CN111505189B - 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 damaging the amoxicillin by influencing the metabolism of lactobacillus acidophilus, and obtains the relation between the metabolite of lactobacillus acidophilus and the metabolic pathway when the amoxicillin is used for the lactobacillus acidophilus, so as to further research the action mechanism of the lactobacillus acidophilus damage induced by the 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.
Amoxicillin is a most commonly used semisynthetic penicillin broad-spectrum beta-lactam antibiotic, and is stable under acidic conditions, and the gastrointestinal absorption rate reaches 90%. The amoxicillin has strong bactericidal effect and strong ability of penetrating cell membranes. Is one of the oral semi-synthetic penicillins which are widely applied at present, and the preparation of the oral semi-synthetic penicillins comprises capsules, tablets, granules, dispersible tablets and the like. Amoxicillin has strong bactericidal action, can kill sensitive bacteria, and is combined with probiotics to expose a plurality of bacteria colonization targets on intestinal mucosa, thereby leaving colonization space for pathogenic bacteria and enabling the pathogenic bacteria to multiply. The formation of dominant microflora leads to destruction of the intestinal micro-ecology. However, the action mechanism of amoxicillin-induced lactobacillus acidophilus damage is not determined at present, so that a basis cannot be provided for the clinical application of amoxicillin in lactobacillus acidophilus application.
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 disturbance of intestinal microbiota, decrease of beneficial bacteria, increase of pathogenic bacteria, decrease of variety diversity of microorganisms, change of metabolic function of flora, and in severe cases, intestinal diseases and the like.
Therefore, a method for establishing the influence of the 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 the amoxicillin on the lactobacillus acidophilus based on metabonomics, which can clarify the action mechanism of the amoxicillin-induced lactobacillus acidophilus injury.
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 the amoxicillin with different concentrations are analyzed by the 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 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 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 needs to be paid attention to, 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 -6 Amoxicillin 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:
the method adopts a 100 multiplied by 2.1mm,1.8 mu m particle size and HSS T3C 18 chromatographic column as a chromatographic column; the temperature of the chromatographic column is 40 ℃;
the mobile phase A is ammonium acetate, the mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3 mL/min -1 ；
The elution procedure was: eluting with 90-70% solution A for 0-1.5 min; then, continuously eluting for 1.5 to 8 minutes by using 70 to 2 percent of solution A; then eluting for 8-9 minutes by using 2-90% of A solution; finally eluting for 9-10 minutes by using 90% of A liquid; wherein the sample introduction temperature is 4 ℃, and 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 the positive and negative ion modes of the electrospray ion sourceThe method comprises the following steps of collecting, wherein the capillary voltage is 3.0kV, the foramen voltage is 40kV, and the extraction voltage is 4.0kV; the temperature of the ion source is 100 ℃, and the temperature of the desolventizing gas is 350 ℃; the air flow of the taper hole is 40 L.h -1 Desolvation flow rate of 800 L.h -1 。
Preferably, the specific steps for obtaining the differential metabolite are:
grouping according to 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: r is 2 X＝0.554，R 2 Y＝0.921，Q 2 ＝0.659；
In the negative ion mode: r 2 X＝0.613，R 2 Y＝0.854，Q 2 ＝0.536。
Preferably, the specific steps of metabolic pathway enrichment for differential metabolites are: 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 400. Mu.L of a mixed solution of acetonitrile and water at equal volume 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, variables with blank values larger than 80% are 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 metabolites with different concentrations to obtain relevant important metabolic pathways for the 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 needs to be paid attention to, 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 and 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 PLS-DA of Lactobacillus acidophilus metabolites after administration of varying concentrations of amoxicillin in an example 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 OPLS-DA profile of the Lactobacillus acidophilus metabolite in comparison to the control group, respectively, after administration of varying concentrations of amoxicillin in accordance with 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 integrated metabolic network constructed by the embodiment of the 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 the biological repeats are respectively 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 multiplied by 10 -4 ，1.0×10 -5 ，1.0×10 -6 Amoxicillin at M concentration, collected 24 hours after dosing.
And step two, performing liquid chromatography-mass spectrometry analysis on the collected 4 groups of lactobacillus acidophilus intracellular metabolites by adopting an ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometer to obtain fragment data information containing retention time and mass-to-charge ratio of different metabolites. Wherein, the conditions of the LC-MS are as follows: the method adopts a 100 multiplied by 2.1mm,1.8 mu m particle size and HSS T3C 18 chromatographic column as a chromatographic column; the temperature of the chromatographic column is 40 ℃; the mobile phase A is ammonium acetate, the mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3 mL/min -1 (ii) a The elution procedure was: eluting with 90-70% solution A for 0-1.5 min; then, continuously eluting for 1.5 to 8 minutes by using 70 to 2 percent of solution A; then eluting for 8-9 minutes by using 2-90% of A solution; finally eluting for 9-10 minutes by using 90% of A liquid; wherein the sample introduction temperature is 4 ℃, and 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 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, and the extraction voltage is 4.0kV; the ion source temperature is 100 ℃, and the desolventizing gas temperature is 350 ℃; the flow rate of the taper hole gas is 40 L.h -1 Desolvation flow rate of 800 L.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: r 2 X＝0.554，R 2 Y＝0.921，Q 2 =0.659; in the negative ion mode: r 2 X＝0.613，R 2 Y＝0.854，Q 2 ＝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. Mu.L of each of the high concentration administration group, the medium concentration administration group and the low concentration administration group was mixed to prepare a quality control sample group.
1. Taking out and culturing to logarithmic growth phaseThe lactobacillus acidophilus is divided into a control group, a high-concentration administration group, a medium-concentration administration group and a low-concentration administration group; wherein the high concentration administration group, the medium concentration administration group, and the low concentration administration group are administered at 1.0 × 10 -4 ，1.0×10 -5 ，1.0×10 -6 Amoxicillin at M concentration, collected 24 hours after dosing. The supernatant was carefully removed from each group of samples, 20. Mu.L of MTT was added, and after 1 hour, MTT-formamide crystals were dissolved in 100. Mu.L of DMSO, and then the absorbance of the solution was measured at 570nm, as 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% glacial glutaraldehyde phosphate-buffered saline at 4 ℃ overnight and then in 1% osmium tetroxide. Then the cells are embedded into 812 epoxy resin after gradient dehydration by ethanol with the concentration of 30% -90%. 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 intracellular metabolites of lactobacillus acidophilus collected at different dosages were subjected to LC-MS analysis by the method of example using 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 FIG. 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 by the method of example are 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. Partial least squares discriminant analysis was performed on lactobacillus acidophilus intracellular metabolites collected at different dosages by the method of example, and the results are shown in fig. 5. 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. Percentage of X and Y matrix information that the PLS-DA classification model can interpret, Q 2 Then, it is calculated by cross-validation to evaluate the prediction power of the PLS-DA model, Q 2 The 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 overfit, and the results were reliable.
7. The method of the example was used to perform an orthogonal partial least squares analysis of the intracellular metabolites of lactobacillus collected at different dosages, the results 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; panel C is control and high dose in positive ion mode; d is a control group and a low dose group in the negative ion mode; e is a control group and a middle dose group under the negative ion mode; f-panel control and high dose groups in 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 enrich the metabolic pathways of the differential metabolites in the lactobacillus acidophilus intracellular metabolites collected by different administration doses, and screen out the related differential metabolites such as tables 1-2 and the related metabolic pathways such as fig. 8. Wherein, CG is a control group; LCG, low dose group; MCG, medium dose group; HCG, high dose group; panel a is control and low dose groups; panel B is control and medium dose groups; panel C is control and high dose groups.
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 can be seen that the content of 6 metabolites was reduced in the low dose group compared to the control group: glycerol, carnosine, orotic acid, nicotinic acid, pseudouridine, tryptophan; at the same time, the content of urocanic acid is increased. There were 6 metabolite content reductions in the medium dose group: glycerol, cytosine, nicotinic acid, L-2, 4-diaminobutyric acid, carnosine and nicotinuric acid. There were 5 metabolite content reductions in the high dose group: cytosine, nicotinic acid, L-2, 4-diaminobutyric acid, ornithine and nicotinuric acid. In contrast, in the high dose group, the content of carnosine, formimino-glutamic acid, and thymine increased.
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 method of example were connected to obtain an integrated metabolic network, and the results are shown in FIG. 9. Wherein, the arrow in the figure indicates that the metabolite is in the up-regulation trend of the administration group relative to the control group, and the arrow in the figure indicates that the metabolite is in the down-regulation trend of the administration group relative to the control group; the L arrow indicates metabolic changes in biomarkers in the low dose group; m arrows indicate metabolic changes in biomarkers in the medium dose groups; h arrows indicate metabolic changes in biomarkers in the high dose group.
From FIG. 9, it can be seen that the obtained biomarker L-tryptophan affects citrate in tryptophan metabolism by indirectly affecting acetyl-CoA and ultimately affects the metabolism of Lactobacillus acidophilus by affecting the Krebs cycle; the biomarker l-2, 4-diaminobutyric acid indirectly affects ornithine by indirectly affecting glyoxylate in glycine, serine and threonine metabolism; the biomarker carnosine affects spermine by affecting beta-alanine in beta-alanine metabolism; the biomarkers thymine, cytosine, orotic acid, pseudouridine affect ornithine indirectly in pyrimidine metabolism by affecting uracil and thus β -alanine, ultimately affecting spermine in arginine and proline metabolism; ornithine and the biomarker glycerol influence oxaloacetate by indirectly influencing pyruvate in glycerolipid metabolism and ultimately influence the metabolism of lactobacillus acidophilus by influencing the tricarboxylic acid cycle; nicotinic acid and nicotinic acid, biomarkers in niacin and nicotinamide metabolism, niacin and nicotinic acid, affect the tricarboxylic acid cycle by affecting succinate, and ultimately affect the metabolism of lactobacillus acidophilus; the biomarkers uric acid and methylamino glutamate in histidine metabolism affect the tricarboxylic acid cycle by affecting glutamate and thus 2-oxoglutarate, ultimately affecting the metabolism of lactobacillus acidophilus.
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 (4)
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;
connecting potential biomarkers in the important metabolic pathways through the metabolic pathways to obtain a comprehensive metabolic network;
the conditions of the LC-MS are as follows:
adopts a HSS T3C 18 chromatographic column with the grain diameter of 100 multiplied by 2.1mm and 1.8 mu m as a chromatographic column; the column temperature of the chromatographic column is 40 ℃;
the mobile phase A is ammonium acetate, the mobile phase B is acetonitrile, and the flow rate of the mobile phase is 0.3 mL.min -1 ；
The elution procedure was: eluting with 90-70% of A solution for 0-1.5 minutes; then continuously eluting for 1.5 to 8 minutes by using 70 to 2 percent of solution A; then eluting for 8 to 9 minutes by using 2 to 90 percent of solution A; finally, eluting for 9 to 10 minutes by using 90 percent of A solution; wherein the sample introduction temperature is 4 ℃, and 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 positive and negative ion modes of an electrospray ion source, wherein the capillary voltage is 3.0kV, the foramen voltage is 40kV, and the extraction voltage is 4.0kV; the temperature of the ion source is 100 ℃, and the temperature of the desolventizing gas is 350 ℃; the air flow of the taper hole is 40 L.h -1 Desolventizing flow rate of 800 L.h -1 ；
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: r 2 X=0.554， R 2 Y=0.921，Q 2 =0.659；
In the negative ion mode: r 2 X=0.613， R 2 Y=0.854，Q 2 =0.536；
Pretreatment of intracellular metabolites of lactobacillus acidophilus prior to LC-MS analysis:
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;
blowing the supernatant fluid by nitrogen, re-dissolving the supernatant fluid by 400 mu L of mixed solution of acetonitrile and water with the same volume before sample injection, and filtering the re-dissolved solution by a 0.22 mu m nylon filter membrane;
the differential metabolites include: glycerol, cytosine, nicotinic acid, L-2, 4-diaminobutyric acid, ornithine, carnosine, orotic acid, nicotinuric acid, pseudouridine, formiminomethyl-glutamic acid, levotryptophan, urocanic acid and thymine.
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 growth phase into 4 groups of 8 biological repeats, respectively feeding to 1.0 × 10 -4 ，1.0×10 -5 ， 1.0×10 -6 Amoxicillin at a concentration of M, collected 24 hours after administration.
3. 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.
4. The metabonomics-based method for establishing the effect of amoxicillin concentration on lactobacillus acidophilus according to claim 1, 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 performed in MarkerLynx 4.1 software and MetabioAnalyst website;
the parameters of the pretreatment are as follows: the retention time is 0 to 10min; the mass number is 50 to 1200Da; a mass number tolerance of 0.01; the mass number window is 0.02; the noise removal level was 6.
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Non-Patent Citations (4)
| Title |
|---|
| Evaluating metabolic response to light exposure in Lactobacillus species via targeted metabolic profiling;Mengyang Xu et al;《Journal of Microbiological Methods》;20161211;第133卷;14-19 * |
| Rapid differentiation of Lactobacillus species via metabolic profiling;Kundi Yang et al;《Journal of Microbiological Methods》;20181023;第154卷;147-155 * |
| 基于气相色谱-质谱技术对用咪康唑处理的白念珠菌的代谢组学研究;杨宇 等;《药学实践杂志》;20150525;第33卷(第3期);209-212 * |
| 黄芩素作用白假丝酵母菌的GC-MS代谢组学研究;吴海棠 等;《第二军医大学学报》;20130228;第34卷(第2期);184-189 * |
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