CN111505189A - Method for establishing influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics - Google Patents
Method for establishing influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics Download PDFInfo
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Abstract
The invention discloses a method for establishing the influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics, which comprises the following steps: carrying out LC-MS analysis on intracellular metabolites of lactobacillus acidophilus after the amoxicillin with different concentrations is administrated, and obtaining fragment information of the metabolites; screening fragment information of the metabolite by establishing an orthogonal partial least square method discriminant analysis model to obtain a potential biomarker; wherein the screening conditions are that the projection value of the variable importance is greater than 1, the absolute value of the difference multiple is greater than 1, and the p value is less than 0.05; carrying out metabolic pathway enrichment on the potential biomarker to obtain relevant important metabolic pathways for development of lactobacillus acidophilus metabolic disorder caused by amoxicillin with different concentrations; potential biomarkers in important metabolic pathways are connected through the metabolic pathways to obtain the comprehensive metabolic network. The invention establishes a method for causing damage to amoxicillin by influencing the metabolism of lactobacillus acidophilus, and obtains the relation between the metabolite of lactobacillus acidophilus and the metabolic pathway when amoxicillin is used for lactobacillus acidophilus, so as to further research the action mechanism of the lactobacillus acidophilus damage induced by amoxicillin.
Description
Technical Field
The invention belongs to the technical field of analysis, and particularly relates to a method for screening an amoxicillin concentration by using a lactobacillus acidophilus metabolite.
Background
Antibiotics have some adverse reactions in clinical use, wherein the gastrointestinal reaction is one of the more common reactions. Such as antibiotic-associated diarrhea. The reason why antibiotics cause diarrhea is that the antibiotics destroy the steady balance of intestinal flora while having an antibacterial effect, and harm probiotics while killing harmful bacteria. It has been shown that lactobacilli are beneficial probiotics for health and cause damage to antibiotics after administration.
The amoxicillin is one of the most commonly used semi-synthetic penicillins, namely broad-spectrum β -lactam antibiotics, is stable under acidic conditions, has the gastrointestinal absorption rate of 90%, has strong sterilization effect and strong cell membrane penetrating capability, is one of the oral semi-synthetic penicillins widely applied at present, and has preparations such as capsules, tablets, granules, dispersible tablets and the like.
Lactobacillus acidophilus is the most representative strain of lactic acid bacteria, is one of few beneficial bacteria in human intestinal tracts, can improve the balance of intestinal flora, enhance immunity, reduce cholesterol level, relieve lactose intolerance and inhibit tumor formation, is considered as a food additive safe to human bodies, and is widely applied to various foods. However, in recent years, in the course of treating diseases, the use of large amounts of antibiotics causes a disturbance in intestinal microflora, a decrease in beneficial bacteria, an increase in pathogenic bacteria, a decrease in the diversity of microorganisms, a change in the metabolic function of the microflora, and in severe cases, intestinal diseases and the like.
Therefore, a method for establishing the influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics is needed.
Disclosure of Invention
It is an object of the present invention to address at least the above-mentioned deficiencies and to provide at least the advantages which will be described hereinafter.
The invention also aims to provide a method for establishing the influence of the concentration of amoxicillin on lactobacillus acidophilus based on metabonomics, which can clarify the action mechanism of amoxicillin-induced lactobacillus acidophilus damage.
To achieve these objects and other advantages in accordance with the present invention, there is provided a method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus based on metabolomics, comprising:
carrying out LC-MS analysis on the intracellular metabolites of lactobacillus acidophilus after the amoxicillin with different concentrations is administrated, and obtaining fragment information of the metabolites.
Screening fragment information of the metabolite by establishing an orthogonal partial least square method discriminant analysis model to obtain a potential biomarker; wherein, the screening conditions are that the projection value of the variable importance is more than 1, the absolute value of the difference multiple is more than 1, and the p value is less than 0.05.
And (3) carrying out metabolic pathway enrichment on the potential biomarker to obtain relevant important metabolic pathways for development of lactobacillus acidophilus metabolic disorder caused by amoxicillin with different concentrations.
Potential biomarkers in important metabolic pathways are connected through the metabolic pathways to obtain the comprehensive metabolic network.
In the scheme, firstly, the intracellular metabolites of the lactobacillus acidophilus damaged by amoxicillin with different concentrations are analyzed by an ultra-high performance liquid chromatography-tandem mass spectrometry technology to obtain information containing retention time and mass-to-charge ratio; establishing an orthogonal partial least square method discriminant analysis model to screen data according to the conditions that the variable importance projection value is greater than 1, the difference multiple absolute value is greater than 1 and the p value is less than 0.05 to obtain a potential biomarker; analyzing chemical structural formulas of retention time, accurate molecular mass and mass spectrum fragment information of the metabolites, and verifying the chemical structural formulas through an online database to obtain metabolites with statistical differences; carrying out metabolic pathway enrichment according to the different metabolites to obtain relevant important metabolic pathways for development of lactobacillus acidophilus metabolic disorder caused by amoxicillin with different concentrations; important metabolic pathways are connected to obtain a comprehensive metabolic network to prompt that clinical medication dosage is emphasized, and a foundation is laid for research, development and use of gastrointestinal tract protection drugs.
Preferably, the intracellular metabolites of lactobacillus acidophilus after administration at different concentrations are obtained by:
dividing Lactobacillus acidophilus cultured to logarithmic phase into 3 groups, and respectively feeding to 1.0 × 10-4，1.0×10-5，1.0×10-6Amoxicillin at M concentration, collected 24 hours after dosing.
In the scheme, the lactobacillus acidophilus is taken as a research object, and is administrated in the logarithmic growth phase, so that the damage effect of the lactobacillus acidophilus on administration due to the influence of self factors in the analysis process is avoided.
Preferably, the conditions of the LC-MS are as follows:
adopting 100 × 2.1.1 mm, 1.8 μm particle size and HSS T3C 18 chromatographic column as chromatographic column, wherein the column temperature of the chromatographic column is 40 deg.C;
the mobile phase A is ammonium acetate, the mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3m L & min-1；
The elution procedure comprises the steps of eluting for 0-1.5 minutes by using 90-70% of the solution A, then continuously eluting for 1.5-8 minutes by using 70-2% of the solution A, then eluting for 8-9 minutes by using 2-90% of the solution A, and finally eluting for 9-10 minutes by using 90% of the solution A, wherein the sample injection temperature is 4 ℃, and the sample injection amount is 5 mu L;
the mass number range of the collected mass spectrum is 50-1200 Da, and all data are collected in a positive ion mode and a negative ion mode of an electrospray ion source, wherein the capillary voltage is 3.0kV, the foramen voltage is 40kV, the extraction voltage is 4.0kV, the ion source temperature is 100 ℃, the desolvation gas temperature is 350 ℃, the taper hole gas flow is 40L & h-1Desolventizing rate 800L. multidot.h-1。
Preferably, the specific steps for obtaining the differential metabolite are:
grouping lactobacillus acidophilus intracellular metabolites after amoxicillin administration with different concentrations, setting a blank control group, and respectively performing principal component analysis, partial least square method discriminant analysis and orthogonal partial least square method discriminant analysis on each group;
screening differential metabolites among groups, and selecting variables of which the variable importance projection value is greater than 1, the absolute value of the difference multiple is greater than 1 and the p value is less than 0.05 as the differential metabolites;
the partial least square method discriminant analysis model fitting ability indexes are respectively as follows:
in positive ion mode: r2X＝0.554，R2Y＝0.921，Q2＝0.659；
In the negative ion mode: r2X＝0.613，R2Y＝0.854，Q2＝0.536。
Preferably, the specific steps for enriching the metabolic pathways of the differential metabolites are as follows: uploading the different metabolites to a metabolic component online analysis webpage, recording the name of each different metabolite, and enriching related pathways.
Preferably, the method further comprises the step of pretreating the intracellular metabolites of lactobacillus acidophilus:
and (3) centrifuging the intracellular metabolites of the lactobacillus acidophilus after the amoxicillin with different concentrations is administered for 10 minutes at low speed and low temperature under the condition of 2000 revolutions respectively to obtain precipitates.
Washing the precipitate with phosphate buffer solution for 3 times, adding 3 times volume of glacial methanol to extract protein for 20min, performing ultrasonication for 15 min, centrifuging at high speed and low temperature at 12000 r for 20min, and collecting supernatant.
The supernatant was dried with nitrogen, redissolved with an equal volume of 400 μ L acetonitrile in water before injection, and filtered through a 0.22 μm nylon filter.
In the above scheme, the culture solution and the like are removed by pretreating the intracellular metabolite of lactobacillus acidophilus to ensure the stability of the intracellular metabolite of lactobacillus acidophilus for LC-MS analysis.
Preferably, before the principal component analysis, the fragment information of the metabolite is processed, the data is processed by an 80% filtering principle, the variable with blank value more than 80% is removed, and preprocessing such as normalization, peak alignment and the like is performed; wherein the preprocessing is performed at the MetabioAnalyst website;
the parameters of the pretreatment are as follows: the retention time is 0-10 min; the mass number is 50-1200 Da; a mass number tolerance of 0.01; the mass number window is 0.02; the noise removal level was 6.
In the scheme, the variable with blank value more than 80% is removed to reduce the missing value caused by the peak which does not exist in the chromatogram so as to influence the judgment of the result.
The invention at least comprises the following beneficial effects:
firstly, analyzing intracellular metabolites of lactobacillus acidophilus damaged by amoxicillin with different concentrations by using an ultra-high performance liquid chromatography-tandem mass spectrometry technology to obtain fragment information containing retention time and a mass-to-charge ratio, establishing a model, and screening data according to the conditions that a variable importance projection value is greater than 1, a difference multiple absolute value is greater than 1 and a p value is less than 0.05 to obtain a potential biomarker; analyzing chemical structural formulas of retention time, accurate molecular mass and mass spectrum fragment information of the metabolites, and verifying the chemical structural formulas through an online database to obtain metabolites with statistical differences; carrying out metabolic pathway enrichment according to the different metabolites to obtain relevant important metabolic pathways for development of lactobacillus acidophilus metabolic disorder caused by amoxicillin with different concentrations; important metabolic pathways are connected to obtain a comprehensive metabolic network to prompt that clinical medication dosage is emphasized, and a foundation is laid for research, development and use of gastrointestinal tract protection drugs.
Secondly, taking lactobacillus acidophilus as a research object, and administering the lactobacillus acidophilus in the logarithmic growth phase to avoid the damage effect of the lactobacillus acidophilus on administration due to self factors in the analysis process.
Furthermore, the culture fluid and the like are removed by pretreatment of the intracellular metabolites of lactobacillus acidophilus to ensure the stability of the intracellular metabolites of lactobacillus acidophilus for the LC-MS analysis.
Finally, the variable with blank value more than 80% is removed to reduce the missing value caused by the peak which does not exist in the chromatogram so as to influence the judgment of the result.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a graph of the MTT assay for Lactobacillus acidophilus following administration of different concentrations in an example of the present invention;
FIG. 2 is a scanning map of the plasma reactivity of Lactobacillus acidophilus after administration of different concentrations in an embodiment of the present invention;
FIG. 3 is a flow chart of each set of total ions collected in a positive ion mode and a negative ion mode based on ultra performance liquid chromatography-tandem mass spectrometry in an embodiment of the present invention;
FIG. 4 is a three-dimensional PCA plot of Lactobacillus acidophilus metabolites after various concentrations of amoxicillin administration in accordance with an embodiment of the present invention;
FIG. 5 is a plot of P L S-DA of Lactobacillus acidophilus metabolites after various concentrations of amoxicillin administration in accordance with embodiments of the present invention;
FIG. 6 is a graph of 200 displacement assays of Lactobacillus acidophilus metabolites after various concentrations of amoxicillin administration in accordance with an embodiment of the present invention;
FIG. 7 is a graph of the OP L S-DA profile of Lactobacillus acidophilus metabolites with respect to a control group, respectively, after administration of varying concentrations of amoxicillin in accordance with embodiments of the present invention;
FIG. 8 is an enrichment map of metabolic pathways constructed on-line on the metaboanalyst platform according to an embodiment of the present invention;
FIG. 9 is a diagram of the comprehensive metabolic network established by the embodiment of the present invention.
Detailed Description
The present invention is further described in detail below with reference to examples so that those skilled in the art can practice the invention with reference to the description.
Examples
A method for establishing the influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics comprises the following steps:
step one, taking lactobacillus acidophilus cultured to logarithmic phase, dividing the lactobacillus acidophilus into four groups, wherein each group has 8 biological repeats, and respectively comprises a high-concentration administration group, a medium-concentration administration group, a low-concentration administration group and a blank control group which are respectively given to 1.0 × 10-4，1.0×10-5，1.0×10-6Amoxicillin at M concentration, collected 24 hours after dosing.
Step two, performing liquid chromatography-quadrupole-time-of-flight mass spectrometer on the collected 4 groups of lactobacillus acidophilus intracellular metabolites to obtain fragment data information containing retention time and mass-to-charge ratio of different metabolites, wherein the conditions of the liquid chromatography-quadrupole-time-of-flight mass spectrometer are that 100 × 2.1.1 mm and 1.8 mu m particle size are adopted, an HSS T3C 18 chromatographic column is adopted as the chromatographic column, the column temperature of the chromatographic column is 40 ℃, a mobile phase A is ammonium acetate, a mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3m L min-1The elution process comprises the steps of eluting for 0-1.5 minutes by using 90-70% of liquid A, then continuously eluting for 1.5-8 minutes by using 70-2% of liquid A, then eluting for 8-9 minutes by using 2-90% of liquid A, and finally eluting for 9-10 minutes by using 90% of liquid A, wherein the sample introduction temperature is 4 ℃, the sample introduction amount is 5 mu L, the mass number range of the collected mass spectrum is 50-1200 Da, and all data are collected in an electrospray ion source positive and negative ion mode, wherein the capillary tube voltage is 3.0kV, the spinal hole voltage is 40kV, the extraction voltage is 4.0kV, the ion source temperature is 100 ℃, and the desolventizing agent temperature is 100 DEG C350 ℃ and the cone hole air flow is 40L h-1Desolventizing rate 800L. multidot.h-1。
And step three, performing noise removal, mass spectrum peak extraction, peak arrangement, peak alignment, normalization and other processing on the fragment data information obtained in the step two by adopting Masslynx software of Waters company to obtain data of peak height, peak area and retention time of each peak. Then, keeping the time range for 0-20 min; the mass number range is 100-1000 Da; mass number tolerance 0.01; mass number window 0.05; after 80% filtering principle processing under the parameter of noise removal degree 6, the variable containing blank value > 80% is removed.
And step four, grouping the data processed in the step three according to the intracellular metabolites of the lactobacillus acidophilus after administration at different concentrations, setting a blank control group, and performing main component analysis on each group of data by adopting SIMCA-P14.1 software to obtain whether the metabolic patterns of different groups are different so as to verify whether the data are reliably obtained.
And step five, grouping the data processed in the step three according to intracellular metabolites of lactobacillus acidophilus after administration at different concentrations, setting a blank control group, and performing partial least square method discriminant analysis on each group of data so as to analyze the change of the metabolic mode of lactobacillus acidophilus after intervention of amoxicillin at different concentrations. The partial least square method discriminant analysis model fitting ability indexes are respectively as follows: in positive ion mode: r2X＝0.554，R2Y＝0.921，Q20.659; in the negative ion mode: r2X＝0.613，R2Y＝0.854，Q2＝0.536。
And step six, grouping the data processed in the step three according to the lactobacillus acidophilus intracellular metabolites after administration at different concentrations, setting a blank control group, carrying out orthogonal partial least square method discriminant analysis on each group of data by adopting SPSS 20.0 software, screening potential biomarkers among the groups, and selecting variables of which the variable importance projection value of the differential metabolites is more than 1, the absolute value of the difference multiple is more than 1 and the p value is less than 0.05 as the differential metabolites.
And seventhly, uploading the different metabolites to a MetabioAnalyst analysis webpage, performing channel enrichment to obtain relevant important metabolic channels of each group, and finally connecting the important relevant metabolites through the metabolic channels to obtain a comprehensive metabolic network map.
And (3) data analysis:
10 μ L were mixed from each of the high concentration administration group, the medium concentration administration group and the low concentration administration group to prepare a quality control sample group.
1. Collecting Lactobacillus acidophilus cultured to logarithmic phase, and dividing into control group, high concentration administration group, medium concentration administration group, and low concentration administration group, wherein the high concentration administration group, medium concentration administration group, and low concentration administration group are respectively administered to 1.0 × 10-4，1.0×10-5，1.0×10-6M concentration of amoxicillin, collected 24 hours after dosing samples from each group were carefully removed of the supernatant, 20. mu. L of MTT was added, 1 hour later MTT-formamide crystals were dissolved in 100. mu. L of DMSO, and the absorbance of the solution was measured at 570nm, the results are shown in FIG. 1.
From the data in FIG. 1, it can be seen that the lower the concentration of amoxicillin, the greater the number of viable cells of Lactobacillus acidophilus, and that the higher the concentration of amoxicillin, the greater the inhibition of Lactobacillus acidophilus activity.
2. The culture medium in the control group, the high concentration administration group, the medium concentration administration group, and the low concentration administration group was removed, and the bacterial suspension was washed 3 times with phosphate buffered saline, and then the bacteria were transferred to a 1.5ml centrifuge tube, and were immobilized in 3% strength glacial glutaraldehyde phosphate buffered saline at 4 ℃ overnight and then in 1% strength osmium tetroxide. Then, after gradient dehydration is carried out by using ethanol with the concentration of 30-90%, the cells are embedded into 812 epoxy resin. After drying, the gold powder was sprayed in a vacuum state and finally observed under a scanning electron microscope, and the results are shown in fig. 2. Wherein A is a scanning electron micrograph of a control group, B is a scanning electron micrograph of a low concentration administration group, C is a scanning electron micrograph of a medium concentration administration group, and D is a scanning electron micrograph of a high concentration administration group.
As can be seen from FIG. 2, the Lactobacillus acidophilus strain in A is normal, the cell wall of a small part of Lactobacillus acidophilus strain in B is damaged, the cell wall of a small part of Lactobacillus acidophilus strain in C is damaged, and the cell wall of a large part of Lactobacillus acidophilus strain in D is damaged; i.e., the greater the concentration administered, the more severely the bacteria are damaged.
3. The method of the example was used to perform LC-MS analysis of intracellular metabolites of Lactobacillus acidophilus collected at different dosages by HPLC-MS, and the results are shown in FIG. 3. Wherein A is a total ion flow diagram collected in a positive ion mode; and B is a total ion flow diagram collected in the negative ion mode.
As can be seen from figure 3, the metabolites of the bacteria will vary after different doses of amoxicillin.
4. The results of the main component analysis of the intracellular metabolites of lactobacillus acidophilus collected at different dosages were shown in fig. 4, wherein C is a control group, L is a low dose group, M is a medium dose group, H is a high dose group, and QC is a quality control sample group.
The principal component analysis is used as an unsupervised learning method, and can truly reflect the clustering condition of the samples. The model group and the blank group are well separated and have no intersection or overlap, which indicates that the two groups of metabolic modes have obvious difference, and the QC samples are gathered more tightly, which indicates that the instrument state is good in the data acquisition process, and ensures the reliability of data. Each point in the principal component analysis PCA score plot represents the bacterial fluid metabolic profile of each experimental sample.
As can be seen from fig. 4, the control group, the high concentration administration group, the medium concentration administration group, and the low concentration administration group were well separated, and were not crossed or overlapped, indicating that there was a significant difference in the metabolic pattern. The QC samples are gathered more tightly, which indicates that the instrument state is good in the data acquisition process, and the reliability of the data is ensured.
5. The results of the partial least squares discriminant analysis of intracellular metabolites of lactobacillus acidophilus collected at different dosages are shown in FIG. 5, where C is a control group, L is a low dose group, M is a medium dose group, H is a high dose group, QC is a quality control sample group, P L S-DA classification model can interpret the percentages of X and Y matrix information, Q2Then the prediction is calculated by cross validation to evaluate the P L S-DA modelCapability, Q2The larger the model is, the better the prediction effect is.
As can be seen from FIG. 5, the control group, the low dose group, the medium dose group and the high dose group were well separated, suggesting that the metabolic pattern of Lactobacillus acidophilus was changed after intervention with amoxicillin of different concentrations, indicating that the effect of amoxicillin of different concentrations on Lactobacillus acidophilus was different.
6. The results of the displacement test on the partial least squares discriminant analysis model are shown in fig. 6. Wherein, A is a 200-time replacement test chart in a positive ion mode; b is a 200-time replacement test chart in the negative ion mode.
As can be seen from fig. 6, this damage model was not over-fitted, and the results were reliable.
7. The results of the orthorhombic partial least squares analysis of intracellular metabolites of lactobacillus acidophilus collected at different dosages are shown in fig. 7, wherein, a is a control group and a low-dose group in a positive ion mode, B is a control group and a medium-dose group in a positive ion mode, C is a control group and a high-dose group in a positive ion mode, D is a control group and a low-dose group in a negative ion mode, E is a control group and a medium-dose group in a negative ion mode, F is a control group and a high-dose group in a negative ion mode, C is a control group, L is a low-dose group, M is a medium-dose group, and H is a high-dose group.
As can be seen in FIG. 7, there was a significant change in the Lactobacillus acidophilus metabolite after amoxicillin administration.
8. The method of the embodiment is adopted to perform metabolic pathway enrichment on differential metabolites in lactobacillus acidophilus intracellular metabolites collected by different administration doses, and relevant differential metabolites are screened out as shown in tables 1-2, and relevant metabolic pathways are shown in fig. 8, wherein CG is a control group, L CG is a low dose group, MCG is a medium dose group, HCG is a high dose group, a graph A is the control group and the low dose group, a graph B is the control group and the medium dose group, and a graph C is the control group and the high dose group.
Table 1: the amoxicillin group and the contrast group with different concentrations in the positive ion mode have different metabolites and content changes: compared with the control group: p <0.05, p < 0.01.
Table 2: the amoxicillin group and the contrast group with different concentrations in the negative ion mode have different metabolites and content changes: compared with the control group: p <0.05, p < 0.01.
From the data in tables 1-2, it is seen that the content of 6 metabolites, including glycerol, carnosine, orotic acid, nicotinic acid, pseudouridine, and tryptophan, was decreased in the low dose group and the content of urocanic acid was increased in the medium dose group, and the content of 6 metabolites, including glycerol, cytosine, nicotinic acid, L-2, 4-diaminobutyric acid, carnosine, and nicotinuric acid, was decreased in the medium dose group, whereas the content of 5 metabolites, including cytosine, nicotinic acid, L-2, 4-diaminobutyric acid, ornithine, and nicotinuric acid, was decreased in the high dose group, as compared to the control group.
Referring to fig. 8, it can be seen that the influence value of the metabolism of proline and alanine is 0.130, the influence value of the metabolism of nicotinic acid and nicotinamide is 0.106, the influence value of the metabolism of glyceride is 0.188, and the influence value of the metabolism of tryptophan is 0.109, which are the most relevant 4 important metabolic pathways for the development and development of the metabolic disorder of lactobacillus acidophilus caused by amoxicillin.
9. The important metabolic pathways obtained by the example method were connected to obtain an integrated metabolic network, and the results are shown in FIG. 9, wherein the arrow in the figure indicates the up-regulation trend of the metabolite in the administration group relative to the control group, and vice versa in the down direction, the arrow in the figure L indicates the metabolic change of the biomarker in the low dose group, the arrow in the figure indicates the metabolic change of the biomarker in the medium dose group, and the arrow in the figure H indicates the metabolic change of the biomarker in the high dose group.
From FIG. 9 it can be seen that the resulting biomarker L-Tryptophan influences citrate in the tryptophan metabolism by indirectly influencing acetyl-CoA and finally influences the metabolism of Lactobacillus acidophilus by influencing the Krebs cycle, that the biomarker l-2, 4-diaminobutyric acid influences ornithine in the glycine, serine and threonine metabolism by indirectly influencing glyoxylate, that the biomarker carnosine influences spermine in the β -alanine metabolism by influencing β -alanine, that the biomarkers thymine, cytosine, orotic acid, pseudouridine influence spermidine in the pyrimidine metabolism by indirectly influencing uracil and thus finally influence spermine in the arginine and proline metabolism, that ornithine and the biomarker glycerol influence oxaloacetate by indirectly influencing pyruvate in the glycerolipid metabolism and thus influence the metabolism of Lactobacillus acidophilus by influencing the Krebs cycle, that the biomarkers nicotinuric acid and nicotinic acid in the niacin and nicotinamide metabolism influence the Krebs cycle by indirectly influencing pyruvate in the glycerolipid metabolism and that the metabolism of Lactobacillus acidophilus by influencing the Krebs cycle, that the biomarkers His and histidine metabolism influence the amino-oxoglutarate metabolism and thus influence the metabolism of Lactobacillus acidophilus by the glutarate cycle and thus influence the final L-glutarate metabolism.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the uses set forth in the specification and examples. It can be applied to all kinds of fields suitable for the present invention. Additional modifications will readily occur to those skilled in the art.
Claims (7)
1. A method for establishing the influence of amoxicillin concentration on lactobacillus acidophilus based on metabonomics is characterized by comprising the following steps:
carrying out LC-MS analysis on intracellular metabolites of lactobacillus acidophilus after the amoxicillin with different concentrations is administrated, and obtaining fragment information of the metabolites;
screening fragment information of the metabolite by establishing an orthogonal partial least square method discriminant analysis model to obtain a potential biomarker; wherein the screening conditions are that the projection value of the variable importance is greater than 1, the absolute value of the difference multiple is greater than 1, and the p value is less than 0.05;
carrying out metabolic pathway enrichment on the potential biomarker to obtain relevant important metabolic pathways for development of lactobacillus acidophilus metabolic disorder caused by amoxicillin with different concentrations;
potential biomarkers in important metabolic pathways are connected through the metabolic pathways to obtain the comprehensive metabolic network.
2. The metabolomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, wherein the intracellular metabolites of lactobacillus acidophilus after the administration of amoxicillin at different concentrations are obtained by:
dividing Lactobacillus acidophilus cultured to logarithmic phase into 4 groups of 8 biological repeats, respectively feeding to 1.0 × 10-4，1.0×10-5，1.0×10-6Amoxicillin at M concentration, collected 24 hours after dosing.
3. The metabolomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, characterized in that: the conditions of the LC-MS are as follows:
adopting 100 × 2.1.1 mm, 1.8 μm particle size and HSS T3C 18 chromatographic column as chromatographic column, wherein the column temperature of the chromatographic column is 40 deg.C;
the mobile phase A is ammonium acetate, the mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3m L & min-1；
The elution procedure comprises the steps of eluting for 0-1.5 minutes by using 90-70% of the solution A, then continuously eluting for 1.5-8 minutes by using 70-2% of the solution A, then eluting for 8-9 minutes by using 2-90% of the solution A, and finally eluting for 9-10 minutes by using 90% of the solution A, wherein the sample injection temperature is 4 ℃, and the sample injection amount is 5 mu L;
the mass number range of the collected mass spectrum is 50-1200 Da, and all data are collected in a positive ion mode and a negative ion mode of an electrospray ion source, wherein the capillary voltage is 3.0kV, the foramen voltage is 40kV, the extraction voltage is 4.0kV, the ion source temperature is 100 ℃, the desolvation gas temperature is 350 ℃, the taper hole gas flow is 40L & h-1Desolventizing agent flow rate 800L ·h-1。
4. The metabolomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, wherein the specific steps for obtaining the potential biomarkers are:
grouping according to the intracellular metabolites of lactobacillus acidophilus after administration at different concentrations, setting a blank control group, and respectively performing principal component analysis, partial least square method discriminant analysis and orthogonal partial least square method discriminant analysis on each group;
screening potential biomarkers among groups, and selecting variables of which the variable importance projection value of the differential metabolite is greater than 1, the absolute value of the difference multiple is greater than 1 and the p value is less than 0.05 as the potential biomarkers;
the partial least square method discriminant analysis model fitting ability indexes are respectively as follows:
in positive ion mode: r2X＝0.554，R2Y＝0.921，Q2＝0.659；
In the negative ion mode: r2X＝0.613，R2Y＝0.854，Q2＝0.536。
5. The metabolomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, wherein the specific steps of metabolic pathway enrichment for potential biomarkers are:
uploading the potential biomarkers to a MetabioAnalyst analysis webpage for enrichment of relevant pathways.
6. The metabolomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, which further comprises, before the LC-MS, a pretreatment of the intracellular metabolites of lactobacillus acidophilus:
centrifuging the acidophilic lactobacillus liquid with different concentrations at low speed and low temperature for 10 minutes under the condition of 2000 revolutions respectively to obtain precipitates;
washing the precipitate with phosphate buffer solution for 3 times, adding 3 times volume of glacial methanol to extract protein for 20min, ultrasonically crushing for 15 min, centrifuging at high speed and low temperature for 20min at 12000 r, and collecting supernatant;
the supernatant was dried with nitrogen, redissolved with an equal volume of 400 μ L acetonitrile in water before injection, and filtered through a 0.22 μm nylon filter.
7. The metabonomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 4, wherein before the principal component analysis, the fragment information of metabolites is preprocessed, the data is processed by 80% filtering principle, and the variables with blank value more than 80% are removed;
wherein the preprocessing is carried out on Marker L ynx 4.1.1 software and MetabioAnalyst website;
the parameters of the pretreatment are as follows: the retention time is 0-10 min; the mass number is 50-1200 Da; a mass number tolerance of 0.01; the mass number window is 0.02; the noise removal level was 6.
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