CN109752473B - Metabonomics analysis method taking amino acid and acylcarnitine as target in blood - Google Patents
Metabonomics analysis method taking amino acid and acylcarnitine as target in blood Download PDFInfo
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Abstract
The metabonomics analysis method taking amino acid and acyl carnitine as target targets in blood comprises the following steps: collecting a certain amount of fresh blood samples to obtain serum or plasma samples, and dividing the serum or plasma samples into a sample group and a control group according to the identification result of blood sample contributors; (II) performing targeted metabonomics pretreatment on each blood sample in the sample group and the control group; thirdly, each blood sample in the sample group and the control group is sent into a high performance liquid chromatography tandem mass spectrometer, and the target object is accurately quantified to obtain the specific value of each target object of each blood sample in the sample group and the control group; (IV) comparing each target object in the sample group and the control group, and selecting target objects with significant difference; and (V) establishing a radar model, drawing the target objects with the significant difference in the radar model, and finding the relation between the target objects with the significant difference.
Description
Technical Field
The invention relates to the field of blood LC-MS/MS metabonomics research, in particular to a metabonomics analysis method taking amino acid and acyl carnitine as targets.
Background
Small molecule metabolites in human bodies are important life substances participating in human body metabolism, and comprise amino acids, organic acids and the like. The level of small molecule metabolites in the body reflects the environment of the cell and has important effects on the life activities of the body.
Amino acids, as components of body proteins, not only constitute basic units for constructing proteins, but also play important physiological functions in the body: (1) when the quality and the amount of protein in the daily diet are proper, the intake amount of nitrogen is equal to the amount of nitrogen discharged from feces, urine and skin, which is called the total balance of nitrogen. In fact, is a balance between the continuous synthesis and breakdown of proteins and amino acids. The daily protein intake of normal people should be kept within a certain range, and when the intake is suddenly increased or decreased, the body can still regulate the protein metabolism to maintain nitrogen balance. Excessive protein intake, beyond the body's ability to regulate, disrupts the balance mechanism. The protein is not eaten at all, the in vivo tissue protein is still decomposed, and the negative nitrogen balance is continuously generated, if the correction is not carried out in time, the antibody is finally killed. (2) The conversion to fat and the catabolism of amino acids produces a-keto acids which, with different properties, are metabolized along the sugar or lipid metabolic pathways. The a-keto acid can be used for synthesizing new amino acid, or can be converted into sugar or fat, or can enter a tricarboxylic cycle for oxidative decomposition into CO2 and H2O, and energy is released. (3) To produce a one-carbon unit, and some amino acids undergo catabolic processes to produce groups containing one carbon atom, including methyl, methylene, alkynyl, cresyl, aminomethyl, and the like. A carbon unit has the following two characteristics: the free form can not exist in the organism; ② tetrahydrofolic acid is used as carrier. Amino acids capable of forming a carbon unit are: serine, tryptophan, histidine, glycine. In addition, methionine (methionine) can provide "active methyl" (one-carbon unit) by S-adenosylmethionine (SAM), so methionine can also generate one-carbon unit. The main physiological function of the one-carbon unit is as a raw material for purine and pyrimidine synthesis, and is a link between amino acids and nucleotides. (4) The participating constitutive enzyme is involved in constitutive enzyme, hormone and partial vitamin. The nitrogen-containing hormone is protein or its derivative, such as growth hormone, thyroid stimulating hormone, epinephrine, insulin, and intestinal juice stimulating hormone. Some vitamins exist as amino acid changes or in combination with proteins. Enzymes, hormones and vitamins play an important role in regulating physiological functions and catalyzing metabolic processes.
Organic acids participate in a plurality of biochemical reactions in the life activities of human bodies and are important life substances of human bodies. Has antibacterial, antiinflammatory, antiviral, mutation resisting, and anticancer effects; can increase coronary artery blood flow, inhibit lipid peroxide generation in brain tissue, soften blood vessel, and promote absorption of calcium and iron elements. The main form of organic acids involved in energy metabolism in the body is acyl carnitine.
Disclosure of Invention
The invention aims to provide an analysis method of blood LC-MS/MS targeted micromolecular target (amino acid and acyl carnitine) metabonomics. The method has the characteristics of strong applicability, high efficiency and the like, and can be widely used for metabonomics research of targeted small molecular target substances (amino acid and acyl carnitine) with various purposes.
In order to achieve the purpose of the invention, the following technical scheme is adopted in the application:
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: it comprises the following steps:
collecting a certain amount of fresh blood samples, separating the blood samples to obtain serum or plasma samples, and dividing the serum or plasma samples into a sample group and a control group according to the identification result of blood sample contributors;
(II) performing targeted metabonomics pretreatment on each blood sample in the sample group and the control group;
thirdly, each blood sample in the sample group and the control group is sent into a high performance liquid chromatography tandem mass spectrometer, and the target object is accurately quantified in the high performance liquid chromatography tandem mass spectrometer, so that a specific numerical value of each target object of each blood sample in the sample group and the control group is obtained;
(IV) comparing each target object in the sample group and the control group, and selecting target objects with significant difference;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: it still includes:
establishing a radar model, drawing the target objects with significant differences selected in the step (IV) in the radar model, and finding out the relation between the target objects with significant differences;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: in the step (one), after collecting the fresh blood sample, standing the fresh blood sample in a blood collecting device for 0.5-3 h, and then centrifuging in a centrifuge with the rotating speed of 3000-4500rmp for 5-15min, wherein the separated supernatant is the blood serum or blood plasma sample, and the same sample is divided into a plurality of parts for storage;
in step (ii), performing targeted metabolomic pretreatment on each serum or plasma sample in the sample population and the control population comprises the following steps:
(i) protein precipitation
Adding each serum or plasma sample in the sample group and the control group and a protein precipitator into the same container according to a certain proportion, fully mixing the liquid in the container for 3-15min at the rotating speed of 1000-;
(ii) solid phase extraction
(ii) loading the supernatant obtained in the step (i) onto an activated solid phase extraction column, loading a certain volume of washing solution on the solid phase extraction column to wash the supernatant, then loading a certain volume of eluent on the solid phase extraction column to elute the solid phase extraction column, collecting the eluent, or drying the eluent, and redissolving the eluent by using a certain volume of redissolving solution to obtain redissolved mixed solution, and sending the eluent or the redissolved mixed solution into a high performance liquid chromatography tandem mass spectrometer to perform mass spectrometry or performing the operation of the step (iii);
(iii) liquid-liquid extraction
Adding a certain volume of an extracting agent into the supernatant obtained in the step (i), the eluent obtained in the step (ii) or the redissolved mixed solution obtained in the step (ii), fully and uniformly mixing the extracting agent for 3-15min at the rotating speed of 1000-;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: in step (b), performing targeted metabolomic pretreatment on each serum or plasma sample in the sample population and the control population further comprises:
(iv) adding a stabilizer
Adding a certain volume of stabilizer to the serum or plasma sample obtained in the step (one), the supernatant obtained in the step (i), the eluent obtained in the step (ii), the redissolved mixed solution obtained in the step (ii), the clarified solution obtained in the step (iii) or the redissolved mixed solution obtained in the step (iii), and reacting the stabilizer with the solution;
or (i) adding a certain volume of stabilizer n-butyl alcohol hydrochloride into the dried supernatant in the step (i), reacting the stabilizer with the solution at 35-70 ℃ for 15-45 minutes, drying, adding a certain volume of redissolution, and finally sending into a high performance liquid chromatography tandem mass spectrometer for mass spectrometry;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps:
the protein precipitant is methanol, acetonitrile, n-butanol, aqueous solution containing 3% -15% perchloric acid, aqueous solution containing 3% -15% trichloroacetic acid or aqueous solution containing 3% -15% sulfosalicylic acid or concentrated hydrochloric acid, and the volume of the protein precipitant is as follows: serum or plasma sample volumes were 1: 1 to 20: 1;
the washing liquid comprises: pure water, physiological saline, PBS phosphate buffer solution, aqueous solution containing 0.05% -0.5% formic acid, aqueous solution containing 1mmol/L-100mmol/L ammonium formate, aqueous solution containing 1mmol/L-100mmol/L ammonium acetate or aqueous solution containing 5-30% methanol or acetonitrile or absolute ethanol, volume of washing solution: serum or plasma sample volumes were 1: 1 to 10: 1;
the eluent comprises: methanol, acetonitrile, n-butanol, a methanol solution containing 0.05% to 0.5% formic acid, an acetonitrile solution containing 0.05% to 0.5% formic acid, an n-butanol solution containing 0.05% to 0.5% formic acid, a methanol solution containing 1mmol/L to 100mmol/L ammonium formate, a methanol solution containing 1mmol/L to 100mmol/L ammonium acetate or an n-butanol solution containing 1mmol/L to 100mmol/L acetonitrile, the volume of the eluent: serum or plasma sample volumes were 1: 2 to 10: 1;
the compound solution comprises: mixing methanol, acetonitrile, n-butanol, methanol solution containing 0.05-0.5% formic acid, acetonitrile solution containing 0.05-0.5% formic acid, n-butanol solution containing 0.05-0.5% formic acid, methanol solution containing 1mmol/L-100mmol/L ammonium formate, methanol solution containing 1mmol/L-100mmol/L ammonium acetate or n-butanol solution containing 1mmol/L-100mmol/L acetonitrile with water, wherein the water accounts for 0-50%, and the volume of the composite solution is as follows: serum or plasma sample volumes were 1: 2 to 5: 1;
the extracting agent is one or two or more of n-hexane, cyclohexane, ethyl acetate, methyl tert-butyl ether and diethyl ether, and the volume of the extracting agent is as follows: serum or plasma sample volumes were 2: 1 to 10: 1;
the stabilizer is methanol, ethanol, n-butanol, perchloric acid, trichloroacetic acid, sulfosalicylic acid, hydrochloric acid, sulfuric acid, nitric acid, vitamin C, dithiothreitol or n-butanol hydrochloride, and the volume of the stabilizer is as follows: serum or plasma sample volumes were 1: 20 to 5: 1;
the small solid-phase extraction column is a C18 solid-phase extraction column, a strong anion exchange SPE column, a strong cation exchange SPE column or a weak cation exchange SPE column;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: in the third step, the method for accurately quantifying the target object in the high performance liquid chromatography tandem mass spectrometer comprises the following steps:
(I) chromatographic retention time separation
Separating the target object by using a C18(150mm) chromatographic column, and eluting a mobile phase by using a gradient at the column temperature of 25-40 ℃ of the chromatographic column, wherein the organic phase is a methanol solution containing 1mmol/L-50mmol/L ammonium formate and 0.1% -0.3% formic acid or a methanol solution containing 1mmol/L-50mmol/L ammonium acetate and 0.1% -0.3% formic acid, and the aqueous phase is an aqueous solution containing 1mmol/L-50mmol/L ammonium formate and 0.1% -0.3% formic acid or an aqueous solution containing 1mmol/L-50mmol/L ammonium acetate and 0.1% -0.3% formic acid.
The elution conditions were:
0- (2-3 min): the organic phase containing 15-35% is eluted with equal degree, and the flow rate is 0.2-0.4 ml/min;
(2.5-3.5 min): eluting at flow rate of 0.2-0.4ml/min when the organic phase ratio is increased from 15-35% to 40-65%;
(2.5-3.5min) - (5.5-6.5 min): contains 40-65% organic phase and is eluted with equal degree, the flow rate is 0.2-0.4 ml/min;
(6-7 min): eluting at flow rate of 0.2-0.4ml/min when the organic phase ratio is increased from 40-65% to 75-90%;
(6-7min) - (9-10 min): contains 75-90% organic phase and is eluted with equal degree, the flow rate is 0.2-0.4 ml/min;
(9.5-10.5 min): eluting at flow rate of 0.2-0.4ml/min when the organic phase ratio is increased from 75-90% to 95-100%;
(9.5-10.5min) - (12.5-13.5 min): contains 95-100% organic phase and is eluted with equal degree, the flow rate is 0.2-0.4 ml/min;
(13-14 min): eluting at the flow rate of 0.2-0.4ml/min when the proportion of the organic phase is reduced from 95-100% to 15-35%;
(13-14min) - (15-16 min): contains 15-35% organic phase with a flow rate of 0.2-0.4 ml/min;
(II) screening and detecting of Mass-to-Charge ratios in Mass Spectrometry
Detecting the mass-to-charge ratios of 21 target objects by adopting a positive ion MRM scanning mode, wherein the specific parameters are as follows:
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: in the step (iv), the selection of the target objects having significant differences includes the steps of:
(A) performing normal test on each target object in the sample group and the control group, wherein when the normal distribution P of a certain target object in the sample group and the control group is less than or equal to 0.05; carrying out the homogeneity test of variance on the target objects between the sample group and the control group, carrying out one-factor variance analysis on the target objects when the P of the homogeneity test of variance is greater than 0.05, and selecting the target objects with the P less than or equal to 0.05; when P of the homogeneity of variance test is less than or equal to 0.05, carrying out mean test of the heterogeneity of variance on the target object, and selecting the target object with P less than or equal to 0.05;
(B) when one normal distribution P in a sample group and a control group of a certain target object is greater than 0.05; carrying out nonparametric inspection on the target objects between the sample group and the control group, and selecting the target objects with P less than or equal to 0.05;
(C) performing logistic regression on each target object in the sample group and the comparison group, and selecting the target object with P less than or equal to 0.05;
(D) respectively collecting the target objects with P less than or equal to 0.05 selected by the single-factor analysis of variance and the mean value test of variance heterogeneity in the step (A), and the target objects with P less than or equal to 0.05 selected in the step (B) and the step (C), namely the target objects with significant difference;
p above is the confidence level.
The invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: the normality test comprises the following steps: KS test (Kolmogorov-Smirno), Jarqe-Bera test, Shapiro-Wilk test, Anderson-Darling test, Cram er-von-Mises test, Pearson's chi-square test, M test (Mudholkar) or D test; the homogeneity test of variance comprises: levene's test, Bartlett test, maximum F-ratio test or maximum variance test; nonparametric tests include: wilcoxon signed rank test, Mann-Whitney U test, Kruskal-Wallis test, Spearman rank correlation test, or Kendall rank correlation test; the one-way anova includes: one-way ANOVA or t test; the mean test for variance heterogeneity comprises: t' test, mixed effect model or variance weighted least squares;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: in the step (five), the establishing of the radar model comprises the following steps:
(1) screening data: selecting a sample group and a control group, wherein the sample group and the control group respectively comprise 40-200 blood samples, and each blood sample comprises: 2-10 target objects with significant differences;
(2) determining an analysis set and a verification set: and (2) grouping a certain proportion of blood samples in the sample group and a proportion of blood samples in the control group into an analysis set and a verification set by adopting a random principle, wherein the analysis set comprises: sample and control groups, including in the validation set: sample group and control group;
(3) the concentration of a target object in each blood sample in the control group in the analysis set is calculated by statistics, the mean value and the median are calculated, and the mean value or the median is used as a control group reference value of the target object;
(4) carrying out statistical calculation and analysis on the concentration of the target object in the step (3) in the concentrated sample group, calculating a mean value and a median, and taking the mean value or the median as a sample group reference value of the target object;
(5) converting the concentration of the target object of the sample group and the control group in the analysis set into a conversion ratio of the target object of the sample group and the control group in the analysis set, wherein the conversion method comprises one of the following steps:
(a) comparing the reference value of the target object in the control group with the reference value of the target object in the sample group, and when the reference value of the control group is more than or equal to the reference value of the sample group, calculating the conversion ratio of the target object in the sample group and the control group in the analysis set by taking the concentration of the target object in the sample group and the control group in the analysis set as a numerator and the reference value of the control group as a denominator; when the reference value of the control group is less than the reference value of the sample group, the concentration of the target object of the sample group and the control group in the analysis set is used as a denominator, and the reference value of the control group is used as a numerator to calculate the conversion ratio of the target object of the sample group and the control group in the analysis set;
(b) comparing the reference value of the target object in the control group with the reference value of the target object in the sample group, and when the reference value of the control group is more than or equal to the reference value of the sample group, calculating the conversion ratio of the target object in the sample group and the control group in the analysis set by taking the concentration of the target object in the sample group and the control group in the analysis set as denominator and the reference value of the control group as numerator; when the reference value of the control group is less than the reference value of the sample group, calculating the conversion ratio of the target objects of the sample group and the control group in the analysis set by taking the concentration of the target objects of the sample group and the control group in the analysis set as a numerator and the reference value of the control group as a denominator;
(6) repeating the steps (3) to (5), converting the concentration of 2-10 target objects contained in the blood samples of the analysis set sample group and the control group into the conversion ratio of the target objects, and selecting only one of the conversion methods in the step (5) in the same model;
(7) the conversion ratios of the above 2-10 target targets for all blood samples in the analysis set were used to generate a radar map to create a model map:
selecting one point as a circular point in the coordinate graph, uniformly making 2-10 rays by taking the circular point as a circle center, wherein included angles between two adjacent rays are the same, and each ray is marked with the same length unit to obtain a radar graph;
labeling the conversion ratio of one target object in the sample group of the analysis set on the same ray of the radar map, similarly labeling the conversion ratio of 2-10 target objects in the sample group of the analysis set on the corresponding ray of the radar map, making a closed line on the radar map between 0-30 quantiles of the conversion ratio of 2-10 target objects in the sample group, making a closed line on the radar map between 70-100 quantiles of the conversion ratio of 2-10 target objects in the sample group, and making a region between the two closed lines as a sample group model interval;
labeling the conversion ratio of one target object in the control group of the analysis set on the same ray of the radar map, similarly labeling the conversion ratios of 2-10 target objects in the control group of the analysis set on the corresponding ray of the radar map, making a closed line on the radar map between 0-30 quantiles of the conversion ratios of 2-10 target objects in the sample group, making a closed line on the radar map between 70-100 quantiles of the conversion ratios of 2-10 target objects in the sample group, and taking the area between the two closed lines as a control group model interval;
(8) establishing model parameters: firstly, calculating the mean value of 2-10 target object concentration conversion values of each blood sample in an analysis set, and recording the mean value as R; in the observation model, the number of the target objects of each blood sample falling in the sample group model interval is marked as N;
(9) the model judging method comprises the following steps: if the conversion value of 2-10 target objects of each blood sample in the analysis set is calculated according to the step (a) in the step (5), judging the sample as the blood sample of the sample group when R is less than 0.85 +/-0.15 and/or N is more than or equal to (0.5 +/-0.4) the number of the target objects, otherwise, judging the sample as the blood sample of the control group; if the conversion value of 2-10 target objects of each blood sample in the analysis set is calculated according to the step (b) in the step (5), when R is more than 1.15 +/-0.15 and/or N is more than or equal to (0.5 +/-0.4) times the number of the target objects, judging the sample as the blood sample of the sample group, otherwise, judging the sample as the blood sample of the control group;
(10) and (3) model verification: taking the concentration of the 2-10 target objects of each blood sample in the sample group and the control group in the verification set as the basis according to the reference value of the corresponding control group and the reference value of the sample group obtained in the steps (3) to (4), replacing the analysis set in the step (5) with the verification set to calculate the conversion value of the target objects, substituting the conversion value of the 2-10 target objects of each blood sample in the sample group and the control group in the verification set into the model, distinguishing the blood samples in the verification set by using the judging methods in the steps (8) and (9), and calculating the sensitivity and the specificity of the model, wherein the sensitivity is the ratio of the number of the blood samples of the sample group in the verification set verified in the model to the number of the blood samples in the verification set; the specificity is the ratio of the number of the blood samples of the control group in the verification set verified by the model to the number of the blood samples of the control group in the verification set, when the sensitivity and the specificity are both greater than 0.75, the model is successfully established, otherwise, the model interval of the sample group and the model interval of the control group in the step (7) are readjusted until the sensitivity and the specificity are both greater than 0.75;
the invention relates to a metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood, which comprises the following steps: the certain proportion in the step (2) is 4:1 to 5: 1; in the step (7), the 0-quantile point is on a ray, no data exists between the dots and the 0-quantile point, the 30-quantile point is on a ray, 30% of data exists between the 0-quantile point and the 30-quantile point, the 70-quantile point is on a ray, 70% of data exists between the 0-quantile point and the 70-quantile point, the 100-quantile point is on a ray, and 100% of data exists between the 0-quantile point and the 100-quantile point.
The targeted small molecule targets are 21 amino acids and acylcarnitines, which are alanine, valine, arginine, citrulline, glycine, histidine, leucine, methionine, ornithine, phenylalanine, tyrosine, glutamic acid, aspartic acid, acetyl-carnitine, propionyl-carnitine, butyryl-carnitine, valeryl-carnitine, caproyl-carnitine, caprylyl-carnitine, myristyl-carnitine, cetyl-carnitine.
The invention has the beneficial effects that:
the blood LC-MS/MS metabonomics analysis method provides a comprehensive and comprehensive analysis method of targeted small molecular target substances (amino acid and acyl carnitine) for the first time, has higher practical value, and can accurately detect 21 amino acid and acyl carnitine substances in blood, including a large-polarity target substance, a medium-polarity target substance and a weak-polarity target substance; an acidic target, a basic target, a neutral target; a stable target and an unstable target. The analysis method can comprehensively and accurately detect the different target objects in the sample group and the control group and carry out radar model analysis on data, is a high-accuracy universal targeted omics analysis method, and has important application prospect in the field of metabonomics, particularly small molecule metabonomics.
The invention will be further illustrated with reference to specific embodiments and the accompanying drawings.
Drawings
FIG. 1 is a block diagram of a method for comparing each target object of the sample group and the control group to select target objects having significant differences in step (IV);
FIG. 2 is a schematic diagram of the metabonomic radar model in step (five); the area between the two closed dotted lines is the sample group model interval and the area between the two closed straight lines is the control group model interval, and for the sake of clarity, the points of conversion values of the target object are not indicated in the figure.
Detailed Description
The invention is further illustrated by the following examples, which are intended to be illustrative only and not limiting.
Collecting a certain amount of fresh blood samples, separating the blood samples to obtain serum or plasma samples, and dividing the serum or plasma samples into a sample group and a control group according to the identification result of blood sample contributors;
after consent of the donors, blood from normal persons and clinically well-identified patients with lung cancer were collected using inert separation gel-accelerating tubes, at least 20 per group. After collecting the fresh blood sample, standing the fresh blood sample in a blood collecting device for 2h, centrifuging at 3000rpm for 10min, taking the supernatant as a serum or plasma sample, and storing at-80 ℃;
(II) performing targeted metabonomics pretreatment on each blood sample in the sample group and the control group;
taking 50uL of a serum or plasma sample, adding 500uL of methanol to precipitate protein, fully mixing for 5min at the rotating speed of 2000rmp, centrifuging the mixed solution for 15min at the rotating speed of 15000rmp, drying the supernatant under nitrogen flow, adding 200uL of stabilizer n-butyl alcohol hydrochloride into residues, fully mixing for 5min at the rotating speed of 2000rmp, reacting for 40min at 55 ℃, drying the reaction solution under nitrogen flow after the reaction is finished, adding 100uL of redissolved solution containing 80% of methanol into the residues, fully mixing for 5min at the rotating speed of 2000rmp, centrifuging the mixed solution for 15min at the rotating speed of 15000rmp, transferring the supernatant into a sample bottle, and analyzing by using a high performance liquid chromatography tandem mass spectrometer, wherein in the example, after protein precipitation, drying and redissolving by adding a stabilizer and a stabilizing agent into the serum or plasma sample, analyzing by using the high performance liquid chromatography tandem mass spectrometer;
thirdly, each blood sample in the sample group and the control group is sent into a high performance liquid chromatography tandem mass spectrometer, and the target object is accurately quantified in the high performance liquid chromatography tandem mass spectrometer, so that a specific numerical value of each target object of each blood sample in the sample group and the control group is obtained;
the treated biological samples were injected by autosampler into an Atlantis dC18 column (2.1 × 150mm, 5 μm, Waters, USA) and the metabolites in the samples were separated, using the following specific chromatographic conditions: the aqueous phase (mobile phase a) was water containing 10mM ammonium acetate and 0.2% formic acid; the organic phase (mobile phase B) was a methanol solution containing 10mM ammonium acetate and containing 0.2% formic acid; the column temperature was 45 ℃; the gradient elution conditions were as follows:
| Time/min | A％ | B％ | flow rate/(mL/min) |
| 0.0 | 75 | 25 | 0.30 |
| 2.5 | 75 | 25 | 0.30 |
| 3.0 | 35 | 65 | 0.30 |
| 5.5 | 35 | 65 | 0.30 |
| 6 | 15 | 85 | 0.30 |
| 9 | 15 | 85 | 0.30 |
| 9.5 | 5 | 95 | 0.30 |
| 12.5 | 5 | 95 | 0.30 |
| 13 | 75 | 25 | 0.30 |
| 15 | 75 | 25 | 0.30 |
And pouring the metabolites subjected to chromatographic separation into a triple quadrupole mass spectrum, and scanning and detecting the alanine by adopting a positive ion multiple reaction monitoring mode. The capillary voltage was 3500V, the desolventizing gas temperature was set at 300 deg.C, the flow rate was 8L/min, and the Nebulizer was set at 35 psi.
(IV) as shown in FIG. 1, comparing each target object in the sample group and the control group, and selecting the target object with significant difference comprises the following steps:
(A) performing normal test on each target object in the sample group and the control group, wherein when the normal distribution P of a certain target object in the sample group and the control group is less than or equal to 0.05; carrying out the homogeneity test of variance on the target objects between the sample group and the control group, carrying out one-factor variance analysis on the target objects when the P of the homogeneity test of variance is greater than 0.05, and selecting the target objects with the P less than or equal to 0.05; when P of the homogeneity of variance test is less than or equal to 0.05, carrying out mean test of the heterogeneity of variance on the target object, and selecting the target object with P less than or equal to 0.05;
(B) when one normal distribution P in a sample group and a control group of a certain target object is greater than 0.05; carrying out nonparametric inspection on the target objects between the sample group and the control group, and selecting the target objects with P less than or equal to 0.05;
(C) performing logistic regression on each target object in the sample group and the comparison group, and selecting the target object with P less than or equal to 0.05;
(D) respectively collecting the target objects with P less than or equal to 0.05 selected by the single-factor analysis of variance and the mean value test of variance heterogeneity in the step (A), and the target objects with P less than or equal to 0.05 selected in the step (B) and the step (C), namely the target objects with significant difference;
p above is the confidence level.
The normality testing method comprises the following steps: KS test (Kolmogorov-Smirno)[1]Jarqe-Bera test[1]Shapiro-Wilk test[1]Anderson-Darling test[1]Cram er-von-Mises test[1]Pearson's chi-square test[1]M test method (Mudholkar)[2]Or D test method[3]。
Nonparametric tests include: wilcoxon signed rank test[4]Mann-Whitney U test[5]Kruskal-Wallis test[6]Spearman rank correlation test[7]Or Kendall rank correlation test[8]。
The homogeneity test of variance comprises: levene's test, Bartlett test, maximum F-ratio test, or maximum variance test[9]。
The one-way anova includes: one-way ANOVA[10]Also includes t-test[11]。
The mean test for variance heterogeneity comprises: t' test[12]Model of mixed effects[12]Variance weighted least squares[12]。
Logistic regression analysis[13]
The indices are as follows:
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[2] Plum, method for checking normality of data [ J ]. proceedings of the institute of biochemistry, 2015 (34): pages 81-82
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Establishing a radar model, drawing the target objects with the significant difference selected in the step (four) in the radar model, and finding the relation between the target objects with the significant difference: it comprises the following steps:
(1) screening data: selecting a sample group and a control group, wherein the sample group comprises 40 lung cancer patients diagnosed in a certain community, the control group comprises 140 non-lung cancer persons diagnosed in a certain community hospital, and each blood sample comprises: 6 target objects; the 6 target objects are citrulline, methionine, valine, glycine, arginine and hexadecanoyl carnitine respectively;
(2) determining an analysis set and a verification set: and (3) grouping the 4:1 blood samples in the sample group and the 4:1 blood samples in the control group into an analysis set and a verification set by adopting a random principle, wherein the analysis set comprises: 32 sample groups and control 112 groups, including in the validation set: 8 sample groups and 28 control groups;
(3) one target object for each blood sample in the control group in the statistical computational analysis set is for example: calculating the mean value of the concentration of citrulline, and taking the mean value as a reference value of a control group of the target object;
(4) carrying out statistical calculation and analysis on the concentration of the target object in the step (3) in the concentrated sample group, calculating a mean value, and taking the mean value as a sample group reference value of the target object;
(5) converting the concentration of the target object of the sample group and the control group in the analysis set into a conversion ratio of the target object of the sample group and the control group in the analysis set, wherein the conversion method comprises the following steps:
(a) comparing the reference value of the target object in the control group with the reference value of the target object in the sample group, and when the reference value of the control group is more than or equal to the reference value of the sample group, calculating the conversion ratio of the target object in the sample group and the control group in the analysis set by taking the concentration of the target object in the sample group and the control group in the analysis set as a numerator and the reference value of the control group as a denominator; when the reference value of the control group is less than the reference value of the sample group, the concentration of the target object of the sample group and the control group in the analysis set is used as a denominator, and the reference value of the control group is used as a numerator to calculate the conversion ratio of the target object of the sample group and the control group in the analysis set;
(6) repeating the steps (3) to (5), and converting the concentrations of methionine, valine, glycine, arginine and hexadecanoyl carnitine included in the blood samples of the group of samples in the analysis set and the control group into the conversion ratio of the target object;
(7) the conversion ratios of the above 6 target targets for all blood samples in the analysis set were used to generate a radar map to create a model map:
as shown in fig. 2, in the coordinate graph, one point is selected as a dot, 6 rays are uniformly made by taking the dot as a circle center, included angles between two adjacent rays are the same, and each ray is marked with the same length unit to obtain a radar map;
labeling the conversion ratio of one target object in the sample group of the analysis set on the same ray of the radar map, and similarly labeling the conversion ratios of 6 target objects in the sample group of the analysis set on the corresponding ray of the radar map, making a closed line on the radar map between 0 quantiles of the conversion ratios of the 6 target objects in the sample group, making a closed line on the radar map between 75 quantiles of the conversion ratios of the 6 target objects in the sample group, wherein the area between the two closed lines is a sample group model interval, namely the area between the two closed dotted lines is a sample group model interval; the 0 quantile point is on a ray, no data exists between the dots and the 0 quantile point, and the 75 quantile point is on a ray, and 75% of data exists between the 0 quantile point and the 75 quantile point;
labeling the conversion ratio of one target object in the control group of the analysis set on the same ray of the radar map, and similarly labeling the conversion ratios of 6 target objects in the control group of the analysis set on corresponding rays of the radar map, making a closed line on the radar map between 25 quantiles of the conversion ratios of 6 target objects in the sample group, making a closed line on the radar map between 100 quantiles of the conversion ratios of 6 target objects in the sample group, wherein the area between the two closed lines is a control group model interval, namely: the area between the two closed straight lines is a comparison group model interval; the points with 25 quantiles are on one ray, 25% of data exists between the points with 0 quantile and 25 quantile, and the points with 100 quantiles are on one ray, and 100% of data exists between the points with 0 quantile and 100 quantile;
(8) establishing model parameters: firstly, calculating the mean value of 6 target object concentration conversion values of each blood sample in an analysis set, and recording the mean value as R; in the observation model, the number of the target objects of each blood sample falling in the sample group model interval is marked as N;
(9) the model judging method comprises the following steps: judging the analysis sample according to the model parameters, judging the sample as the blood sample of the sample group when R is less than 0.9 and N is more than or equal to 4, otherwise, judging the sample as the blood sample of the control group;
(10) and (3) model verification: and (3) model verification: taking the concentration of 6 target objects of each blood sample in the sample group and the control group in the verification set according to the reference value of the control group and the reference value of the sample group obtained in the steps (3) to (4), replacing the analysis set in the step (5) with the verification set to calculate the conversion value of the target objects, substituting the conversion value of the 6 target objects of each blood sample in the sample group and the control group in the verification set into the model, distinguishing the blood sample in the verification set by using the judgment methods in the steps (8) and (9), calculating the sensitivity and the specificity of the model, and obtaining the sensitivity of 0.90 by using the ratio of the number of the blood samples of the sample group in the verification set verified in the model to the number of the blood samples in the verification set; the specificity is the ratio of the number of the blood samples of the control group in the verification set verified in the model to the number of the blood samples of the control group in the verification set, the obtained specificity is 0.88, and when the sensitivity and the specificity are both more than 0.75, the model is successfully established.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention by those skilled in the art should fall within the protection scope of the present invention without departing from the design spirit of the present invention.
Claims (8)
1. A metabonomics analysis method taking amino acid and acyl-carnitine as target targets in blood is characterized in that: it comprises the following steps:
collecting a certain amount of fresh blood samples, separating the blood samples to obtain serum or plasma samples, and dividing the serum or plasma samples into a sample group and a control group according to the identification result of blood sample contributors;
(II) performing targeted metabonomics pretreatment on each blood sample in the sample group and the control group;
thirdly, each blood sample in the sample group and the control group is sent into a high performance liquid chromatography tandem mass spectrometer, and the target object is accurately quantified in the high performance liquid chromatography tandem mass spectrometer, so that a specific numerical value of each target object of each blood sample in the sample group and the control group is obtained;
(IV) comparing each target object in the sample group and the control group, and selecting target objects with significant difference;
building a radar model, drawing the target objects with significant differences selected in the step (IV) in the radar model, and finding out the relation between the target objects with significant differences; in the step (five), the establishing of the radar model comprises the following steps: (1) screening data: selecting a sample group and a control group, wherein the sample group and the control group respectively comprise 40-200 blood samples, and each blood sample comprises: 2-10 target objects with significant differences; (2) determining an analysis set and a verification set: and (2) grouping a certain proportion of blood samples in the sample group and a proportion of blood samples in the control group into an analysis set and a verification set by adopting a random principle, wherein the analysis set comprises: sample and control groups, including in the validation set: sample group and control group; (3) the concentration of a target object in each blood sample in the control group in the analysis set is calculated by statistics, the mean value and the median are calculated, and the mean value or the median is used as a control group reference value of the target object; (4) carrying out statistical calculation and analysis on the concentration of the target object in the step (3) in the concentrated sample group, calculating a mean value and a median, and taking the mean value or the median as a sample group reference value of the target object; (5) converting the concentration of the target object of the sample group and the control group in the analysis set into a conversion ratio of the target object of the sample group and the control group in the analysis set, wherein the conversion method comprises one of the following steps: (a) comparing the reference value of the target object in the control group with the reference value of the target object in the sample group, and when the reference value of the control group is more than or equal to the reference value of the sample group, calculating the conversion ratio of the target object in the sample group and the control group in the analysis set by taking the concentration of the target object in the sample group and the control group in the analysis set as a numerator and the reference value of the control group as a denominator; when the reference value of the control group is less than the reference value of the sample group, the concentration of the target object of the sample group and the control group in the analysis set is used as a denominator, and the reference value of the control group is used as a numerator to calculate the conversion ratio of the target object of the sample group and the control group in the analysis set; (b) comparing the reference value of the target object in the control group with the reference value of the target object in the sample group, and when the reference value of the control group is more than or equal to the reference value of the sample group, calculating the conversion ratio of the target object in the sample group and the control group in the analysis set by taking the concentration of the target object in the sample group and the control group in the analysis set as denominator and the reference value of the control group as numerator; when the reference value of the control group is less than the reference value of the sample group, calculating the conversion ratio of the target objects of the sample group and the control group in the analysis set by taking the concentration of the target objects of the sample group and the control group in the analysis set as a numerator and the reference value of the control group as a denominator; (6) repeating the steps (3) to (5), converting the concentration of 2-10 target objects contained in the blood samples of the analysis set sample group and the control group into the conversion ratio of the target objects, and selecting only one of the conversion methods in the step (5) in the same model; (7) the conversion ratios of the above 2-10 target targets for all blood samples in the analysis set were used to generate a radar map to create a model map: selecting one point as a circular point in the coordinate graph, uniformly making 2-10 rays by taking the circular point as a circle center, wherein included angles between two adjacent rays are the same, and each ray is marked with the same length unit to obtain a radar graph; labeling the conversion ratio of one target object in the sample group of the analysis set on the same ray of the radar map, similarly labeling the conversion ratio of 2-10 target objects in the sample group of the analysis set on the corresponding ray of the radar map, making a closed line on the radar map between 0-30 quantiles of the conversion ratio of 2-10 target objects in the sample group, making a closed line on the radar map between 70-100 quantiles of the conversion ratio of 2-10 target objects in the sample group, and making a region between the two closed lines as a sample group model interval; labeling the conversion ratio of one target object in the control group of the analysis set on the same ray of the radar map, similarly labeling the conversion ratios of 2-10 target objects in the control group of the analysis set on the corresponding ray of the radar map, making a closed line on the radar map between 0-30 quantiles of the conversion ratios of 2-10 target objects in the sample group, making a closed line on the radar map between 70-100 quantiles of the conversion ratios of 2-10 target objects in the sample group, and taking the area between the two closed lines as a control group model interval; (8) establishing model parameters: firstly, calculating the mean value of 2-10 target object concentration conversion values of each blood sample in an analysis set, and recording the mean value as R; in the observation model, the number of the target objects of each blood sample falling in the sample group model interval is marked as N; (9) the model judging method comprises the following steps: if the conversion values of 2-10 target objects of each blood sample in the analysis set are calculated according to the step (a) in the step (5), when R <0.85 and/or N is more than or equal to 0.5 times the number of the target objects, the sample is judged to be the blood sample of the sample group, otherwise, the sample is judged to be the blood sample of the control group; if the conversion value of 2-10 target objects of each blood sample in the analysis set is calculated according to the step (b) in the step (5), when R is more than 1.15 and/or N is more than or equal to 0.5 times the number of the target objects, the sample is judged to be the blood sample of the sample group, otherwise, the sample is judged to be the blood sample of the control group; (10) and (3) model verification: taking the concentration of the 2-10 target objects of each blood sample in the sample group and the control group in the verification set as the basis according to the reference value of the corresponding control group and the reference value of the sample group obtained in the steps (3) to (4), replacing the analysis set in the step (5) with the verification set to calculate the conversion value of the target objects, substituting the conversion value of the 2-10 target objects of each blood sample in the sample group and the control group in the verification set into the model, distinguishing the blood samples in the verification set by using the judging methods in the steps (8) and (9), and calculating the sensitivity and the specificity of the model, wherein the sensitivity is the ratio of the number of the blood samples of the sample group in the verification set verified in the model to the number of the blood samples in the verification set; and (3) the specificity is the ratio of the number of the blood samples of the control group in the verification set verified by the model to the number of the blood samples of the control group in the verification set, when the sensitivity and the specificity are both greater than 0.75, the model is successfully established, otherwise, the model interval of the sample group and the model interval of the control group in the step (7) are readjusted until the sensitivity and the specificity are both greater than 0.75.
2. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 1, which comprises: in the step (one), after collecting the fresh blood sample, standing the fresh blood sample in a blood collecting device for 0.5-3 h, and then centrifuging in a centrifuge with the rotating speed of 3000-4500rmp for 5-15min, wherein the separated supernatant is the blood serum or blood plasma sample, and the same sample is divided into a plurality of parts for storage;
in step (ii), performing targeted metabolomic pretreatment on each serum or plasma sample in the sample population and the control population comprises the following steps:
(i) protein precipitation
Adding each serum or plasma sample in the sample group and the control group and a protein precipitator into the same container according to a certain proportion, fully mixing the liquid in the container for 3-15min at the rotating speed of 1000-; or blowing the supernatant to dry under nitrogen;
(ii) solid phase extraction
(ii) loading the supernatant obtained in the step (i) onto an activated solid phase extraction column, loading a certain volume of washing solution on the solid phase extraction column to wash the supernatant, then loading a certain volume of eluent on the solid phase extraction column to elute the solid phase extraction column, collecting the eluent, or drying the eluent, and redissolving the eluent by using a certain volume of redissolving solution to obtain redissolved mixed solution, and sending the eluent or the redissolved mixed solution into a high performance liquid chromatography tandem mass spectrometer to perform mass spectrometry or performing the operation of the step (iii);
(iii) liquid-liquid extraction
Adding a certain volume of an extracting agent into the supernatant obtained in the step (i), the eluent obtained in the step (ii) or the redissolved mixed solution obtained in the step (ii), fully mixing the mixture for 3-15min at the rotating speed of 1000-.
3. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 2, which comprises: in step (b), performing targeted metabolomic pretreatment on each serum or plasma sample in the sample population and the control population further comprises:
(iv) adding a stabilizer
Adding a certain volume of stabilizer to the serum or plasma sample obtained in step (one), the supernatant obtained in step (i), the eluent obtained in step (ii), the reconstituted mixed solution obtained in step (ii), the clarified solution obtained in step (iii) or the reconstituted mixed solution obtained in step (iii), and allowing the stabilizer to react with the solution; or (i) adding a certain volume of stabilizer n-butyl alcohol hydrochloride into the dried supernatant in the step (i), reacting the stabilizer with the solution at 35-70 ℃ for 15-45 minutes, drying, adding a certain volume of redissolution, and finally sending into a high performance liquid chromatography tandem mass spectrometer for mass spectrometry.
4. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 3, which comprises: the protein precipitant is methanol, acetonitrile, n-butanol, aqueous solution containing 3% -15% perchloric acid, aqueous solution containing 3% -15% trichloroacetic acid or aqueous solution containing 3% -15% sulfosalicylic acid or concentrated hydrochloric acid, and the volume of the protein precipitant is as follows: serum or plasma sample volumes were 1: 1 to 20: 1; the washing liquid comprises: pure water, physiological saline, PBS phosphate buffer solution, aqueous solution containing 0.05% -0.5% formic acid, aqueous solution containing 1mmol/L-100mmol/L ammonium formate, aqueous solution containing 1mmol/L-100mmol/L ammonium acetate or aqueous solution containing 5-30% methanol or acetonitrile or absolute ethanol, volume of washing solution: serum or plasma sample volumes were 1: 1 to 10: 1; the eluent comprises: methanol, acetonitrile, n-butanol, a methanol solution containing 0.05% to 0.5% formic acid, an acetonitrile solution containing 0.05% to 0.5% formic acid, an n-butanol solution containing 0.05% to 0.5% formic acid, a methanol solution containing 1mmol/L to 100mmol/L ammonium formate, a methanol solution containing 1mmol/L to 100mmol/L ammonium acetate or an n-butanol solution containing 1mmol/L to 100mmol/L acetonitrile, the volume of the eluent: serum or plasma sample volumes were 1: 2 to 10: 1; the compound solution comprises: mixing methanol, acetonitrile, n-butanol, methanol solution containing 0.05-0.5% formic acid, acetonitrile solution containing 0.05-0.5% formic acid, n-butanol solution containing 0.05-0.5% formic acid, methanol solution containing 1mmol/L-100mmol/L ammonium formate, methanol solution containing 1mmol/L-100mmol/L ammonium acetate or n-butanol solution containing 1mmol/L-100mmol/L acetonitrile with water, wherein the water accounts for 0-50%, and the volume of the composite solution is as follows: serum or plasma sample volumes were 1: 2 to 5: 1; the extracting agent is one or two or more of n-hexane, cyclohexane, ethyl acetate, methyl tert-butyl ether and diethyl ether, and the volume of the extracting agent is as follows: serum or plasma sample volumes were 2: 1 to 10: 1; the stabilizer is methanol, ethanol, n-butanol, perchloric acid, trichloroacetic acid, sulfosalicylic acid, hydrochloric acid, sulfuric acid, nitric acid, vitamin C, dithiothreitol or n-butanol hydrochloride, and the volume of the stabilizer is as follows: serum or plasma sample volumes were 1: 20 to 5: 1; the solid phase extraction column is a C18 solid phase extraction column, a strong anion exchange SPE column, a strong cation exchange SPE column or a weak cation exchange SPE column.
5. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 4, which comprises: in the third step, the method for accurately quantifying the target object in the high performance liquid chromatography tandem mass spectrometer comprises the following steps:
(I) separation of chromatographic retention times
The treated biological samples were injected into a 2.1 x 150mm, 5 μm Atlantis dC18 column by an autosampler to separate the metabolites from the samples, with specific chromatographic conditions: the aqueous phase was water containing 10mM ammonium acetate and 0.2% formic acid; the organic phase was a methanol solution containing 10mM ammonium acetate and containing 0.2% formic acid; the column temperature was 45 ℃; the gradient elution conditions were as follows:
(II) screening and detecting mass-to-charge ratio in mass spectrum
Detecting the mass-to-charge ratios of 21 target objects by adopting a positive ion MRM scanning mode, wherein the specific parameters are as follows: the capillary voltage is 3500V, the desolventizing gas temperature is set to 300 ℃, the flow rate is 8L/min, and the Nebulizer is set to 35 psi;
6. the metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 5, which comprises: in the step (iv), the selection of the target objects having significant differences includes the steps of: (A) performing normal test on each target object in the sample group and the control group, wherein when the normal distribution P of a certain target object in the sample group and the control group is less than or equal to 0.05; carrying out the homogeneity test of variance on the target objects between the sample group and the control group, carrying out one-factor variance analysis on the target objects when the P of the homogeneity test of variance is greater than 0.05, and selecting the target objects with the P less than or equal to 0.05; when P of the homogeneity of variance test is less than or equal to 0.05, carrying out mean test of the heterogeneity of variance on the target object, and selecting the target object with P less than or equal to 0.05; (B) when one normal distribution P in a sample group and a control group of a certain target object is greater than 0.05; carrying out nonparametric inspection on the target objects between the sample group and the control group, and selecting the target objects with P less than or equal to 0.05; (C) performing logistic regression on each target object in the sample group and the comparison group, and selecting the target object with P less than or equal to 0.05; (D) respectively collecting the target objects with P less than or equal to 0.05 selected by the single-factor analysis of variance and the mean value test of variance heterogeneity in the step (A), and the target objects with P less than or equal to 0.05 selected in the step (B) and the step (C), namely the target objects with significant difference; p above is the confidence level.
7. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 6, which comprises: the normality test comprises the following steps: KS test (Kolmogorov-Smirno), Jarqe-Bera test, Shapiro-Wilk test, Anderson-Darling test, Cram er-von-Mises test, Pearson's chi-square test, M test (Mudholkar) or D test; the homogeneity test of variance comprises: levene's test, Bartlett test, maximum F-ratio test or maximum variance test; nonparametric tests include: wilcoxon signed rank test, Mann-Whitney U test, Kruskal-Wallis test, Spearman rank correlation test, or Kendall rank correlation test; the one-way anova includes: one-way ANOVA or t test; the mean test for variance heterogeneity comprises: t' test, mixed effect model or variance weighted least squares.
8. The metabolomics analysis method for targeting amino acids and acylcarnitines in blood according to claim 7, which comprises: the certain proportion in the step (2) is 4:1 to 5: 1; in the step (7), the 0-quantile point is on a ray, no data exists between the dots and the 0-quantile point, the 30-quantile point is on a ray, 30% of data exists between the 0-quantile point and the 30-quantile point, the 70-quantile point is on a ray, 70% of data exists between the 0-quantile point and the 70-quantile point, the 100-quantile point is on a ray, and 100% of data exists between the 0-quantile point and the 100-quantile point.
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