Method for analyzing methylmercury-tolerant key metabolite of tetrahymena thermophila

Abstract

The invention relates to the technical field of biological analysis, in particular to a method for analyzing methylmercury-tolerant key metabolites of tetrahymena thermophila. The method comprises the following steps: respectively culturing tetrahymena thermophila in a culture medium containing methyl mercury to respectively obtain experimental groups; culturing tetrahymena thermophila in a culture medium without methyl mercury to obtain a control group; then carrying out pretreatment on tetrahymena thermophila of the experimental group and the control group, then carrying out detection and quality control, respectively collecting data of metabolites of the experimental group and the control group, and then carrying out data processing; and finally, screening the differential metabolites of the experimental group and the control group, and then carrying out pathway enrichment analysis by adopting KEGG to obtain the change of the metabolic pathway related to the response of the tetrahymena thermophila to the methylmercury stress, and further selecting the key metabolite related to the metabolic pathway as the key metabolite related to the methylmercury thermophila to the tetrahymena thermophila.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological analysis, and in particular to a method for analyzing key metabolites of Tetrahymena thermophila resistant to methylmercury. BACKGROUND

[0002] Methylmercury (MeHg) is an extremely toxic environmental pollutant with high neurotoxicity, bioaccumulation and biomagnification effects, which poses a serious threat to the ecosystem and human health. As a single-cell eukaryote at the bottom of the food chain, Tetrahymena thermophila may play an important role in the process of methylmercury entering the food chain by ingesting methylmercury in the water environment. Therefore, studying the mechanism of its resistance to methylmercury is of great significance for understanding the response of organisms to heavy metal pollutants and environmental remediation.

[0003] Metabolomics is a new research method for qualitative and quantitative analysis of all metabolites in the body, which can comprehensively reflect the metabolic state and physiological process of the body. At present, although metabolomics has certain application in biological toxicology research, there is no reported method for systematic analysis of key metabolites of Tetrahymena thermophila resistant to methylmercury. The existing research methods often have low sensitivity and cannot fully reflect the changes of metabolites, making it difficult to accurately identify the key metabolites of Tetrahymena thermophila resistant to methylmercury.

[0004] Therefore, it is essential to provide a technical solution that can solve the above technical problems. SUMMARY

[0005] To solve the above problems, the purpose of the present application is to provide a method for analyzing key metabolites of Tetrahymena thermophila resistant to methylmercury. The present application can detect under methylmercury treatment, screen for differentially expressed metabolites under two concentrations, and analyze the changes of key metabolites of Tetrahymena thermophila resistant to methylmercury and the corresponding pathway changes under two concentrations of methylmercury treatment.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The present application provides a method for analyzing key metabolites of Tetrahymena thermophila resistant to methylmercury, characterized in that it comprises the following steps:

[0008] (S1) Culturing Tetrahymena thermophila in a culture medium containing methylmercury to obtain an experimental group;

[0009] Culturing Tetrahymena thermophila in a culture medium without methylmercury to obtain a control group;

[0010] (S2) treating the experimental group and the control group of T. thermophila in step (S1) to obtain an extract; then detecting and quality-controlling the extract, collecting the metabolite data of the experimental group and the control group respectively, and then processing the data;

[0011] (S3) combining the differential metabolites of the experimental group and the control group to perform KEGG pathway enrichment analysis, obtaining the change of the metabolic pathway of T. thermophila responding to methylmercury stress, and then selecting the key metabolites related to the metabolic pathway as the key metabolites of T. thermophila tolerating methylmercury.

[0012] In an embodiment of the present application, in step (S1), the temperature during the culture is 27℃, and the time is 20-28h.

[0013] In an embodiment of the present application, in step (S2), the concentration of methylmercury in the culture medium containing methylmercury is 1-10 pg / mL and 1-10 ng / mL.

[0014] In an embodiment of the present application, in step (S2), the data processing includes baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization of the extracted data.

[0015] In an embodiment of the present application, the metabolites are identified based on mass-to-charge ratio (M / z), secondary fragments and isotope distribution.

[0016] In an embodiment of the present application, in the data processing process, any peak with more than 50% missing values in the group is removed, the remaining 0 values are replaced with half of the minimum value of all ion intensities of all samples, and screening is performed according to the qualitative results of the metabolites.

[0017] In an embodiment of the present application, the differential metabolites are screened by Score scoring: the metabolites obtained by qualitative analysis are screened according to the scoring of the metabolite qualitative results, and when the score of the metabolite is ≥36, it is determined as a differential metabolite.

[0018] When the score of the metabolite is <36, it is determined that the qualitative result is inaccurate and is deleted.

[0019] In an embodiment of the present application, when screening the differential expression metabolites, R language and bioinformatics package are used for screening the differential expression metabolites, and the screening standard is p value <0.05 and VIP >1.

[0020] In an embodiment of the present application, the differential metabolites are subjected to metabolic pathway enrichment analysis based on the KEGG database.

[0021] Preferably, the KEGG ID of the differential metabolite is used for pathway enrichment analysis to obtain metabolic pathway enrichment results; and hypergeometric test is applied to find out the pathway entries significantly enriched in the significantly differentially expressed metabolites compared with the whole background.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] The method for analyzing key metabolites of Tetrahymena thermophila tolerating methylmercury provided by the present application can simultaneously analyze a large number of metabolites, greatly improving the throughput and efficiency of sample analysis.

[0024] The method for analyzing key metabolites of Tetrahymena thermophila tolerating methylmercury provided by the present application can perform unbiased large-scale detection on thousands of small molecule metabolites in biological samples, and the coverage is much wider than traditional single or a few metabolite detection methods.

[0025] The method for analyzing key metabolites of Tetrahymena thermophila tolerating methylmercury provided by the present application can provide comprehensive metabolite information, including small molecule organic matter, metabolic pathways, metabolites, etc., so as to better understand the overall mechanism and mutual correlation of the metabolic system in the organism. DETAILED DESCRIPTION

[0026] The present application will be described in detail below in combination with specific embodiments.

[0027] In the following examples, if not specifically stated, the reagents are commercially available reagents, and the detection means and methods used are conventional detection means and methods in the art.

[0028] Example 1

[0029] The present embodiment provides a method for analyzing key metabolites of Tetrahymena thermophila tolerating methylmercury, specifically comprising the following steps:

[0030] (S1) Tetrahymena cell culture and collection, the control group Tetrahymena culture conditions were set to be placed in a centrifuge tube containing SPP medium (2% peptone, 0.1% yeast extract, 0.2% glucose, 0.003% ferric citrate, and 1% of 10000 U / mL penicillin and 10000 mg / L streptomycin), cultured in a 27°C constant temperature shaker. The treatment group Tetrahymena culture conditions were SPP medium containing different concentrations of methyl mercury (concentrations were 1 ng / mL (denoted as Me-H-T) and 1 pg / mL (denoted as Me-T) (calculated as mercury)). After 24 hours of treatment, the centrifuge tube was placed in a centrifuge at 10000 rpm, 4°C, centrifuged for 6 min, and the supernatant was discarded after centrifugation to collect the precipitate, which was Tetrahymena cells. The Tetrahymena cells cultured under normal conditions and the Tetrahymena cells treated with methyl mercury for 24 hours were collected separately.

[0031] (S2) Tetrahymena cell pretreatment, the collected cells were loaded into 1.5 ml EP tubes, two small steel balls and 400 μL of L-2-chlorophenylalanine-containing methanol-water solution (V:V = 4:1) were added, pre-cooled in a -40°C refrigerator for 2 min, and then put into a grinder for 2 min (60 Hz). After grinding, ultrasonic extraction in ice water bath for 10 min, -40°C refrigerator for 30 min. After standing, centrifuged at 1200 rpm for 10 min (4°C), and 300 μL of the supernatant after centrifugation was loaded into an LC-MS sample vial and dried. After drying, 300 μL of methanol-water solution (V:V = 1:4) was added to the sample vial, vortexed for 30 s, ultrasonicated in an ice water bath for 3 min, and -40°C for 2 hours. Then the extracted solution was centrifuged at 4°C 1200 rpm for 10 min, 150 μL of supernatant was taken with a syringe, filtered with a 0.22 μm organic phase needle filter, transferred to an LC-MS sample vial, and stored at -80°C for subsequent LC-MS analysis. The quality control sample (QC) was prepared by mixing equal volumes of all sample extracts.

[0032] (S3) On-machine detection and quality control, the analysis instrument is a liquid chromatography-mass spectrometry system composed of an ACQUITY UPLC I-Class plus ultra-high performance liquid and a QE plus high-resolution mass spectrometer, equipped with a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) for analyzing the metabolic profile in ESI positive and ESI negative ion modes. ACQUITY UPLC HSS T3 chromatographic column (1.8 μm, 2.1 x 100 mm) is used in both positive and negative modes. The gradient elution system consists of (A) water (containing 0.1% formic acid) and (B) acetonitrile, using the following gradient: 0 min, 5% B; 2 min, 5% B; 4 min, 30% B; 8 min, 50% B; 10 min, 80% B; 14 min, 100% B; 15 min, 100% B; 15.1 min, 5% B; 16 min, 5% B, with a flow rate of 0.35 mL / min and a column temperature of 45°C. All samples are kept at 10°C during analysis. The injection volume is 5 μL. The mass range is from 70 m / z to 1050 m / z. The first mass spectrometry scan resolution is 70000, and the second mass spectrometry scan resolution is 17500, with collision energies of 10, 20 and 40 eV, respectively. The working mode of the mass spectrometer is as follows: spray voltage, 3800 V (+) and 3000 V (-); sheath gas flow, 35 arbitrary units; auxiliary gas flow, 8 arbitrary units; capillary temperature: 320°C; Aux gas heater temperature, 350°C; s lens RF level, 50. The unfiltered data matrix is imported into the R package for principal component analysis (PCA) to observe the overall distribution between samples and the stability of the entire analysis process. In order to prevent overfitting, a 7-fold cross-validation PCA model is obtained, and the observation of the close clustering of QC samples indicates that the experiment has good stability and repeatability.

[0033] (S4) Data preprocessing of the raw data was performed using Progenesis QI v3.0 software, including baseline filtering, peak detection, integration, retention time correction, peak alignment and normalization. The main parameters were 5 ppm precursor tolerance, 10 ppm product tolerance and 5% product ion threshold. The compounds were identified based on accurate mass-to-charge ratio (M / z), secondary fragments and isotope distribution using The Human Metabolome Database (HMDB), Lipidmaps (V2.3), METLIN database. Any peak with more than 50% missing values (ion intensity = 0) was removed and replaced with half of the minimum value. The compounds were screened according to the qualitative results of the scoring (Score). The screening standard was 36 points (full score 80 points), and less than 36 points was considered inaccurate and deleted. The positive and negative ion data were combined into a screened data matrix.

[0034] (S5) Screening of differentially expressed metabolites. R language and bioinformatics package were used for screening of differentially expressed metabolites, wherein metabolites with p value < 0.05 and VIP > 1 were defined as differentially expressed metabolites.

[0035] Compared with the control group, 323 differentially expressed metabolites were screened in the Me-T group, including 230 up-regulated differentially expressed metabolites and 93 down-regulated differentially expressed metabolites. Compared with the control group, 382 differentially expressed metabolites were screened in the Me-H-T group, including 331 up-regulated differentially expressed metabolites and 51 down-regulated differentially expressed metabolites. According to the calculation of the fold change after treatment, the top three metabolites with expression difference were as shown in Tables 1-4. The metabolites mentioned in the tables may be key metabolites for the tolerance of T. thermophila to methylmercury.

[0036] Table 1 Top three metabolites with up-regulated expression difference after 24 hours of treatment with 1 pg / mL methylmercury

[0037]

[0038] Table 2 Top three metabolites with up-regulated expression difference after 24 hours of treatment with 1 ng / mL methylmercury

[0039]

[0040] Table 3 Top three metabolites with down-regulated expression difference after 24 hours of treatment with 1 pg / mL methylmercury

[0041]

[0042] Table 4 Top 3 metabolites with the largest fold change of down-regulation after 24 hours of 1 ng / mL methylmercury treatment

[0043]

[0044]

[0045] (S6) KEGG pathway enrichment analysis Based on the KEGG database, the differential metabolites were subjected to metabolic pathway enrichment analysis. The KEGG ID of the differential metabolites was used for pathway enrichment analysis to obtain the metabolic pathway enrichment results. Hypergeometric test was used to find out the pathway entries that were significantly enriched in the significantly differentially expressed metabolites compared with the whole background; the calculation formula is as follows:

[0046]

[0047] Wherein, N is the total number of metabolites; n is the number of differentially expressed metabolites in N; M is the number of metabolites annotated to a certain pathway; m is the number of differential metabolites annotated to a certain pathway. According to the P value, the smaller the P value, the more significant, the results are shown in Tables 5-6.

[0048] Table 5 Top 3 pathways with significant enrichment after 24 hours of 1 pg / mL methylmercury treatment

[0049]

[0050] Table 6 Top 3 pathways with significant enrichment after 24 hours of 1 ng / mL methylmercury treatment

[0051]

[0052] The KEGG enrichment analysis results show that when the growth environment of Tetrahymena thermophila changes from normal nutritional conditions to methylmercury treatment conditions, the related metabolic pathways such as purine metabolism, phenylalanine metabolism, ABC transporter, citric acid cycle, and biosynthesis of valine, leucine and isoleucine are significantly enriched, and it is inferred that the above metabolic pathways are the key metabolic pathways of Tetrahymena thermophila exposed to methylmercury.

[0053] The above description of the embodiments is for the convenience of those skilled in the art to understand and use the invention. Those skilled in the art can easily make various modifications to these embodiments, and apply the general principles described herein to other embodiments without having to go through creative labor. Therefore, the present application is not limited to the above embodiments, and the improvements and modifications made by those skilled in the art without departing from the scope of the present application should be within the scope of protection of the present application.

Claims

1. A method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila, characterized in that: The following steps are involved: (S1) Tetrahymena thermophila was cultured in a medium containing methylmercury to obtain experimental groups; The control group was obtained by culturing Tetrahymena thermophila in a medium without methylmercury; (S2) pre-treating the thermophilic Tetrahymena in the experimental group and the control group in step (S1) to obtain an extract; then testing and quality controlling the extract, collecting metabolite data from the experimental group and the control group, and then performing data processing; (S3) KEGG pathway enrichment analysis was performed based on the differential metabolites obtained between the experimental and control groups to obtain changes in metabolic pathways related to the response of Tetrahymena thermophila to methylmercury stress, and then key metabolites related to the metabolic pathways were selected as key metabolites of Tetrahymena thermophila to methylmercury.

2. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: In step (S1), during the incubation process, the temperature is 27°C and the time is 20 to 28 hours.

3. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: In step (S2), the concentrations of methylmercury in the culture medium containing methylmercury are 1-10 pg / mL and 1-10 ng / mL, respectively.

4. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: In step (S2), the data processing includes performing baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization on the extracted data.

5. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 4, characterized in that: Metabolites were identified based on mass-to-charge ratio, secondary fragments, and isotope distribution.

6. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 4, characterized in that: During data processing, any peak with more than 50% missing values ​​in the group was removed, the remaining 0 values ​​were replaced with half of the minimum value of all ion intensities for all samples, and the metabolites were screened based on their qualitative results.

7. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: Differential metabolites are screened by Score: the qualitative metabolites are screened according to the metabolite qualitative results. When the metabolite score is ≥36 points, it is determined to be a differential metabolite; When the metabolite score was less than 36 points, the qualitative result was determined to be inaccurate and deleted.

8. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: When screening differentially expressed metabolites, R language and bioinformatics packages were used for screening of differentially expressed metabolites, and the screening criteria were p value < 0.05 and VIP > 1.

9. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: Metabolic pathway enrichment analysis of differential metabolites was performed based on the KEGG database.

10. The method for analyzing key metabolites of methylmercury tolerance in Tetrahymena thermophila according to claim 1, characterized in that: Pathway enrichment analysis was performed using the KEGG ID of differential metabolites to obtain metabolic pathway enrichment results; A hypergeometric test was applied to identify pathway entries that were significantly enriched among significantly differentially expressed metabolites compared to the entire background.

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