Serum biomarkers for screening or detection of growth hormone injection and their application

Abstract

The present invention discloses a serum biomarker for screening or detecting growth hormone injection and its application; the serum biomarker includes 20 different metabolites, and by analyzing the expression of the metabolite group, it can be determined whether the sample has been injected with growth hormone. The present invention also provides a screening method for the biomarker group, which uses metabolomics technology to analyze human blood samples to obtain a metabolite combination related to growth hormone injection. The screening method of the present invention has wide universality, high accuracy, and good stability, and is expected to be used for screening growth hormone injection.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular, relates to a serum biomarker for detecting growth hormone injection-related biomarkers and an application thereof. Background Art

[0002] Growth hormone (GH) is an endogenous peptide hormone that has the functions of protein synthesis and lipid catabolism. It plays a role in regulating the growth and metabolism of the body and promoting bone growth and skeletal muscle protein synthesis. GH is widely used in the clinical treatment of dwarfism caused by growth hormone deficiency. At the same time, due to the biological role of GH in promoting synthesis and its difficult detection characteristics, GH has also attracted great attention among athletes and fitness enthusiasts. Many people regard GH as an athletic performance enhancer, which leads to the random intake of GH. However, excessive intake of GH may cause some side effects, such as increasing the risk of cardiovascular and cerebrovascular diseases and diabetes. Therefore, it is necessary to establish a set of appropriate detection methods and standards to avoid the harm of excessive GH intake to human health.

[0003] So far, the methods for detecting GH injection include direct detection and indirect detection. The direct detection method includes directly measuring the content of circulating GH in the blood, and also includes separately measuring the content of 22kDa GH and total GH and calculating the ratio of the two to determine whether the subject has injected GH (J Clin Endocrinol Metab. 2001; 86 (4): 1731-1737). However, the characteristics of GH rhythmic secretion and the short detection window period greatly limit the application of direct measurement methods. The indirect detection method uses immunoassay method, using GH-sensitive biomarkers IGF-I and P-III-NP as detection objects (J Clin Endocrinol Metab. 2014; 99 (6): 2259-2268). Compared with GH, the half-life of this biomarker in the body is longer, but studies have shown that the P-III-NP content is affected by injuries and diseases and lasts for 2-3 weeks. This means that there is an urgent need to find suitable markers to detect GH injection.

[0004] With the development of high-sensitivity and high-throughput mass spectrometers, omics, especially metabolomics, has become a new method for finding and discovering metabolic biomarkers when the human body is stimulated by diseases or drugs, and is widely used in the study of disease mechanisms and the search for biomarkers. Amna Al-Nesf et al. used metabolomics analysis methods to screen out 7 and 2 metabolites from blood and urine, respectively, to form a biomarker panel that can be used to detect potential autologous blood transfusion in subjects. Compared with a single marker, the detection window of this method is extended by 21 times (7d Vs.8h), and false positives that may be caused by infection or hemolysis are avoided (Metabolites. 2022; 12(5):425). Sock-Hwee Tan et al. conducted a proteomic study on blood samples from healthy men taking GH and placebo, and found eight GH-sensitive proteins in plasma that could serve as candidate biomarkers, but two of them, IGFBP3 and IGF-ALS, were significantly reduced immediately after GH was discontinued (SciRep. 2017; 7(1): 10039).

[0005] The above literature reports indicate the potential application of biomarker combinations in hormone drug detection, and metabolites, as the end products of life activities, are more stable than proteins. Currently, there is a lack of research on metabolite biomarker panels for GH detection alone. Summary of the invention

[0006] The purpose of the present invention is to provide a group of metabolite markers based on serum metabolomics, and the use of the product in the detection of growth hormone injection, so as to solve the problems existing in the above-mentioned prior art. The marker group can detect whether the subject has injected growth hormone by liquid chromatography-mass spectrometry, and has the detection characteristics of high sensitivity, high accuracy and strong anti-interference.

[0007] The technical solution of the present invention is specifically described as follows.

[0008] The invention discloses an application of serum biomarkers in preparing a kit for detecting growth hormone injection. The kit is used to detect whether growth hormone has been injected. The kit comprises serum biomarkers, and the serum biomarkers comprise the following 20 metabolites: 2-hydroxy-8-methylchromene-2-carboxylate, L-glutamine, betaine, xanthine, uridine, indoleacetic acid, glycodeoxycholate, nicotinic acid, oleic acid, L-cystine, 3-oxo-6-acetamidohexanoate, glycochenodeoxycholate, 2-hydroxy-4-(1-oxo-1,3-dihydro-indene-2-ylidene)-but-2-enoic acid, LO-phosphoserine, trans-cinnamic acid, 2,2-enoursodeoxycholic acid, 2,2-enoursodeoxycholic acid, L-histidine, biliverdin and 2-aminobut-2-enoate.

[0009] In the present invention, during the detection, the expression level of metabolic markers in the serum of the subject is used as an evaluation index. When the expression levels of 3-oxo-6-acetamidohexanoate, 22-enoursodeoxycholic acid, glycochenodeoxycholate, glycodeoxycholate, betaine, L-histidine, primary oleic acid, xanthine, biliverdin, and L-glutamine in the serum sample to be tested are upregulated, and at the same time, the expression levels of 2-aminobut-2-enoate, L-β-phenylalanine, 2-hydroxy-3-methylphenylpyruvate, uridine, 2-hydroxy-8-methylchromene-2-carboxylate, trans-cinnamic acid, indoleacetic acid, L-cystine, nicotinic acid, and 2-hydroxy-4-(1-oxo-1,3-dihydro-indene-2-ylidene)-but-2-enoic acid are downregulated, it means that the sample is a sample injected with growth hormone, that is, it is determined that the subject has been injected with growth hormone.

[0010] In the present invention, during the detection, the detection window of the metabolic markers in the serum of the subject is 72h.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] 1. The screening method of the present invention has wide universality. Compared with the currently published international commercial GH immunoassay kit method, it has low detection cost and high throughput, and is expected to be used for large-scale screening of growth hormone injection.

[0013] 2. A single biomarker is easily affected by various factors, resulting in inaccurate test results. This method uses a combination of multiple biomarkers for joint monitoring to improve resistance to interference from external factors, thereby improving the accuracy of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the biomarker panel screening scheme.

[0015] Figure 2 PCA diagram of the GH injection group and the non-injection group.

[0016] Figure 3 The PLS-DA diagram shows significant differences between the GH-injected group and the non-injected group.

[0017] Figure 4 This is the ROC analysis result diagram. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art should understand that the specific implementation cases described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The present invention provides a method for screening a biomarker group, using metabolomics technology to analyze human blood samples to obtain a metabolite combination related to growth hormone injection; the screening method is as follows:

[0020] Step 1: Collect human blood samples and pre-treat them. Collect venous blood from the subjects, centrifuge it, take the upper serum, and extract metabolites;

[0021] Step 2: Using liquid chromatography-mass spectrometry to detect the sample pretreated in step 1;

[0022] Step 3: Process the raw data obtained in step 2 to screen out all differential metabolites related to GH injection;

[0023] Step 4: Further, the metabolites screened out in step 3 were subjected to receiver operating characteristic (ROC) curve analysis, and the top 20 metabolites with the best classification ability were screened out as the serum biomarker group associated with GH injection.

[0024] As a preferred embodiment of the screening method of the present invention, in step 1, the human blood sample is derived from a healthy subject or a patient with growth hormone deficiency; the GH injection can be a single GH injection or multiple GH injections.

[0025] As a preferred embodiment of the screening method of the present invention, in step 2, the liquid chromatography-mass spectrometry is ultra-high performance liquid chromatography-tandem mass spectrometry; the liquid chromatography uses a polar or non-polar chromatographic column to separate and detect metabolites in serum, preferably, a polar chromatographic column is used for separation.

[0026] As a preferred embodiment of the screening method of the present invention, in step 3, the data processing method includes data format conversion, peak extraction and alignment; compound identification; data normalization; data screening; missing value filling; compound deduplication; multivariate analysis; and searching for differential metabolites.

[0027] As a preferred embodiment of the screening method of the present invention, in step 4, the differential metabolite ROC analysis uses PLS-DA as a classification method, and determines the classification effect and biomarker group according to the area under the curve (AUC) value.

[0028] According to the above technical scheme, the present invention provides a group of serum markers for detecting whether growth hormone is injected, wherein the metabolite composition of the biomarker group includes the following 20 metabolites: 2-hydroxy-8-methylchromene-2-carboxylate, L-glutamine, betaine, xanthine, uridine, indoleacetic acid, glycodeoxycholate, nicotinic acid, oleic acid, L-cystine, 3-oxo-6-acetamidohexanoate, glycochenodeoxycholate, 2-hydroxy-4-(1-oxo-1,3-dihydro-indene-2-ylidene)-but-2-enoic acid, LO-phosphoserine, trans-cinnamic acid, 22-enoursodeoxycholic acid, 22-enoursodeoxycholic acid, L-histidine, biliverdin and 2-aminobut-2-enoate.

[0029] In one embodiment, when the expression levels of the metabolites 3-oxo-6-acetamidohexanoate, 22-enoursodeoxycholic acid, glycochenodeoxycholate, glycodeoxycholate, betaine, L-histidine, oleic acid, xanthine, biliverdin, and L-glutamine are upregulated, and at the same time, the expression levels of 2-aminobut-2-enoate, L-β-phenylalanine, 2-hydroxy-3-methylphenylpyruvate, uridine, 2-hydroxy-8-methylchromene-2-carboxylate, trans-cinnamic acid, indoleacetic acid, L-cystine, nicotinic acid, and 2-hydroxy-4-(1-oxo-1,3-dihydro-inden-2-ylidene)-but-2-enoic acid are downregulated, it indicates that the sample is from a subject injected with growth hormone.

[0030] Furthermore, the present invention provides a marker combination for detecting GH injection, which is composed of the above 20 metabolites.

[0031] The present invention uses metabolites as markers related to GH injection, which are more stable than proteins or polypeptides and more suitable for GH injection-related detection.

[0032] The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0033] Unless otherwise specified, the experimental methods used in the implementation cases are all conventional methods; the materials and reagents used are all commercially available unless otherwise specified. The data acquisition instrument system in the embodiment mainly includes ultra-high performance liquid chromatography (UPLC) and tandem mass spectrometry (Triple-TOF). The chromatographic column uses Waters ACQUITY UPLC BEH Amide column (2.1×100mm, 1.7μm,); mass spectrometry grade methanol, acetonitrile, water, formic acid and ammonium formate are all purchased from Thermo Fisher.

[0034] Example

[0035] 1.1 Volunteer recruitment, sample collection, and metabolite extraction

[0036] 14 volunteers were recruited, and the recruitment conditions were: healthy males, aged 18-31 years old, with no history of chronic diseases, no diabetes, no regular medication, no smoking, strenuous exercise, alcohol, caffeine intake, etc.

[0037] The volunteers were arranged to receive GH injection, a single subcutaneous injection, a dose of 0.1 IU / Kg.

[0038] Blood sample collection: Blood samples were collected from volunteers 24 hours before injection and 72 hours after injection. The blood was stored in blood collection tubes without anticoagulants, and the collection volume was not less than 4 mL of venous blood.

[0039] The blood samples were immediately centrifuged at 2000×g for 10 min at room temperature, and the upper serum was collected and dispensed into clean sterile centrifuge tubes at 200 μL / tube and stored at −80°C.

[0040] The steps for metabolite extraction are as follows: thaw the serum sample at 4°C for 30-60 minutes; add 200 μL of the sample to 800 μL of an extraction reagent with a ratio of methanol to acetonitrile of 1:1; shake thoroughly for 1 minute to precipitate the protein; ultrasonically extract the metabolites in a 40kHz ultrasonic cleaner at 4°C; place in a -20°C environment for 30 minutes to further precipitate the protein; centrifuge at 16000×g for 20 minutes at 4°C, take 700 μL of the upper layer solution into a new, clean, sterile centrifuge tube, and blow dry with nitrogen at room temperature; add 120 μL of a reconstitution solution (methanol:acetonitrile:water=2:2:1), shake and mix for 2 minutes, centrifuge at 16000×g for 20 minutes at 4°C, take 100 μL of the supernatant and place it in a 200 μL liner tube.

[0041] Preferably, 10 μL of each sample is mixed into one tube as a mixed sample.

[0042] 1.2 Serum samples were analyzed and tested using liquid chromatography-time-of-flight mass spectrometry;

[0043] The chromatographic conditions for sample detection using a polar column were as follows: ACQUITY UPLC BEH Amide column (2.1 × 100 mm, 1.7 μm);

[0044] The mobile phase A was composed of an aqueous solution containing 10 mM ammonium formate and 0.1% formic acid, and the mobile phase B was composed of 100% acetonitrile; the column temperature was 25 °C; the mobile phase flow rate was 0.3 mL / min; the injection volume was 5 μL; and the gradient time was 20 min. The mass spectrometry conditions were: when detecting in positive ion mode, the voltage was 5.5 kV; when detecting in negative ion mode, the voltage was -4.5 kV. The mass range of the first-stage full scan was set to 100-1000 m / z, with an accumulation time of 0.15 s; the electrospray ionization temperature was 550 °C, and the nebulizer gas, auxiliary gas, and curtain gas were 60, 60, and 35 psi, respectively; the collision energy was 35 ± 15 eV. The secondary ion scan range was 80-1000 m / z, and the accumulation time was 35 ms. The information-dependent data acquisition mode was used to collect data; the first 15 ions with the strongest signal intensity were selected for fragmentation.

[0045] 1.3 Data processing and screening of differential metabolites

[0046] Liquid chromatography-mass spectrometry was used to analyze the test samples to obtain raw mass spectrometry data, and the data were converted into mgf and mzXML formats using MSConvert software; XC-MS was used for peak extraction and peak alignment; metabolite identification was performed using the MetDNA online database; normalization was performed based on the sum of all peaks in each sample; peaks with a missing value ratio higher than 50% in all samples were removed; the coefficient of variation (CV) of the quality control samples was calculated, and further, peaks with a CV value greater than 30% in the quality control samples were removed; for the retained peaks, the K nearest neighbor method (KNN) was used to fill in the missing values; further, multivariate analysis was performed on the retained peaks, including principal component analysis (PCA) (such as Figure 2 ) and partial least squares discriminant analysis (PLS-DA) (e.g. Figure 3 ), calculate the feature variable projection importance (VIP score) of PLS-DA.

[0047] Based on the previous identification results, duplicate metabolites were removed and unique peak-metabolite relationships were retained. First, unsuccessful peaks were removed; second, preferably, peaks with parent ion isotope peaks of [M] were retained; on this basis, peaks with adduct ions of +H or -H were retained; and peaks with the highest credibility were further retained.

[0048] Among the metabolites after deduplication, the difference fold between the metabolite expression levels before and after injection and the VIP value were calculated, and metabolites with a VIP value greater than 1 were retained as differential metabolites.

[0049] 1.4 ROC analysis

[0050] The MetaboAnalyst online database (http: / / www.metaboanalyst.ca) was used to perform receiver operating characteristic curve (ROC) analysis of the above differential metabolites, and PLS-DA was used as the classification method after data normalization using log10 transformation. Nine samples were used as training sets to evaluate the importance of peaks, and classification models were constructed using the top 3, 5, 10, 20, 36, and 71 important peaks (metabolites). The remaining 5 samples were used as validation sets, and the classification effect of the model was evaluated based on the area under the curve (AUC) and the biomarker group was determined.

[0051] 1.5 Filter Results

[0052] The GH injection-related biomarker group was screened by performing metabolite identification, deduplication and ROC analysis of differential metabolites. A total of 465 metabolites were identified using the positive and negative modes of the polarity column; metabolites with VIP score>1 were considered candidate differential metabolites, and a total of 71 candidate differential metabolites were screened. The top 3, 5, 10, 20, 36, and 71 differential metabolites were used to establish a biomarker model based on the PLS-DA algorithm, and the AUC of the model ROC was 0.622, 0.711, 0.813, 0.911, 0.94, and 0.964, respectively, and the 95% confidence intervals were 0.111-1, 0.247-1, 0.247-1, 0.333-1, 0.383-1, and 0.581-1, respectively. When all differential metabolites were used for ROC analysis, the AUC value was the highest, which was 0.964. When the number of metabolites was reduced to 20, the AUC value did not differ much. In the validation set, the sensitivity and specificity of the samples were 0.8 and 0.8, respectively. Therefore, this test preferentially uses the top 20 differential metabolites with the largest VIP values ​​as the biomarker group for GH injection detection. Among them, 3-oxo-6-acetamidohexanoate, 22-enoursodeoxycholic acid, glycochenodeoxycholate, glycodeoxycholate, betaine, L-histidine, oleic acid, xanthine, biliverdin, and L-glutamine expression levels were upregulated, and 2-aminobut-2-enoate, L-β-phenylalanine, 2-hydroxy-3-methylphenylpyruvate, uridine, 2-hydroxy-8-methylchromene-2-carboxylate, trans-cinnamic acid, indoleacetic acid, L-cystine, nicotinic acid, and 2-hydroxy-4-(1-oxo-1,3-dihydro-indene-2-ylidene)-but-2-enoic acid were downregulated.

[0053] Table 1 shows the 20 metabolites that make up the marker panel and their VIP values.

[0054]

[0055] Table 1

[0056] In summary, the present invention analyzed the metabolite differences between sample groups by detecting serum samples of 14 subjects based on the UPLC-MS / MS platform and multivariate statistical analysis. A group of detection markers that can be used for GH injection was screened out. The analysis results show that PLS-DA can obviously separate the GH injection group from the non-injection group; when the ROC classification model is constructed with 20 metabolites, the AUC value is above 0.9, and the model classification effect is good; the sensitivity and specificity of the validation set are 0.8 and 0.8, respectively. The metabolite marker substance group of the present invention is of great value for GH injection detection, and related detection kits or methods can be developed for laboratory detection of GH injection.

[0057] The metabolite biomarker panel proposed in the present invention has the advantages of strong applicability and high accuracy in the detection of GH injection. The above advantages are due to the fact that the mixed multiple markers have stronger anti-interference ability than a single marker, and the application of metabolomics technology also improves the detection throughput.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the protection scope of the present invention. Those skilled in the art should understand that the technical solution of the invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. Use of a serum biomarker in preparing a kit for screening or detecting growth hormone injection, characterized in that: The kit is used to detect whether growth hormone is injected. The kit includes serum biomarkers, which include the following 20 metabolites: 2-hydroxy-8-methylchromene-2-carboxylate, L-glutamine, betaine, xanthine, uridine, indoleacetic acid, glycodeoxycholate, niacin, oleic acid, L-cystine, 3-oxo-6-acetamidohexanoate, glycochenodeoxycholate, 2-hydroxy-4-(1-oxo-1,3-dihydro-indene-2-ylidene)-but-2-enoic acid, LO-phosphoserine, trans-cinnamic acid, 22-enoursodeoxycholic acid, 2-hydroxy-3-methylphenylpyruvate, L-histidine, biliverdin and 2-aminobut-2-enoate.

2. The use according to claim 1, characterized in that: During the test, the expression level of metabolic markers in the subject's serum was used as an evaluation indicator.

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