Use of nine dipeptides as markers for risk assessment of gout flares

Abstract

The application provides nine biomarkers-dipeptide metabolites, which can reflect the growth rate of serum-induced monosodium urate crystals and are used for evaluating the risk of gout attack of an individual. The method provided by the application can objectively reflect the difference in the growth rate of serum-induced monosodium urate crystals and the risk of gout attack of patients in different health states without complex instruments and monitoring methods.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically involving the application of nine dipeptides as biomarkers for assessing the risk of gout attacks. Background Technology

[0002] Acute gout is an acute arthritis characterized by severe joint pain. Its main pathological feature is the deposition of monosodium urate crystals, which form when serum urate concentration is elevated. These crystals chronically deposit in joints and tissues, and can directly induce joint inflammation. Clinical manifestations include sudden onset of pain, edema, redness, and limited joint movement. How to conduct risk assessment for acute gout attacks and evaluate individual differences in susceptibility to attacks is an important research topic.

[0003] Significant progress has been made in the study of gout pathogenesis, diagnosis, and treatment. However, risk assessment for acute gout attacks is still in its early stages. Current predictive indicators for acute gout mainly rely on biomarkers such as uric acid and CRP. However, these indicators are easily affected by individual differences and have limited sensitivity and specificity. Therefore, there is an urgent need to explore biomarkers that can specifically assess the risk of acute gout attacks for individuals with different characteristics. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides nine newly discovered serum biomarkers for assessing gout attack risk: Hisidinyl-Tryptophan, Seryl-Isoleucine, Leucyl-Isoleucine, Tryptophyl-Histidine, Isomer 2 of Prolyl-Threonine, Glutamyl-Serine, Leucyl-Leucine, 5-L-Glutamyl-taurine, and Hisidinyl-Alanine. A previous method for assessing gout attack risk by inducing the growth rate of urate monosodium crystals in serum has been developed. Significant differences in the growth rate of urate monosodium crystals were observed in serum samples from different healthy individuals, thus allowing for the assessment of gout attack risk based on the urate monosodium crystal growth rate. The detection results include EC... 50 The smaller the value, the faster the corresponding serum-induced growth rate of monosodium urate crystals, and vice versa (patent application CN202311431091.X). In subsequent mechanistic studies, metabolomics research was conducted on the two serum samples with the most significant differences in induced monosodium urate crystal growth rates, and nine dipeptide substances affecting the growth rate of monosodium urate crystals were discovered.

[0005] This invention also provides a biomarker that affects the growth rate of monosodium urate crystals and / or is used to assess the risk of gout attacks, said biomarker being selected from any one, two, or more of the following:

[0006] Histidinyl-Tryptophan, Seryl-Isoleucine, Leucyl-Isoleucine, Tryptophyl-Histidine, Isomer 2 of Prolyl-Threonine, Glutamyl-Serine, Leucyl-Leucine, 5-L-Glutamyl-taurine, Histidinyl-Alanine.

[0007] According to an embodiment of the present invention, the biomarker is a serum metabolite.

[0008] According to an embodiment of the present invention, the growth rate of monosodium urate crystals and / or the risk of gout attacks are determined by detecting the levels of the aforementioned biomarkers in serum samples. According to an embodiment of the present invention, the higher the level of the aforementioned biomarkers in serum samples, the faster the growth rate of monosodium urate crystals. According to an embodiment of the present invention, the higher the level of the aforementioned biomarkers in serum samples, the higher the risk of gout attacks. According to an embodiment of the present invention, the higher the level of the aforementioned biomarkers in serum samples, the faster the growth rate of monosodium urate crystals, and the higher the EC50. 50 The smaller the size, the higher the risk of a gout attack. EC 50 The Hill Slope, representing the point in time when the urate monosodium crystal reaches its maximum growth rate, describes the time at which the crystal reaches its maximum growth rate. The Hill Slope measures the maximum slope of the curve, describing the maximum rate of crystal growth.

[0009] According to an embodiment of the present invention, the growth rate of serum-induced monosodium urate crystals can be assessed by detecting the aforementioned biomarkers in serum samples. Since the growth of monosodium urate crystals is positively correlated with gout attacks, the faster and more numerous the monosodium urate crystals grow, the higher the risk of a gout attack. Therefore, the levels of the aforementioned biomarkers in serum can be used to assess an individual's risk of gout attacks.

[0010] The present invention also provides the use of the above-mentioned biomarkers in the preparation of products for determining the growth rate of monosodium urate crystals and / or assessing the risk of gout attacks.

[0011] According to an embodiment of the present invention, the product is, for example, a reagent kit.

[0012] The present invention also provides the use of reagents for detecting the levels of the above-mentioned biomarkers in the preparation of products for determining the growth rate of monosodium urate crystals and / or assessing the risk of gout attacks.

[0013] According to an embodiment of the present invention, the reagent is a reagent for detecting the content level of biomarkers in serum samples.

[0014] According to an embodiment of the present invention, the product is, for example, a reagent kit.

[0015] According to the embodiments of the present invention, the content levels of the above-mentioned biomarkers can be detected by methods such as liquid chromatography, liquid chromatography-mass spectrometry, ion exchange chromatography, capillary electrophoresis, and colorimetry.

[0016] The present invention also provides a product for determining the growth rate of monosodium urate crystals and / or assessing the risk of gout attacks, the product comprising reagents for detecting the levels of the aforementioned biomarkers.

[0017] According to an embodiment of the present invention, the reagent is a reagent for detecting the content level of biomarkers in serum samples.

[0018] According to an embodiment of the present invention, the product is, for example, a reagent kit.

[0019] According to the embodiments of the present invention, the content levels of the above-mentioned biomarkers can be detected by methods such as liquid chromatography, liquid chromatography-mass spectrometry, ion exchange chromatography, capillary electrophoresis, and colorimetry.

[0020] This invention provides a method for screening the above-mentioned biomarkers, the screening method comprising the following steps:

[0021] (1) Calculate the time point EC corresponding to the maximum growth rate of uric acid monosodium crystals induced in serum samples from the gout group and the non-gout group. 50 ;

[0022] (2) Screening out the gout group EC 50 The smallest serum samples (e.g., the serum samples with the fastest growth rate, such as the first 30 cases) and non-gout EC 50 The largest serum sample (e.g., the serum sample with the slowest growth rate, such as the first 30 cases);

[0023] (3) Perform metabolomics analysis on the two groups of serum samples in step (2);

[0024] (4) Select those related to EC 50 Metabolites with significant correlations (e.g., negative correlations) are called biomarkers.

[0025] According to the embodiment of the present invention, step (1) specifically involves: reading the absorbance values ​​(Y) of serum samples from the gout group and non-gout group after mixing with alkaline uric acid solution at different time points (X) and creating a change curve; obtaining the maximum absorbance value (Top value), the minimum absorbance value (Bottom value), and the symmetry value S of the curve based on the curve; and obtaining the Hill Slope value (maximum slope value of the curve) and the corresponding time point EC where the Hill Slope appears through a parameter fitting equation. 50 value.

[0026] According to an embodiment of the present invention, the alkaline uric acid solution is a sodium hydroxide solution of uric acid; preferably, the concentration of the sodium hydroxide solution is 0.5 mol / L-1.0 mol / L, for example 0.6 mol / L, 0.667 mol / L, 0.7 mol / L, or 0.8 mol / L. According to an embodiment of the present invention, the concentration of uric acid in the alkaline uric acid solution is 30 mg / mL-40 mg / mL, for example 32 mg / mL, 33.3 mg / mL, 35 mg / mL, 38 mg / mL, or 40 mg / mL. Optionally, the alkaline uric acid solution is filtered. According to an embodiment of the present invention, the volume ratio of serum sample to alkaline uric acid solution is 5-20:1, preferably 8-15:1, for example 9:1, 10:1, 11:1, 12:1, 13:1, or 14:1.

[0027] According to an embodiment of the present invention, the absorbance value (Y) change at different time points (X) is read by an absorbance monitor (e.g., a multifunctional cell imaging microplate detector); preferably, the detection wavelength is 520nm-540nm, more preferably 525nm-535nm, for example 530nm; preferably, the absorbance value is detected every 1min-10min, for example every 5min; preferably, the detection is performed continuously for 1h-5h, for example 2h, 3h, 4h.

[0028] According to an embodiment of the present invention, the formula for the parameter fitting equation is:

[0029] Denominator=(1+(2^(1 / S)-1)*((EC 50 / X)^HillSlope))^S,

[0030] Numerator = Top – Bottom

[0031] Y=Bottom+(Numerator / Denominator);

[0032] in:

[0033] Y represents the absorbance value;

[0034] X represents the time point corresponding to the absorbance value;

[0035] Top represents the maximum absorbance value of the curve (i.e., MAX{Y});

[0036] Bottom is the minimum absorbance value of the curve (i.e., MIN{Y});

[0037] S represents the degree of symmetry of the curve, with values ​​S∈[0,1] (approximately 1 indicates a more symmetrical fitted curve, while approximately 0 indicates a less symmetrical curve).

[0038] HillSlope is the maximum slope value used to measure the slope of the curve, which describes the maximum rate of crystal growth.

[0039] EC 50 The corresponding time point when the Hill slope appears (used to describe the time point when the crystal reaches its maximum growth rate).

[0040] According to an embodiment of the present invention, the fitting method is asymmetric sigmoidal.

[0041] According to an embodiment of the present invention, in step (2) and / or (3), the serum sample is frozen at -80°C.

[0042] According to an embodiment of the present invention, in step (3), the sample analysis is performed using the RapidLC-MS Analysis for DeepMarker MT metabolomics platform.

[0043] Preferably, serum samples are labeled using a dansyl labeling kit before metabolomics analysis.

[0044] Preferably, data analysis is performed using IsoMS Pro software.

[0045] According to the embodiment of the present invention, step (3) is specifically performed as follows:

[0046] ① Serum sample aliquoting and mixed sample preparation: Serum samples are aliquoted and a portion is used for single-channel analysis, backup, and mixed sample preparation. The mixed sample is composed of a portion of serum from each sample.

[0047] ② Sample processing and supernatant collection: Add methanol to the serum sample, vortex, centrifuge, let stand, and centrifuge again.

[0048] Collect the supernatant and dry it;

[0049] ③ Amine / phenolic secondary metabolome sample labeling: After reconstituted the sample with water, add reagents for labeling treatment. The labeling reagents are the reagents in the dansyl labeling kit (including buffer, labeling reagent, quenching reagent and pH adjustment reagent).

[0050] ④ Sample preparation for LC-UV quantification and liquid chromatography-mass analysis: After using the sample from step ③ of LC-UV quantification, mix the labeled sample and prepare the quality control sample. Perform liquid chromatography-mass analysis after all samples are prepared.

[0051] ⑤ Liquid chromatography-mass analysis: Perform liquid chromatography-mass analysis and periodically analyze quality control samples and calibration samples to monitor instrument stability;

[0052] ⑥ Data Acquisition and Processing: After acquiring liquid chromatography-mass analysis data, upload it to IsoMS Pro software for format conversion, quality check, and processing analysis;

[0053] ⑦ Data grouping and normalization: The data are classified into groups, the information of stable metabolites is preserved, and the data of each group is normalized.

[0054] ⑧ Metabolite identification: Metabolite identification is performed on the processed data to determine the types and quantities of metabolites in the sample.

[0055] According to an embodiment of the present invention, the metabolite identification parameters are as follows:

[0056] CIL database ID retention time error 10 seconds LI database ID retention time error 75 seconds CIL database ID quality error 10ppm Quality error of LI database ID 10ppm Quality error of "MCID" database ID 10ppm

[0057] The beneficial effects of this invention are:

[0058] ①This invention provides nine biomarkers-dipeptide metabolites that may affect the growth rate of monosodium urate crystals in vivo. These biomarkers can reflect the growth rate of monosodium urate crystals induced by human serum and indirectly assess an individual's risk of gout attacks.

[0059] ②The method provided by this invention can objectively reflect the differences in the growth rate of serum-induced monosodium urate crystals and the risk of gout attacks in patients with different health conditions without the need for complex instruments and monitoring methods.

[0060] ③ Using dipeptides as a starting point, explore the potential mechanisms of gout development and progression, such as the upstream and downstream pathways of acute gout inflammatory attacks, to provide medical guidance for individual prevention.

[0061] ④ By studying dipeptides, we can explore the mechanisms of gout development and provide guidance for clinical treatment and medication. They can also be used to predict individual recurrence risk and provide timely intervention. Attached Figure Description

[0062] Figure 1The volcano plot of the metabolomics detection results of this invention shows the differences in metabolites in the serum of the gout group (with rapid induced growth of monosodium urate crystals) and the non-gout group (with slow induced growth of monosodium urate crystals).

[0063] Figure 2 The results showed good differentiation between the two groups of serum PCA.

[0064] Figure 3 These are two groups of serum metabolites and EC. 50 Based on the Pearson correlation coefficient, this invention selected the top 9 dipeptides with the Pearson correlation coefficient as the chosen serum biomarkers for assessing the risk of gout attacks.

[0065] Figure 4 The results show that the EC50 of the dipeptide was obtained by in vitro reproducibility. As the dipeptide solution concentration increased, the EC50 also increased. 50 The trend showed a decrease, which verified the conclusion that dipeptide promotes the growth of monosodium urate crystals. Detailed Implementation

[0066] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. It should be understood that the following embodiments are merely illustrative and explanatory of the present invention, and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above content of the present invention are covered within the scope of protection intended by the present invention.

[0067] Unless otherwise stated, the raw materials and reagents used in the following examples are commercially available products or can be prepared by known methods.

[0068] Example 1

[0069] Thirty serum samples from the gout group and 30 serum samples from the non-gout group that induced rapid growth of uric acid monosodium crystals were screened for metabolomics studies. Differential metabolites between the two groups were screened out, and the top nine metabolites with the greatest differences were selected.

[0070] 1. Materials

[0071] Uric acid, purchased from Sigma-Aldrich, USA.

[0072] Sodium hydroxide was purchased from Aladdin Reagent (Shanghai) Co., Ltd.

[0073] The 384-hole plate was purchased from Agilent Technologies (China) Co., Ltd.

[0074] Fetal bovine serum was purchased from Wuhan Pronosei Life Science Technology Co., Ltd.

[0075] Mass spectrometry grade methanol was purchased from Thermo Fisher.

[0076] Dansyl labeling kit for amine and phenolic metabolomics, MLO-1001-KT, purchased from Nova Medical Testing Inc., including buffer reagent A (sodium carbonate / sodium bicarbonate). 12 C-labeled reagent B.12 (dansyl chloride), 13 C-labeled reagent B.13 (13C2-dansyl chloride), reagent C-quenching reagent (sodium hydroxide), reagent D-pH adjusting reagent (formic acid solution).

[0077] 2. Gout group EC 50 The smallest number of serum samples from the top 30 cases and non-gout EC groups 50 Screening of the top 30 largest serum samples

[0078] The absorbance values ​​(Y) of serum samples from the gout group and non-gout group, as well as samples mixed with alkaline uric acid solution, were recorded at different time points (X) to create change curves. Based on the curves and parameter fitting equations, the corresponding time point EC at which the Hill slope occurred was obtained. 50 Value. Screening for EC in the gout group. 50 The smallest 30 serum samples (fastest growth rate) and non-gout EC 50 The top 30 largest serum samples (with the slowest growth rate).

[0079] EC 50 The specific procedures for value determination are as follows:

[0080] (1) Dissolve 0.4g of sodium hydroxide in 15mL of deionized water to prepare a 0.667mol / L NaOH solution;

[0081] (2) Dissolve 0.1g of uric acid in 3mL of (1) to prepare an alkaline uric acid solution;

[0082] (3) Filter (2) using a 0.45μm PDVF filter membrane to prepare a filtered alkaline saturated uric acid solution;

[0083] (4) Add 30 μL of serum sample to a 384-well plate;

[0084] (5) Add 3 μL of the alkaline saturated uric acid solution filtered from (3) to the 384-well plate;

[0085] (6) The absorbance at 530 nm was measured using a multifunctional cell imaging microplate analyzer (Cytation5), with measurements taken every 5 minutes for 3 hours. The detection time and absorbance change at each measurement point were recorded. The detection data for each sample were then fitted using a fitting formula to obtain the EC value. 50 .

[0086] The fitting method is asymmetric sigmoidal, and the formula for the parameter fitting equation is:

[0087] Denominator=(1+(2^(1 / S)-1)*((EC 50 / X)^HillSlope))^S,

[0088] Numerator = Top – Bottom

[0089] Y=Bottom+(Numerator / Denominator);

[0090] in:

[0091] Y represents the absorbance value;

[0092] X represents the time point corresponding to the absorbance value;

[0093] Top represents the maximum absorbance value of the curve (i.e., MAX{Y});

[0094] Bottom is the minimum absorbance value of the curve (i.e., MIN{Y});

[0095] S represents the degree of symmetry of the curve, with values ​​S∈[0,1] (approximately 1 indicates a more symmetrical fitted curve, while approximately 0 indicates a less symmetrical curve).

[0096] HillSlope is the maximum slope value used to measure the slope of the curve, which describes the maximum rate of crystal growth.

[0097] EC 50 The corresponding time point when the Hill slope appears (used to describe the time point when the crystal reaches its maximum growth rate).

[0098] 3. Metabolomics Detection Process

[0099] (1) The gout group EC obtained from the above screening 50 The smallest number of serum samples from the top 30 cases and non-gout EC groups 50 The top 30 serum samples were aliquoted according to the analysis channel. After thawing (serum samples were stored at -80°C) and vortexing, 30 μL of each sample was transferred to the corresponding 1.5 mL centrifuge tube. Each sample was divided into three fractions for single-channel analysis (30 μL / channel), backup sample preparation, and pooled sample preparation. For pooled sample preparation, 90 μL of serum was aspirated from each sample and combined into one sample, vortexed, and labeled to serve as a reference sample.

[0100] (2) Add 90 μL of pre-chilled mass spectrometry grade methanol to each centrifuge tube containing 30 μL of serum, vortex thoroughly and centrifuge at low speed, place in a -20℃ freezer for 1 hour, and then centrifuge the sample at 4℃ (12000 rpm, 10 min). Transfer 90 μL of supernatant to a new centrifuge tube and dry using a vacuum concentrator.

[0101] (3) For aliquoted samples to be analyzed for amine / phenolic secondary metabolomics: add 25 μL of mass spectrometry grade water to reconstitute the dried samples from step (2), and label the samples strictly according to the standard operating procedure (SOP) and kit requirements. First, add 12.5 μL of buffer reagent A and 37.5 μL of [unclear - possibly a reagent name or reagent] to the sample. 12 C-labeled reagent B.12 (for labeling individual and pooled samples) or 13 C-labeled reagent B.13 (for mixed sample labeling only) was vortexed and incubated at 40°C for 45 minutes. After incubation, 7.5 μL of reagent C was added to quench excess labeling reagent, and the mixture was incubated at 40°C for 10 minutes. Finally, 30 μL of pH adjustment reagent D was added.

[0102] (4) Quantify the labeled amine / phenolic secondary metabolome using LC-UV according to standard operating procedures. Based on the quantitative results, use equal amounts of... 13 C-labeled mixed sample added to 12 C-labeled individual samples were used for liquid chromatography-mass spectrometry (LC-MS) analysis. Quality control samples were prepared simultaneously before the LC-MS analysis. 13 C-labeled mixed samples and 12 Equal volumes of C-labeled mixed samples were thoroughly mixed and used as quality control samples. Liquid chromatography-mass spectrometry (LC-MS) analysis was performed on all samples after preparation.

[0103] (5) Perform LC-MS analysis strictly according to the standard operating procedure (Rapid LC-MS Analysis for DeepMarker MT). Simultaneously, analyze quality control samples and retention time calibration samples every 12 samples to monitor instrument operational stability.

[0104] (6) A total of 65 data points were collected for liquid chromatography-mass spectrometry (LC-MS) analysis (single-channel data, 65 LC-MS data points per channel, including 60 sample data points and 5 quality control sample data points). After data collection and export, all data were uploaded to IsoMS Pro 1.2.20 for data processing and analysis. After format conversion and quality checks, the data were processed and analyzed.

[0105] (7) The 65 data points from each channel were divided into three groups according to their grouping: 30 data points were labeled as the AG group, 30 data points as the HC group, and 5 quality control data points as the QC group. Among them, only metabolites that appeared in at least 80% of the samples in at least one group were retained to remove unstable information. After filtering, each group of data was normalized according to the total useful signal ratio.

[0106] (8) Perform metabolite identification, analyze the differentially expressed metabolites between the two groups, and screen for metabolites similar to EC. 50 (EC 50 The top nine metabolites with the highest Pearson correlation coefficients between the time points corresponding to the appearance of Hill slope (used to describe the time points corresponding to the maximum growth rate of monosodium urate crystals) and the time points corresponding to the appearance of Hill slope.

[0107] 4. Analysis and Conclusion

[0108] Figure 1 This is a volcano plot of the metabolomics detection results of this invention. Significant differences in metabolites were found between serum samples from the gout group (with rapid induction of uric acid monosodium crystal growth) and the non-gout group (with slow induction of uric acid monosodium crystal growth). Specifically, compared to the non-gout group, the gout group showed upregulation of 138 metabolites and downregulation of 62 metabolites. Figure 2 The results showed that the two groups of serum PCA were well differentiated, and there were significant differences in the overall composition of serum metabolites between the two groups.

[0109] like Figure 3 As shown, based on the metabolomics analysis results of the two groups of samples, EC was obtained. 50 The top 30 dipeptides with the highest negative Pearson correlation coefficient were identified. Statistical analysis was then used to screen EC... 50 The top nine dipeptides with the highest negative correlation coefficient among the Pearson correlation coefficients showed a significant correlation with the growth rate of monosodium urate crystals, and may have the most significant effect on promoting the growth of monosodium urate crystals.

[0110] The nine dipeptides are as follows:

[0111] Histidinyl-Tryptophan (A-T2716);

[0112] Seryl-Isoleucine (A-T1193);

[0113] Leucyl-Isoleucine (A-T2150);

[0114] Tryptophyl-Histidine (A-T844);

[0115] Isomer 2 of Prolyl-Threonine, isomer 2 (A-937) of prolyl-threonine, with the structural formula as follows:

[0116]

[0117] Glutamyl-Serine (A-116);

[0118] Leucyl-Leucine (A-1574);

[0119] 5-L-Glutamyl-taurine, 5-L-glutamyl-taurine (A-T3);

[0120] Histidinyl-Alanine (A-T2333).

[0121] Example 2

[0122] In vitro reproducibility experiments were conducted on the nine differentially metabolized dipeptides screened in Example 1 to verify the interaction between the dipeptides and EC. 50 Correlation of (growth rate).

[0123] 1. Materials

[0124] Uric acid (purchased from Sigma-Aldrich, USA)

[0125] Sodium hydroxide (purchased from Aladdin Reagent (Shanghai) Co., Ltd.)

[0126] 384-hole plate (purchased from Agilent Technologies (China) Co., Ltd.)

[0127] Fetal bovine serum (purchased from Wuhan Pronosai Life Science Technology Co., Ltd.)

[0128] Nine dipeptide standards: Histidinyl-Tryptophan, Seryl-Isoleucine, Leucyl-Isoleucine, Tryptophyl-Histidine, Isomer 2 of Prolyl-Threonine, Glutamyl-Serine, Leucyl-Leucine, 5-L-Glutamyl-taurine, and Histidinyl-Alanine (purchased from Shanghai Ammonia Biotechnology Co., Ltd.)

[0129] 2. Verification Process

[0130] (1) Dissolve 0.4g of sodium hydroxide in 15mL of deionized water to prepare a 0.667mol / L NaOH solution;

[0131] (2) Dissolve 0.1g of uric acid in 3mL of (1) to prepare an alkaline uric acid solution;

[0132] (3) Filter (2) using a 0.45μm PDVF filter membrane to prepare a filtered alkaline saturated uric acid solution;

[0133] (4) The dipeptide was dissolved in fetal bovine serum to form a 0.37 mol / L dipeptide solution, and then serially diluted with fetal bovine serum to a total of 6 concentration gradients;

[0134] (5) Add 20 μL of fetal bovine serum to a 384-well plate;

[0135] (6) Add 10 μL of dipeptide solution of different concentrations of (4) to the 384-well plate containing fetal bovine serum in (5), and repeat 3 wells for each concentration.

[0136] (7) Add 3 μL of the alkaline saturated uric acid solution filtered from (3) to the 384-well plate of (6);

[0137] (8) Place the plate into a multifunctional cell imaging microplate analyzer (Cytation5) to detect the change in absorbance at a wavelength of 530 nm. Detect the absorbance every 5 min for 3 h.

[0138] (9) Record the detection time and absorbance changes at each detection point;

[0139] (10) Substitute the detection data of each sample into the fitting formula to perform fitting and obtain EC. 50 ;

[0140] (11) Analysis of EC 50 Correlation with dipeptide solution concentration.

[0141] 3. Analysis and Conclusion

[0142] Analysis results as follows Figure 4 As shown, the EC50 of the growth curve of sodium urate monocrystals 50 There is a significant negative correlation between EC and the concentration of the dipeptide solution; the higher the concentration of the dipeptide solution, the higher the EC. 50 The smaller the size, the faster the growth rate of monosodium urate crystals, and compared to the blank control group in fetal bovine serum, EC... 50 The downward trend is significant.

[0143] The above experimental results verify that dipeptides accelerate the growth rate of monosodium urate crystals. Therefore, it is speculated that the ease of monosodium urate crystal growth in the human body is related to dipeptides. Dipeptide concentration can be used as an indicator to assess the ease of crystal growth and as a serum marker to further assess the individual's risk of gout attacks.

[0144] The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Use of a biomarker selected from any one, two or more of: Histidinyl-Tryptophan, Seryl-Isoleucine, Leucyl-Isoleucine, Tryptophyl-Histidine, Isomer 2 of Prolyl-Threonine, Glutamyl-Serine, Leucyl-Leucine, 5-L-Glutamyl-taurine, Histidinyl-Alanine, in the manufacture of a product that affects the growth rate of monosodium urate crystals. The biomarker is a serum metabolite. The structural formula of Isomer 2 of Prolyl-Threonine is .

2. Use according to claim 1, characterized in that, The higher the level of the biomarker of claim 1 in a serum sample, the faster the growth rate of monosodium urate crystals.

3. Use according to claim 2, characterized in that, The biomarker is selected from any one, two or more of: Histidinyl-Tryptophan, Seryl-Isoleucine, Leucyl-Isoleucine, Tryptophyl-Histidine, Isomer 2 of Prolyl-Threonine, Glutamyl-Serine, Leucyl-Leucine, 5-L-Glutamyl-taurine, Histidinyl-Alanine.

4. A method of screening for biomarkers affecting the growth rate of monosodium urate crystals, characterized in that, The screening method comprises the following steps: (3) performing metabolomics analysis on the two groups of serum samples in step (2) respectively; The structural formula of Isomer 2 of Prolyl-Threonine is ; In step (3), high-efficiency chemical isotope labeling-liquid chromatography-mass spectrometry metabolomics platform is used for sample analysis. (1) Calculate the time point EC of serum samples of gout group and non-gout group inducing uric acid monosodium crystals to reach the maximum growth rate 50 ; (2) Gout group ECs were selected 50 Minimal serum sample and non-gout group ECs 50 Maximal serum sample; The serum sample is labeled using a dansyl labeling kit before metabolomics analysis. (4) selecting metabolites significantly correlated with EC 50 as the biomarkers.

5. The screening method according to claim 4, characterized in that, Step (1) is specifically: reading the serum samples of gout group and non-gout group mixed with alkaline uric acid solution at different time points Absorbance value changes, making change curve; According to the curve, the maximum absorbance value of the curve, the minimum absorbance value of the curve, the symmetry degree value S of the curve; Through parameter fitting equation, obtain Hill slope HillSlope value and the corresponding time point EC of Hill slope appearance 50 Value.

6. The screening method according to claim 4 or 5, characterized in that, ​ 7. The screening method according to claim 6, characterized in that, ​

Images