CN116784283A - Research method for identifying influence of bacterial metabolism on rare earth regulation biological metabolic process - Google Patents

Abstract

The invention discloses a research method for identifying the effects of bacterial metabolism on rare earth elements regulating biological metabolic processes, including expanded cultivation of Caenorhabditis elegans, inactivation of Escherichia coli, experimental grouping, exposure to rare earth elements, action of Escherichia coli, sample collection, metabolism In several steps including sample preparation, metabolic data detection and analysis, and metabolic pathway analysis, NMR spectral analysis and probability quotient normalization were used for data processing. Cross-validation and permutation tests were used to analyze the fitting effect of the constructed model. From the metabolites It explores the impact of bacterial metabolism on rare earth elements' regulation of biological metabolism, and solves the reliability verification problem of exploring the impact of bacterial metabolism on rare earth elements' regulation of biological metabolism from the overall biological metabolism level.

Description

A research method to identify the influence of bacterial metabolism on rare earth-regulated biological metabolic processes

Technical field

The invention relates to a research method for identifying the influence of bacterial metabolism on rare earth-regulated biological metabolic processes, and belongs to the field of microorganisms.

Background technique

In recent years, rare earth elements have been widely used in various fields, resulting in a large amount of rare earth elements being released into the environment. The biological hazards of these elements are still unclear, and they pose a huge threat to the ecological environment and human body. In view of its ecological threat, there are still uncertainties about the pros and cons of its large-scale application, and there is currently a lack of corresponding standards for its application. In-depth exploration of its endogenous biological mechanism is needed to standardize the use of rare earth elements and conduct research on rare earth toxicity risks in the future. Assessment is urgently needed to provide evidence support.

Other experimental models such as rats have long experimental cycles, high experimental costs, and large individual differences. Using Caenorhabditis elegans can solve the above problems. Caenorhabditis elegans can grow and develop in a variety of bacteria. During laboratory culture, it is mainly fed uracil-deficient Escherichia coli. The metabolism of Escherichia coli can affect the life span, metabolism and other biological processes of the nematode. However, its various metabolic factors have a negative impact on C. elegans. The influence model of C. elegans is still unclear, and there are still various obstacles in experimental design and factor analysis. How to design experimental parameters, effectively process the parameters, and isolate usable data has become the current method of using C. elegans to replace rats. Major obstacles faced by other experimental models make it difficult to replace C. elegans in the experimental field.

Contents of the invention

The purpose of the present invention is to provide a research method for identifying the influence of bacterial metabolism on rare earth-regulated biological metabolic processes.

The technical solution to achieve the object of the present invention is: a research method for identifying the influence of bacterial metabolism on rare earth-regulated biological metabolic processes, which is characterized by including the following steps:

S1: Expanded culture of Caenorhabditis elegans: Cultivate Caenorhabditis elegans on agar plates coated with Escherichia coli, conduct large-scale culture, collect nematodes in the egg-laying period for synchronization, and culture the eggs in the culture medium to stage L4;

S2: Preparation of inactivated E. coli;

S3: Experimental grouping: Put the L4 stage wild-type C. elegans nematodes described in S1 into a petri dish and divide them into four groups: live bacteria control group, dead bacteria control group, live bacteria experimental group, and dead bacteria experimental group. ;

S4: Model construction: Add chemical reagents to the nematode petri dish described in S3 to expose the nematodes, and provide E. coli as food and incubate;

S5: Sample collection: Collect nematode samples after step S4 and wait for the next step of processing;

S6: Sample preparation: The nematode samples collected in S5 need to be extracted, enriched, concentrated, resuspended and sent for metabolomics analysis;

S7: Metabolite detection and analysis: Obtain metabolic data based on NMR, and use relevant software and statistical methods to identify metabolites;

S8: Metabolic pathway analysis: Use relevant databases to perform enrichment analysis of metabolic pathways.

The above method can be used to effectively establish the OPLS-DA model. From the perspective of metabolite analysis, the impact of rare earth elements on the metabolism of C. elegans can be tracked, and effective correlation demonstration can be carried out to prepare for the next step of using C. elegans to conduct experiments on rats and other animals. Experimental models are used to prepare alternative theoretical foundations.

Further, the specific method for preparing inactivated E. coli in step S2 is high-temperature inactivation.

It should be noted that the inactivation effects produced by different inactivation methods are different, and the residues produced by the inactivation will have a certain impact on the experimental results.

Further, the density of nematodes used in the experiment in step S3 was 10,000 per dish, the bacterial species added to the live bacteria control group and live bacteria experimental group were active Escherichia coli, and the dead bacteria control group and dead bacteria experimental group The added strain was inactivated Escherichia coli.

Nematode density will affect the concentration of metabolites received by a single nematode, and the data obtained under different nematode densities will be different.

Further, the incubation time in step S4 is 10 days, the chemical reagent added to the control group is dimethyl sulfoxide, the chemical reagent added to the experimental group is rare earth solution, and the volumes of chemical reagents added to the control group and the experimental group are the same.

The incubation time affects the development stage of nematodes, and the metabolic intensity and metabolic mode of nematodes may be different; the concentration of reagents will affect the concentration of metabolites and will also affect the parameter test results. Therefore, it is necessary to pay attention to the strict control of each parameter during the culture process.

Furthermore, the method for extracting metabolites in step S6 is low-temperature extraction after grinding and crushing, and the extraction reagent is a mixed solution of methanol:water solution with a volume of 2:1 stored at -20°C.

Further, the specific method for obtaining the metabolic profile based on NMR in step S7 is to use a Bruker Avance III HD600-MHz nuclear magnetic resonance spectrometer to conduct ¹ H NMR metabolic profile and analysis. The test is equipped with a TXI probe, scanning 512 times, and 73,728 points in F1. , 12019.230Hz spectrum width, 1024 transients, cycle delay 4s.

To increase the repeatability of test data, the same parameters were used for each test.

Further, normalization processing is performed before metabolite identification in step S7. The specific steps are:

A1: Use the NMR processing software BrukerTopspin4.0.2 to perform data processing using one-dimensional exponential window multiplication, Fourier transform and phase correction of free induction attenuation to obtain NMR data;

A2: Import the above NMR data into Matlab2014a;

A3: Conduct chemical shift control with the internal standard trimethylsilyl propionic acid set to 0ppm, excluding areas around the signals such as water, TSP and methanol;

A4: NMR spectrum alignment;

A5: Use probability quotient normalization to process the data to compensate for concentration differences between samples; during the analysis process, use ChenomxNMRsuite8.4 and reference compounds to perform relevant identification of metabolites.

Further, in the process of data normalization by probability quotient in step A5, PubChem and KEGG database baseline search and matching are used, and then the converted values, KEGG codes, and metabolite names are integrated to draw a metabolic signal network diagram.

The above process can normalize the data and perform noise reduction on the measured data, making the influence of parameters more obvious.

Adopting the above technical solution, the present invention has the following beneficial effects:

The present invention uses nuclear magnetic resonance spectrum analysis and probability quotient normalization for data processing, uses cross-validation and permutation testing to analyze the fitting effect of the constructed model, and explores the effects of bacterial metabolism on rare earth elements in regulating biological metabolism from the perspective of metabolites. The impact solves the problem of reliability verification in exploring the influence of bacterial metabolism on rare earth elements in regulating biological metabolism from the overall metabolic level of organisms.

Description of the drawings

In order to make the content of the present invention easier to understand clearly, the present invention will be described in further detail below based on specific embodiments and in conjunction with the accompanying drawings, wherein

Figure 1 shows the PCA score plot of four groups of C. elegans samples;

Figure 2 shows the OPLS-DA diagram of the live bacteria and dead bacteria lanthanum experimental groups;

Figure 3 is a cross-validation diagram;

Figure 4 is a permutation test diagram;

Figure 5 shows the differential metabolite analysis diagram between the live bacterial lanthanum experimental group and the dead bacterial lanthanum experimental group;

Figure 6 is an analysis diagram of the differential metabolic pathways between the live bacterial lanthanum experimental group and the dead bacterial lanthanum experimental group.

Detailed ways

In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation modes.

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, rather than all embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

Therefore, the following detailed description of the embodiments of the invention provided in the appended drawings is not intended to limit the scope of the claimed invention, but rather to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

It should be noted that similar reference numerals and letters represent similar items in the following figures, therefore, once an item is defined in one figure, it does not need further definition and explanation in subsequent figures.

(Example 1)

A research method of the present invention for identifying the influence of bacterial metabolism on rare earth-regulated biological metabolic processes includes the following steps:

S1: Expanded culture of Caenorhabditis elegans: Cultivate Caenorhabditis elegans on agar plates coated with Escherichia coli, conduct large-scale culture, collect nematodes in the egg-laying period for synchronization, and culture the eggs in the culture medium to stage L4;

S2: Preparation of inactivated E. coli;

S3: Experimental grouping: Put the L4 stage wild-type C. elegans nematodes described in S1 into a petri dish and divide them into four groups: live bacteria control group, dead bacteria control group, live bacteria experimental group, and dead bacteria experimental group. , each group sets 5 samples;

S4: Model construction: Add chemical reagents to the nematode petri dish described in S3 to expose the nematodes, and provide E. coli as food and incubate;

S5: Sample collection: After step S4, centrifuge the nematode samples;

S6: Sample preparation: The nematode samples collected in S5 need to be extracted, enriched, concentrated, resuspended and sent for metabolomics analysis;

S7: Metabolite detection and analysis: Obtain metabolic data based on NMR, and use relevant software and statistical methods to identify metabolites;

S8: Metabolic pathway analysis: Use relevant databases to perform enrichment analysis of metabolic pathways.

The specific method for preparing inactivated E. coli in step S2 is high-temperature inactivation.

The density of nematodes used in the experiment in step S3 was 10,000 per dish, and the amount of culture medium was 6 mL. The strains added to the live bacteria control group and live bacteria experimental group were active Escherichia coli, and the dead bacteria control group and dead bacteria were added. The bacterial strain added to the bacteria experimental group was inactivated Escherichia coli; all samples provided 200 mg/mL OP50 400 μL as food.

The incubation time in step S4 is 10 days, the chemical reagent added to the control group is dimethyl sulfoxide, the chemical reagent added to the experimental group is lanthanum solution, and the volumes of chemical reagents added to the control group and the experimental group are the same.

The method of extracting metabolites in step S6 is low-temperature extraction after grinding and crushing. The extraction reagent is a mixed solution of methanol:aqueous solution with a volume of 2:1 stored at -20°C. In the specific operation, the tissue is grinded and crushed and then concentrated. The samples were then resuspended and sent for metabolic data analysis.

The specific method for obtaining the metabolic profile based on NMR in step S7 is to use a BrukerAvanceIIIHD 600-MHz nuclear magnetic resonance spectrometer to perform NMR measurement of ¹ HNMR metabolic profile and analysis. The test was equipped with a TXI probe, scanning 512 times, 73,728 points in F1, 12019.230 Hz spectrum width, 1024 transients, cycle delay 4s.

In step S7, normalization processing is also performed before metabolite identification. The specific steps are:

A1: Use the NMR processing software BrukerTopspin4.0.2 to perform data processing using one-dimensional exponential window multiplication, Fourier transform and phase correction of free induction attenuation to obtain NMR data;

A2: Import the above NMR data into Matlab2014a;

A3: Conduct chemical shift control with the internal standard trimethylsilyl propionic acid set to 0ppm, excluding areas around the signals such as water, TSP and methanol;

A4: NMR spectrum alignment;

A5: Use probability quotient normalization to process the data to compensate for concentration differences between samples; during the analysis process, use ChenomxNMRsuite8.4 and reference compounds to perform relevant identification of metabolites.

In the above process, variables with a p value less than 0.05 were selected as differential metabolites.

In the process of data normalization by probability quotient in step A5, PubChem and KEGG database baseline search and matching are used, and then the converted values, KEGG codes, and metabolite names are integrated to draw a metabolic signal network diagram.

NMR analysis was performed on nematode metabolites under the same rare earth element exposure conditions and different E. coli activity. After metabolite identification and peak area quantification, unsupervised PCA analysis was used to conclude that bacterial metabolism significantly affects the metabolism of nematodes. ; Carry out model reliability analysis through supervised OPLS-DA analysis, and then use t-test for significance analysis.

During the experiment, 35 samples were obtained, and Figure 1-6 was obtained after analyzing the data.

Figure 1 shows the PCA test results. As can be seen from Figure 1, under the action of dead bacteria, the metabolic data of the rare earth element-regulated group and the control group are not clearly distinguished; under the action of live bacteria, rare earth elements significantly regulate the metabolism of nematodes, which shows that the nematodes under the action of dead bacteria are affected by rare earth elements. The regulatory impact is smaller than that of nematodes under the action of live bacteria, which are regulated by rare earth elements.

Figure 2-4 shows the OPLS-DA analysis, cross-validation, and permutation test results. Cross-validation and permutation tests were used to verify the constructed model. After fitting calculation, the parameters were obtained as R ² Y = 0.998 and Q ² = 0.992. The two values are very close to 1. The model fitting effect is good. The same Under rare earth element conditions, bacterial metabolism significantly affected the metabolism of nematodes.

Figure 5 shows differential metabolite analysis. In the figure, nuclear magnetic resonance spectroscopy shows that compared with nematodes fed live bacteria, the levels of 35 metabolites in nematodes fed dead bacteria changed significantly, with 18 species increasing and 17 species decreasing. The metabolites specifically changed include alanine, glutamic acid, glutamine, citric acid, aspartic acid, trimethylamine, phosphatidylcholine, glycerophosphocholine, trimethylamine oxide, betaine, taurine, Increases in the concentrations of glycine, glycerol, inosine, dCTP, guanosine diphosphate, ADP and nicotinamide adenine dinucleotide (NAD); in nematodes fed dead bacteria, leucine, isoleucine, valerine Amino acid, arginine, succinic acid, dimethylamine, lysine, acetylcarnitine, choline, inositol, threonine, uracil, fumaric acid, tryptophan, indoxyl sulfate, yellow The concentrations of purine and adenosine monophosphate decreased, indicating that bacterial metabolism significantly affected the change levels of metabolites, thereby affecting related metabolic pathways.

In Figure 6, the metabolite pathways are enriched. Under the same rare earth element conditions, bacterial metabolism significantly affects the metabolic changes of nematodes, mainly involving the following metabolic pathways: glycine, serine and threonine metabolism; pantothenate and coenzyme a biosynthesis; aminoacyl-tRNA biosynthesis; purine metabolism; glyoxylic acid and dicarboxylic acid metabolism; glutathione metabolism; porphyrin and chlorophyll metabolism; pyrimidine metabolism; arginine biosynthesis, and D-glutamine and D-glutamate metabolism. These results indicate that bacterial metabolism affects the metabolic regulation of rare earth elements in C. elegans .

The above-mentioned specific embodiments further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above-mentioned are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (8)

1. A research method for identifying the influence of bacterial metabolism on the metabolic process of rare earth regulation organisms is characterized by comprising the following steps:

s1: enlarged culture of caenorhabditis elegans: culturing caenorhabditis elegans on an agar plate coated with escherichia coli, performing large-scale culture, collecting nematodes in spawning period, performing synchronization treatment, and culturing eggs in a culture medium to L4;

s2: preparing inactivated escherichia coli;

s3: experimental grouping: placing the L4-phase wild caenorhabditis elegans in a culture dish, and dividing the L4-phase wild caenorhabditis elegans into four groups, namely a live bacteria control group, a dead bacteria control group, a live bacteria experimental group and a dead bacteria experimental group;

s4: model construction: adding a chemical reagent into the nematode culture dish in the step S3 to expose nematodes, and providing escherichia coli as food for incubation;

s5: sample collection: collecting a nematode sample after the step S4 is finished, and waiting for the next treatment;

s6: sample preparation: the nematode sample collected in the step S5 is subjected to metabonomics analysis by metabolite extraction, enrichment, concentration and resuspension sample feeding;

s7: metabolite detection assay: obtaining metabolic data based on NMR, and identifying metabolites by adopting related software and statistical means;

s8: metabolic pathway analysis: enrichment analysis of metabolic pathways was performed using a relational database.

2. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: the specific method for preparing the inactivated escherichia coli in the step S2 is high-temperature inactivation.

3. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: the density of nematodes used in the experiment in the step S3 is 10000 per dish, the strains added in the live bacteria control group and the live bacteria experimental group are active escherichia coli, and the strains added in the dead bacteria control group and the dead bacteria experimental group are inactive escherichia coli.

4. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: and in the step S4, the incubation time is 10 days, the chemical reagent added in the control group is dimethyl sulfoxide, the chemical reagent added in the experimental group is rare earth solution, and the chemical reagent added in the control group and the experimental group have the same volume.

5. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: the metabolite is extracted in the step S6 by grinding and crushing, then extracting at low temperature, wherein the volume of an extracting reagent is 2 when the extracting reagent is stored at-20 ℃: methanol of 1: mixed solution of aqueous solutions.

6. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: the specific method for obtaining the metabolic spectrum based on NMR in the step S7 is to use a BrukerAvance IIIHD-600-MHz nuclear magnetic resonance spectrometer ¹ Nuclear magnetic resonance measurement of HNMR metabolic profile and analysis, equipped with TXI probe in test, scan 512 times, 73,728 points in F1, 12019.230Hz spectrum width, 1024 transients, cyclic delay 4s.

7. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 1, wherein the method comprises the steps of: the normalization treatment is also carried out before the metabolite identification in the step S7, and the specific steps are as follows:

a1: performing data processing by using one-dimensional exponential window multiplication, fourier transformation and phase correction of free induction attenuation of Bruker Topspin4.0.2 of nuclear magnetic resonance processing software to obtain nuclear magnetic resonance data;

a2: importing the nuclear magnetic resonance data into Matlab2014a;

a3: performing chemical shift contrast under the condition that the position of the internal standard trimethylsilicopropionic acid is set to be 0ppm, and removing the surrounding areas of signals such as water, TSP, methanol and the like;

a4: aligning nuclear magnetic resonance spectra;

a5: normalizing the data using a probability quotient to compensate for concentration differences between samples; during the analysis, the metabolites were identified relatedly using chenomxnmrsuice 8.4 and the reference compound.

8. The method for identifying bacterial metabolism affecting rare earth regulated biological metabolic processes according to claim 7, wherein:

and (C) in the step (A5), in the process of normalizing the data by using the probability quotient, carrying out baseline searching and matching by using PubCHem and KEGG databases, and then integrating the converted numerical value, KEGG code and metabolite name to draw a metabolic signal network diagram.