Marker related to asymptotheca and kit thereof

Abstract

The invention discloses a marker related to asymptomatic hyperthyroidism and a kit of the marker. The marker is one or any combination of more than one of 15 differential metabolites. The marker has relatively high specificity and sensitivity, can be used for distinguishing ALS (ALS asymptomatic disorder) patients from normal people, is used for early detection of ALS, and strives for time for early intervention and treatment of the patients.

Description

Technical Field

[0001] The present invention relates to the field of markers, and in particular to an ALS-related marker and a kit thereof. Background Art

[0002] Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a rapidly progressive neurodegenerative disease that mainly damages upper and lower motor neurons and the trunk, limbs, and head and facial muscles they control, ultimately affecting respiratory function, and causing respiratory failure about three to five years after onset. ALS patients may also experience other overlapping symptoms, such as cognitive and behavioral changes, and even frontotemporal dementia, resulting in very different clinical manifestations. The peak incidence of ALS usually occurs in people over 45 years old, with a higher incidence in men than in women. The exact cause is not yet fully understood, and multiple factors such as genetics and environment may play a role in the onset of the disease. The median survival time of ALS from onset to death is very short, only 2 to 4 years, and the diagnosis delay is 10 to 16 months, so there is an urgent need to optimize diagnostic accuracy to identify the disease faster and more reliably (Vidovic M et al., Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis. Cells. 2023; 12(5): 736, incorporated herein by reference).

[0003] Metabolomics is the science of studying the endogenous metabolites produced by biological systems after external stimulation or disturbance and their changing rules, and is a research method to explore the metabolic pathways of biological systems. At present, metabolomics has been widely used to study the mechanism of disease and discover new diagnostic biomarkers. In the analytical technology of metabolomics research, high performance liquid chromatography-mass spectrometry (HPLC-MS) is considered to be one of the best analytical techniques for metabolite spectrum and metabolomics research, with high selectivity, sensitivity and repeatability. In addition, the liquid chromatography-mass spectrometry metabolomics method based on high efficiency isotope labeling (HP-CIL) has higher analytical efficiency and higher precision qualitative and quantitative results (Xu et al., Development of High-Performance Chemical Isotope Labeling LC-MS forProfiling the Human Fecal Metabolome.Anal.Chem.87,829-836 (2015), incorporated herein by reference), which has opened up a new path for the study of metabolomics.

[0004] Most ALS patients are in a hypermetabolic state. Using HP-CIL-based metabolomics technology to study the endogenous metabolic changes in ALS patients and identify potential small molecule biomarkers to understand the pathogenesis of ALS will help achieve early diagnosis, improve diagnostic accuracy, and develop new treatment strategies, thereby providing patients with better treatment options and prognosis (Krokidis MG. Transcriptomics and Metabolomics in Amyotrophic Lateral Sclerosis. Adv Exp Med Biol. 2020; 1195: 205-212, incorporated herein by reference). Therefore, it is very promising to establish a highly sensitive and specific ALS screening method. Summary of the invention

[0005] The object of the present invention is to provide a biomarker composition and method for assessing ALS risk or diagnosing ALS using an established HP-CIL-based liquid chromatography-mass spectrometry method.

[0006] To achieve the above-mentioned object, the present invention provides a marker related to ALS, characterized in that the marker is one or any combination of more than one of the following 15 differential metabolites, and the 15 differential metabolites are Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 4-Aminobutyraldehyde, Hypotaurine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid.

[0007] Furthermore, the marker is one or more of the following four differential metabolites that are upregulated in combination; the four differential metabolites are 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 4-Aminobutyraldehyde, and Hypotaurine.

[0008] Further, the marker is a significant downregulation of one or more of the following 11 differential metabolites, wherein the 11 differential metabolites are Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid.

[0009] The present invention also provides a kit, characterized in that it contains the ALS-related markers.

[0010] The sample used for detecting biomarkers in the present invention is serum.

[0011] Compared with the prior art, the technical solution of the present invention proposes for the first time 15 differential metabolites, including Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 4-Aminobutyraldehyde, Hypotaurine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid, and any combination thereof as ALS diagnostic markers. The diagnostic markers have high specificity and sensitivity, can be used to distinguish ALS patients from normal people, and are used for early detection of ALS, so as to buy time for patients to carry out early intervention and treatment.

[0012] The determination method of the present invention can fully and comprehensively reflect the variation of metabolites between ALS patients and normal people, find diagnostic markers for ALS, and provide favorable technical support for the early diagnosis and prognosis of ALS. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a distribution diagram of the identified metabolites in databases at different levels.

[0014] Figure 2 It is the volcano plot analysis diagram of metabolites in the samples of group P and group HC.

[0015] Figure 3 It is the PLS-DA analysis diagram of the samples in the P group and the HC group.

[0016] Figure 4 It is the ROC analysis (463 metabolites) diagram of the samples in the P group and the HC group.

[0017] Figure 5 It is the ROC analysis (AUC top 15 metabolites) graph of samples in group P and group HC.

[0018] Figure 6 It is a pathway analysis bubble chart. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, and the examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by reference to the accompanying drawings are exemplary, are intended to be used to explain the present invention, and are not to be construed as limitations of the present invention. Those who do not indicate specific techniques or conditions in the embodiments are carried out according to the techniques or conditions described in the documents in this area or according to the product specification. Those who do not indicate the manufacturer of reagents or instruments used are all conventional products that can be obtained commercially.

[0020] The embodiment of the present invention uses liquid chromatography-mass spectrometry technology based on high-efficiency chemical isotope labeling to detect metabolites in the serum of ALS patients and find diagnostic markers for ALS. The specific method includes: collecting serum from ALS patients, analyzing it by chromatography-mass spectrometry after processing, visually displaying the differences in metabolic spectra between ALS patients and normal people by establishing univariate and multivariate statistical models, obtaining differential metabolites between the two groups, and preliminarily identifying these substances.

[0021] The method for measuring the ALS diagnostic marker combination comprises the following steps.

[0022] Step 1, obtaining clinical serum samples from ALS patients and control samples from normal subjects;

[0023] Step 2, using HP-CIL liquid chromatography-mass spectrometry coupled metabolomics to analyze and identify the metabolites in the serum of ALS patients and normal subjects;

[0024] Step 3: In the univariate analysis, the parameters FC>1.2 and p<0.05 were set as the criteria for determining the preliminary differential metabolites.

[0025] Step 4, using the selection criteria of VIP value greater than 1 in the PLS-DA model in multivariate analysis and P value less than 0.05 in the nonparametric test, differential metabolites were obtained.

[0026] The chromatographic and mass spectrometric conditions of the HP-CIL liquid chromatography-mass spectrometry metabolomics method in step 2 include:

[0027] (1) Chromatographic column: Agilent eclipse plus reversed-phase C18 column (150 x 2.1 mm, 1.8 μm particle size);

[0028] (2) Mobile phase: A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid)

[0029] (3) Elution gradient (phase B): 0-10 min (25-99%), 10-15 min (99%), 15-15.1 min (99-25%), 15.1-18 min (25%)

[0030] (4) Column temperature: 40°C

[0031] (5) Flow rate: 400 μL / min

[0032] (6) Mass spectrometry scanning range: 220-1000Da

[0033] The serum sample pretreatment method for the HP-CIL liquid chromatography-mass spectrometry metabolomics method in step 2 includes: (1) Add 90 μL of pre-cooled mass spectrometry grade methanol to a centrifuge tube containing 30 μL of serum. After sufficient vortexing and low-speed centrifugation, place the sample in a -20°C refrigerator for 1 hour, and then high-speed centrifuge the sample at 4°C (12000 rpm, 10 min). Pipette 90 μL of supernatant into a new centrifuge tube and blow dry with nitrogen. (2) The four-channel multi-metabolome labeling (amines / phenols + carboxyls + hydroxyls + carbonyls) is strictly carried out in accordance with the kit and standard operating procedures. (3) Samples are mixed and equal amounts of 13 The C-labeled mixed sample was added to 12 C-labeled samples for LC-MS analysis.

[0034] Embodiment 1:

[0035] 1. Experimental materials and test conditions

[0036] 1. Serum sample status

[0037] The serum samples were from 140 ALS patients (P group) and 35 normal subjects (HC group).

[0038] 2. Sample Pretreatment

[0039] (1) The samples were centrifuged at high speed (12000r / min, 10min) at 4°C, and the supernatant was transferred to a new centrifuge tube and aliquoted according to the analysis channel. Each sample was divided into an analytical sample for four-channel metabolomics analysis (30μL / channel) and a mixed sample as a reference sample. The mixed sample was prepared by taking part of the supernatant from each sample and merging them into one sample, which was vortexed to form a mixed sample and aliquoted into 30μL per tube.

[0040] (2) Metabolite extraction of analytical samples and mixed samples: Add 90 μL of pre-cooled mass spectrometry grade methanol to a centrifuge tube containing 30 μL of serum, vortex thoroughly and centrifuge at low speed, place in a -20°C refrigerator for 1 hour, and then centrifuge the samples (analytical samples and mixed samples) at 4°C at high speed (12000 rpm, 10 min). Pipette 90 μL of supernatant into a new centrifuge tube and blow dry with nitrogen to obtain the extract.

[0041] (3) The labeling experiment of the analytical samples and mixed samples was carried out simultaneously: the labeling of the four-channel multi-metabolome (amines / phenols + carboxyls + hydroxyls + carbonyls) was carried out strictly in accordance with the HP-CIL metabolomics analysis kit (HP-CIL metabolomics analysis kit I: amine and phenol metabolite analysis; HP-CIL metabolomics analysis kit II: carboxylic acid metabolite analysis; HP-CIL metabolomics analysis kit III: hydroxyl metabolite analysis; HP-CIL metabolomics analysis kit IV: carbonyl metabolite analysis) and standard operating procedures. The analytical samples were labeled by 12 C was used to label the mixed sample. 12 C and 13 C is marked.

[0042] (4) Add equal volumes of 13 The C-labeled mixed sample was added to 12 The C-labeled analytical samples are mixed as samples for on-machine testing for liquid mass spectrometry analysis. 13 The C-labeled mixed sample was added to 12 C-labeled mixed samples were used as quality control samples.

[0043] 3. Liquid chromatography mass spectrometry test

[0044] The samples were analyzed using an Agilent ultra-high performance liquid chromatography time-of-flight mass spectrometer. Chromatographic column: Agilent eclipse plus reversed-phase C18 column (150x 2.1mm, 1.8μm particle size). Mobile phase: A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid). Elution gradient (phase B): 0-10min (25-99%), 10-15min (99%), 15-15.1min (99-25%), 15.1-18min (25%). Column temperature: 40°C. Flow rate: 400μL / min. Mass spectrometry scanning range: 220-1000Da. Data analysis and processing used IsoMS Pro software and NovaMT metabolomics database.

[0045] (II) Results

[0046] After the above serum samples were analyzed by ultra-high performance liquid chromatography time-of-flight mass spectrometry, the test data results were analyzed and processed using IsoMS Pro software.

[0047] First, the metabolites detected in the above serum samples were identified using the three-level database. A total of 6707 metabolites were detected, of which 6090 (90.8%) metabolites were identified or presumed. The test results are as follows: Figure 1 As shown, the distribution of identified metabolites in different levels of databases is shown. Level 1: According to the accurate molecular weight and retention time, the derivatization standard database (CIL Library) is matched. CIL Library contains experimental information of more than 1,500 metabolites. Among them, 392 metabolites can be identified in this database. Level 2: The associated metabolite database (LILibrary) is used to identify the remaining metabolites. LI Library includes more than 9,000 metabolites associated with metabolic pathways, and can provide highly reliable presumptive results based on accurate molecular weight and predicted retention time. Among them, 833 metabolites can be identified in this database. Level 3: According to the accurate molecular weight, the remaining metabolites are matched in the MyCompoundID (MCID) database. This database includes 8,021 known endogenous metabolites (0-level reaction database), 375,809 metabolites predicted by primary metabolic reactions (1-level reaction database), and 10,583,901 predicted metabolites generated by two-step metabolic reactions (2-level reaction database). In this database, 1312, 2769 and 784 metabolites were matched in the level 0, level 1 and level 2 databases, respectively. Subsequently, a volcano plot was drawn using the fold difference (FC) and p-value ( Figure 2Figure : Volcano plot of differential metabolites between ALS group (P group) and healthy control group (HC group). Note: The screening criteria were differential fold FC>1.2 or <0.83 and significant P-value<0.05). The differential fold was calculated by calculating the ratio of the average values ​​of the analysis results of ALS group (P group) and healthy control group (HC group). The p value was from student's t-test, and the q value was from FDR-adjusted p-value (Storey's q-value). As a result, 1225 differential metabolites were obtained, including Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 2,5-Dihydroxybenzoic The differential metabolites included acid, N-Hydroxy-L-valine, 4-Aminobutyraldehyde, Hypotaurine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid. The PLS-DA analysis was further performed in multivariate statistical analysis. Figure 3 : In the PLS-DA analysis of the ALS group (P group) and the healthy control group (HC group), it can be seen that the metabolic profiles of the serum of ALS patients and the serum of the healthy control group are significantly different. Based on the above screening, 463 differential metabolites were further screened as candidate biomarkers for ALS according to the variable weight value (VIP value) greater than 1.

[0048] At the same time, the Biomarker Analysis module on the MetaboAnalyst website (www.metaboanalyst.ca) was used to perform receiver operating curve (ROC) analysis on the 463 differential metabolites screened out ( Figure 4 : Figure 4It is the ROC analysis (463 metabolites) diagram of the samples of the P group and the HC group, and the receiver operating curve (ROC) analysis of the 463 differential metabolites screened out under the selection criteria of difference fold FC>1.2 or <0.83, P-value<0.05, and variable weight value (VIP)>1).Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovalericacid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1ofThreonolactone, 2,4-Dihydroxybutanoic were further discovered by area under the curve (AUC) acid, 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 4-Aminobutyraldehyde, Hypotaurine, 2,3-Dihydroxy-p-cumic acid, 2-Aminooctanoic acid The 15 differential metabolites with higher AUC can better distinguish ALS patients from normal people, and determine their sensitivity and specificity. The binary logistic regression algorithm of Metaboanalyst was used to construct a diagnostic model, and the logistic regression model was obtained: logit(P)=log(P / (1-P))=3.745+2.583Threonolactone-1.801N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid-2.092,2,2-Trifluoroethanol-0.66Pyridoxal+0.703Dehydroascorbic acid-0.945Isomer1of Threonolactone-0.2492,4-Dihydroxybutanoic acid+4.6114-Hydroxyaniline+6.2362-Amino-2-methyl-1,3-propanediol-0.4832,5-Dihydroxybenzoic acid-0.535N-Hydroxy-L-valine-9.441 4-Aminobutyraldehyde+1.704Hypotaurine-0.272,3-Dihydroxy-p-cumic acid-1.1752-Aminooctanoic acid, where P=Pr(y=1|x) the best threshold (or cutoff value) for predicting P is 0.87, and its accuracy in predicting ALS was tested, and the results were AUC: 0.967; Sensitivity: 0.842; Specificity: 0.943. Figure 5Figure 3 is the ROC curve of the top 15 metabolites in terms of AUC value among the differential metabolites.

[0049] In order to apply metabolomics to clinical diagnosis, treatment or pathophysiological research, it is necessary to clarify the physiological functions of metabolites. The Pathway Analysis module on the MetaboAnalyst website (www.metaboanalyst.ca) was used to perform metabolic pathway analysis on 1225 metabolites with high confidence in level 1 and level 2 using Homo sapiens (KEGG) ( Figure 6 :Bubble diagram of differential metabolite enrichment pathways). The results showed that significant changes were found in tyrosine metabolism, vitamin B6 metabolism, glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, taurine and low taurine metabolism pathways ( Figure 6 ).

[0050] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A marker related to ALS, characterized in that: The marker is one or any combination of more than one of the following 15 differential metabolites, and the 15 differential metabolites are Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbicacid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 4-Aminobutyraldehyde, Hypotaurine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid.

2. The ALS-related marker according to claim 1, characterized in that The marker is an up-regulation of one or a combination of more than one of the following four differential metabolites; the four differential metabolites are 4-Hydroxyaniline, 2-Amino-2-methyl-1,3-propanediol, 4-Aminobutyraldehyde, and Hypotaurine.

3. The ALS-related marker according to claim 1, characterized in that: The marker is a significant downregulation of one or more of the following 11 differential metabolites, wherein the 11 differential metabolites are Threonolactone, N-Acetyl-2-Oxo-4-hydroxy-5-aminovaleric acid, 2,2,2-Trifluoroethanol, Pyridoxal, Dehydroascorbic acid, Isomer 1 of Threonolactone, 2,4-Dihydroxybutanoic acid, 2,5-Dihydroxybenzoic acid, N-Hydroxy-L-valine, 2,3-Dihydroxy-p-cumic acid, and 2-Aminooctanoic acid.

4. A kit, characterized in that: Contains the ALS-related markers described in any one of claims 1-3.

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