Urine marker for ischemic acute kidney injury as well as screening method and application of urine marker

Abstract

The invention relates to the technical field of biology, in particular to a urine marker for ischemic acute kidney injury as well as a screening method and application of the urine marker. The urine marker comprises a metabolism marker and a protein marker, the metabolism marker comprises Pyroglumic acid, L-Leucine, Mannitol, Homocysteine, [gamma]-Aminobubic acid, 4-Hydroxy-L-glumic acid, Dulcitol, Ophthemic acid, Biocytin, 2-oxo-4-methylidylthio-butymic acid, the protein marker comprises a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker and a protein marker, and the protein marker comprises the following components: a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker and a protein marker. The protein marker comprises the following components: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1 and HEBP2. The occurrence of the ischemic acute kidney injury can be early judged by noninvasive detection of the expression levels of the metabolic marker and the protein marker in urine of a patient suffering from a major cardiac surgery. The urine marker of the ischemic acute kidney injury is screened by adopting a mass spectrum multi-omics technology and a mathematical statistics method, theoretical support is provided for diagnosis and treatment of patients with the ischemic acute kidney injury, early discovery and early treatment of the patients with the ischemic acute kidney injury are realized, and the urine marker has great economic value and social benefit.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, in particular to urine markers related to ischemic acute kidney injury, and also relates to a screening method for urine markers of ischemic acute kidney injury, and the use of urine markers of ischemic acute kidney injury. Background Art

[0002] Acute kidney injury (AKI) is a common acute and critical illness in clinical practice, with a high incidence and great harm. It is an important cause of death and disability in critically ill patients, bringing a heavy economic burden to patients and society. Ischemia, major surgery, sepsis, drugs and poisons can all cause AKI, among which ischemic AKI caused by ischemia-reperfusion injury is the most common. Its pathogenesis is complex and there is a lack of effective treatment. The current diagnosis of AKI still follows the criteria used many years ago, that is, based on the increase in serum creatinine value and the decrease in urine volume. However, the increase in serum creatinine value and the decrease in urine volume are not early diagnostic indicators of AKI, that is, serum creatinine and urine volume often change after renal dysfunction has been determined. Therefore, predicting or early diagnosing AKI before the occurrence of renal dysfunction and cooperating with treatment are beneficial to reducing the risk of the disease.

[0003] A biomarker refers to a measurable and quantifiable biological parameter that reflects a physiological or pathological process. An ideal AKI biomarker should have high sensitivity, specificity and reproducibility, and be able to predict the risk of AKI onset before the occurrence of renal dysfunction, reflect the severity of renal injury and predict its consequences. In recent years, although some AKI biomarkers with potential clinical value have been discovered, including markers reflecting glomerular filtration rate, such as serum cystatin C, hepcidin, etc.; markers reflecting tubular epithelial cell injury or tissue injury reactivity, such as blood / urine neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, liver-type fatty acid-binding protein, etc.; markers related to inflammation, such as urinary interleukin-18, chemokine-activated protein, etc.; markers related to cell cycle arrest, such as urinary insulin-like growth factor-binding protein-7, tissue inhibitor of metalloproteinase-2, etc. However, the predictive ability of most potential biomarkers still needs to be clinically verified. Therefore, a screening method with high sensitivity and good specificity for potential markers of ischemic AKI needs to be established. Summary of the Invention

[0004] In view of the above deficiencies of the prior art, the purpose of the present invention is to provide a urine marker for ischemic acute kidney injury, its screening method and application, aiming to reduce the problem of false positives that easily occur in the screening of markers for ischemic acute kidney injury by single omics technology.

[0005] Mass spectrometry-based omics technologies can rapidly and efficiently analyze target molecules in complex matrices, with advantages such as high sensitivity and good repeatability. Metabolomics can obtain the final products of gene expression in batches, and proteomics can dynamically describe gene regulation. Therefore, the combined multi-omics analysis of metabolomics and proteomics helps to further reveal the internal mechanism of gene expression regulation and construct an overall regulatory network. Urine is a non-invasive and rich source of liquid, and urine biomarkers have become an attractive tool for early diagnosis, especially for the diagnosis of kidney diseases and the exploration of molecular mechanism changes in kidney pathology. In summary, the present invention combines metabolomics and proteomics for combined analysis, screens urine biomarkers for ischemic acute kidney injury through multi-omics technologies, provides theoretical support for the diagnosis and treatment of patients with ischemic acute kidney injury, and realizes early detection and early treatment of patients with ischemic acute kidney injury, with great economic value and social benefits.

[0006] The technical solution of the present invention is as follows:

[0007] In the first aspect of the present invention, there is provided a urine biomarker for ischemic acute kidney injury, wherein the urine biomarker includes a metabolic biomarker and a protein biomarker, and the metabolic biomarker includes: Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid; the protein biomarker includes: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1.

[0008] Optionally, the metabolic biomarker further includes: 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid, and the protein biomarker further includes: GLB1, HEBP2.

[0009] In the second aspect of the present invention, there is provided a screening method for the urine biomarker for ischemic acute kidney injury according to the present invention, which includes the following steps:

[0010] Step 1: Collect preoperative and postoperative urine samples from patients with ischemic acute kidney injury caused by cardiac major surgery;

[0011] Step 2: Centrifuge the preoperative and postoperative urine samples respectively and collect the supernatant, and use ultra-high performance liquid chromatography-high resolution mass spectrometry to measure the metabolites and proteins in the supernatant of different groups to obtain the urine substance analysis map before and after the occurrence of ischemic acute kidney injury;

[0012] Step 3: Use bioinformatics analysis software to preprocess the analysis spectra of urine substances to obtain omics data;

[0013] Step 4: Based on the untargeted metabolomics analysis method, screen for differential metabolites and their metabolic pathways through multivariate statistical analysis and univariate statistical analysis;

[0014] Step 5: Based on the proteomics analysis method, screen for differential proteins and their metabolic pathways through visualization methods and enriched pathway analysis;

[0015] Step 6: Based on the correlation analysis method of metabolomics and proteomics, perform a common enrichment pathway analysis of the differential metabolites obtained from metabolomics and the differential proteins obtained from proteomics through biological function combination, identify the metabolic pathways most relevant to ischemic acute kidney injury, label the differential metabolites and differential proteins participating in the same metabolic pathway, and finally use the differential metabolites and differential proteins on the main metabolic pathway as the urine markers for ischemic acute kidney injury.

[0016] Optionally, the urine in Step 1 is from patients with ischemic acute kidney injury caused by cardiac and great vessel surgery, and the cardiac and great vessel surgery includes one of coronary artery bypass surgery, cardiac valve surgery, heart transplantation, aortic surgery, and congenital heart disease surgery; meanwhile, the patients do not meet the following characteristics: having advanced chronic kidney disease, being over 80 years old, having malignant tumors, and having been clearly diagnosed with ischemic acute kidney injury before surgery.

[0017] Optionally, the data preprocessing in Step 3 includes peak extraction, peak identification, peak matching, peak alignment, reference substance comparison, and normalization.

[0018] Optionally, the differential proteins in Step 5 include: BAX, CADM1, CD14, CD276, CD40, CDH2, CDH5, CILP2, CNTFR, CNTN1, DLK1, EGF, FOLR1, GAS6, GGH, GLG1, GRB2, ICOSLG, IGFBP1, IGFBP2, IGFBP3, IGHG2, IGHM, IGHV1-8, IGHV3-72, IGHV4-34, IL10RB, IL2RB, IL6R, JAM3, MADCAM1, MMP9, MMRN1, MST1, MXRA8, NCSTN, NEGR1, NEO1, NRXN3, PDGFRB, PECAM1, PIK3IP1, PLAU, PTPRM, PVR, SHMT1, SNED1, SPINT2, TMPRSS2, VASN.

[0019] In a third aspect of the present invention, there is provided an application of the urinary biomarker for ischemic acute kidney injury described in the present invention in the preparation of an early diagnostic reagent for ischemic acute kidney injury.

[0020] In a fourth aspect of the present invention, there is provided an early diagnostic reagent for ischemic acute kidney injury, which comprises the urinary biomarker for ischemic acute kidney injury described in the present invention.

[0021] In a fifth aspect of the present invention, there is provided an application of the urinary biomarker for ischemic acute kidney injury described in the present invention in the preparation of an early diagnostic kit for ischemic acute kidney injury.

[0022] In a sixth aspect of the present invention, there is provided an early diagnostic kit for ischemic acute kidney injury, which comprises the urinary biomarker for ischemic acute kidney injury described in the present invention.

[0023] Beneficial effects: The present invention provides a urinary biomarker for ischemic acute kidney injury. Compared with the biomarkers for ischemic acute kidney injury screened by single omics technology (210 potential metabolic biomarkers and 306 potential protein biomarkers) which are prone to false positive problems, the combined application of metabolomics and proteomics can effectively reduce the false positive problems of the screening results (among them, 10 metabolic biomarkers and 15 protein biomarkers). The urinary biomarker includes metabolic biomarkers and protein biomarkers. The metabolic biomarkers include: Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid, 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid; The protein biomarkers include: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1, HEBP2. By non-invasively detecting the expression levels of the above-mentioned metabolic biomarkers and protein biomarkers in the urine of patients undergoing cardiac surgery, the occurrence of ischemic acute kidney injury can be judged early. The present invention also provides a method for screening urinary biomarkers for ischemic acute kidney injury by using mass spectrometry multi-omics technology and mathematical statistics methods, which provides theoretical support for the diagnosis and treatment of patients with ischemic acute kidney injury, realizes early detection and early treatment of patients with ischemic acute kidney injury, and has great economic value and social benefits. Description of the Drawings

[0024] Figure 1Total ion chromatogram of quality control samples of urine metabolites in patients with ischemic AKI caused by cardiac surgery; among them, A is the positive ion mode and B is the negative ion mode.

[0025] Figure 2 In A and B are the OPLS-DA graphs and volcano plots of urine metabolites in patients with ischemic AKI caused by cardiac surgery respectively.

[0026] Figure 3 Enrichment pathway analysis graph of differential proteins in urine of patients before and after the occurrence of ischemic AKI caused by cardiac surgery; among them, A is map04330 (Notch signaling pathway), map04630 (Jak-STAT signaling pathway), map01521 (EGFR tyrosine kinase inhibitor resistance), and B is map01523 (Antifolateresistance), map04514 (Cell adhesion molecules), map05202 (Transcriptionalmisregulation in cancer); purple represents the enrichment pathway, red represents up-regulated proteins, and blue represents down-regulated proteins.

[0027] Figure 4 Bubble chart of pathway enrichment analysis of differential metabolites and differential proteins; in the figure, triangles represent metabolic index pathways, circles represent protein index pathways, the size of the graph represents the number of differential metabolites and differential proteins annotated to this pathway, and the color of the graph represents the pathway significance.

[0028] Figure 5 Spearman correlation analysis of differential metabolites and differential proteins; the abscissa represents differential metabolites and the ordinate represents differential proteins. The matrix color block represents the Spearman rank correlation number. The darker the color, the stronger the correlation; red represents positive correlation and blue represents negative correlation; *P<0.05, **P<0.01. Detailed implementation mode

[0029] The present invention provides a urine biomarker for ischemic acute kidney injury, its screening method and application. To make the purpose, technical solution and effect of the present invention clearer and more definite, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] The embodiments of the present invention provide a urine biomarker for ischemic acute kidney injury. Among them, the urine biomarker includes a metabolic biomarker and a protein biomarker. The metabolic biomarker includes: Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid, 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid; the protein biomarker includes: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1, HEBP2.

[0031] By non-invasively detecting the expression levels of the above-mentioned metabolic biomarkers and protein biomarkers in the urine of patients undergoing major cardiac surgery, the occurrence of ischemic acute kidney injury can be judged early, providing theoretical support for the diagnosis and treatment of patients with ischemic acute kidney injury, realizing early detection and early treatment of patients with ischemic acute kidney injury, and having great economic value and social benefits.

[0032] The embodiments of the present invention also provide a screening method for the urine biomarker of ischemic acute kidney injury as described above, which includes the following steps:

[0033] Step 1: Collect the urine samples before and after surgery of patients with ischemic acute kidney injury caused by major cardiac surgery, where the urine sample before surgery is equivalent to the baseline control group;

[0034] Step 2: Centrifuge the urine samples before and after surgery respectively and collect the supernatant. Use ultra-high performance liquid chromatography-high resolution mass spectrometry to determine the metabolites and proteins in the supernatant of different groups, and obtain the urine substance analysis map before and after the occurrence of ischemic acute kidney injury;

[0035] Step 3: Use bioinformatics analysis software to perform data preprocessing on the analysis map of urine substances, such as peak extraction, peak identification, peak matching, peak alignment, reference substance comparison and normalization, etc., to obtain omics data including retention time, mass-to-charge ratio, peak area and mass spectrometry secondary fragmentation;

[0036] Step 4: Based on the non-targeted metabolomics analysis method, screen for differential metabolites and their metabolic pathways and regulatory networks through multivariate statistical analysis and univariate statistical analysis;

[0037] Step 5: Based on the proteomics analysis method, screen for differential proteins and their metabolic pathways and regulatory networks through univariate statistical analysis, visualization method and enriched pathway analysis;

[0038] Step 6: Based on the correlation analysis method of metabolomics and proteomics, perform pathway analysis of co-enrichment of biological functions on the differential metabolites obtained from metabolomics and the differential proteins obtained from proteomics, identify the metabolic pathways most relevant to ischemic acute kidney injury, label the differential metabolites and differential proteins participating in the same metabolic pathway, and finally use the differential metabolites and differential proteins on the main metabolic pathways as the urine markers for ischemic acute kidney injury.

[0039] In the embodiment of the present invention, mass spectrometry multi-omics technology and mathematical statistics methods are used to screen urine markers for ischemic acute kidney injury, providing theoretical support for the diagnosis and treatment of patients with ischemic acute kidney injury, realizing early detection and early treatment of patients with ischemic acute kidney injury, and having great economic value and social benefits. The markers in the embodiment of the present invention are derived from urine because urine is easy to collect, rich in potential markers, and can reflect numerous biochemical pathways in the body.

[0040] Further, the urine in Step 1 is from patients with ischemic acute kidney injury caused by cardiac and major vascular surgeries, and cardiac and major vascular surgeries include but are not limited to coronary artery bypass surgery, cardiac valve surgery, heart transplantation, aortic surgery, and congenital heart disease surgery; meanwhile, such patients do not meet the following characteristics: having advanced chronic kidney disease (end-stage renal disease, kidney transplantation), being over 80 years old, having a malignant tumor, and having been clearly diagnosed with AKI before surgery.

[0041] Further, the differential proteins related to ischemic AKI in Step 5 include BAX, CADM1, CD14, CD276, CD40, CDH2, CDH5, CILP2, CNTFR, CNTN1, DLK1, EGF, FOLR1, GAS6, GGH, GLG1, GRB2, ICOSLG, IGFBP1, IGFBP2, IGFBP3, IGHG2, IGHM, IGHV1-8, IGHV3-72, IGHV4-34, IL10RB, IL2RB, IL6R, JAM3, MADCAM1, MMP9, MMRN1, MST1, MXRA8, NCSTN, NEGR1, NEO1, NRXN3, PDGFRB, PECAM1, PIK3IP1, PLAU, PTPRM, PVR, SHMT1, SNED1, SPINT2, TMPRSS2, VASN.

[0042] Furthermore, the urine markers related to ischemic AKI reflected by the correlation analysis of metabolomics and proteomics in step 6 include Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid, 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid, GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1, HEBP2.

[0043] An embodiment of the present invention provides an application of the urine markers of ischemic acute kidney injury as described above in the preparation of early diagnostic reagents for ischemic acute kidney injury.

[0044] An embodiment of the present invention provides an early diagnostic reagent for ischemic acute kidney injury, which includes the urine markers of ischemic acute kidney injury as described above.

[0045] An embodiment of the present invention provides an application of the urine markers of ischemic acute kidney injury as described above in the preparation of early diagnostic kits for ischemic acute kidney injury.

[0046] An embodiment of the present invention provides an early diagnostic kit for ischemic acute kidney injury, which includes the urine markers of ischemic acute kidney injury as described above.

[0047] The present invention will be further described below through specific embodiments.

[0048] Example 1

[0049] 1. Sample collection

[0050] Forty urine samples before and after surgery were collected from 20 patients with ischemic AKI caused by cardiac surgery. According to the classification of AKI by Kidney Disease: Improving Global Outcomes (KDIGO) in 2012, the patients included those with grades 1-3 of AKI.

[0051] The above patients did not meet the following characteristics: having advanced chronic kidney disease (end-stage renal disease, kidney transplantation), being over 80 years old, having malignant tumors, and having a clearly diagnosed AKI before surgery.

[0052] 2. Metabolomics

[0053] (1) Pretreatment of urine samples

[0054] Take 100 μL of urine sample and directly add 300 μL of pre-cooled methanol and acetonitrile (2:1, v / v) for dilution and mix with 10 μL of internal standard (Leucine-d3, 13 C9-Phenylalanine, Tryptophan-d5, 13 C3-Progesterone). After vortexing for 1 minute, incubate at -20 °C for 2 hours, centrifuge at 4000 g for 20 minutes, and then transfer the supernatant for vacuum freeze-drying. The dried supernatant was redissolved in 150 μL of 50% methanol-water (v:v) solution, centrifuged at 4000 g for 30 minutes, and the supernatant was subjected to ultra-high performance liquid chromatography-high resolution mass spectrometry analysis. In addition, 10 μL of each sample was taken for mixing to obtain a quality control sample for evaluating the repeatability and stability of the liquid chromatography-mass spectrometry data acquisition process.

[0055] (2) Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis

[0056] The bench-top Orbitrap mass spectrometer was connected to an ESI source. The chromatographic column was Waters ACQUITY UHPLC BEH C18 (1.7 μm, 2.1 mm × 100 mm, Waters, USA). The flow rate was 0.35 mL / min, the injection volume was 5 μL, and the column temperature was 45 °C. The mobile phase composition in the positive ion mode was (A) aqueous solution containing 0.1% formic acid and (B) methanol containing 0.1% formic acid. The mobile phase composition in the negative ion mode was (A) 10 mM ammonium formate aqueous solution and (B) methanol-aqueous solution of 10 mM ammonium formate (95:5, v:v). The elution gradient was: 0 - 1 min, 2% B; 1 - 9 min, 2% - 98% B; 9 - 12 min, 98% B; 12 - 12.1 min, 98% - 2% B; 12.1 - 15 min, 2% B.

[0057] The mass spectrometry settings were as follows: spray voltage, 3.8 kV in the positive ion mode and 3.2 kV in the negative ion mode; spray gas flow rate was 40 arb; auxiliary gas flow rate was 10 arb; auxiliary gas heater temperature was 350 °C; capillary temperature was 320 °C. The full scan range was set to 70 - 1050 m / z, the high resolution was 70000, AGC was 3e6, and the maximum ion injection time was 100 ms. The top 3 precursor ions with the highest abundance were selected for subsequent MS / MS fragmentation, the maximum ion injection time was 50 ms, the resolution was 30000, and AGC was 1e5. The collision energies were 20, 40, and 60 eV. To reduce systematic errors, the samples were randomly sorted, and one quality control sample was inserted among every 10 samples.

[0058] (3) Metabolite biomarker screening and visualization analysis

[0059] ①Metabolite biomarker screening

[0060] The raw mass spectrometry data is imported into Compound Discovery software for data preprocessing, including peak extraction, retention time correction, missing value filling, background peak marking, metabolite identification, etc. (such as Figure 1 ). The databases for metabolite identification include BGI database, mzCloud, and ChemSpider (HMDB, KEGG, LipidMaps), etc. The identified data is then imported into metaX for data preprocessing and subsequent analysis. Data preprocessing includes: performing normalization to obtain relative peak areas; correcting batch effects in the data; calculating the coefficient of variation (CV) of the relative peak areas in all quality controls, and deleting ion peaks with a CV greater than 30%.

[0061] ②Visual identification

[0062] After the data is subjected to Log2 logarithmic transformation and normalization, differential analysis is performed, including OPLS-DA plots for multivariate statistical analysis and volcano plots for univariate statistical analysis (such as Figure 2 shown as A and B in). Metabolites that simultaneously meet the criteria of VIP value ≥ 1, (|FC|) ≥ 1.2, and p value < 0.05 are selected as differential metabolites. By comparing databases such as HMDB, and using the KEGG database for pathway annotation, a total of 210 differential metabolites are identified.

[0063] 3. Proteomics

[0064] (1) Pretreatment of urine samples

[0065] ①Transfer approximately 5 mL of urine sample to a 50 mL centrifuge tube and add 25 mL of pre-cooled acetone. Place it at -20 °C overnight. After centrifuging the urine sample at 25000 g and 4 °C for 15 minutes, discard the supernatant of each sample. Add an appropriate amount of SDS-free protein lysate to the precipitated urine sample and use an automatic grinder to promote the dissolution of urinary proteins. After centrifuging at 25000 g at 4 °C, collect the supernatant, add DTT with a final concentration of 10 mM, incubate in a 37 °C water bath for 30 minutes, then add IAM with a final concentration of 55 mM to the reduced sample, and let the reduced sample stand overnight at room temperature in the dark. Finally, use the Bradford assay kit to determine the protein concentration.

[0066] ②Trypsin digestion of urinary proteins. 100 μg of protein solution is mixed with 4 volumes of 50 mM NH 4 HCO 3Solution dilution. According to the mass ratio of protein:enzyme of 40:1, 2.5 μg of trypsin was added to the above solution. After digesting the protein solution at 37 °C for 4 hours, the enzymatic hydrolysate was desalted and vacuum dried using a Strata X column. The Shimadzu LC-20AB high-performance liquid chromatography system was used to perform fractional enrichment of the peptide samples after enzymatic hydrolysis.

[0067] (2) Nano-Ultra Performance Liquid Chromatography-High Resolution Mass Spectrometry Analysis

[0068] ① The Orbitrap Exploris 480 system was used for peptide analysis and protein quantification of the urine proteome. The liquid chromatograph was a Thermo 3000 UHPLC equipped with a nano-ESI ion source. Data-dependent acquisition (DDA) library construction: The fractionally dried peptide samples were dissolved with the initial mobile phase (2% ACN, 0.1% FA aqueous solution). The flow rate for DDA mode detection was 500 nL / min, and the liquid phase gradient was as follows: 0 - 5 minutes, 5% mobile phase B (98% ACN, 0.1% FA); 5 - 120 minutes, 5% - 25% B; 120 - 160 minutes, 25% - 35% B; 160 - 170 minutes, 35% - 80% B; 170 - 175 minutes, 80% B; 175 - 175.5 minutes, 80% - 5% B; 175.5 - 180 minutes, 5% B.

[0069] The main parameter settings of the mass spectrometer were as follows: the ion source voltage was 1.9 kV; the MS1 scan range was m / z 350 - 1650; the MS1 resolution was 120000; the maximum injection time was 90 milliseconds; the MS / MS collision type was HCD; the collision energy NCE = 30; the MS / MS resolution was 30000; the dynamic exclusion duration was 120 seconds. The AGC was set to 300% for MS and 100% for MS / MS. The precursor peptide ions for MS / MS scans met the requirements: the charge range was from 2+ to 6+, and the top 30 precursor ions with an intensity > 2e4.

[0070] ② Protein quantification by data-independent acquisition (DIA): The dried urine protein peptide samples were re-dissolved with mobile phase A (2% ACN, 0.1% FA), injected into the nanoLC-MS system and analyzed at a flow rate of 500 nL / min. The gradient program for liquid phase elution was as follows: 0 - 5 minutes, 5% mobile phase B (98% ACN, 0.1% FA); 5 - 90 minutes, 5% - 25% B; 90 - 100 minutes, 25% - 35% B; 100 - 108 minutes, 35% - 80% B; 108 - 113 minutes, 80% B; 113 - 113.5 minutes, 80% - 5% B; 113.5 - 120 minutes, 5% B. The MS parameters in DIA mode were set as follows: power supply voltage 1.9 kV, MS1 scan range m / z 400 - 1250; the resolution of MS1 was 120,000; MIT 90 milliseconds. For MS / MS, the scan range was m / z 400 - 1250, and the fragmented ions were scanned in 50 consecutive windows equally, with a resolution of 30,000. The MS / MS collision type was HCD, the collision energy NCE was set to 30, MIT was in automatic mode, and AGC was set to 300% for MS and 1000% for MS / MS respectively.

[0071] (3) Screening and visualization analysis of protein markers

[0072] ① Screening of protein markers

[0073] In the protein identification of DDA, the MaxQuant software was used for peptide identification, and the retrieved database was uniprot_homosapiens_irt.fasta. Trypsin was used for peptide cleavage of proteins, allowing a maximum of two missed cleavages. Methyl carbamate was used for the fixed modification of cysteine (C) sites, and methionine (M), acetyl group (N-terminal protein), glutamine (Q) were converted to pyroglutamic acid (N-terminal Q), and deamidation (NQ) was used for possible modification of peptides. The false discovery rate for peptide map matching was set to 1%, and the shortest peptide length was seven amino acids. The DIA MS data was processed by the software Spectronaut, and the retention time (RT) was corrected by iRT peptides. The mass spectrometry used consecutive windows to acquire fragmented ions, and the false positive control was set to 1%. The differential urine proteome was screened by the MSstats software package, and the differential proteins were screened after normalization. Among them, (|FC|) ≥ 2 and p value < 0.05 were used as the judgment criteria for significant differences, and a total of 306 differential proteins were identified.

[0074] ② Visual identification

[0075] The differential urine proteome was subjected to visualization analysis, and KEGG was used for enrichment pathway analysis of differential proteins (such as Figure 3)。Enriched metabolic pathways include: Notch signaling pathway (map04330), Jak-STAT signaling pathway (map04630), EGFR tyrosine kinase inhibitor resistance (map01521), Antifolate resistance (map01523), Cell adhesion molecules (map04514), Transcriptional misregulation in cancer (map05202); Enriched differential proteins include: BAX, CADM1, CD14, CD276, CD40, CDH2, CDH5, CILP2, CNTFR, CNTN1, DLK1, EGF, FOLR1, GAS6, GGH, GLG1, GRB2, ICOSLG, IGFBP1, IGFBP2, IGFBP3, IGHG2, IGHM, IGHV1-8, IGHV3-72, IGHV4-34, IL10RB, IL2RB, IL6R, JAM3, MADCAM1, MMP9, MMRN1, MST1, MXRA8, NCSTN, NEGR1, NEO1, NRXN3, PDGFRB, PECAM1, PIK3IP1, PLAU, PTPRM, PVR, SHMT1, SNED1, SPINT2, TMPRSS2, VASN. The above differential proteins may be potential urine markers for ischemic AKI.

[0076] 4. Integrated analysis of metabolomics and proteomics

[0077] Using KEGG database annotation and visual analysis of KEGG pathways, the differential metabolites obtained from metabolomics and the differential proteins obtained from proteomics were integrated to obtain a bubble chart of pathway enrichment analysis (as Figure 4 ). It can be seen from the figure that the metabolic pathways most relevant to ischemic AKI include glutathione metabolism, galactose metabolism, fructose and mannose metabolism, estrogen signaling pathway, cysteine and methionine metabolism, central carbon metabolism in cancer, biotin metabolism, biosynthesis of amino acids, arginine and proline metabolism, and antifolate resistance pathway. Table 1 lists the differential metabolites and differential proteins on the co-annotated metabolic pathways, Figure 5 indicating the Spearman correlation analysis of differential metabolites and differential proteins.

[0078] Table 1. Differential metabolites and differential proteins on the co-annotated metabolic pathways

[0079]

[0080] The metabolites and proteins involved in the same metabolic pathway are labeled, and finally, the differentially expressed metabolites and proteins annotated on the KEGG pathway together are used as urine markers for ischemic AKI. After analysis, the metabolic markers among the above urine markers are: Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid, 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid; the protein markers are: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1, HEBP2. Among them, the metabolic markers Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid are consistent with the reference substances.

[0081] Therefore, the differential protein-protein associated molecules and differential metabolite-protein associated molecules may be potential urine markers for ischemic AKI, which can be used as specific molecular targets for ischemic AKI and applied to the early diagnosis and evaluation of ischemic AKI, as well as the prevention and treatment of AKI diseases.

[0082] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or changes can be made according to the above description, and all such improvements and changes should fall within the protection scope of the appended claims of the present invention.

Claims

1. A urine marker for ischemic acute kidney injury, characterized in that: The urine markers include metabolic markers and protein markers. The metabolic markers include: Pyroglutamic acid, L-Leucine, Mannitol, Homocysteine, γ-Aminobutyric acid; the protein markers include: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1.

2. The urine marker for ischemic acute kidney injury according to claim 1, characterized in that The metabolic markers also include: 4-Hydroxy-L-glutamic acid, Dulcitol, Ophthalmic acid, Biocytin, 2-oxo-4-methylthio-butanoic acid; the protein markers also include: GLB1, HEBP2.

3. A method for screening urine markers for ischemic acute kidney injury according to claim 1, characterized in that: The following steps are involved: Step 1: Collect urine samples before and after surgery from patients with ischemic acute kidney injury caused by major cardiac surgery; Step 2: The preoperative urine samples and the postoperative urine samples are centrifuged respectively to collect the supernatants, and the metabolites and proteins in the supernatants of different groups are determined by ultra-high performance liquid chromatography-high resolution mass spectrometry to obtain the urine material analysis spectrum before and after the occurrence of ischemic acute kidney injury; Step 3: Use bioinformatics analysis software to preprocess the analytical spectra of urine substances to obtain omics data; Step 4: Based on the non-targeted metabolomics analysis method, differential metabolites and their metabolic pathways were screened through multivariate statistical analysis and univariate statistical analysis; Step 5: Based on proteomics analysis methods, differentially expressed proteins and their metabolic pathways were screened through visualization methods and enrichment pathway analysis; Step 6: Based on the association analysis method of metabolomics and proteomics, the differential metabolites obtained by metabolomics and the differential proteins obtained by proteomics are analyzed by common enrichment pathways combined with biological functions to identify the metabolic pathways most relevant to ischemic acute kidney injury, and the differential metabolites and differential proteins involved in the same metabolic pathway are annotated. Finally, the differential metabolites and differential proteins on the main metabolic pathways are used as urine markers for the ischemic acute kidney injury.

4. The method for screening urine markers for ischemic acute kidney injury according to claim 3, characterized in that: The urine in step 1 comes from a patient with ischemic acute kidney injury caused by cardiac and great vascular surgery, wherein the cardiac and great vascular surgery includes one of coronary artery bypass surgery, heart valve surgery, heart transplantation, aortic surgery, and congenital heart disease surgery; and at the same time, the patient does not meet the following characteristics: Patients with advanced chronic kidney disease, over 80 years old, malignant tumors, or confirmed ischemic acute kidney injury before surgery.

5. The method for screening urine markers for ischemic acute kidney injury according to claim 3, characterized in that: The data preprocessing described in step 3 includes peak extraction, peak identification, peak matching, peak alignment, reference comparison and normalization.

6. The method for screening urine markers for ischemic acute kidney injury according to claim 3, characterized in that: The differential proteins described in step 5 include: BAX, CADM1, CD14, CD276, CD40, CDH2, CDH5, CILP2, CNTFR, CNTN1, DLK1, EGF, FOLR1, GAS6, GGH, GLG1, GRB2, ICOSLG, IGFBP1, IGFBP2, IGFBP3, IGHG2, IGHM, IGHV1-8, IGHV3-72, IGHV4-34, IL10RB, IL2RB, IL6R, JAM3, MADCAM1, MMP9, MMRN1, MST1, MXRA8, NCSTN, NEGR1, NEO1, NRXN3, PDGFRB, PECAM1, PIK3IP1, PLAU, PTPRM, PVR, SHMT1, SNED1, SPINT2, TMPRSS2, and VASN.

7. Use of the urine marker of ischemic acute kidney injury according to claim 1 in the preparation of an early diagnostic reagent for ischemic acute kidney injury.

8. An early diagnostic reagent for ischemic acute kidney injury, characterized in that: Comprising the urine marker of ischemic acute kidney injury as described in claim 1.

9. Use of the urine marker of ischemic acute kidney injury according to claim 1 in preparing an early diagnosis kit for ischemic acute kidney injury.

10. A kit for early diagnosis of ischemic acute kidney injury, characterized in that: Comprising the urine marker of ischemic acute kidney injury as described in claim 1.

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