CN111721862A - Method for identifying depression energy metabolism abnormal pathway based on stable isotope tracing metabonomics - Google Patents
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Abstract
The invention belongs to the technical field of stable isotope tracing metabonomics, and provides a method for identifying an energy metabolism abnormal pathway of depression based on stable isotope tracing. The method provided by the invention has important scientific values for understanding the pathogenesis of depression and researching and developing novel antidepressant drugs, and provides a new method for researching and researching the action mechanism of the disease and the drug target and researching and developing new drugs.
Description
Technical Field
The invention belongs to the technical field of stable isotope tracing metabonomics, and particularly relates to a method for identifying an abnormal pathway of depression energy metabolism based on stable isotope tracing metabonomics. Based on the research of the technology on the abnormal pathway of the energy metabolism of the depression, a new way is developed to know the pathogenesis of the depression and discover the potential target of the disease.
Background
The serious hazard of depression to human life has attracted high attention, but because the pathogenesis is unknown and abnormally complex, the existing antidepressant drugs on the market developed based on the theory of monoamine neurotransmitters are not ideal, the drug control rate is only about 30%, although various pathogenesis recognitions such as neuroendocrine, neuroimmunity, neurotrophic factors, inflammation hypothesis and the like are developed, the ideal antidepressant drugs developed according to the pathogenesis hypotheses are not on the market. The method shows that another way is needed to know the pathological mechanism of depression from different visual angles, and is necessary to solve diagnosis and treatment of difficult and complicated diseases and to explore and discover a more ideal novel antidepressant drug.
In the early period, through conventional metabonomics, the concentration of metabolites in body fluid of both depression patients and chronic mild unpredictable stress (CUMS) model rats is remarkably changed, wherein various metabolites such as glucose, lactic acid, pyruvic acid, citric acid, alpha-ketoglutaric acid, succinic acid, aspartic acid, glutamic acid, palmitic acid, glycerol and the like are positioned on an energy metabolism pathway of a body and are related to tricarboxylic acid cycle (TCA), pyruvic acid metabolism, fatty acid metabolism and the like.
The stable isotope tracing technology is an effective tool required for metabolic pathway research, and the technology replaces radioactive isotopes with isotopes which naturally exist in organisms and have stable atomic nucleus structures, and is used as a tracer to accurately trace the activity rule of trace compounds in vivo. The stable isotope tracing technology is combined with the metabonomics technology, so that the defect that the conventional metabonomics can only detect the total amount of terminal metabolites and cannot distinguish which metabolic pathway the metabolites come from can be overcome.
Disclosure of Invention
The invention aims to provide a method for identifying an abnormal pathway of energy metabolism of depression based on stable isotope labeled metabonomics, a new method for recognizing pathogenesis of depression and discovering potential targets.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for identifying an abnormal pathway of energy metabolism of depression based on stable isotope tracing metabonomics is characterized in that stable isotope labeling is carried out on glucose which is an energy metabolism precursor substance, a normal-phase chromatographic column and a reverse-phase chromatographic column are utilized to comprehensively characterize energy metabolism compounds, and the exact metabolic pathway of the abnormal energy metabolism of depression is determined according to isotope distribution characteristics of metabolites at the downstream of glucose.
The method comprises the following specific steps:
(1) introducing an energy metabolism precursor substance marked by a stable isotope into a sample body of the depression to be detected, and collecting animal body fluid and tissue samples when a stable isotope signal reaches a steady state;
(2) adopting a high performance liquid chromatography-mass spectrometry combination (HPLC-MS full scan mode) to carry out comprehensive data acquisition on the energy metabolism compound, and comprehensively representing the energy metabolism compound through a normal phase chromatographic column HILIC and a reverse phase chromatographic column T3, wherein the energy metabolism compound comprises a polar metabolite and a nonpolar metabolite;
(3) importing the collected original data into Compound discover 3.0 software (Thermo Fisher scientific), and performing peak deconvolution, peak allocation, retention time alignment and normalized data processing; identifying the target metabolites by using a sample which is not marked by an isotope, and matching the target metabolites with an online database and a local database according to accurate m/z, retention time and secondary mass spectrum information to realize one-by-one identification of the target metabolites;
(4) constructing isotope labeling Workflow to perform data processing on a labeling sample, extracting an isotope feature list by means of Python software, analyzing the proportion of each isotope peak of labeled metabolites, determining the metabolic pathway of depression energy metabolism abnormality according to the isotope distribution features of key metabolites, and finding potential action targets of depression.
The stable isotope labeled energy metabolism precursor substance in the step (1) comprises:13c-glucose,15N-glutamine or13C-palmitic acid; the steady isotope signal reaches steady state as the isotope peak of the target metabolite tends to stabilize and no longer increases.
The mass spectrum in the step (2) is a high resolution mass spectrum Orbitrap, and the specific analysis conditions are as follows:
the normal phase chromatographic column is a SeQuant ZIC-cHILIC chromatographic column, 2.1 mm multiplied by 150 mm, 3 μm, Merck, USA, mobile phase A: 10 mM ammonium acetate aqueous solution pH =3.25, B: acetonitrile; the column temperature is 35 ℃, the flow rate is 0.3 mL/min, and the sample injection amount is 5 mul;
elution gradient: 0min, 95% of B, 0-8 min, 95-85% of B, 8-10 min, 85-81% of B, 10-22 min, 81-60% of B, 22-25 min, 60-95% of B, 22-25 min, 95% of B;
the ion source adopts HESI, and the ion mode is collected, and the mass spectrum parameters are as follows: 55 arbitrary units of sheath gas, 15 arbitrary units of auxiliary gas, 3arbitrary units of purge gas, spray voltage (-) 2.5 kV, the temperature of a capillary ion transmission tube is 320 ℃, the S-lens voltage is 65.0, the heating temperature is 450 ℃, the scanning range is 60-900 (m/z), the resolution is 35,000, the maximum injection time is 120 ms, and the target ion number is automatically controlled by gain 1e6 ions;
the reverse phase column was a Waters ACQUITYUPLC HSST3 column, 2.1 mm X100 mm, 1.8 μm, Waters, USA, mobile phase A: 0.1% formic acid water, B: 0.1% formic acid acetonitrile; the column temperature is 35 ℃, the flow rate is 0.2 mL/min, and the sample injection amount is 5 mul;
elution gradient: 0-2 min, 2% B, 2-3 min, 2-35% B, 3-28 min, 35-98% B, 28-30 min, 98% B, 30-32 min, 98-2% B, 32-34 min, 2% B;
the ion source adopts HESI, and the positive and negative ion switching mode is used for collection, and the mass spectrum parameters are as follows: 35 arbitraryunits of sheath gas, 10 arbitraryunits of auxiliary gas, 3.0 kV of spraying voltage (+), 2.7 kV of spraying voltage (-), 300 ℃ of capillary tube (ion transmission tube), 55.0 of S-lens voltage, 300 ℃ of heating temperature, 100-1500 (m/z) of scanning range, 35,000 of resolution, 120 ms of maximum injection time and 1e6 ions of automatic gain control target ion number.
The online database in step (3) is mzCloud, HMDB or KEGG.
The experimental system comprises cells, plants, animal models or human body experiments. As in the case of studying depression, labeled energy metabolism precursor substance13C6Glucose was introduced into depressed-like rats and samples were taken when the stable isotope signal reached steady state.
The mass spectrum in the step (2) is a high-resolution mass spectrum (such as Orbitrap and the like), the high-resolution mass spectrum is the key for acquiring high-quality mass spectrum data, and the accuracy of isotope peak identification is directly determined by accurate mass precision. Meanwhile, the energy metabolism compound is comprehensively characterized by adopting a normal phase chromatographic column (HILIC) and a reverse phase chromatographic column (T3), the metabolic pathways are complex and various, the variety of related metabolites is various, and the comprehensive characterization of the metabolic pathways is the key for ensuring the analysis of the subsequent metabolic pathways.
The stable isotope labeled metabonomics technology is used for carrying out isotope labeling on glucose serving as an energy metabolism precursor, comprehensive characterization of energy metabolism compounds is realized through a positive-phase chromatographic column and a reverse-phase chromatographic column, the metabolic pathway of abnormal energy metabolism of the depression is determined according to the isotope distribution characteristics of key metabolites, a new method is provided for researching the pathogenesis of diseases, and a new thought is provided for discovery of drug targets and research and development of new drugs.
Drawings
FIG. 1 is a stable isotope labeled metabonomics data processing flow;
FIG. 2 is a schematic diagram of the metabolic pathway of tracer metabolites, note: the underlined metabolites were significantly elevated in the model group compared to the blank group;
FIG. 3 is a key metabolite isotope distribution profile in which a, b, c, d, e, f, g, h, i, j, k, l are pyruvic acid, cysteine, glycerol-3-phosphocholine, dihydrothymine, fumaric acid, malic acid, aspartic acid, α -ketoglutaric acid, glutamic acid, γ -aminobutyric acid, ornithine and citrulline, respectively;
FIG. 4Is dihydrothymine13C, tracing a schematic diagram; in the figure: the open circles represent unlabeled carbon atoms: (12C) (ii) a The filled circles represent the labeled carbon atoms produced by PDH: (13C) (ii) a Filled squares represent labeled carbon atoms produced by PC: (13C）；
FIG. 5 shows cysteine and glycerocholine phosphate13C tracing principle diagram, wherein: the open circles represent unlabeled carbon atoms: (12C) (ii) a The filled circles represent the labeled carbon atoms produced by PDH: (13C) (ii) a Filled squares represent labeled carbon atoms produced by PC: (13C）。
Detailed Description
The invention is illustrated below with reference to specific examples. These examples are intended only to illustrate the invention and do not limit the scope of the invention in any way.
Example 1: the method for identifying the dysthymia energy metabolism abnormal pathway based on stable isotope tracing is characterized in that stable isotope labeling is carried out on energy metabolism precursor glucose, a normal-phase chromatographic column and a reverse-phase chromatographic column are utilized to comprehensively characterize energy metabolism compounds, and the exact metabolic pathway of dysthymia energy metabolism abnormal is determined according to the isotope distribution characteristics of glucose downstream metabolites.
The method comprises the following specific steps:
(1) the labeled energy metabolism precursor substance13C6Glucose is introduced into the bodies of the depressed rats and the blank control rats, samples are collected after 6 hours of continuous tail vein bolus injection, and unlabeled samples are collected before the introduction of the isotope tracer for subsequent identification.
(2) The method comprises the following steps of collecting data by adopting a high performance liquid chromatography-mass spectrometry (HPLC-MS) full-scan mode, and comprehensively representing energy metabolism compounds by using a normal phase chromatographic column (HILIC) and a reverse phase chromatographic column (T3), wherein the specific analysis conditions are as follows:
the normal phase column was a SeQuant ZIC-cHILIC column (2.1 mm. times.150 mm, 3 μm, Merck, USA), mobile phase A: 10 mM aqueous ammonium acetate (pH = 3.25), B: and (3) acetonitrile. The column temperature was 35 ℃, the flow rate was 0.3 mL/min, and the sample volume was 5. mu.l. Elution gradient: 0min, 95% of B, 0-8 min, 95-85% of B, 8-10 min, 85-81% of B, 10-22 min, 81-60% of B, 22-25 min, 60-95% of B, 22-25 min, 95% of B. The ion source adopts HESI, and the ion mode is collected, and the mass spectrum parameters are as follows: 55 arbitrary units of sheath gas, 15 arbitrary units of auxiliary gas, 3arbitrary units of purge gas, spray voltage (-) 2.5 kV, 320 ℃ of temperature of a capillary tube (an ion transmission tube), 65.0 of S-lens voltage, 450 ℃ of heating temperature, 60-900 (m/z) of scanning range, 35,000 of resolution, 120 ms of maximum injection time and 1e6 ions of automatic gain control target ion number.
Reverse phase column was a Waters ACQUITYUPLC HSST3 column (2.1 mm. times.100 mm, 1.8 μm, Waters, USA), mobile phase A: 0.1% formic acid water, B: 0.1% formic acid acetonitrile. The column temperature was 35 ℃, the flow rate was 0.2 mL/min, and the sample volume was 5. mu.l. Elution gradient: 0-2 min, 2% of B, 2-3 min, 2-35% of B, 3-28 min, 35-98% of B, 28-30 min, 98% of B, 30-32 min, 98-2% of B, 32-34 min and 2% of B. The ion source adopts HESI, and the positive and negative ion switching mode is used for collection, and the mass spectrum parameters are as follows: 35 arbitrary units of sheath gas, 10 arbitrary units of auxiliary gas, 3.0 kV of spraying voltage (+), 2.7 kV of spraying voltage (-), 300 ℃ of temperature of a capillary tube (an ion transmission tube), 55.0 of S-lens voltage, 300 ℃ of heating temperature, 100-1500 (m/z) of scanning range, 35,000 of resolution, 120 ms of maximum injection time and 1e6 ions of automatic gain control target ion number.
(3) And importing the collected original data into Compound discover 3.0 software, and performing data processing such as peak deconvolution, peak allocation, retention time alignment, normalization and the like. Identifying the target metabolites by using a sample before introducing an isotope tracer, and identifying the target metabolites one by one according to the matching of accurate m/z, retention time and secondary mass spectrum with an online database (mzCloud, HMDB, KEGG and the like) and a local database (User Library). The data processing (figure 1) is carried out on the marked sample by the self-constructed isotope marking Workflow, the isotope feature list is extracted by means of Python software, the proportion of each isotope peak of the marked metabolites is analyzed, and the specific path of the depression energy metabolism abnormality is determined according to the isotope distribution characteristics of key metabolites.
For chronic mild unpredictable stress CUMS rat serum was subjected to identification and isotope distribution profiling, and 617 metabolites were identified in total, of which1378C-labeled metabolites, the CUMS model group had a significant change in the concentration of 28 key metabolites compared to the blank group, with 4 key metabolites being significantly elevated (FIG. 2). The isotope peaks of the pyruvic acid of the model group are all increased remarkably, the isotope peaks of TCA intermediate metabolites downstream of the pyruvic acid are all lower than those of the blank group remarkably, and the isotope peaks of the dihydrothymine, the glycerophosphorylcholine and the cysteine upstream of the pyruvic acid are increased remarkably. Indicating that the elevation of pyruvate is due to too slow downstream metabolism rather than insufficient upstream sources, it is speculated that depression causes the TCA cycle to be hindered, and abnormally elevated pyruvate may be dismissed by gluconeogenesis or other pathways to synthesize other substances.
(4) By analyzing the isotope distribution characteristics (figure 3) of the key node metabolites, potential new targets of the depression are searched.
① model group dihydrothymine [ M +3 ]]The significant increase in the peak suggests that Pyruvate Carboxylase (PC) may have higher activity in depression. According to dihydrothymine13The tracing principle diagram (fig. 4) shows that:13C3direct production of pyruvate by PC13C3Oxaloacetic acid, to13C3Pyruvate Generation by PDH13C2Oxaloacetic acid. It is therefore speculated that depression may lead to PC activation. Oxaloacetate production by aspartate Aminotransferase (AST)13C3-an aspartic acid, a salt of a carboxylic acid,13C3production of aspartic acid in combination with carbamyl phosphate13C3Uracil nucleotides, which are then converted by methylation into13C3Thymine nucleotide, eventually hydrolyzed to yield13C3-thymine.
② Glycerol choline phosphate and cysteine [ M +1 ] in model group]The significant elevation of the peak suggests that depression may lead to activation of the gluconeogenic pathway, presumably depression may activate the first step reaction of the gluconeogenic pathway, i.e. Pyruvate Carboxylase (PC) and phosphoenolpyruvate carboxykinase (PEPC), may have higher activity in depression.Based on glycerocholine phosphate and cysteine13The tracing principle diagram (fig. 5) shows that: the increase in isotopic abundance of model group glycerocholine phosphate M1 indicates that,13C1pyruvic acid through13C1Indirect formation of dihydroxyacetone-phosphate13C1-glycerophospholipids, followed by13C1Production of glycerophospholipids13C1-egg phosphoric acid and13C1-glycerocholine phosphate. An increase in the isotopic abundance of cysteine M1 indicates that,13C1pyruvic acid through13C1Indirect production of glyceraldehyde-3-phosphate13C1-serine, then13C1Production of serine13C1-cysteine.
The results of the above studies indicate that the isotope distribution characteristics of key metabolites and the differential metabolites13The C tracing principle diagram can find abnormal pathways of depression energy metabolism. The depression possibly causes the TCA cycle to be blocked, abnormally increased pyruvate can participate in pyrimidine biosynthesis pathway, phospholipid synthesis pathway and amino acid metabolism pathway by activating gluconeogenesis pathway, and PC, AST and PEPC can be new targets of depression.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention should not be limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention, the technical solutions and their solutions disclosed in the present invention, and their equivalents and changes should be covered by the protection scope of the present invention.
Claims (5)
1. A method for identifying an energy metabolism abnormal pathway of depression based on stable isotope tracing is characterized in that: stable isotope labeling is carried out on energy metabolism precursor glucose, a normal-phase chromatographic column and a reverse-phase chromatographic column are utilized to comprehensively characterize energy metabolism compounds, and the exact metabolic pathway of depression energy metabolism abnormality is determined according to isotope distribution characteristics of glucose downstream metabolites.
2. The method for identifying pathways of energy metabolism disorder in depression based on stable isotope tracing as claimed in claim 1, wherein: the method comprises the following specific steps:
(1) introducing an energy metabolism precursor substance marked by a stable isotope into a sample body of the depression to be detected, and collecting animal body fluid and tissue samples when a stable isotope signal reaches a steady state;
(2) adopting a high performance liquid chromatography-mass spectrometry combination (HPLC-MS full scan mode) to carry out comprehensive data acquisition on the energy metabolism compound, and comprehensively representing the energy metabolism compound through a normal phase chromatographic column HILIC and a reverse phase chromatographic column T3, wherein the energy metabolism compound comprises a polar metabolite and a nonpolar metabolite;
(3) importing the collected original data into Compound discover 3.0 software (Thermo Fisher scientific), and performing peak deconvolution, peak allocation, retention time alignment and normalized data processing; identifying the target metabolites by using a sample which is not marked by an isotope, and matching the target metabolites with an online database and a local database according to accurate m/z, retention time and secondary mass spectrum information to realize one-by-one identification of the target metabolites;
(4) constructing isotope labeling Workflow to perform data processing on a labeling sample, extracting an isotope feature list by means of Python software, analyzing the proportion of each isotope peak of labeled metabolites, determining the metabolic pathway of depression energy metabolism abnormality according to the isotope distribution features of key metabolites, and finding potential action targets of depression.
3. The method for identifying pathways of energy metabolism disorder in depression based on stable isotope tracing as claimed in claim 2, wherein: the stable isotope labeled energy metabolism precursor substance in the step (1) comprises:13c-glucose,15N-glutamine or13C-palmitic acid; the steady isotope signal reaches steady state as the isotope peak of the target metabolite tends to stabilize and no longer increases.
4. The method for identifying pathways of energy metabolism disorder in depression based on stable isotope tracing as claimed in claim 2, wherein: the mass spectrum in the step (2) is a high resolution mass spectrum Orbitrap, and the specific analysis conditions are as follows:
the normal phase chromatographic column is a SeQuant ZIC-cHILIC chromatographic column, 2.1 mm multiplied by 150 mm, 3 μm, Merck, USA, mobile phase A: 10 mM ammonium acetate aqueous solution pH =3.25, B: acetonitrile; the column temperature is 35 ℃, the flow rate is 0.3 mL/min, and the sample injection amount is 5 mul;
elution gradient: 0min, 95% of B, 0-8 min, 95-85% of B, 8-10 min, 85-81% of B, 10-22 min, 81-60% of B, 22-25 min, 60-95% of B, 22-25 min, 95% of B;
the ion source adopts HESI, and the ion mode is collected, and the mass spectrum parameters are as follows: 55 arbitrary units of sheath gas, 15 arbitrary units of auxiliary gas, 3arbitrary units of purge gas, spray voltage (-) 2.5 kV, the temperature of a capillary ion transmission tube is 320 ℃, the S-lens voltage is 65.0, the heating temperature is 450 ℃, the scanning range is 60-900 (m/z), the resolution is 35,000, the maximum injection time is 120 ms, and the target ion number is automatically controlled by gain 1e6 ions;
the reverse phase column was a Waters ACQUITYUPLC HSST3 column, 2.1 mm X100 mm, 1.8 μm, Waters, USA, mobile phase A: 0.1% formic acid water, B: 0.1% formic acid acetonitrile; the column temperature is 35 ℃, the flow rate is 0.2 mL/min, and the sample injection amount is 5 mul;
elution gradient: 0-2 min, 2% B, 2-3 min, 2-35% B, 3-28 min, 35-98% B, 28-30 min, 98% B, 30-32 min, 98-2% B, 32-34 min, 2% B;
the ion source adopts HESI, and the positive and negative ion switching mode is used for collection, and the mass spectrum parameters are as follows: 35 arbitraryunits of sheath gas, 10 arbitraryunits of auxiliary gas, 3.0 kV of spraying voltage (+), 2.7 kV of spraying voltage (-), 300 ℃ of capillary tube (ion transmission tube), 55.0 of S-lens voltage, 300 ℃ of heating temperature, 100-1500 (m/z) of scanning range, 35,000 of resolution, 120 ms of maximum injection time and 1e6 ions of automatic gain control target ion number.
5. The method for identifying pathways of energy metabolism disorder in depression based on stable isotope tracing as claimed in claim 2, wherein: the online database in step (3) is mzCloud, HMDB or KEGG.
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