KR102666417B1 - Bacterial phenotype-based aquatic ecosystem disturbance evaluation method and use thereof - Google Patents

Abstract

The present invention relates to a method for assessing aquatic ecological disturbance based on bacterial phenotypes and its use. The present invention concentrates groundwater samples, analyzes them by flow cytometry, and classifies them into phenotypes according to cell size and complexity. In addition, the classified phenotypes were grouped, the differences in the phenotypes according to the groundwater samples were confirmed, and compared with the control group, it was confirmed that the degree of contamination and the source of contamination could be inferred according to the differences in the phenotypes of the groundwater bacterial flora, thereby improving groundwater ecology. It was confirmed that it could be used for disturbance evaluation.

Description

Bacterial phenotype-based aquatic ecosystem disturbance evaluation method and use thereof}

The present invention relates to a method for assessing aquatic ecological disturbance based on bacterial phenotype and its use.

In order to safely and effectively manage water resources such as groundwater, drinking water, agricultural water, and industrial water, a monitoring system to detect waterborne microorganisms is required. Because the cell density or distribution of waterborne microorganisms can pose a fatal threat to public health, it is important to accurately characterize aquatic microorganisms. Currently, 90% of the U.S. population does not have access to high-quality purified water, and approximately 7.15 million Americans suffer from diseases caused by contaminated water every year, and the U.S. healthcare system costs $3.3 billion to manage water quality and diseases caused by it. It is used for prevention. If water quality is assessed using only indicator microorganisms such as fecal E. coli or waterborne microorganisms, information about other threats that cause water pollution may be missed, and it is necessary to analyze the entire bacterial community to derive meaningful information. Therefore, a new perspective approach to water quality management is required, which can be achieved through a method that can obtain overall information about bacterial communities in the aquatic ecosystem.

In most countries, the standard method for detecting indicator microorganisms and waterborne microorganisms is a culture-based method that can detect microorganisms relatively simply and inexpensively. However, culture-based methods are labor-intensive, time-consuming, and unsuitable for on-site online monitoring systems, and most waterborne microorganisms exist in nutrient-limited water in a viable but uncultivable state. , Culture-based analysis through sampling is particularly problematic as it may represent sample representativeness and heterogeneity of the relevant aquatic environment, which inevitably leads to low detection efficiency. The recent trend to introduce molecular biological assays, including polymerase chain reaction, high-throughput sequencing, and oligo-based biosensors, has led to the introduction of water-borne microorganisms rather than culture-based methods. For , high-throughput sequencing using specific primer sets, such as those using 16s rRNA and primers from the amoA gene, has led to a fast and sensitive detection method, and can also provide detailed taxonomic and diversity information about aquatic bacterial communities. However, despite the development of these molecular biological approaches, there are limitations to their application to automated or online monitoring due to factors such as interference with molecular reactions depending on sampling conditions, complexity of the analysis process, and analysis cost. Therefore, a new approach that can overcome the limitations of existing detection methods is needed.

Meanwhile, flow cytometry (FCM) is an alternative technology that can confirm the physical and chemical properties of cells or particles based on light scattering behavior and fluorescence. Advances in optical procedures and detectors have improved both the performance of analytical instruments, such as sensitivity and particle size resolution, and the robustness of analytical systems, such as computational capacity and optical stability, over the past few decades. These technological improvements have enabled FCM to be used in clinical and water quality applications. It has been applied not only to evaluation but also to early diagnosis of microbial contamination of food. Compared to existing culture-based technologies and molecular biological analysis, FCM can provide results with high precision and accuracy, and the measurement time can measure more than 10,000 events or cells, making it comparable to existing methods. In comparison, it has the advantage of enabling rapid analysis. Due to the high accuracy and large measurement scale of FCM, analytical methods related to online or automated water quality assessment in water reuse systems, wastewater treatment plants and drinking water supply networks are being explored.

Accordingly, the present inventors confirmed that aquatic ecological disturbances could be analyzed by obtaining phenotypic information of bacterial communities using FCM and evaluating microbial characteristics of water samples using this, thereby completing the present invention.

The object of the present invention is to obtain cells by filtering a first water sample obtained from a water source;

2) producing a concentrate by mixing the cells obtained in step 1) with a second water body sample obtained from the same water source as the water source in step 1) but having a volume of 1/50 of the first water body sample.

3) Based on the data analyzed by flow cytometry on the concentrate produced through step 2), forward scatter channel (FSC), side scatter channel (SSC), fluorescein isothiocyanate Selecting data with fluorescein isothiocyanate (FITC) and phycoerythrin (PE) values exceeding 10 ³ ;

4) The data selected in step 3) is displayed on a two-dimensional coordinate plane expressed by X and Y coordinates, each composed of n grid cells, and located in one of five groupings, A grouping step wherein the and

[Equation 1]

(X,Y)=(FSC,SSC);

[Equation 2]

(SSC > n/2) > (FSC > n/2);

[Equation 3]

(SSC > n/2) < (FSC > n/2);

[Equation 4]

(SSC > n/2) ∩ (FSC < (n/2+1));

[Equation 5]

(SSC < (n/2+1)) ∩ (FSC > (n/2+1)); and

[Equation 6]

(SSC < (n/2+1)) ∩ (FSC < (n/2+1));

5) evaluating the ecological disturbance of the water body sample based on the grouped information; providing a method for evaluating ecological disturbance of the water body sample including the following.

In order to achieve the above object, the present invention includes the steps of 1) filtering a first water body sample obtained from a water source to obtain cells;

2) producing a concentrate by mixing the cells obtained in step 1) with a second water body sample obtained from the same water source as the water source in step 1) but having a volume of 1/50 of the first water body sample.

3) Based on the data analyzed by flow cytometry on the concentrate produced through step 2), forward scatter channel (FSC), side scatter channel (SSC), fluorescein isothiocyanate Selecting data with fluorescein isothiocyanate (FITC) and phycoerythrin (PE) values exceeding 10 ³ ;

4) The data selected in step 3) is displayed on a two-dimensional coordinate plane expressed by X and Y coordinates, each composed of n grid cells, and located in one of five groupings, A grouping step wherein the and

[Equation 1]

(X,Y)=(FSC,SSC);

[Equation 2]

(SSC > n/2) > (FSC > n/2);

[Equation 3]

(SSC > n/2) < (FSC > n/2);

[Equation 4]

(SSC > n/2) ∩ (FSC < (n/2+1));

[Equation 5]

(SSC < (n/2+1)) ∩ (FSC > (n/2+1)); and

[Equation 6]

(SSC < (n/2+1)) ∩ (FSC < (n/2+1));

5) evaluating the ecological disturbance of the water body sample based on the grouped information; providing a method for evaluating ecological disturbance of the water body sample including;

The present invention concentrated groundwater samples, analyzed them using flow cytometry, and classified them into phenotypes based on cell size and complexity. In addition, the classified phenotypes were grouped, the differences in the phenotypes according to the groundwater samples were confirmed, and compared with the control group, it was confirmed that the degree of contamination and the source of contamination could be inferred according to the differences in the phenotypes of the groundwater bacterial flora, thereby improving groundwater ecology. By confirming that it can be used for disturbance evaluation, the level of groundwater ecological disturbance in the contaminated area can be quickly and significantly discerned, making it useful for related industries.

Figure 1 shows a flow chart of the cell phenotype-based groundwater ecological disturbance evaluation method of the present invention.
Figure 2 is a schematic diagram of the flow cytometry results of the present invention converted into data and data processing and analysis methods.
Figure 3 is a diagram comparing the β-diversity analysis results of the cell phenotype-based groundwater ecological disturbance method and the phylogenetic analysis method of the present invention (A: phylogenetic analysis, B: phenotype-based analysis).
Figure 4 is a diagram grouping phenotypes within grid cells according to cell size and complexity in the cell phenotype-based groundwater ecological disturbance method of the present invention.
Figure 5 is a diagram showing hierarchical clusters based on Bray-Curtis distance calculated using subgroups and abundance of phenotypes in the dry season.
Figure 6 is a diagram showing the quantitative distribution heatmap of each phenotype in the dry season.
Figure 7 is a diagram confirming the phenotypic distribution between each group in the hierarchical cluster heatmap data of the dry season.

Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. In the following description, detailed descriptions of techniques well known to those skilled in the art may be omitted. Additionally, when describing the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description may be omitted. In addition, the terminology used in this specification is a term used to appropriately express preferred embodiments of the present invention, and may vary depending on the intention of the user or operator or the customs of the field to which the present invention belongs.

Therefore, definitions of these terms should be made based on the content throughout this specification. Throughout the specification, when a part is said to “include” a certain element, this means that it may further include other elements rather than excluding other elements, unless specifically stated to the contrary.

The present invention includes the steps of 1) filtering a first water body sample obtained from a water source to obtain cells;

2) producing a concentrate by mixing the cells obtained in step 1) with a second water body sample obtained from the same water source as the water source in step 1) but having a volume of 1/50 of the first water body sample;

3) Based on the data analyzed by flow cytometry on the concentrate produced through step 2), forward scatter channel (FSC), side scatter channel (SSC), fluorescein isothiocyanate Selecting data with fluorescein isothiocyanate (FITC) and phycoerythrin (PE) values exceeding 10 ³ ;

4) The data selected in step 3) is displayed on a two-dimensional coordinate plane expressed by X and Y coordinates, each composed of n grid cells, and located in one of five groupings, A grouping step wherein the and

[Equation 1]

(X,Y)=(FSC,SSC)

[Equation 2]

(SSC > n/2) > (FSC > n/2)

[Equation 3]

(SSC > n/2) < (FSC > n/2)

[Equation 4]

(SSC > n/2) ∩ (FSC < (n/2+1))

[Equation 5]

(SSC < (n/2+1)) ∩ (FSC > (n/2+1))

[Equation 6]

(SSC < (n/2+1)) ∩ (FSC < (n/2+1))

5) evaluating the ecological disturbance of the water body sample based on the grouped information; providing a method for evaluating ecological disturbance of the water body sample including;

“Water source” in the present invention refers to a source of water, such as the point where a river begins, such as a watershed or groundwater discharge point near a watershed or the area near it, or a water body that supplies water to public drinking water supplies and wells. It means.

The "concentrate" of the present invention is made by filtering the microorganisms in a first water body sample obtained from a water source and filtering them into a second water body sample obtained from the same water source having a volume of 1/n of the volume of the first water body sample. By resuspending, the volume is reduced by n times compared to the first water body sample, but the number of microorganisms is maintained or increased. In one embodiment of the present invention, the second water body sample is compared to the first water body sample. Therefore, it was resuspended at 1/50 times the volume and concentrated 50 times, but is not limited to this.

As used herein, “disturbance” refers to an event that disrupts community structure and changes available resources, material availability, or the physical environment. Disturbances can affect ecological communities in different ways, depending on the frequency and intensity of their occurrence. Additionally, disturbance also means contamination, and can be classified as organic pollution or inorganic pollution depending on the source of pollution. Organic pollution is caused by organic substances such as bacteria, viruses, parasites, organic substances derived from animals and plants, food, excrement, and natural organic substances such as volatile substances, or synthetic organic substances such as medicines, pesticides, plastics, detergents, oil, and gasoline. This means disturbing the aquatic ecosystem. On the other hand, inorganic pollution excludes organic substances, including heavy metals such as mercury, cadmium, and chromium, inorganic compounds such as sulfur oxides, nitrogen oxides, bases, salts, or ozone, radioactive substances such as uranium, cesium, iodine, and radon, yellow dust, silt, and other pollutants. This means that inorganic substances such as particulate matter such as fine dust and ultrafine dust act as pollutants and disturb the aquatic ecosystem.

"Flow cytometry (FCM)" of the present invention is an analysis method that detects cells or particles based on a laser, and passes a fluidly flowing suspension of cells through an electronic detector to determine cell size, shape, or fluorescence. It is an analysis method that selects, counts, and observes data.

The "forward scatter channel (FSC)" of the present invention is a channel that detects a laser passing through cells in a straight line in flow cytometry, and detects information related to the size or diameter of the cells with the laser passing through the cells. It is a channel that does.

The "side scatter channel (SSC)" of the present invention is a channel that collects light scattered vertically (90 degrees) by a laser irradiated on a cell, and is a channel that collects light scattered vertically (90 degrees) by the nuclear structure of the cell, granule structure, etc. This is a channel that detects information related to the complexity of cells from the scattered laser.

The "grid cell" of the present invention is a unit grid with a single value or attribute, and means that cells are gathered together to form one large grid or image.

According to one embodiment of the present invention, after step 4), a step of analyzing α-diversity and β-diversity from the phenotype group may be further included.

“α-diversity” in the present invention refers to the average species diversity in an ecosystem such as a local-scale habitat or sample, and represents species richness. α-diversity is an indicator that determines how diverse species are distributed in an ecosystem, regardless of the number of species among them.

“β-diversity” of the present invention refers to the degree to which species composition changes according to various environmental gradients, and refers to the degree to which species composition or diversity between small points in a large space (region of interest) It is an indicator of difference. β-diversity is an indicator used to compare differences between communities and predict community changes due to environmental changes.

According to one embodiment of the present invention, the value detected by FSC in step 3) may be cell size.

According to one embodiment of the present invention, the value detected by SSC in step 3) may be cell complexity.

According to one embodiment of the present invention, the number n in step 4) may be 50 to 3000, preferably 100 to 2500, but is not limited thereto.

According to one embodiment of the present invention, the grouping that satisfies Equation 2 of step 4) may be a highly active (HA) phenotype group.

The high activity phenotype group of the present invention is a phenotype in which microbial cells are large in size and intracellular complexity is increased, and reflects a state of active microbial growth and metabolic activity, which is an indicator of aquatic ecological disturbance due to organic pollution. It may be a group.

According to one embodiment of the present invention, the grouping that satisfies Equation 3 of step 4) may be a highly reproducible (HR) phenotype group.

The highly reproducible phenotype group of the present invention is a phenotype in which the size of the microbial cell is large, but the intracellular complexity is reduced compared to the size value, and organic pollution in the aquatic ecosystem is reduced after microbial growth, and cell metabolism is reduced. , it may be a phenotypic group in which microbial growth increases due to organic pollution, and then cellular metabolism begins to decrease due to a decrease in organic pollutants.

According to one embodiment of the present invention, the grouping that satisfies Equation 4 in step 4) may be an active small-sized (AS) phenotype group.

The active small-cell phenotype group of the present invention is a phenotype of aquatic ecology disturbed by inorganic pollution, which is a pollutant that does not affect cell proliferation but can increase intracellular complexity, resulting from the introduction of inorganic pollutants into the cell. It may be a phenotypic group in which only active metabolic activity is induced.

According to one embodiment of the present invention, the grouping that satisfies Equation 5 of step 4) may be a large-sized (LS) phenotype group.

The large phenotype group of the present invention is a phenotype in which the size of the microbial cells is large but the intracellular complexity is stable, and the oligotrophy state, which is the basic nutritional state of the aquatic ecosystem, is restored after microbial growth such as organic pollution, thereby protecting the organism from organic pollutants. The disturbance may be due to the phenotypic group that has been purified.

According to one embodiment of the present invention, the grouping that satisfies Equation 6 of step 4) may be a dormant (dormant or starved, D) phenotype group.

The dormant phenotype group of the present invention is a phenotype in an oligotrophic state, which is the basic nutritional state of aquatic ecology, and is a microbial phenotype in aquatic ecology with small cell size and low intracellular complexity, and is a phenotype group that is not exposed to any pollutants. It could be. It is known that more than 70% of groundwater environments that are not exposed to pollution or disturbance have the same phenotype as D.

The HA, HR, AS, LS, and D phenotypes of the present invention can each be located in a quadrant of a grid cell, and the specific grouping is shown in Figure 4.

According to one embodiment of the present invention, the water source may be selected from the group consisting of groundwater, river, brook, lake, reservoir, and spring, Preferably, it is ground water, but is not limited thereto.

Hereinafter, the present invention will be described in more detail through examples. These examples are merely for illustrating the present invention in more detail, and it will be apparent to those skilled in the art that the scope of the present invention is not limited to these examples.

<Example 1> Water body sample preparation

In order to evaluate aquatic ecological disturbance based on the microbial phenotype of the present invention, water samples were concentrated and prepared. Specifically, in order to concentrate microorganisms in the groundwater sample, 1 L of groundwater sample was obtained, pre-filtered with 8 μm cellulose filter paper, and then filtered with 0.25 μm mixed cellulose ester (MCE) filter paper using a vacuum pump. and filtered. Afterwards, the cell sample collected on the filter paper was resuspended in 20 ml of groundwater to prepare a 50-fold cell enrichment sample.

<Example 2> Preparation of flow cytometry and phenotypic diversity analysis

<2-1> Cell staining and flow cytometry

To evaluate aquatic ecological disturbance based on the microbial phenotype of the present invention, 50-fold cell enrichment samples were analyzed using flow cytometry (FCM). Specifically, the 50-fold cell enrichment sample was reacted for 15 minutes in the dark using the LIVE/DEAD BacLight Bacterial Viability kit (Invitrogen, OR, USA). Afterwards, it was analyzed using a CytoFLEX flow cytometer (CytoFLEX V0-B3-R2, Beckman Coulter, CA, USA) equipped with a 50 mW laser emitting a wavelength of 488 nm, two light scattering detectors, and five fluorescence detectors. The sheath buffer (Beckman coulter) was used as a carrier fluid for the FCM of the present invention, and fluorescein isothiocyanate (fluorescein) from the forward scatter channel (FSC) and side scatter channel (SSC) Isothiocyanate (FITC, 525/40 nm) and phycoerythrin (PE, 585/42 nm) fluorescence signals were recorded for 1 minute at a flow rate of 10 μl/min, and the FSC threshold was set to 8,000. From the FCM results, complexity such as cell size of FSC and karyotype of SSC were used as phenotypes. FCM fingerprinting, including signal filtering, denoising, and binning, was obtained by the methods of Examples 2-2 to 2-4 below.

<2-2> Noise removal (denoising)

For microbial phenotypic analysis of the present invention, noise was removed. Specifically, the FCM results output in ".fcs" format in Example 2-1 were converted to a ".csv" file using the R packages "flowCore" and "biobase". Afterwards, results satisfying the criteria {(FSC > 10 ³ ) ∩ (SSC > 10 ³ ) ∩ (FITC > 10 ³ ) ∩ (PE > 10 ³ )} were collected, and the collected data were subjected to a noise removal process. converted to . Noise signals can arise from a variety of situations in the analysis of environmental samples, with particles including debris fragments, cell aggregates, and self-fluorescent generating bacteria being common noise sources. In the present invention, in order to collect only data from actual cells, noise signals were removed from the data set. Negative control samples were prepared as samples sterilized by autoclave and samples without staining. Afterwards, the area of the FITC-PE biplot where the event of the negative control was observed in the FCM biplot generated using the CytExpert program (ver. 2.4.0.28, Beckman coulter) was designated as the noise-area. It was designated as . Afterwards, a scatter plot was created using the FITC and PE intensity values on the x-axis and y-axis, excluding the noise area, respectively. In the generated region, the region where cell events were observed was identified and gated using the R package alphahull. SYTO9 and PI fluorescence identified live cells according to the ratio of fluorescence intensity, and the fluorescence intensity values observed in the gate area were extracted as a live cell data set.

<2-3> Data normalization and binning

Cells with different phenotypes were identified at different positions in the FCM scatterplot, and the distribution pattern of FCM events in the scatterplot may be representative of the cell phenotype, so using the fixed-bin method, the FSC-SSC scatterplot was divided into 40 × 40 It was divided into equal grid cells (grid-cells), and the event frequency of each grid-cell was counted. The event frequency of each grid-cell was summarized in a feature table. All data analysis processes were performed in R (ver. 4.0.2, http://www.R-project.org), and the FCM-based phenotypic diversity script was downloaded from the GitHub repository (https://github.com/ecobiolab) /FCM-based-phenotypicdiversity.git).

<2-4> Ecological evaluation based on phenotypic alpha and beta diversity

Apa and beta diversity according to phenotype was analyzed and used for ecological evaluation. Specifically, Shannon's diversity index was calculated using the feature table generated after binning in Example 2-3 and the R package vegan. The significance of the difference in alpha-diversity between the unperturbed and perturbed groups was then assessed using the Wilcoxon signed rank test calculated using the R package ggpubr. Afterwards, the correlation between the samples was confirmed and visualized using a principal component analysis plot using the stats of the R package. The significance of each clustered group in each PCA plot was tested using permutational multivariate analysis of variance (PERMANOVA) in the vegan package. Ecological disturbance was evaluated based on the pair-wise distance between the undisturbed sample and the tested sample in each PCA plot.

<2-5> Phenotypic identification according to disturbance

To interpret the FCM fingerprint as a phenotypic structure, n × n grid cells were grouped into phenotypic subcommunities, and each group was designated as shown in Table 1 below. Since general groundwater is in an oligotrophic state in which microorganisms rarely proliferate, the HA group in the phenotypic groups below is a group with large cell size and high cell complexity, and various metabolic activities of cells are affected by pollutants. It is an active group. The HR group is a group with larger cells and somewhat lower cell complexity compared to the HA group. After various metabolic activities of cells increase due to pollutants, cell complexity decreases over time, leading to a decrease in pollutants or It is a group that progresses to stabilization of aquatic ecology, and since the size of cells in the HA group and HR group increases due to pollutants, it can be inferred that aquatic ecological disturbance is induced by organic pollutants, which are pollutants related to cell proliferation. You can. The AS group is a group in which the cell size is small but the complexity of the cells is increased. It can be inferred that the cell complexity is increased due to the introduction of contaminants, and that it is a chemical disturbance that is not related to cell proliferation. there is. The LS group is in a state in which the cell size is increased, and it can be inferred that the cell complexity enters a plateau after the pollutant is metabolized by microorganisms over time after exposure to the contaminant, and the D group is the cell Since the size and complexity of the group are measured to be smaller than those of other groups, it can be inferred that they are a group that has not been exposed to a pollutant, a long period of time has passed since exposure to a pollutant, or an unpolluted group in a oligotrophic state. In fact, it is known that more than 70% of microbial phenotypes in uncontaminated groundwater show the same phenotype as group D. The phenotypic criteria for each grouping of the present invention are shown in Table 1 below.

group phenotype highly active (HA) {(SSC > n/2) > (FSC > n/2)} Highly reproducible (HR) {(SSC > n/2) < (FSC > n/2)} active small-sized (AS) {(SSC > n/2) ∩ (FSC < (n/2+1))} Large-sized (LS) {(SSC < (n/2+1)) ∩ (FSC > n/2+1)} Dormant (dormant or starved, D) {(SSC < (n/2+1)) ∩ (FSC < (n/2+1))}

Then, using a table grouped by phenotype, the phenotypic structure of groundwater microbial communities was compared using hierarchical clustering with the R function hclust based on Bray-Curtis dissimilarity. To determine whether the phenotypes could reflect the disturbance caused by pollutants in the microbiome, the pollution status was indicated in the phenotypic clusters determined in the analysis. We then identified the phenotypic communities comprised of disturbed samples and used indicator species analysis using the R package indicspecies to identify key phenotypic differences between disturbed and undisturbed groundwater microbiomes. Confirmed. The overall analysis process of Examples 1 to 2 is shown in Figure 1, and the FCM data analysis process is shown in Figure 2.

<Example 3> Taxonomic evaluation of groundwater ecological disturbance

In dry season groundwater samples, microbiome ecological disturbance was confirmed by the method described in Examples 1 and 2 above. As a control, a molecular biological analysis method using 16s rRNA was used. Specifically, the groundwater samples included the contaminated group (C, n = 8) exposed to perchlorethylene (PCE), a contamination source, the fringed group (F, n = 8) around the contamination source, and the local background, which was the source of water supply in the region. They were classified into group B (B, n=4) and uncontaminated group (G, n=27) and collected during the dry season. For the 16s rRNA gene in the collected groundwater samples, 718,046 total and 439,935 quality-filtered reads were obtained. Afterwards, clustering was performed using 3,158 OTUs for the dry season. The results are shown in Table 2 below.

As a result, as shown in Table 2 above, Shannon's diversity calculated from OTUs in the dry season was in the order F > C > B > G, but there was no significant difference between each group.

However, in the expression-based evaluation using the FCM analysis of the present invention, in order to identify the phenotypic characteristics of 2,048 living cells in samples from each season with FCM data, the grid cell unit size was set to 100, 625, 1,600, and 2,500. As a result of calculating and comparing the diversity of selected phenotypes, Shannon diversity increased as the number of grid cells increased in the dry season, regardless of the level of contamination, except for the case of using 100 grid cells, 625, 1,600, and 2,500 grids. It was confirmed that the difference in shannon diversity between groups using cells was significant. In particular, the difference in Shannon diversity between the C and G groups was significant at 625, 1,600, and 2,500 grid cells, while the diversity of F and B was not significantly different from that of the G group at 625 and 2,500 grid cells. Among them, when shannon diversity was analyzed using 1,600 grid cells, significant differences were shown for each group.

<Example 4> Phylogenetic and phenotypic evaluation of ecological disturbance

Using a Bray-Curtis distance-based NMDS plot, we confirmed whether each groundwater sample could be distinguished through phylogenetic and phenotypic β-diversity. In the dry season, the NMDS plot of OTUs clearly separated the G group and the contaminated groups C, F, and B (ANOSIM, R = 0.69 and P < 0.05) (Figure 3a). In FCM's NMDS (ANOSIM, R = 0.29 and P < 0.05), group C and B and G samples overlapped with G and contamination groups (Figure 3B). In intra- and inter-group comparisons, OTU distances were twice as high as FCM distances. To prevent biased comparisons due to large differences in intra-group distances, the differences in inter- and intra-group distances were compared to the intra-group distances. The ratios were compared. As a result, there was a clear deviation in FCM-distances (14.5%, G-C; 25.3%, G-F; and 7.1%, G-B), and OTU-distances (7.3%, G-C; 7.8%, G-F; 5.7%, G-B). There was no significant difference. It was confirmed that the OTU distance order was not consistent: F > C > B.

<Example 5> Identification of bacterial taxa related to perchlorethylene disturbance

To identify FCM fingerprints as phenotypic features and link them to response mechanisms to disturbances, 1,600 FCM grid cells were classified into phenotypic units (Figure 4) and cell distribution patterns were compared using hierarchical clustering. Using 0.2 distance as a cutoff in the dry season, 19 contaminated area samples and 3 G group samples were designated as Dd1 (disturbance + dry season) (Figure 5). The remaining 25 G group samples and 4 contaminated samples (C 2 region, B 2 region) were clustered into Ud1, Ud2, and Ud3 (Ud = undisturbed + dry season) (Figure 5). Ud1, Ud2, and Ud3 were clustered with mean intra-distances of 0.07 ± 0.04 and 0.10 ± 0.04, respectively, whereas the intra-distance of Dd1 was higher.

The cell phenotypic composition of each cluster was visualized with a heatmap (Figure 6), and it was confirmed that cells in Dd1 were distributed in HA (34.6 ± 7.4%), AS (26.7 ± 5.6%), and D (33.0 ± 8.5%) ( Figure 6). Meanwhile, in Ud1 and Ud3, cells were concentrated in D (83.7 ± 5.1%, Ud1; 63.7 ± 6.0%, Ud3), and in Ud2, as an exception, the density of cells in D was reduced compared to Ud1 and Ud3. (23.4 ± 4.1%, HA, 10.2 ± 4.7%, AS, 48.2 ± 2.6%, D, 14.1 ± 5.0%, HR) (Figure 7). Regardless of the phenotypic cluster, less than 5% of cells were observed in the LS subcommunity, and the HA and AS subcommunities of Dd1 were significantly increased compared to Ud1 to Ud3. In contrast, the D subcommunity of Ud1 to Ud3 was significantly increased than D1.

As a result of indicator species analysis, a total of 88 grid cells were identified as indicators of Dd1 (indicator value > 0.5 and P < 0.05), with 93.2% of Dd1 belonging to HA (46) or AS (36), and the remaining 6 It was confirmed that grid cells belong to D (Table 3). Among the undisturbed clusters in the dry season, 40 and 16 grid cells were identified as indicators (indicator values > 0.5 and P < 0.05) for Ud1 and Ud2, respectively, but no indicator grid cells were identified for Ud3. All Ud1s representing grid cells were derived from HR (13), HA (2) and LS (1), and D and Ud2 representing grid cells containing them (Table 4).

Therefore, the present invention concentrated groundwater samples, analyzed them using flow cytometry, and classified them into phenotypes according to cell size and complexity. In addition, the classified phenotypes were grouped, the differences in the phenotypes according to the groundwater samples were confirmed, and compared with the control group, it was confirmed that the degree of contamination and the source of contamination could be inferred according to the differences in the phenotypes of the groundwater bacterial flora, thereby improving groundwater ecology. It was confirmed that it could be used for disturbance evaluation.

Claims (11)

1) Obtaining cells by filtering a first water sample obtained from a water source;
2) producing a concentrate by mixing the cells obtained in step 1) with a second water body sample obtained from the same water source as the water source in step 1) but having a volume of 1/50 of the first water body sample;
3) Based on the data analyzed by flow cytometry on the concentrate produced through step 2), forward scatter channel (FSC), side scatter channel (SSC), fluorescein isothiocyanate Quantifying the fluorescence expression of fluorescein isothiocyanate (FITC) and phycoerythrin (PE) and selecting data with a quantified fluorescence expression value exceeding 10 ³ ;
4) The data selected in step 3) above is displayed on a two-dimensional coordinate plane expressed as , wherein the and
[Equation 1]
(X,Y)=(FSC,SSC);
[Equation 2]
(SSC > n/2) > (FSC >n/2);
[Equation 3]
(SSC > n/2) < (FSC >n/2);
[Equation 4]
(SSC > n/2) ∩ (FSC <(n/2+1));
[Equation 5]
(SSC < (n/2+1)) ∩ (FSC >(n/2+1)); and
[Equation 6]
(SSC < (n/2+1)) ∩ (FSC <(n/2+1));
5) evaluating the ecological disturbance of the water body sample based on the grouping; a method for evaluating ecological disturbance of the water body sample, including:
The quantified fluorescence expression value of FSC in step 3) is the cell size,
The quantified fluorescence expression value of SSC in step 3) is cell complexity,
The group that satisfies equation 2 of step 4) is a highly active (HA) phenotype group, which is a phenotype of aquatic ecology disturbed by exposure to organic pollutants in which cell size and cell complexity are increased compared to the baseline value of the control group. ego,
The group that satisfies Equation 3 is a highly reproducible (HR) phenotype group, which is an aquatic ecology phenotype in which cell complexity is reduced compared to the baseline value of the control group due to a decrease in pollutants after exposure to organic pollution and cell proliferation,
The grouping that satisfies Equation 4 is an active small-sized (AS) phenotype group, which refers to an aquatic ecology disturbed by exposure to inorganic pollutants in which cell complexity increased compared to the baseline value of the control group after the introduction of inorganic pollutants. is the phenotype of,
The grouping that satisfies Equation 5 above is a large-sized (LS) phenotype group, which is the phenotype of the aquatic ecology in which the disturbance has been purified after disturbance of the aquatic ecology by organic pollutants,
The grouping that satisfies Equation 6 is a dormant or starved (D) phenotype group, which is a phenotype of an aquatic ecosystem that is not exposed to pollutants or disturbances. According to clause 1,
The method further includes analyzing α-diversity and β-diversity from the phenotypic group after step 4). delete delete According to clause 1,
The method wherein the n number in step 4) is 50 to 3000. delete delete delete delete delete According to clause 1,
The method according to claim 1, wherein the water source is selected from the group consisting of groundwater, river, brook, lake, reservoir, and spring.