Method for detecting influence of dead algae on structure and function of sediment microbial community

Abstract

The invention provides a method for detecting the influence of dead algae on the structure and function of a sediment microbial community. The method comprises the following steps: collecting a sample; measuring the physical and chemical properties of the sediments and the overlying water; extracting total DNA (deoxyribonucleic acid) of the sediment, then performing high-throughput sequencing, and performing OUT (optical under test) clustering on the sequence to obtain microflora composition distribution; diversity analysis is carried out based on OUT clustering, a co-occurrence network of microorganisms is constructed, and FAPROTAX function prediction is carried out; analyzing the abundance of functional genes related to phosphorus, nitrogen, sulfur and carbon cycles in the sediment; evaluating the functional potential of microorganisms in the sediment; and based on partial least square path modeling, analyzing a relationship among nutrient substances, microbial communities, functional genes and a nutrition cycle process of the sediment. By combining a plurality of modules such as microflora composition, interaction and functional gene abundance, the influence of dead algae on the microflora can be more comprehensively and accurately detected.

Description

Technical Field

[0001] The invention relates to the technical field of ecological environment protection, and in particular to a method for detecting the influence of dead algae on the structure and function of sediment microbial community. Background Art

[0002] When algae die, their decomposition process consumes oxygen in the water, causing serious impacts on the aquatic ecosystem, leading to hypoxia or even death of other aquatic organisms. When dead algae are deposited, it may also cause changes in the number and composition of microorganisms in the water-sediment system, thereby affecting their functions. For example, certain bacteria may proliferate to degrade decaying organic matter, affecting the competition and cooperation between microorganisms. Microbial communities in sediments play a vital role in ecological functions such as organic matter degradation, nutrient cycling, and sediment improvement. In addition, benthic organisms (such as Spirulina and other aquatic organisms) rely on nutrients in water and sediments, and the decomposition products of dead algae may affect the living space and food sources of benthic organisms.

[0003] Therefore, it is very important to study the impact of dead algae on the structure and function of sediment microbial communities. Understanding the factors and pathways that affect the nutrient cycle process in sediments after the deposition of dead algae will help with algal bloom management and eutrophication control. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for detecting the influence of dead algae on the structure and function of sediment microbial community.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A method for detecting the effect of dead algae on the structure and function of sediment microbial communities comprises the following steps:

[0007] (1) collecting samples, wherein the samples include water samples, algae samples and sediment samples, performing a death treatment on the algae samples, and then adding the algae samples to the ecosystem composed of the sediment samples and the water samples;

[0008] (2) cultivating the ecosystem;

[0009] (3) Determination of the physical and chemical properties of sediments and overlying water of the cultivated ecosystem;

[0010] (4) extracting total DNA from sediments of the cultivated ecosystem, and then performing high-throughput sequencing, performing OTU clustering on the sequences, and obtaining the distribution of microbial community composition;

[0011] (5) performing diversity analysis based on the OTU clustering, wherein the diversity analysis includes Alpha diversity analysis and Beta diversity analysis;

[0012] (6) constructing a co-occurrence network of microorganisms based on the OTU clustering and analyzing the interactions between different microorganisms;

[0013] (7) performing FAPROTAX analysis based on the OTU clustering to predict the functional potential of the microorganisms in the sediment;

[0014] (8) Detecting functional genes in sediments of the cultivated ecosystem based on high-throughput qPCR method, and analyzing the abundance of functional genes related to phosphorus, nitrogen, sulfur, and carbon cycles in sediments of the cultivated ecosystem;

[0015] (9) determining the activity of microbial enzymes in the sediment of the cultured ecosystem to evaluate the functional potential of the microorganisms in the sediment;

[0016] (10) The relationship between nutrients, microbial communities, functional genes, and nutrient cycling processes in the sediments of the cultivated ecosystems was analyzed based on partial least squares path modeling.

[0017] Some biodiversity indicators may not be sensitive enough to changes in microbial community structure, and it is difficult to accurately reflect the actual ecological state. The method of the present invention involves not only the physical and chemical properties of sediments and overlying water, but also the composition of microbial communities, the abundance of functional genes in sediments, etc. By studying the interactions of microorganisms and changes in their community structure, the dynamics of sediment microbial communities can be more comprehensively understood. Through the abundance of functional genes, FAPROTAX analysis, and sediment enzyme activity and analysis, the functional potential of microorganisms is deeply studied, thereby revealing the multiple effects of dead algae on the function of microbial communities; by exploring the interactions between microorganisms, the competition or symbiotic relationship between organisms in the microbial community can be revealed, avoiding the one-sidedness that may be caused by a single method.

[0018] The present invention combines multiple modules such as community composition, interaction and functional gene abundance, and can more comprehensively and accurately detect the impact of dead algae on microbial communities. It also confirms through partial least squares path modeling that the key factors affecting the circulation processes of different nutrients are also different, with better timeliness and applicability. The prediction results have a certain guiding role in the control of algal blooms in eutrophic lakes.

[0019] Preferably, the nutrient cycling process includes cycling processes of carbon, nitrogen, phosphorus and sulfur.

[0020] In some implementations, the partial least squares structural equation modeling is performed using smart PLS software.

[0021] Preferably, the culture period is not less than 80 days. If the culture period is too short, the long-term ecological process of the microbial community in response to algal death and deposition cannot be revealed, and when the culture period exceeds 80 days, the ecological process tends to be stable. Therefore, the culture period does not need to be too long, preferably not more than 100 days.

[0022] Preferably, the high-throughput sequencing in step (4) includes 16S rRNA gene sequencing and ITS sequence sequencing. 16S rRNA gene sequencing can usually only identify bacteria and archaea, and cannot provide information about fungi, viruses and other microbial groups. In addition, for some bacterial species, the variability of the 16S rRNA gene is low, which may cause some bacteria to be misclassified, affecting the accuracy of the data. The present invention improves the accuracy and comprehensiveness of the measurement results through 16S rRNA gene sequencing and ITS sequence sequencing.

[0023] In some embodiments, the 16S rRNA gene sequencing specifically includes PCR amplification and product electrophoresis detection.

[0024] Furthermore, the PCR amplification primers for 16S rRNA gene sequencing correspond to the 16S V4 region primers (515F and 806R).

[0025] Furthermore, the electrophoresis detection of the PCR products of the 16S rRNA gene sequencing includes detecting the fragment length and concentration of the PCR products by 1% agarose gel electrophoresis.

[0026] In some embodiments, the ITS sequence sequencing specifically includes PCR amplification and product electrophoresis detection.

[0027] Furthermore, the PCR amplification primers for ITS sequence sequencing correspond to the ITS1 region primers (ITS5-1737F and ITS2-2043R).

[0028] Furthermore, the electrophoresis detection of the PCR product of the ITS sequence sequencing includes detecting the fragment length and concentration of the PCR product by 1% agarose gel electrophoresis.

[0029] Preferably, the determination of the physical and chemical properties of the sediment in step (3) includes determining the contents of TN (total nitrogen), TP (total phosphorus), TOC (total carbon) and dissolved oxygen as well as the Eh value and pH value in the sediment.

[0030] Preferably, the determination of the physicochemical properties of the overlying water in step (3) comprises determining the NH4 + 、NO3 - PO4 3- , Fe 2+ and TOC content.

[0031] Preferably, the Alpha diversity analysis includes calculating ACE and Shannon index and plotting the calculation results.

[0032] In some embodiments, the calculation formula of the ACE index is as follows:

[0033]

[0034] Among them, S ace is the ACE index; S abund is the number of high-abundance species; S rare is the number of low-abundance species; C ace is the coverage estimate, calculated as C ace =1-F1 / N rare , F1 is the number of species observed only once, Nrare is the sum of the number of individuals of all low-abundance species; γ 2 ace is the correction factor, calculated as Among them, F i is the abundance of the ith species.

[0035] In some implementations, the Shannon index is calculated as follows:

[0036]

[0037] Where H' is the Shannon index; S is the total number of species in the community; p i is the ratio of the number of individuals of the i-th species to the total number of individuals.

[0038] Preferably, the Beta diversity analysis adopts the Euclidean method.

[0039] Preferably, the microbial enzyme comprises one or more of alkaline phosphatase, polyphosphate kinase, and polyphosphate esterase.

[0040] Preferably, the algae sample after the death treatment is added in a plum blossom-shaped injection manner.

[0041] Preferably, the death treatment comprises subjecting the algae sample to centrifugation with distilled water to obtain an algae cell solution, and then culturing the algae cell solution in the dark to allow the algae to die naturally.

[0042] More preferably, the number of days for the light-proof culture is more than 15 days.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention combines multiple modules such as microbial community composition, interaction and functional gene abundance, and can more comprehensively and accurately predict the impact of dead algae on microbial communities. It also confirms through partial least squares path modeling that the key factors affecting the circulation processes of different nutrients are also different, with better timeliness and applicability. The prediction results have a certain guiding role in the control of algal blooms in eutrophic lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure shows the results of the physical and chemical properties test of the sediment surface layer (I) and the overlying water (II) in different treatment groups after algae death deposition. The values ​​of each group are expressed as the mean value with standard deviation, and different letters represent significant differences (p<0.05);

[0046] Figure 2 The distribution diagram of bacterial community (a) and fungal community (b) composition in different treatment groups after algae death and sedimentation;

[0047] Figure 3 Alpha diversity diagram of bacterial and fungal communities in different treatment groups after algae death and deposition;

[0048] Figure 4 Beta diversity diagram of bacterial and fungal communities in different treatment groups after algae death and deposition;

[0049] Figure 5 The interactive network diagram (I) and node distribution diagram (II) of bacterial and fungal communities in different treatment groups after algae death and deposition;

[0050] Figure 6 Schematic diagram of the changes in the abundance of functional genes in different treatment groups after algae death and deposition;

[0051] Figure 7 This is the predicted map of sediment surface microbial functions in different treatment groups after algae death and deposition;

[0052] Figure 8 The schematic diagram is the activity of main metabolic enzymes and the content of main metabolites in different treatment groups after algae death and deposition;

[0053] Fig. 9 Schematic diagram of the relationship between sediment nutrients, bacterial and fungal communities and functional genes and C cycling processes for partial least squares path modeling (PLS-PM);

[0054] Fig.10 Schematic diagram of the relationship between sediment nutrients, bacterial and fungal communities and functional genes and N cycling processes for partial least squares path modeling (PLS-PM);

[0055] Fig.11Schematic diagram of the relationship between sediment nutrients, bacterial and fungal communities and functional genes and P cycling processes for partial least squares path modeling (PLS-PM);

[0056] Fig.12 The relationship between sediment nutrients, bacterial and fungal communities and functional genes and S cycling processes was analyzed for partial least squares path modeling (PLS-PM);

[0057] Fig.13 This is a diagram showing the overall effects of sediment nutrients, bacterial communities, fungal communities and functional genes on the C, N, P and S cycles in different treatment groups after algal death and deposition. DETAILED DESCRIPTION

[0058] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0059] Hereinafter, methods not specifically described are conventional methods in the art, and reagents, instruments, etc. not specifically described are conventional reagents or instruments in the art.

[0060] Example 1

[0061] A method for detecting the effect of dead algae on the structure and function of sediment microbial communities comprises the following steps:

[0062] 1. Sample collection and processing

[0063] 1.1. Sample collection

[0064] Collecting water samples, algae samples and sediment samples. In this embodiment, water samples, algae samples and sediment samples were all collected from Meiliang Bay, Taihu Lake (31°28′1.92″N, 120°10′40.80″E).

[0065] 1.2 Sample processing

[0066] (1) Treatment of natural death of algae:

[0067] The natural mixed algae (i.e., algae sample) taken from Taihu Lake were treated with different eutrophication states according to different chlorophyll a contents (control: normal, chlorophyll a concentration less than 2.6 μg / L; Amend 1: mesotrophic, chlorophyll a concentration greater than or equal to 2.6 μg / L and less than 20 μg / L; Amend 4: eutrophic, chlorophyll a concentration greater than or equal to 20 μg / L and less than 56 μg / L; Amend 12: hypereutrophic, chlorophyll a concentration greater than or equal to 56 μg / L). In this embodiment, the chlorophyll a contents added to the normal, mesotrophic, eutrophic, and hypereutrophic groups were 0 μg / L, 12 μg / L, 48 μg / L, and 150 μg / L, respectively, and 4 parallel tests were set up for each group.

[0068] Each group of algae samples was centrifuged with distilled water to obtain a concentrated algae cell solution, which was then cultured in the dark for 15 days to allow the algae to die naturally.

[0069] (2) The dead algae samples after the above treatment were added to the sediment samples in a plum blossom-shaped injection manner to simulate the state of dead algae deposited on the sediment surface in a natural ecosystem, thereby obtaining a control group and multiple treatment groups (Amend1, Amend4 and Amend12).

[0070] The control group and multiple treatment groups were cultured indoors for 80 days (culture conditions: cultured in the dark at 25°C and 96.4-97.3KPa for 80 days). Studies have found that the culture time is too short to reveal the long-term ecological process of microbial community response to algal death deposition, so the preferred culture time is ≥ 80 days.

[0071] 2. Microbial community analysis

[0072] 2.1. Physical and chemical characteristics of the sediment surface and overlying water

[0073] Sediment samples from the control group and the treatment group were taken and sliced ​​every 2 cm. The sliced ​​sediment samples were freeze-dried, ground and sieved through a 100-mesh (0.150 mm) sieve under vacuum for subsequent TN, TP, TOC and dissolved oxygen content analysis and Eh value and pH value determination. TN content was determined by alkaline potassium persulfate oxidation analysis; TP content determination included calcination of the sample at high temperature (450°C) and extraction with 3.5 mol / L hydrochloric acid, followed by SRP analysis; TOC content was determined by TOC analyzer (MultiNC3100 TOC analyzer; Jena Analytical Company, Germany); Eh value, dissolved oxygen and pH value were determined by Danish Unisense microelectrode system (Unisense MC-232-7180, Arhus, Denmark).

[0074] Water samples from the control group and the treatment group were taken and filtered through a 0.45 μm filter to determine the NH4 + 、NO3 - PO4 3- , Fe 2+ and TOC content, NH4 + The content of NO3 was determined by Nessler reagent method. - The content of PO4 was determined by UV spectrophotometry. 3- The content of Fe was determined by molybdenum blue method. 2+ The content of TOC was determined by the o-phenanthroline method, and the content of TOC was determined by a TOC analyzer (Multi NC3100 TOC analyzer; Jena Analytical Company, Germany).

[0075] Figure 1 The effects of different concentrations of dead algae deposition on the physicochemical properties of the surface sediment (I) and overlying water (II) after 80 days of long-term cultivation are shown. The values ​​of each group are expressed as the mean with standard deviation, and different letters represent significant differences (p<0.05). The results show that dead algae deposition has a significant effect on DO, Eh, TN, TP and TOC in the surface sediment, but not on pH. Furthermore, with the increase in the amount of dead algae deposition, the DO value shows a downward trend, the Eh and TOC values ​​show a trend of first decreasing and then increasing, and the TN and TP values ​​show an upward trend. The pH value is relatively stable throughout the cultivation period, with an average value of 7.7±0.05.

[0076] Dead algae deposition makes NO3 - NH3 - PO4 3- and Fe 2+ As in Amend1, NO3 - The content reached the highest value, and the difference was significant compared with the control group (p < 0.05).

[0077] 2.2 Determination of microbial community composition and distribution

[0078] 2.2.1. DNA extraction: Total DNA was extracted from sediment samples for subsequent microbial community analysis.

[0079] The steps for extracting total DNA are as follows:

[0080] After obtaining the original experimental samples, the sample DNA was extracted according to the instructions of the kit (Microchip Bio, name: Advanced Soil DNA Kit, catalog number: ADX1170-02). After the extraction, the total amount and purity of DNA were tested using the Qubit 4.0 (Thermo Fisher Scientific, Waltham, USA) instrument to ensure that the DNA concentration was uniformly diluted to 20 ng / μL.

[0081] 2.2.2 High-throughput sequencing: Use 16S rRNA gene sequencing (for bacteria) and ITS sequence sequencing (for fungi) to analyze the composition of the microbial community, obtain OTUs (operational taxonomic units) data, and calculate the relative abundance of OTUs in the samples.

[0082] The steps for 16S rRNA gene sequencing are as follows:

[0083] (1) PCR amplification and product electrophoresis detection

[0084] Using the extracted DNA as a template, PCR amplification was performed using specific primers with barcodes and Premix Taq (the enzymes used for different fragment selections may be different, and the actual enzyme used is subject to the amplification report) according to the selection of the sequencing region.

[0085] (1.1) Primer corresponding region: 16S V4 region primers (515F and 806R), for identification of bacterial diversity.

[0086] (1.2) PCR reaction system: see Table 1 below.

[0087] Table 1

[0088] Reagent name Dosage 2x Premix Taq 25μl Primer-F (10 μM) 1μl Primer-R (10 μM) 1μl DNA 50ng Nucleas-free water Add to 50μl

[0089] (1.3) PCR reaction conditions: a, 94°C for 5 min; b, 30 cycles of 94°C / 30 s, 52°C / 30 s, 72°C / 30 s; c, 72°C / 10 min; d, 4°C hold.

[0090] (1.4) PCR instrument: BioRad S1000 (Bio-Rad Laboratory, CA).

[0091] (1.5) PCR product electrophoresis detection: 1% agarose gel electrophoresis was used to detect the fragment length and concentration of PCR products. Samples with main band length within the normal range (e.g. 16S V4: 290-310 bp / 16S V4-V5: 400-450 bp, etc.) can be used for further experiments.

[0092] (2) Pooling and gel purification: After comparing the concentrations of PCR products using GeneTools Analysis Software (Version 4.03.05.0, SynGene), the required volume of each sample was calculated according to the principle of equal mass, and the PCR products were mixed. The PCR mixed product was recovered by Gel Extraction Kit (Omega, USA), and the target DNA fragment was recovered by elution with TE buffer.

[0093] (3) Library construction and sequencing:

[0094] (3.1) Library construction: The library was constructed according to the standard process of ALFA-SEQ DNA Library Prep Kit, and the size of the library fragments was evaluated on the Qsep400 high-throughput nucleic acid protein analysis system (Hangzhou Houze Biotechnology Co., Ltd., China), and the concentration of the library was measured using Qubit4.0 (Thermo Fisher Scientific, Waltham, USA).

[0095] (3.2) Sequencing: The constructed amplicon library was sequenced by PE250 using Illumina or MGI platform (Guangdong Magigene Biotechnology Co., Ltd. Guangzhou, China).

[0096] ITS sequence sequencing refers to 16S rRNA gene sequencing, the difference lies in the primer corresponding region: ITS1 region primers (ITS5-1737F and ITS2-2043R): identification of fungal diversity.

[0097] The steps to analyze the composition of microbial communities and obtain OTUs data include:

[0098] (1) Sequencing data processing

[0099] (1.1) Paired-end Raw Reads data filtering: fastp (an ultra-fast all-in-one FASTQ preprocessor, version 0.14.1, https: / / github.com / OpenGene / fastp) was used to perform sliding window quality trimming (-W 4-M 20) on the paired-end Raw Reads data. At the same time, according to the primer information at both ends of the sequence, the cutadapt software (https: / / github.com / marcelm / cutadapt / ) was used to remove the primers to obtain the paired-end Clean Reads after quality control.

[0100] (1.2) Paired-end Clean Reads splicing: For paired-end sequencing data, it is necessary to use usearch-fastq_mergepairs (V10, http: / / www.drive5.com / usearch / , with preset parameters including the minimum overlap length set to 16 bp and the maximum mismatch allowed in the overlap region of the spliced ​​sequence of 5 bp) to filter out non-compliant tags and obtain the original spliced ​​sequence (Raw Tags) based on the overlap relationship between PEreads.

[0101] (1.3) Raw Tags sequence quality filtering: fastp (an ultra-fast all-in-one FASTQ preprocessor, version 0.14.1, https: / / github.com / OpenGene / fastp) was used to perform sliding window quality trimming (-W 4-M20) on the Raw Tags data to obtain valid spliced ​​fragments (Clean Tags).

[0102] (2) Representative sequences and species annotations

[0103] (2.1) OTU: OTU, or operational taxonomic units, is one of the most common terms in microbiological research. The platform provides the following three methods, and the default clustering method is UPARSE:

[0104] a UPARSE (RC Edgar.highly accurate OTU sequences from microbialamplicon reads.Nature methods, 2019, 10(10):996);

[0105] b UNOISE3 (RC Edgar.UNOISE2: Improved error-correction for Illumina 16Sand ITS amplicon read.bioRxiv, 2016);

[0106] c UCLUST (RC Edgar. Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 2010, 26(19):2460-2461).

[0107] (2.2) ASV: ASV stands for amplicon sequence variant. The platform provides the following two methods:

[0108] a DADA2: Callahan B .et al.DADA2: high-resolution sample inference fromIllumina amplicon data.Nat Methods.2016a,13:581-583;

[0109] b Deblur:Amir,A.et al.Deblur Rapidy Resolves Single-NucleotideCommunity Sequence Patterns.Msystems.2017,2(2).

[0110] (2.3) Species annotation of representative sequences: Use usearch-sintax / blast to compare the representative sequences of each OTU with databases such as SILVA (16S), RDP (16S), Greengenes (16S), SILVA (18S), Unite (ITS), NR (Bacteria), NR (Archaea), and NR (Fungi) to obtain species annotation information (the confidence threshold is set to 0.8 by default, and the default databases are SILVA (16S), SILVA (18S), and Unite (ITS)), so as to understand the species origin of all sequences. The taxonomy results of species annotation are divided into 7 levels, namely kingdom (L1), phylum (L2), class (L3), order (L4), family (L5), genus (L6), and species (L7).

[0111] (2.4) Removal of contaminated representative sequences (OTU / asv): Remove representative sequences annotated as chloroplasts or mitochondria (16S amplicons) and their tags that cannot be annotated to the kingdom level, and obtain the number of valid tag sequences (No. of seqs) and the representative sequence taxonomy comprehensive information table (OTU_table) for each sample used for analysis.

[0112] (3) Representative sequence statistics

[0113] (3.1) OTU_Table: Based on the OTU_table after removing singleton OTU / ASV, chimeras and contaminated sequences, count the number of reads and OTU / ASVs contained in the sample or group.

[0114] (3.2) Pan_Core species analysis: R software (V5.1.3) was used to count the union (Pan) and intersection (Core) of the target taxonomic level in different sample numbers to assess whether the sample size was sufficient.

[0115] Figure 2 The relative abundance of microorganisms at the phylum level under different concentrations of dead algae deposition conditions, including bacterial (a) and fungal (b) communities, is shown. The most common bacterial phylum is Proteobacteria, followed by Nitrobacteria and Chlorophyta, while Ascomycota and Ascomycota account for the majority of fungal OTUs in the four micro-ecosystems. The deposition of dead algae significantly changed the relative abundance of dominant species in the bacterial community, but the relative abundance of the fungal community and the order of the dominant species in the bacterial and fungal communities did not change significantly. At the same time, different concentrations of dead algae deposition also have different effects on the reproduction of microorganisms. For example, the relative abundance of green algae in the bacterial community in Amend12 was significantly decreased (p<0.05) (from 0.156±0.004 in the control group to 0.140±0.002 in Amend12), and the relative abundance of acidobacteria in the bacterial community in Amend1 was significantly increased (p<0.05) (from 0.0393±0.001 in the control group to 0.045±0.001 in Amend1).

[0116] 3. Microbial community diversity analysis based on OTUs (operational taxonomic units) data

[0117] 3.1. Alpha Diversity (including calculation of ACE and Shannon index)

[0118] (1) The ACE index is an index used to estimate the species richness in a community. It can calculate the number of unobserved species in a sample based on information such as the number of individuals and species richness of the species in the sample. The ACE index is often used to assess the diversity and stability of species in an ecosystem. It divides species into high-abundance species and low-abundance species based on the abundance data of species in the sample (usually with 10 as the boundary, species above 10 are high-abundance species, and species below or equal to 10 are rare species). The calculation formula of the ACE index is as follows:

[0119]

[0120] in:

[0121] ·S abund is the number of high-abundance species.

[0122] ·S rare is the number of low-abundance species.

[0123] ·C ace is the coverage estimate, calculated as

[0124] F1 is the number of species observed only once (singletons).

[0125] ·N rare is the sum of the number of individuals of all low-abundance species.

[0126] · is a correction factor, calculated as where F i is the abundance of the i-th species, , .

[0127] The larger the ACE index is, the larger the number of species in the community is, and it can provide an estimate of the number of species actually present in the community. This index takes into account the contribution of rare species and high-abundance species, as well as the number of species that have not been observed in the sample, so it is an index that comprehensively considers the species richness and sample coverage in the sample.

[0128] (2) The Shannon index is an index that comprehensively considers species richness and evenness. It considers the ratio of the number of individuals of each species to the total number of individuals in the community and calculates a comprehensive value based on this. The higher the Shannon index, the richer the species diversity of the community and the more evenly distributed the species. The calculation formula of the Shannon index is:

[0129]

[0130] in:

[0131] H′ is the Shannon index.

[0132] S is the total number of species in the community.

[0133] ·p i is the ratio of the number of individuals of the i-th species to the total number of individuals.

[0134] The Shannon index ranges from 0 to 0. The higher the value, the higher the community diversity. When there is only one species in the community, its Shannon index is 0, that is, the community diversity is 0. The Shannon index increases as the number of species in the community increases or the relative abundance of species becomes more balanced; if the relative abundance of different species increases, the Shannon index decreases.

[0135] 3.2 Beta Diversity

[0136] Using the Euclidean method

[0137] The Euclidean method is a distance measurement method based on species composition data, which is used to calculate the difference between two communities. The Euclidean distance is a distance often used in multivariate analysis. The distance between two communities is obtained by calculating the square root of the sum of the squares of the abundance differences of each species in the two communities. This method takes into account the abundance differences of species and is sensitive to the size of abundance. Its calculation formula is as follows:

[0138]

[0139] in:

[0140] ·E ij represents the Euclidean distance between sample i and sample j.

[0141] ·S in represents the abundance of the nth species in sample i.

[0142] ·S jn represents the abundance of the nth species in sample j.

[0143] n represents the nth species in the sample.

[0144] The size of the Euclidean distance depends on the abundance of the input, and its value range is [0, +∞]. The larger the Euclidean distance, the greater the difference between the two samples. Since the Euclidean distance is symmetric, when dealing with double zero data (i.e., species that are not detected in both samples), it assumes that the meaning of these double zero data is the same, so the Euclidean distance should be used with caution when there are many zero values ​​in the sample pair.

[0145] 3.3 Calculation and visualization of Alpha diversity and Beta diversity

[0146] Use the MegaGene Cloud Platform for calculation and visualization

[0147] Figure 3 , Figure 4 The Alpha and Beta diversity of bacterial and fungal communities in different treatment groups after algal death and deposition are shown. The values ​​of each group are presented as mean and standard deviation, and different letters indicate significant differences (p<0.05).

[0148] Compared with the fungal community, dead algae deposition significantly affected the alpha diversity of the bacterial community in the surface sediments ( Figure 3 (a) Figure 3 (b)). With the increase of dead algae deposition concentration, the Ace index of bacterial community decreased in Amend1 and Amend12, but increased significantly in Amend4 (p<0.05); the Shannon index of bacterial community increased in Ameng1 and Amend4, but decreased significantly in Amend12 (p<0.05). Principal coordinate analysis (PCoA) was used to explore the similarities and differences in community structure among different groups ( Figure 4 ). Multivariate analysis of variance (PERMANOVA) showed that the deposition of dead algae had significantly different effects on bacterial and fungal communities (ADONIS: [bacteria: R2 = 0.412, p = 0.001; fungi: R2 = 0.336, p = 0.001]). In the bacterial and fungal community analyses, the total explained percentages of principal component 1 (PC1) and principal component 2 (PC2) were 54.6% and 51.7%, respectively (PERMANOVA, p < 0.001), indicating that the results were sufficient to explain the differences in community composition. In addition, all dead algae amendment groups in the bacterial community were significantly different from the control group (p < 0.05), while in the fungal community, only Amend4 was significantly different from the control group (p < 0.05). The results show that the structure of the bacterial community is easily affected by the deposition of dead algae compared to the fungal community, and the greater the amount of algae deposition, the more obvious the effect.

[0149] 4. Construct the co-occurrence network of bacteria and bacteria, fungi and fungi based on OTUs abundance data, and analyze the interactions between different microorganisms (such as symbiotic and antagonistic relationships).

[0150] Gephi software can be used for visualization, including:

[0151] (1) Data preparation

[0152] The OUT data of bacterial and fungal communities were analyzed for intragroup correlation on the Kidio platform, and an Excel table of cor and p values ​​was obtained, and OTUs with p < 0.05 were retained. The weight of the edge data is the absolute value of cor, and a positive cor is a positive edge, and a negative cor is a negative edge.

[0153] Then the following table containing node and edge information is obtained. The node data usually includes the node ID and label, and the edge data includes the source node, the target node and the optional weight.

[0154] Take the Control group in the bacterial interaction network diagram as an example, as shown in Table 2 below.

[0155] Table 2

[0156]

[0157]

[0158] (2) Import data

[0159] Open Gephi and select "Open" or "Import Spreadsheet" in the "File" menu to load the data file. For edge data, select "Edge List" to import and ensure that the data format meets the requirements of Gephi.

[0160] (3) Visual graphics

[0161] After importing the data, Gephi will generate a preliminary network diagram. The positions of the nodes are usually random, and the diagram can be adjusted according to needs.

[0162] The topological parameters in the interaction network were calculated using Gephi software, as shown in Table 3.

[0163] Table 3 Topological parameters of bacterial and fungal symbiotic networks

[0164]

[0165] Among them, the average degree is the average of the degrees of all nodes in the network, which can reflect the overall connectivity of the network. The higher the degree, the more complex the network. Density refers to the ratio of the frequency of actual edges to possible edges. The higher the degree, the more complex the network. The average clustering coefficient measures the degree of modular structure in the network. The larger the degree, the tighter the network interaction, the more stable the community structure and the higher the community activity. The cohesiveness of the network = density + average clustering coefficient. The modularity represents the proportion of tightly connected node groups in the total node network. The higher the degree, the more modular the network. The number of modules can reflect the community structure characteristics of the network. The more modules, the more obvious the community structure in the network, that is, the nodes within the community are more tightly connected, while the connections between communities are relatively few.

[0166] It can be seen that the number of nodes and total edges in the bacterial community increased, while the number of nodes and total edges in the fungal community decreased; the average degree and average clustering coefficient in the bacterial community increased, while the average degree and average clustering coefficient in the fungal community decreased (except Amend12). Algal death deposition increased the connectivity and complexity of the bacterial network and promoted the mutual relationship between bacterial groups, especially the symbiotic relationship; algal death deposition reduced the connectivity and complexity of the fungal network and inhibited the mutual relationship between fungal groups, especially the symbiotic relationship.

[0167] Figure 5 (I) shows the co-occurrence network of bacterial and fungal operational taxonomic units (OTUs) after dead algae deposition. Each node in the network represents an OTU, the color of the node indicates the module in which the node is located, and the connecting lines between nodes are edges, indicating the interaction between two OTUs. Positive edges indicate symbiotic relationships, and negative edges indicate antagonistic relationships (Poudel et al., 2016). The nodes are classified to identify key species in the dead algae deposition network ( Figure 5 (as shown in (II)).

[0168] After the deposition of dead algae, the symbiotic patterns of bacterial and fungal communities changed ( Figure 5 (I)). After the deposition of dead algae, the total number of centers and edges of bacterial communities increased, while the total number of centers and edges of fungal communities decreased. In the bacterial network, the proportion of positive edges gradually increased with the increase of dead algae deposition concentration (Control: 55.6%; Amend1: 51.8%; Amend4: 60.9%; Amend12: 63.9%), indicating that a robust symbiotic relationship was maintained in the bacterial symbiotic network and gradually increased with the increase of dead algae deposition concentration. Correspondingly, the proportion of negative edges decreased with the increase of algae deposition, from 44.3% in the control group to 36.0% in Amend12. However, in all bacterial networks, the proportion of positive edges was higher than that of negative edges. It is worth noting that although the proportion of positive edges in both bacterial and fungal networks was higher than that of negative edges, the proportion of positive edges in fungal networks was much higher than that of negative edges. In the fungal network, the proportion of negative edges increased with the increase of dead algae sediments (Control: 8.9%, Amend1: 13.5%, Amend4: 18.4%, Amend12: 11.2%), indicating that competition between fungal communities gradually intensified with the increase of dead algae sediment concentration. Average degree and density are two key indicators of network complexity, and the average clustering coefficient plays an important role in the activity and stability of the network. After the deposition of dead algae, the average degree, average clustering coefficient and density of the bacterial network increased (except Amend1, where the density was lower than the control group), while the average degree, average clustering coefficient and density of the fungal network decreased.

[0169] In addition, the key species of different microbial communities were identified by calculating the intra-module connectivity (Zi) and inter-module connectivity (Pi) of the nodes. Figure 5 (II)). The key nodes were analyzed and calculated using the “rnetcaeto” package (V4.1.1) of the R software. The calculation results can divide the node attributes into module hubs (module center points, nodes with high connectivity inside the module), connectors (connection nodes, nodes with high connectivity between two modules) and peripherals (peripheral nodes, nodes that do not have high connectivity inside or between modules). Generally, the remaining nodes except peripherals are called key nodes.

[0170] Table 4 Relative distribution of key species in bacterial and fungal networks

[0171]

[0172]

[0173] Table 5 Distribution of key phyla in bacterial and fungal networks

[0174]

[0175] After 80 days of cultivation, most of the network interaction nodes in the bacterial network were classified as peripheral nodes, with a percentage of more than 90%, while all nodes in the control group and Amend1 in the fungal network were peripheral nodes, with only a few connectors appearing in Amend4 and Amend12 (Amend4: 0.40%, Amend12: 0.50%). Key species in bacterial communities came from connectors and module hubs, while key species in fungal communities came from connectors. In all groups, bacterial communities had more key species than fungal communities. The bacterial community had 38 key species in the control group, 29 key species in Amend1, 63 key species in Amend4, and 23 key species in Amend12, while the fungal community had no key species in the control group and Amend1, and two key species in Amend4 and Amend12. As the concentration of dead algae deposition increased, the percentage of OTUs listed as key species in Amend1 and Amend12 in the bacterial network decreased, while the percentage of OTUs listed as key species in Amend4 in the bacterial network increased, while the percentage of OTUs listed as key species in all dead algae amendment groups in the fungal network increased. Amend4 had the most keystone species in the bacterial network, and new keystone species not found in other microecological systems appeared, including Euryarchaeota (3.1%), Verrucomicrobia (3.1%), Armatimonadetes (1.5%), Asgardaeota (1.5%), and 7AO6 (1.5%). At the same time, the results showed that the basal species of the fungal community only appeared in Amend4 and Amend12, which were Ascomycetes (Rozellomycota) and some undetermined species.

[0176] 5. Functional gene abundance analysis

[0177] High-throughput qPCR was used to analyze the effect of dead algae deposition on the abundance of each functional gene using univariate data.

[0178] The high-throughput qPCR procedure includes:

[0179] (1) DNA extraction and quantification

[0180] As mentioned above, no further details will be given here.

[0181] (2) qPCR absolute quantification

[0182] Prepare qPCR amplification system: prepare 16S absolute quantitative system using 96-well plate or eight-well tube strip according to the following system (Table 6), mix thoroughly, centrifuge and place at 4°C for later use.

[0183] Table 6

[0184] Roche LightCycler 480 SYBR Green I Maste 10μL 16S-F(10μM) 0.4μL 16S-R (10μM) 0.4μL Nuclease-free Water 7.2μL Total 18μL

[0185] Edit the sample loading table according to the experimental list, sample volume and standard (one plate experiment includes 96 wells, which can be used for 26 samples, 5 standards, and 1 negative, and each is repeated three times for parallel experiments)

[0186] Add samples in the arranged sample order, add 2 μL of sample to each reaction system, and after adding samples, stick the sealing film on the 96-well plate and place the sample plate on the real-time fluorescence PCR instrument;

[0187] Follow the steps below to set up the on-machine program:

[0188] Table 7

[0189]

[0190]

[0191] (3) qPCR chip reaction

[0192] The qualified DNA samples were added to a 384-well plate as a sample plate (Sample Sourceplate), and the primers (see Table 8 below) and reagents used for qPCR were added to another 384-well plate as a primer plate (Assay Sourceplate). The sample plate and primer plate reagents were added to the microwells of the high-throughput qPCR chip SmartChip MyDesign Chip (Takara Biomedical Technology, Clontech) using a high-throughput automatic micro-addition device SmartChip Multisample Nanodispenser (Takara Biomedical Technology), and qPCR reactions and fluorescence signal detection were performed in the SmartChip Real-Time PCR System (WaferGen Biosystems USA), and amplification curves and melting curves were automatically generated.

[0193] The absolute quantitative information of 16S rRNA gene was obtained by fluorescence quantitative PCR (LineGene 9600 Plus). The absolute quantitative information of each gene in each sample was obtained according to the absolute quantitative conversion of 16S rRNA gene. The formula is as follows:

[0194] 16S rRNA relative quantification / 16S rRNA absolute quantification = gene relative quantification / gene absolute quantification.

[0195] Based on the absolute gene abundance statistics table, the absolute gene abundance heat map was drawn using R software (Version: 3.6.1).

[0196] Table 8

[0197]

[0198]

[0199]

[0200] After 80 days of incubation, dead algae deposition increased the abundance of many functional genes involved in C, N, P, and S cycles in sediments, but did not show a concentration-dependent pattern ( Figure 6 For example, after the deposition of dead algae, the abundance of sga gene (carbon compound degradation), amoB gene (nitrification), nirK1 gene (denitrification), nirS2 gene (denitrification), phoD gene (organic P mineralization), pqqc gene (inorganic P dissolution), and yedZ gene (S oxidation) increased significantly (p < 0.05). In contrast, the abundance of the S oxidation gene soxY decreased significantly after the deposition of dead algae (p < 0.05).

[0201] Through FAPROTAX functional prediction analysis, such as Figure 7 (Prediction of microbial functions on the sediment surface 80 days after the deposition of dead algae, purple represents the control group, blue represents Amend1, green represents Amend4, and orange represents Amend12) As shown, compared with the control, the proportion of microorganisms involved in aerobic ammonia oxidation, manganese respiration, dark hydrogen oxidation and nitrate reduction in Amend1 was significantly increased (p<0.05). Among the four functions that were significantly improved (p<0.05) in Amend1, only the dark hydrogen oxidation function was improved in Amend4 (p<0.05), and the nitrate reduction function was improved in Amend12 (p<0.05). At the same time, compared with the control group, the proportion of microorganisms involved in aerobic nitrite oxidation, nitrification, urea decomposition, methanotrophy, dark sulfur oxidation, hydrocarbon degradation, sulfur compound respiration and sulfate respiration in Amend4 increased significantly (p<0.05), while the improvement of urea decomposition, methanotrophy, dark sulfur oxidation and hydrocarbon degradation in Amend12 was higher than that in Amend4. It is noteworthy that only Amend12 had a significantly lower proportion of microorganisms involved in dark oxidation of sulfide and methanogenesis by methyl reduction compared with the control group (p<0.05), while only Amend12 had a significantly higher proportion of microorganisms involved in predation or ectoparasitism and methanol oxidation (p<0.05). The microbial community involved in sulfur transformation, as predicted by FAPROTAX analysis, was greatly altered in the dead algae amendment group.

[0202] 6. Determination of Sediment Enzyme Activity

[0203] The activities of important microbial enzymes (alkaline phosphatase, polyphosphate kinase, polyphosphate esterase) in sediments were determined to evaluate the functional potential of microorganisms in different treatment groups.

[0204] The determination method of microbial enzymes is:

[0205] At the end of the experiment, sediment samples were collected from each microcosm and washed three times with 100 mM phosphate buffered saline (PBS, pH 7.2-7.4). The suspension was then ground with PBS using a tissue homogenizer. The homogenate was then sonicated at a frequency of 20 kHz for 2 minutes and centrifuged at 10,000 g for 10 minutes. All of the above operations were performed at 4°C. The liquid supernatant was collected for microbial assays. Finally, the enzyme activity was determined using a double antibody sandwich enzyme-linked immunosorbent assay (ELISA).

[0206] Figure 8 The results show the effect of dead algae deposition on the activity of key enzymes in the phosphorus cycle in surface sediments. After the algae die and deposit, the enzyme activity of microorganisms related to the phosphorus cycle increases significantly, especially the activity of alkaline phosphatase, which helps the transformation of inorganic phosphorus in the sediments.

[0207] 7. Data analysis and result prediction

[0208] The relationships between sediment nutrients, bacterial and fungal communities and functional genes and C (a), N (b), P (c) and S (d) cycles were analyzed based on partial least squares path modeling (PLS-PM). The positive and negative effects are represented by red and blue arrows, respectively, and the path coefficients with no significant differences are shown as gray dashed lines: *p < 0.05, **p < 0.01, and ***p < 0.001, using smart The data of the C cycle model are as follows: total organic carbon (TOC) content in sediments, bacterial β diversity (NMS1), fungal β diversity (NMS1), absolute abundance of carbon functional gene rbcL, absolute abundance of carbon functional gene sga, functional prediction of methane production (Methanogenesis) in carbon cycle process, functional prediction of urea decomposition (Ureolysis) in carbon cycle process; the data of the N cycle model are as follows: total nitrogen (TN) content in sediments, bacterial β diversity (NMS1), fungal β diversity (NMS1), fungal α diversity (ACE index), absolute abundance of nitrogen functional gene amoB, absolute abundance of nitrogen functional gene nirK, aerobic nitrite oxidation (Aerobic nitrite The data introduced into the P cycle model are: sediment total phosphorus (TP) content, bacterial β diversity (NMS1), fungal β diversity (NMS1), fungal α diversity (ACE index), absolute abundance of phosphorus functional gene phoD, absolute abundance of phosphorus functional gene pqqC, activity of alkaline phosphatase (ALP) in phosphorus cycle, activity of polyphosphate kinase (PPK) in phosphorus cycle, activity of polyphosphate esterase (PPX) in phosphorus cycle; the data introduced into the S cycle model are: sediment total organic carbon (TOC) content, bacterial β diversity (NMS1), fungal β diversity (NMS1), fungal α diversity (ACE index), absolute abundance of sulfur functional gene soxY, absolute abundance of sulfur functional gene yedZ, function prediction of dark oxidation of sulfur compounds in sulfur cycle, function prediction of respiration of sulfur compounds in sulfur cycle, function prediction of sulfate respiration in sulfur cycle.

[0209] Fig. 9Represents the C cycle process, among which, Sediment nutrients: TOC in basic physical and chemical indicators; Bacteria: indicator NMS1 in β (Beta) diversity; Fungi: indicator NMS1 in β diversity; C genes: rbcL and sga in functional genes; C cycle process: Methanogenesis and Ureolysis functions in functional prediction.

[0210] Fig.10 Represents the N cycle process, among which, Sediment nutrients: TN in basic physical and chemical indicators; Bacteria: indicator NMS1 in β diversity; Fungi: indicator NMS1 in β diversity, indicator ACE in α diversity; Ngenes: amoB and nirK in functional genes; N cycle process: Aerobic nitriteoxidation and Nitrification functions in functional prediction.

[0211] Fig.11 Represents the P cycle process, among which, Sediment nutrients: TP in basic physical and chemical indicators; Bacteria: indicator NMS1 in β diversity; Fungi: indicator NMS1 in β diversity, indicator ACE in α diversity; Pgenes: phoD and pqqC in functional genes; P cycle process: ALP, PPK, PPX in enzyme activity are closely related to the phosphorus metabolism process.

[0212] Fig.12 Represents the S cycle process, among which, Sediment nutrients: TOC in basic physical and chemical indicators; Bacteria: indicator NMS1 in β diversity; Fungi: indicator NMS1 in β diversity, indicator ACE in α diversity; Sgenes: soxY and yedZ in functional genes; S cycle process: dark oxidation ofsulfur compounds, respiration of sulfur compounds, and sulfate respiration functions in functional prediction.

[0213] Fig.13 It is the total effect of sediment nutrients, bacterial communities, fungal communities and functional genes on the C, N, P and S cycling processes.

[0214] Fit indices include SRMR (<0.08), d_ULS (<0.95), and d_G (<0.95), indicating that the model has a good fit to the sample data. The relevant parameter table of the CNPS model constructed in this embodiment is shown below, which basically meets the above requirements.

[0215] Table 9 Quality criteria for model fitting

[0216]

[0217]

[0218] The relationships among sediment nutrients, microbial communities, functional genes, and nutrient cycling processes were elucidated by partial least squares path modeling (PLS-PM) analysis. Sediment nutrients had different effects on fungal communities in different nutrient cycling models. Sediment nutrients had direct positive effects on bacterial and fungal communities in nitrogen and phosphorus cycling models (p<0.05), while they had direct negative effects on fungal communities in carbon and sulfur cycling models (p<0.05). It is also worth noting that sediment nutrients only had direct positive effects on phosphorus cycling processes (p<0.05), and only had direct negative effects on nitrogen cycling processes (p<0.05). Compared with fungi, bacteria were more closely associated with functional genes, for example, bacterial communities had direct positive effects on nitrogen functional genes (p<0.05) and direct negative effects on sulfur functional genes (p<0.05). In addition, sediment nutrients had a significant effect on phosphorus cycle (p<0.05), bacterial communities had a greater impact on carbon cycle (p<0.05), and functional genes had a greater impact on nitrogen and sulfur cycle (p<0.05). Fig.13 ), sediment nutrients contributed the most to the phosphorus cycle, bacterial communities had the greatest impact on the carbon cycle, while nitrogen and sulfur cycles were mainly driven by functional genes.

[0219] Specific as Fig. 9 The results showed that bacterial diversity was significantly positively correlated with the C cycle process (p<0.05), indicating that the increase in bacterial diversity promoted the C cycle process, such as improving glycolysis and urea metabolism. - and NH4 + The content of nitrogen reflects the nitrogen cycle process, including nitrification, aerobic ammonia oxidation, aerobic nitrite oxidation, and nitrate reduction. Fig.10As shown, the correlation between functional genes and nitrogen cycle processes is stronger than that between sediment nutrients and nitrogen cycle processes (p<0.05); the increase in the abundance of nitrogen functional genes such as amoB and nirK improved aerobic nitrite oxidation and nitrification during the nitrogen cycle. The increase in the abundance of amoB genes generally reflects an increase in the number of ammonia oxidizing bacteria (AOB) or archaea (AOA) in the environment. This leads to enhanced ammonia oxidation capacity, thereby accelerating the first step of nitrification. PO4 in the overlying water 3- The content and ALP, PPK and PPX activities in the surface sediments were used as indicators of changes in the P cycling process after the deposition of dead algae. These indicators increased 80 days after the deposition of dead algae. Fig.11 The results showed that sediment nutrients were significantly correlated with the phosphorus cycle (p<0.05). The increase in TP content had a positive effect on the phosphorus cycle, such as increasing the activities of ALP, PPK and PPX. These enzymes are related to phosphorus metabolism in sediments, and their increased activities contribute to the PO4 3- Release into water bodies. Fig.12 The functional genes were highly correlated with the S cycle process (p<0.05), and the functional potentials of dark oxidation of sulfur compounds, sulfur compound respiration, and sulfate respiration were all increased, indicating that the S cycle process was improved by increasing the abundance of S functional genes in surface sediments. This phenomenon is consistent with the HT-qPCR results of S cycle genes, such as the increase in the abundance of the functional gene yedZ, which promotes the sulfite reduction process. In summary, after the deposition of dead algae, sediment nutrients are considered to strongly affect the P cycle process, while biological factors are considered to strongly affect the C, N, and S cycle processes.

[0220] It is worth noting that the effects of lower concentrations (Amend1 or Amend4) of dead algae deposition on microbial behavior and nutrient cycling processes may be more significant than those of higher concentrations (Amend12). Therefore, although the control of eutrophication and algal blooms has been strengthened to a certain extent, the effects of low-concentration algal blooms or residual algal blooms on nutrient cycling processes should still be of concern. Understanding the factors and pathways that affect sediment nutrient cycling processes after dead algae deposition will help algal bloom management and eutrophication control.

[0221] The above detailed description of the present invention is intended to enable persons familiar with the art to understand the contents of the present invention and implement them. It does not limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the effect of dead algae on the structure and function of sediment microbial communities, characterized in that: The steps include: (1) collecting samples, wherein the samples include water samples, algae samples and sediment samples, performing a death treatment on the algae samples, and then adding the algae samples to the ecosystem composed of the sediment samples and the water samples; (2) cultivating the ecosystem; (3) Determination of the physical and chemical properties of sediments and overlying water of the cultivated ecosystem; (4) extracting total DNA from sediments of the cultivated ecosystem, then performing high-throughput sequencing, and performing OUT clustering on the sequences to obtain the distribution of microbial community composition; (5) performing diversity analysis based on the OTU clustering, wherein the diversity analysis includes Alpha diversity analysis and Beta diversity analysis; (6) constructing a co-occurrence network of microorganisms based on the OTU clustering and analyzing the interactions between different microorganisms; (7) performing FAPROTAX analysis based on the OTU clustering to predict the functional potential of the microorganisms in the sediment; (8) Detect the functional genes in the sediments of the cultivated ecosystem based on the high-throughput qPCR method, and analyze the abundance of functional genes related to phosphorus, nitrogen, sulfur, and carbon cycles in the sediments of the cultivated ecosystem; (9) determining the activity of microbial enzymes in the sediment of the cultured ecosystem to evaluate the functional potential of the microorganisms in the sediment; (10) The relationship between nutrients, microbial communities, functional genes, and nutrient cycling processes in the sediments of the cultivated ecosystems was analyzed based on partial least squares path modeling.

2. The method for detecting the effect of dead algae on the structure and function of sediment microbial communities according to claim 1, characterized in that: The nutrient cycle process includes the cycle processes of carbon, nitrogen, phosphorus and sulfur.

3. The method for detecting the effect of dead algae on the structure and function of sediment microbial communities according to claim 1, characterized in that: The number of days for the cultivation is not less than 80 days.

4. According to the method for detecting the impact of dead algae on the structure and function of sediment microbial communities according to claim 1, the high-throughput sequencing in step (4) includes 16S rRNA gene sequencing and ITS sequence sequencing.

5. The method for detecting the influence of dead algae on the structure and function of sediment microbial community according to claim 1, wherein the determination of the physical and chemical properties of the sediment in step (3) comprises determining the contents of TN, TP, TOC and dissolved oxygen as well as the Eh value and pH value in the sediment; and / or, The determination of the physicochemical properties of the overlying water in step (3) includes determining the NH4 + 、NO3 - PO4 3- , Fe 2+ and TOC content.

6. The method for detecting the effect of dead algae on the structure and function of sediment microbial communities according to claim 1, wherein the Alpha diversity analysis comprises calculating ACE and Shannon index and plotting the calculation results.

7. According to the method for detecting the effect of dead algae on the structure and function of sediment microbial community according to claim 6, the calculation formula of the ACE index is as follows: in, S ace is the ACE index; S abund is the number of high-abundance species; S rare is the number of low-abundance species; C ace is the coverage estimate, calculated as C ace =1-F1 / N rare , F1 is the number of species observed only once, Nrare is the sum of the number of individuals of all low-abundance species; γ 2 ace is the correction factor, calculated as Among them, F i is the abundance of the ith species; The calculation formula of the Shannon index is: Where H' is the Shannon index; S is the total number of species in the community; p i is the ratio of the number of individuals of the i-th species to the total number of individuals.

8. The method for detecting the effect of dead algae on the structure and function of sediment microbial communities according to claim 1, wherein the Beta diversity analysis adopts the Euclidean method.

9. The method for detecting the effect of dead algae on the structure and function of sediment microbial communities according to claim 1, wherein the microbial enzyme comprises one or more of alkaline phosphatase, polyphosphate kinase, and polyphosphate esterase.

10. The method for detecting the effect of dead algae on the structure and function of sediment microbial community according to claim 1, wherein the algae sample after the dead algae treatment is added in a plum blossom-shaped injection manner.

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