds (CRAM-like) and used as molecular markers for RDOC. The abundance percentage of CRAM-like molecules was calculated. For all molecules identified as CRAM-like, the corrected aromaticity index (AI) was calculated sequentially. mod) and nominal oxidation state of carbon (NOSC). Calculate the AI ​​of all CRAM-like molecules in each sample. mod The arithmetic mean of NOSC.

[0059] 2.5 Experimental Results Table 1: Distribution of dissolved organic matter molecules in eight chemical regions (I-VIII) based on FT-ICRMS analysis S-day28 185 248 129 1 0 1812 43 124 S-day2 325 230 37 1 70 1002 41 89 S-day0 240 207 11 1 10 663 31 108 Q-day0 149 161 11 0 0 392 23 42 Q-day2 206 216 49 2 1 695 38 57 Q-day28 191 246 113 1 6 1585 59 161 The results are as follows Figure 5-7 As shown in Table 1, the abundance of CRAM-like molecules in the experimental group on day 28 was 65.57%, lower than the 69.12% in the control group; the average AI of CRAM-like molecules in the experimental group was... mod The NOSC concentration in the experimental group increased from 0.3993 to 0.4109, a significant increase of 0.0116%, far less than that in the control group (from 0.3522 to 0.4067, an increase of 0.0545). The average NOSC concentration in the experimental group increased from -0.4272 to -0.2312 (an increase of 0.1960), consistently higher than that in the control group (from -0.4664 to -0.4098, an increase of 0.0566). These results indicate that wastewater input significantly reduces the chemical stability of RDOCs.

[0060] Example 3: Microbial Community Structure Analysis 3.1 DNA extraction and 16S rRNA gene sequencing 500 mL of water samples were collected at the same time points as in Example 2 (days 0, 2, and 28), and the microorganisms were collected by filtration through a 0.22 μm glass fiber membrane. Total DNA was extracted using a DNA extraction kit, and PCR amplification was performed using universal primers 338F / 806R for the V3-V4 region of the bacterial 16S rRNA gene. Paired-end sequencing was then performed on the Illumina platform.

[0061] 3.2 Data Analysis Quality control, noise reduction, and ASV clustering were performed using the QIIME2 workflow, and species annotation was performed using the SILVA database. The Chao1 and Shannon indices (Alpha diversity) were calculated, and principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) (Beta diversity) were performed based on the Bray-Curtis distance. Relative abundance histograms at the family and genus levels were plotted.

[0062] 3.3 Experimental Results like Figure 8 As shown, Alpha diversity analysis revealed that the Chao1 and Shannon indices of the experimental group were significantly lower than those of the control group on days 2 and 28 (p<0.05), indicating that wastewater input led to a decrease in microbial species richness and community evenness. Figure 9 As shown, Beta diversity analysis revealed that the cumulative explanatory power of the first two axes of PCoA was 58%, the NMDS stress value was 0.035, and the experimental group and the control group were clearly separated, indicating that sewage input significantly altered the microbial community structure.

[0063] like Figure 10 As shown, community composition analysis revealed that on day 0, the initial community was dominated by Moraxellaceae (21.50%) and Pseudomonasceae (18.11%); on day 2, the experimental group was dominated by Trichophytonceae (…). Comamonadaceae The percentage of Enterobacteriaceae ( ) increased sharply to 32.60%. Enterobacteriaceae The figure increased to 12.08%, which is considered to be above average. Comamonas (30.48%) and Citrobacter (12.08%) became the dominant group; on day 28, the experimental group community underwent a fundamental remodeling, with the Rallariaceae family ( Reyranellaceae ,20.70%) and Rhodococcus ( Rhodocyclaceae (8.60%) replaced the previous dominant group, belonging to the horizontal level. Reyranella (20.70%) became the absolutely dominant genus. The results indicate that wastewater input drives the microbial community from a state dominated by... Comamonas and Citrobacter The directional succession of the "degradation-type" group, represented by [the group name], is based on [the following]. Reyranella The "synthetic" group is represented by this.

[0064] Example 4: Metagenomics and Metatranscriptomics Reveal Key Functional Genes in Response to Wastewater Input 4.1 RNA extraction and metatranscriptome sequencing 1 L of water samples were collected at the same time points as in Example 2 (days 0, 2, and 28), and microorganisms were collected by filtration through a 0.22 μm glass fiber membrane. Total RNA was extracted using an RNA extraction kit, and after rRNA removal, strand-specific metagenomic sequencing (PE150) was performed using the Illumina platform. Simultaneously, total DNA was extracted from water samples at the same time points and metagenomic sequencing (PE150) was performed.

[0065] 4.2 Metagenomic Data Analysis The raw metagenomic data underwent quality control and assembly. Prodigal was used to predict open reading frames, and a non-redundant gene set was constructed. Diamond was used to align gene sequences to the COG and KEGG databases to obtain functional gene abundance (normalized by TPM). Based on the functional abundance matrix, the differences in functional gene abundance between group S and group Q at different time points were compared.

[0066] 4.3 Metatranscriptome Data Analysis Raw metatranscriptome data underwent quality control and rRNA removal. De novo assembly was performed using Trinity to predict open reading frames and construct a non-redundant gene set. Diamond annotation of the COG and KEGG databases was used to obtain gene expression levels (normalized to TPM). Differential expression analysis was performed using DESeq2 to identify significantly differentially expressed genes between the experimental and control groups, as well as between different time points within the experimental group. KEGG pathway enrichment analysis was then conducted on these differentially expressed genes.

[0067] 4.4 Experimental Results (1) Changes in functional potential revealed by metagenomics like Figure 11 As shown in a, COG functional annotations show that the abundance of functional genes related to energy production and conversion (C), amino acid transport and metabolism (E), transcription (K), inorganic ion transport and metabolism (P), and signal transduction (T) continued to increase over time in the control group; while in the experimental group, the abundance of these genes increased significantly on the second day of culture and then fell back to the initial level by the 28th day.

[0068] KEGG secondary pathway analysis ( Figure 11 b) Further evidence: In the experimental group, the abundance of functional genes related to pathways such as cell motility, signal transduction, and membrane transport showed a trend of first significantly increasing and then decreasing; while in the control group, the abundance of these pathways showed a continuous increase.

[0069] (2) Gene expression changes revealed by metatranscriptomics like Figure 12 As shown in Figure a, the COG functional expression profile reveals that core functional categories, including C, E, K, P, and T, also exhibited a trend of initial upregulation followed by downregulation in the experimental group. KEGG pathway expression analysis ( Figure 12 b) indicates that in the experimental group, the expression of secondary pathways related to cell motility, signal transduction, membrane transport, and cell growth and death all showed a trend of first increasing and then decreasing. The corresponding tertiary pathways mainly include flagellar assembly, two-component system, ABC transporter and cell cycle.

[0070] (3) Temporal variation characteristics of major functional genes Combined metagenomic and metatranscriptomic results revealed distinct temporal changes in the functional genes of the microbial community after wastewater input: On day 2 of culture (short-term stress period), the abundance and expression levels of functional genes related to chemotaxis, flagellar assembly, two-component systems, ABC transporters, and exogenous pollutant degradation significantly increased; on day 28 of culture (long-term stationary period), the metabolic focus shifted to endogenous maintenance, with functional genes related to purine metabolism, amino acid biosynthesis, and pyruvate metabolism becoming dominant, and their expression levels significantly higher than on day 2, while exogenous degradation-related functional genes, which were highly expressed on day 2, were significantly downregulated. In contrast, the control group maintained a stable co-metabolic pattern throughout, without exhibiting this temporal change.

[0071] Example 5: Correlation analysis between microbial communities and key metabolic pathways 5.1 Data Source This embodiment conducts Spearman correlation analysis based on the following data: Microbial community composition data: derived from the 16S rRNA gene amplicon sequencing results of Example 3, namely the relative abundance of genus-level microorganisms in the control group (Q group) and experimental group (S group) samples at each time point (days 0, 2, and 28).

[0072] Key metabolic pathway data: derived from the results of the metatranscriptome differential expression analysis in Example 4, mainly including exogenous pollutant degradation pathways specifically activated during the short-term stress period (such as dioxin degradation, polycyclic aromatic hydrocarbon degradation, atrazine degradation, etc.) and endogenous synthesis pathways specifically enriched during the long-term stable period (such as valine / leucine / isoleucine biosynthesis, lysine biosynthesis, purine metabolism, etc.).

[0073] 5.2 Correlation Analysis Results like Figure 13 As shown, Spearman correlation analysis identified microbial groups significantly associated with key metabolic pathways: Short-term stress period (day 2): Comamonas and Citrobacter The relative abundance of the genera was significantly positively correlated with the degradation pathways of exogenous pollutants (ρ>0.7, p<0.05), indicating that these two types of bacteria are the core functional groups that respond rapidly after sewage input and dominate the degradation of exogenous organic matter.

[0074] Long-term stable period (day 28): Reyranella The relative abundance of the genera was significantly positively correlated with endogenous synthetic pathways (ρ>0.7, p<0.05), indicating that this bacterial community is the dominant group that later shifts to anabolism and maintains cellular homeostasis.

[0075] Other genera (such as) Pseudomonas , AcinetobacteThe absolute values ​​of the correlation coefficients between r and the aforementioned key pathways did not exceed 0.6, and did not reach the strong correlation threshold.

[0076] 5.3 Conclusion The correlation analysis results further validated the findings of Examples 3 and 4: Wastewater input drives the directional succession of microbial communities, causing different functional groups to sequentially gain dominance, thereby determining changes in community function. Comamonas , Citrobacter and Reyranella They are the key microbial groups that drive this functional transformation.

[0077] As demonstrated by the examples, the following technical effects were achieved after conducting experiments using the method of this invention: Wastewater input reduces the proportion and chemical stability of inert dissolved organic carbon, weakening the carbon sink function of water bodies; simultaneously, it significantly alters the microbial community structure, reducing community richness and evenness, with the community undergoing directional succession throughout the cultivation phase. The abundance and transcriptional activity of microbial functional genes exhibit temporal differences, with high expression of genes related to exogenous pollutant degradation during the short-term stress period, while endogenous metabolic genes dominate during the long-term stable period. The experiments clarified the core microbial groups and corresponding functional pathways at different stages, confirming that this method can simultaneously analyze the characteristics of organic carbon molecules, microbial communities, and gene expression changes, accurately revealing the intrinsic mechanisms by which wastewater affects the carbon cycle of water bodies, and is suitable for water-related ecological assessment work.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, alterations, and equivalent transformations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for assessing the impact of wastewater input on aquatic carbon sinks and microbial communities based on multi-omics integration, characterized in that, The method includes the following steps: Step a) Determine the basic nutrient parameters of the target water body to determine its initial carbon-nitrogen ratio; Step b) Based on the measurement results of step a), a micro-universe simulation culture system was constructed, and a control group without sewage and an experimental group with 5% sewage were set up. The simulation culture was carried out under constant temperature and dark conditions of 20±1℃, and water samples were collected at different culture stages. Step c) The dissolved organic carbon concentration in the control group and experimental group in step b) was determined by the biodegradable dissolved organic carbon method to obtain the degradation curve of dissolved organic carbon concentration over time; the end time of the fastest degradation rate stage in the experimental group was determined as the sampling time point of the short-term stress period, and the starting time of the degradation curve of the experimental group tending to flatten and the concentration stabilizing was determined as the sampling time point of the long-term stable period. Step d) Select water samples based on the sampling time points for the short-term stress period and long-term stable period determined in step c). Use solid phase extraction combined with Fourier transform ion cyclotron resonance mass spectrometry to analyze the molecular composition of dissolved organic matter. Use carboxyl-like alicyclic substances as molecular markers of inert dissolved organic carbon (RDOC) to obtain the abundance ratio and chemical stability index of inert dissolved organic carbon (RDOC). Step e) Take water samples at the sampling time points determined in step c) for the short-term stress period and the long-term stable period, extract total microbial DNA and total RNA, and perform 16S rRNA gene amplicon sequencing, metagenomic sequencing and metagenomic sequencing, respectively. Step f) Based on the 16S rRNA gene amplicon sequencing results in step e), analyze the diversity and composition changes of the microbial community in the control group and the experimental group, obtain the relative abundance of each microbial group, and identify the main microbial groups that have changed. Step g) Based on the metagenomic and metatranscriptomic sequencing results in step e), analyze the changes in the abundance and transcriptional activity of functional genes in the control group and the experimental group, and identify the key functional genes that have changed. Step h) combines the abundance percentage and chemical stability index of inert dissolved organic carbon (RDOC) obtained in step d), the major microbial groups that have changed obtained in step f), and the key functional genes that have changed obtained in step g) to identify the core microbial groups and their key functional genes that are significantly associated with changes in inert dissolved organic carbon (RDOC), thereby assessing the impact of wastewater input on water carbon sinks.

2. The method for assessing the impact of wastewater input on water body carbon sinks and microbial communities based on multi-omics integration according to claim 1, characterized in that, The method for defining the carboxyl-like alicyclic substances mentioned in step d) is as follows: in the Van Krevelen diagram obtained by Fourier transform ion cyclotron resonance mass spectrometry analysis, the molecular region with a hydrogen-to-carbon ratio between 0.7 and 1.5 and an oxygen-to-carbon ratio between 0.1 and 0.67 is defined as a carboxyl-like alicyclic substance.

3. A method for assessing the impact of wastewater input on water body carbon sinks and microbial communities based on multi-omics integration, as described in claim 1 or 2, characterized in that... The chemical stability indicators mentioned in step d) include the average modified aromaticity index and the average nominal carbon oxidation state of carboxyl-like alicyclic molecules; wherein, a decrease in the modified aromaticity index or an increase in the nominal carbon oxidation state characterizes a decrease in the chemical stability of inert dissolved organic carbon (RDOC).

4. The method for assessing the impact of wastewater input on water body carbon sinks and microbial communities based on multi-omics integration according to claim 1, characterized in that, The microbial groups that changed in step f) include: one or more microbial groups whose relative abundance increased by more than 10% in the experimental group during the short-term stress period, compared with the control group; and one or more microbial groups whose relative abundance increased by more than 10% in the long-term stable period.

5. The method for assessing the impact of wastewater input on water body carbon sinks and microbial communities based on multi-omics integration according to claim 1, characterized in that, The key functional genes that change in step g) are determined according to the following steps: Step 1) During the short-term stress period, the expression levels of functional genes in the experimental group changed significantly compared to the initial point, which can be identified as key functional genes that have changed. Step 2) During the long-term stable period, the expression levels of functional genes in the experimental group changed significantly compared to the short-term stress period, which can be identified as key functional genes that have changed. Step 3) In the control group, the expression levels of the above genes did not show this temporal variation pattern; Step 4) The criteria for determining a significant up- or down-adjustment are: the absolute value of the change factor compared to the initial point or the previous period is greater than or equal to 2, and the p-value is less than 0.05 after statistical testing to be significant.

6. The method for assessing the impact of wastewater input on water body carbon sinks and microbial communities based on multi-omics integration according to claim 1, characterized in that, The method for identifying significantly related major microbial taxa and their key functional genes in step h) is as follows: Spearman correlation analysis was performed on the relative abundance of each microbial group obtained in step f) and the abundance and expression level of key functional genes obtained in step g). The absolute value of the correlation coefficient was greater than 0.7 and the p value was less than 0.05 as the judgment criteria to identify the microbial groups and functional genes that were statistically significantly associated with the inert dissolved organic carbon (RDOC) index.

7. A method for assessing the impact of wastewater input on aquatic carbon sinks and microbial communities based on multi-omics integration according to any one of claims 1-6, characterized in that, It also includes a comprehensive assessment, which includes: based on the results of steps d) to h), comprehensively determining whether the wastewater input leads to a decrease in the chemical stability of inert dissolved organic carbon (RDOC), and identifying the core microbial groups and functional genes driving this change.

8. The application of the method according to any one of claims 1-7, characterized in that, This includes applications in water body carbon sink health assessment, wastewater ecological risk early warning, or water ecological restoration effect evaluation, where the water body includes estuaries, coastal areas, lakes, and rivers.

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