CN115184501A - Application of serum metabolite combination screening in diagnosis of dystonia - Google Patents

Abstract

The object of the present invention is to provide the application of a reagent for detecting serum metabolite levels in the preparation of a kit for diagnosing dystonia, wherein the serum metabolites include: 2,3-diaminopropionic acid, phenylalanyl phenylalanine acid, arachidonic acid, L-pyroglutamic acid, L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine, D-aspartic acid, pyruvic acid, taurine, 1-oleylglycerol, L-threonic acid, maleamic acid, DL-malic acid, vitamin C and citric acid etc. (Table 1.).

Description

Application of serum metabolite combination screening in the diagnosis of dystonia

technical field

The invention belongs to the field of medical biology, and relates to the application of serum metabolomes in the diagnosis of dystonia.

Background technique

Dystonia is a complex and highly variable neuromotor disorder characterized by persistent or intermittent muscle contractions. According to statistics, the prevalence of dystonia is 16 per 100,000, and it is the third most common movement disorder after essential tremor and Parkinson's disease. Dystonia can be a manifestation of many neurological diseases or a combination of other diseases, or it can be an isolated symptom. Isolated dystonia is a dystonic syndrome in which dystonia is the only phenotype other than tremor. Currently, several genes have been implicated in child- and adolescent-onset dystonia, but most common adult-onset dystonias tend to be sporadic with no clear etiology.

Metabolomics is the qualitative and quantitative study of all low-molecular-weight metabolites at a certain time in the metabolic process to reflect changes in organisms to external stimuli or genetic modifications. Metabolites can include endogenous metabolites, drug metabolites, environmental chemicals, and chemicals produced from gut flora, among others. Changes in gene expression and proteins will inevitably produce metabolic changes in cells, tissues, and organs, and metabolites are the final products of cellular regulatory processes. Metabolomics is an emerging post-genomics research field following genomics, proteomics, and transcriptomics. It is an important part of systems biology with rapid, efficient, non-invasive, high sensitivity and high specificity. specialty. Numerous studies have shown that the metabolome plays an important role in neurodevelopment as well as in brain diseases such as Parkinson's (PD), Alzheimer's disease (AD) and multiple sclerosis. Metabolic markers provide important references for early diagnosis and treatment of diseases. Currently, the relationship between the metabolome and dystonia is unknown. Metabolomics provides an important research tool for the exploration of disease biomarkers, early diagnosis of diseases, and effective therapeutic targets.

SUMMARY OF THE INVENTION

In the present invention, a total of 1768 characteristic substances are identified by the non-targeted metabolomics technology based on gas chromatography-mass spectrometry (GS-MS), and then the patients with dystonia are identified by partial least squares regression analysis. The levels of 242 metabolites in serum were significantly different from healthy people (p<0.05, VIP>1 and FC>2or<0.5). Further analysis using the Human Metabolome Database found that 44 of these metabolites (Table 1.) were reliably annotated and named in the database and served as key metabolites and combinations for further study in dystonia. The identification and discovery of these metabolites provides an important reference for further research to discover biomarkers and effective therapeutic targets in patients with dystonia.

The object of the present invention is to provide the application of a reagent for detecting serum metabolite levels in the preparation of a kit for diagnosing dystonia, wherein the serum metabolites include:

2,3-Diaminopropionic acid, Phenylalanylphenylalanine, Arachidonic acid, L-pyroglutamic acid, L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine Purine, D-Aspartic Acid, Pyruvate, Taurine, 1-Oleylglycerol, L-Threonic Acid, Maleamic Acid, DL-Malic Acid, Vitamin C, Citric Acid, Paraxanthine, 14 (S)-HDHA and L-serine etc. (Table 1.).

The method for detecting serum metabolite levels of the present invention is a non-targeted metabolomics method based on GC-MS.

Another object of the present invention is to provide a product for diagnosing dystonia, the product comprising a reagent for detecting the level of serum metabolites, wherein the serum metabolites include:

2,3-Diaminopropionic acid, Phenylalanylphenylalanine, Arachidonic acid, L-pyroglutamic acid, L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine Purine, D-Aspartic Acid, Pyruvate, Taurine, 1-Oleylglycerol, L-Threonic Acid, Maleamic Acid, DL-Malic Acid, Vitamin C, Citric Acid, Paraxanthine, 14 (S)-HDHA and L-serine etc. (Table 1.).

Another object of the present invention is to provide a kit for diagnosing dystonia, the kit includes 2,3-diaminopropionic acid, phenylalanylphenylalanine, arachidonic acid, and L-pyroglutamic acid , L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine, D-aspartic acid, pyruvic acid, taurine, 1-oleylglycerol, L-threonic acid, Single or combined reagents of maleamic acid, DL-malic acid, vitamin C citric acid, paraxanthine, 14(S)-HDHA and L-serine, etc. (Table 1.).

Another object of the present invention is to provide a method for detecting dystonia by means of a kit, that is, serum metabolomics analysis of isolated dystonia patients, as shown in FIG. 1 . Include the following steps:

1. Serum samples were all thawed on ice, and quality control (QC) samples (made by mixing equal volumes of each serum sample) were used to estimate an average profile representative of all analytes encountered during analysis,

2. Treat serum samples with 2 volumes of methanol, then centrifuge at 14,000g for 10 minutes at 4°C.

3. Metabolomic analysis of the supernatant was carried out by high performance liquid chromatography-mass spectrometry, and the sample analysis was carried out on a Waters ACQUITY ultra-high performance liquid chromatography system (Milford, MA) and a Waters Q-TOF micromass system (Manchester, UK). The analysis was performed in both positive and negative ionization modes,

4. The original GC-MS data is extracted, aligned, and de-redundant, and then exported to a peak dataset file including variables (rt_mz), observations (samples), and peak intensity (abundance), etc., and further normalized processing. ,

5. The standardized data set is imported into the R language (version, 3.5.3, which is the language and operating environment for statistical analysis and plotting.) The system performs Partial least squares Discriminant Analysis (PLS- DA), R2X (PCA) or R2Y (PLS-DA) is defined as the proportion of variance in the data explained by the model, indicating goodness of fit, and Q2 is defined as the proportion of variance in the model predictable data, indicating the predictability of the current model The values of R2X, R2Y and Q2 were used as indicators to assess the robustness of the pattern recognition model, and the significantly different metabolites were determined by the PLS-DA model for VIP values >1 and two-tailed t-test for the normalized peak intensity P value ( <0.05) combination was determined, fold change (FC) was calculated as the binary logarithm of the mean peak intensity ratio between the two groups, and VIP estimated the importance of each variable in the PLS-DA model; variables with a VIP score >1 were very high in the model important role.

6. Use Receiver Operating Characteristic Curve (ROC) to diagnose the importance of various metabolites in assessing diseases, and establish models to discover important disease biomolecular markers and combinations.

The beneficial effects of the present invention are mainly manifested in the following aspects:

Dystonia is the third major motor neurological disorder after essential tremor and Parkinson's disease. The present invention is known to be the first to systematically analyze the serum metabolome of patients with isolated dystonia, and for the first time to find that the relative abundance of various metabolites (Table 1.) in the serum of patients is significantly reduced, P<0.05, VIP>1andFC> 2or<0.5.

The changes in the abundance of these serum metabolites may play an important role in the occurrence, development and prognosis of patients with isolated dystonia. Valuable candidate target.

The English language and terms appearing in the manual are explained as follows:

FC is the abbreviation of fold change, which refers to the ratio of the concentration of a given metabolite in patient serum to that in normal human serum.

P value, the basis used in statistics to judge whether the hypothesis is true.

ROC, short for receiver operating characteristic curve, is used to evaluate the predictive ability of the model.

VIP is the abbreviation of Variable Importance for the Projection, which measures the impact strength and explanatory power of the expression pattern of each metabolite on the classification and discrimination of each group of samples.

Description of drawings

Figure 1. Technical flow of serum metabolite analysis in patients with dystonia.

FIG. 2A . Partial least squares discriminant analysis (PLS-DA) analysis of serum metabolomics in patients with dystonia. The results showed that the overall characteristics of the serum metabolome in patients with dystonia were significantly different from those in healthy controls.

Figure 2B, the volcano plot visually shows that in both positive and negative ionization modes, the abundance of a large number of metabolites (242 species) in the serum of patients with dystonia is significantly different from that of healthy controls. Criteria for significant difference: P<0.05, VIP>1 and FC>2or<0.5, where P is the statistical P value; VIP is the parameter used to estimate the importance of variables; FC is the abbreviation of Fold change, which is the expression of specific metabolites in muscle. Ratio of abundance in serum of dystonic patients to that in healthy control serum.

Figure 2C, PLS-DA analysis showing the top 20 serum metabolites most important to differentiate dystonia patients from healthy controls.

Figure 2D. Receiver operating curve (ROC) was drawn, and the area under the ROC curve (AUC) was used to evaluate the effect of the top 20 most important differential serum metabolites in the differential diagnosis of dystonia. The results show that using the differential metabolites and combinations we identified can accurately and effectively distinguish dystonia patients from healthy controls, AUC=1.

Detailed ways

The present invention is further illustrated by the following specific examples, but not as a limitation of the present invention.

Embodiment 1, detection process

1. Metabolomic analysis of the gut microbiome in patients with isolated dystonia

1. Serum samples were all thawed on ice, and Quantity Control (QC) samples (made by mixing equal volumes of each serum sample) were used to estimate an average profile representative of all analytes encountered during analysis,

2. Treat serum samples with 2 volumes of methanol, then centrifuge at 14,000g for 10 minutes at 4°C.

3. Metabolomic analysis of the supernatant was carried out by high performance liquid chromatography-mass spectrometry, and the sample analysis was carried out on a Waters ACQUITY ultra-high performance liquid chromatography system (Milford, MA) and a Waters Q-TOF micromass system (Manchester, UK). The analysis was performed in both positive and negative ionization modes,

4. The original GC-MS data is extracted, aligned, and de-redundant, and then exported to a peak dataset file including variables (rt_mz), observations (samples), and peak intensity (abundance), etc., and further normalized processing. ,

5. Import the file into biostatistics software (R (version, 3.5.3), perform Partial least squares Discriminant Analysis (PLS-DA), R2X (PCA) or R2Y (PLS-DA) definition The proportion of variance in the data explained for the model, indicating goodness of fit, Q2 is defined as the proportion of variance in the model predictable data, indicating the predictability of the current model, the values of R2X, R2Y and Q2 are used as indicators to evaluate Robustness of the pattern recognition model, significantly different metabolites were determined by a combination of VIP values >1 for the PLS-DA model and a P value (<0.05) for the normalized peak intensity by a two-tailed t-test, and fold changes (FC) between the two groups were determined. Binary logarithm of mean peak intensity ratio, VIP estimates the importance of each variable in the PLS-DA model; variables with a VIP score >1 were important in the model.

6. Use Random Forest to evaluate the importance of metabolic components, build models, and discover important biomolecular markers.

Research result:

1. Through the partial least squares regression analysis of the metabolome, 242 metabolites with significantly different expression levels in the serum of isolated dystonia patients were identified (p<0.05, VIP>1and FC>2or<0.5 ).

2. Through the analysis of differential metabolites, it was found that 2,3-diaminopropionic acid (increased), phenylalanylphenylalanine (decreased), arachidonic acid (decreased), L-pyroglutamic acid ( Decreased), L-Aspartic Acid (Increased), L-Glutamate (Decreased), Glyceryl Oleate (Decreased), Hypoxanthine (Decreased), D-Aspartic Acid (Increased), Acetone acid (reduced), taurine (reduced), 1-oleylglycerol (reduced), L-threonic acid (reduced), maleamic acid (reduced), DL-malic acid (reduced), vitamin C ( Significant changes in serum abundance of citric acid (increased), paraxanthine (increased), 14(S)-HDHA (decreased) and L-serine (decreased), etc., may be involved in various neurotransmissions in the body It can be used as a biomarker for the differential diagnosis of dystonia and provide a new target for subsequent disease treatment. (Table 2 and Figure 2)

Table 1. Significant changes in serum metabolomic levels in patients with isolated dystonia. (p<0.05, VIP>1andFC>2or<0.5).

Claims (5)

1. Application of a reagent for detecting serum metabolite levels in the preparation of a kit for diagnosing dystonia, wherein the serum metabolites include: 2,3-diaminopropionic acid, phenylalanylphenylalanine, arachidonic acid Oleic acid, L-pyroglutamic acid, L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine, D-aspartic acid, pyruvic acid, taurine, 1-olene Glycerol, L-threonic acid, maleamic acid, DL-malic acid, vitamin C, citric acid, paraxanthine, 14(S)-HDHA and L-serine. 2 . The application according to claim 1 , wherein the method for detecting serum metabolite levels is a non-targeted metabolomics method by GC-MS. 3 . 3. application according to claim 1, is characterized in that, the detection method of test kit, comprises the following steps: (1) Serum samples were all thawed on ice, quality control samples were used to estimate mean profiles representing all analytes encountered during analysis, (2) Serum samples were treated with 2 times the volume of methanol, and then centrifuged at 14,000g for 10 minutes at 4°C. (3) Metabolomics analysis of the supernatant was carried out by high performance liquid chromatography-mass spectrometry, and the sample analysis was carried out using Waters ACQUITY ultra-high performance liquid chromatography system and Waters Q-TOF micromass system in two modes of positive and negative ionization analyze, (4) The original GC-MS data is extracted, aligned, and de-redundant, and then exported as a peak dataset file including variables (rt_mz), observations and peak intensities, etc., and further standardized processing. (5) Import the file into biostatistics software, perform partial least squares regression analysis, R2X (PCA) or R2Y (PLS-DA) is defined as the proportion of variance in the data explained by the model, indicating goodness of fit, Q2 is defined For the proportion of variance in the model predictable data, indicating the predictability of the current model, the values of R2X, R2Y and Q2 were used as indicators to evaluate the robustness of the pattern recognition model, and the significantly different metabolites were determined by the PLS-DA model. P values (<0.05) for normalized peak intensities were determined in combination with VIP values >1 and two-tailed t-tests, and fold changes (FC) were calculated as the binary logarithm of the ratio of mean peak intensities between the two groups, and VIP was estimated in the PLS-DA model. The importance of each variable; variables with VIP score > 1 have a very important role in the model, (6) Use the receiver operating characteristic curve to diagnose the importance of various metabolites in evaluating diseases, and establish models to discover important disease biomolecular markers and combinations. 4. A product for diagnosing dystonia, characterized in that the product comprises a reagent for detecting serum metabolite levels, wherein the serum metabolites include: 2,3-diaminopropionic acid, phenylalanyl phenylalanine acid, arachidonic acid, L-pyroglutamic acid, L-aspartic acid, L-glutamic acid, glyceryl oleate, hypoxanthine, D-aspartic acid, pyruvic acid, taurine, 1-oleylglycerol, L-threonic acid, maleamic acid, DL-malic acid, vitamin C, citric acid, paraxanthine, 14(S)-HDHA and L-serine. 5. A kit for diagnosing dystonia, comprising 2,3-diaminopropionic acid, phenylalanylphenylalanine, arachidonic acid, L-pyroglutamic acid, L-aspartic acid Amino acid, L-glutamic acid, glyceryl oleate, hypoxanthine, D-aspartic acid, pyruvic acid, taurine, 1-oleylglycerol, L-threonic acid, maleamic acid, DL-malic acid, vitamin C citric acid, paraxanthine, 14(S)-HDHA and L-serine.

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