Application of blood differential protein combination in the preparation of reagents for detecting ASD

Abstract

The present invention relates to the field of reagent application technology, and in particular to the use of a group of blood difference protein combinations in the preparation of reagents for detecting autism spectrum disorders (ASD), wherein the blood difference protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein; wherein when the blood difference protein combination is 12, the accuracy and specificity are optimal. That is, the present invention determines a blood difference protein combination that can be used to detect autism spectrum disorders, and the blood difference protein combination is used to prepare a reagent for detecting autism spectrum disorders, which can improve the objectivity, accuracy and specificity of autism spectrum disorder detection.

Description

Technical Field

[0001] The present invention relates to the technical field of reagent application, and in particular to the application of a blood differential protein combination in the preparation of a reagent for detecting ASD. Background Art

[0002] Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders in children. The etiology and pathogenesis of the disease are not yet clear. ASD is highly heterogeneous and has no precise detection criteria. It is usually detected using the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). It is not only subjective, but also often leads to delayed or misdiagnosed diagnosis. Even for skilled doctors, it takes 15-18 months for children to be diagnosed. Early diagnosis and intervention of children with ASD can improve their prognosis. Therefore, it is urgent to find detection reagents to assist in the diagnosis of ASD, which is of great significance for the early detection and intervention of the disease.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a group of blood difference protein combinations for use in the preparation of reagents for detecting ASD, aiming to solve the problems that the existing detection methods have a certain degree of subjectivity and are prone to cause detection delays or misdiagnosis.

[0005] The technical solution of the present invention is as follows:

[0006] The use of a group of blood difference protein combinations in the preparation of a reagent for detecting ASD, wherein the blood difference protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein.

[0007] The use of the blood difference protein combination in the preparation of a reagent for detecting ASD, wherein the blood difference protein combination is C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein.

[0008] The use of the blood differential protein combination in the preparation of a reagent for detecting ASD, wherein blood is collected, complement and coagulation pathway proteins in the blood are analyzed by mass spectrometry multiple reaction monitoring (MRM) proteomics technology, and then the blood differential protein combination for detecting ASD in the blood is confirmed by a machine learning method.

[0009] The blood difference protein combination is used in the preparation of a reagent for detecting ASD, wherein determining the presence of the blood difference protein combination in the blood further comprises: using ELISA (enzyme-linked immunosorbent assay) technology to detect each protein in the blood difference protein combination one by one, and determining the concentration change of the detected blood difference protein.

[0010] The use of the blood difference protein combination in the preparation of a reagent for detecting ASD, wherein determining the presence of the blood difference protein combination in the blood further comprises: using protein chip technology to simultaneously detect the blood difference proteins and determine the concentration changes of the detected blood difference proteins.

[0011] Beneficial effects: The present invention provides an application of a group of blood differential protein combinations in the preparation of reagents for detecting ASD, wherein the blood differential protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein. That is, the present invention determines a blood differential protein combination that can be used to detect autism spectrum disorders, and the blood differential protein combination is used to prepare a reagent for detecting autism spectrum disorders, which can improve the objectivity, accuracy and specificity of autism spectrum disorder detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the experimental process of Example 1 of the present invention;

[0013] Figure 2 AD in the figure is the experimental result diagram of a proteome consisting of 12 differentially expressed proteins obtained by using a machine learning method in Example 1 of the present invention;

[0014] Figure 3 This is a result diagram of the mass spectrometry expression values ​​of 12 blood differential protein markers in Example 1 of the present invention. DETAILED DESCRIPTION

[0015] The present invention provides a group of blood differential protein combinations for use in preparing a reagent for detecting ASD. In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0017] Existing technology uses the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) to detect ASD, but it is subjective and often leads to delayed or misdiagnosed diagnosis. Even for skilled doctors, it may take 15-18 months for a child to be diagnosed.

[0018] Based on this, the present invention provides a group of blood difference protein combinations for use in preparing a reagent for detecting ASD, wherein the blood difference protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein.

[0019] Blood sampling is convenient and can better reflect the physiological process of the body, making it one of the best sources of disease markers. However, it is difficult to find blood protein markers with high specificity and accuracy that can be used for clinical diagnosis. Previous studies have shown that ASD is a multifactorial and complex disease. Therefore, the present invention provides a group of differential protein combinations related to complement and coagulation cascades in the blood for use in the preparation of reagents for detecting ASD. By using any 10 or more proteins in the blood differential protein combination (a total of 12) as a protein marker for detecting ASD, the accuracy and specificity of the reagent can be improved; and the application of the reagent has a certain objectivity, so that the detection accuracy is higher and the specificity is stronger.

[0020] Studies have shown that complement and coagulation pathway proteins are associated with ASD. In this study, 33 proteins involved in this pathway in the plasma of ASD children and control children were compared and analyzed by proteomics mass spectrometry multiple reaction monitoring technology (MRM). The present invention uses machine learning methods to obtain a proteome consisting of 12 differentially expressed proteins, and applies the proteome to the preparation of reagents for detecting ASD, so that the reagents have accuracy and specificity for detecting ASD; finally, the complement and coagulation-related proteins of children with ASD were systematically detected and analyzed, and their structures further support that changes in blood complement and coagulation-related proteins are associated with ASD, and some complement and coagulation proteins have the ability to detect ASD.

[0021] In a preferred embodiment, the blood difference protein combination is C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein; when this protein combination is used as an ASD detection marker, it has certain objectivity, accuracy and specificity.

[0022] In one embodiment, the blood difference protein combination is a combination of C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, and FGA protein. When this protein combination is used as an ASD detection marker, it has certain objectivity, accuracy and specificity.

[0023] In one embodiment, the blood difference protein combination is a combination of C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein. When this protein combination is used as an ASD detection marker, it also has certain objectivity, accuracy and specificity.

[0024] In one embodiment, blood is collected, and the presence of the blood differential protein combination is confirmed by MRM proteomics technology and machine learning methods. By using MRM proteomics technology to analyze complement and coagulation pathway proteins in the blood, and then using a method combined with machine learning, the blood differential protein combination used to detect ASD in the blood can be accurately confirmed, thereby realizing the detection of ASD using reagents.

[0025] In one embodiment, determining the presence of the blood differential protein combination in the blood further comprises: using ELISA technology to detect each protein in the blood differential protein combination one by one, and determining the concentration change of the detected blood differential protein. Using ELISA to detect each protein in the blood differential protein combination one by one makes the determination of protein concentration more accurate.

[0026] In one embodiment, determining the presence of a blood differential protein combination in the blood further comprises: using protein chip technology to simultaneously detect the blood differential proteins and determine the concentration changes of the detected blood differential proteins. Using protein chip technology to simultaneously detect the blood differential proteins makes the determination of protein concentration more efficient.

[0027] The following further examples are given to illustrate the present invention in detail. It should also be understood that the following examples are only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Some non-essential improvements and adjustments made by those skilled in the art based on the above content of the present invention belong to the scope of protection of the present invention.

[0028] Example 1

[0029] The present invention uses a method combining MRM proteomics technology and machine learning to identify potential protein substances in the complement and coagulation cascades in the blood of ASD, such as Figure 1 As shown, the method comprises the following steps: first, extracting proteins from plasma samples of children with ASD and children in the control group; second, comparing the protein spectrum data of complement and coagulation cascades in the online database with the data in the samples, screening out the detectable protein types in the complement and coagulation cascades, and performing targeted mass spectrometry on the samples to determine the relative content of proteins in each complement and coagulation cascade (which can reduce the influence of certain high-abundance proteins); third, using statistics to find blood differential proteins; finally, using machine learning to find blood differential protein combinations, and realizing the application in the preparation of reagents for detecting ASD; the analytical method uses MRM proteomics and machine learning to find ASD blood protein markers, which has higher sensitivity and better specificity than traditional proteomics methods.

[0030] Specifically, the experimental flow diagram of this embodiment is shown in Figure 1 By comparing and analyzing blood samples from 30 ASD children and healthy controls, 33 complement and coagulation cascade-related proteins were identified; among them, 16 were differentially expressed proteins, as shown in Table 1:

[0031]

[0032] Afterwards, machine learning was performed using complement and coagulation-related plasma proteins. All of the following methods were performed using a 5-fold test. Five decision tree-based machine learning methods were selected to analyze the data and the most appropriate machine learning method was selected. This method was used to rank the importance of each protein pair and grouping, and then the cumulative AUC method was used to help find the best marker combination with the least number of potential biomarkers. The method calculated the AUC (area under the curve, area under the ROC curve) of multiple marker combinations in sequence based on the logistic regression algorithm according to their importance from high to low. When the marker combination reached 1, it was considered to be the best marker combination. The above blood difference protein combination (a total of 12) can be used to detect blood protein markers for ASD. The final experimental results are as follows: Figure 2 As shown in FIG. 1 , the results of the mass spectrometry expression values ​​of the 12 blood differential proteins are compared as follows Figure 3 As shown, Figure 3The y-axis in the figure represents the corresponding intensity of mass spectra; ** represents p<0.01; *** represents p<0.001; *** represents p<0.0001.

[0033] Figure 2 A in the figure uses 5 decision tree-based machine learning methods to find the optimal way to distinguish, and judges the indicators (the sum of ROC, F1, Precision and Recall values) to obtain the Catboost model that is most suitable for the analysis of this set of data. Figure 2 B in the figure uses the Catboost calculation method to measure the importance of each protein in distinguishing ASD from the control group. The higher the value, the stronger the distinguishing power. Figure 2 C in the figure calculates the discrimination efficiency starting from the highest protein in B. The first point represents that there is only one protein with a discrimination efficiency of 0.96, the second point represents that 5 proteins have a discrimination effect of 0.97, the second point represents that 9 proteins have a discrimination effect of 0.97, and the fourth point represents that 12 proteins have a discrimination effect of 1 (that is, when more than 12 proteins are used as protein markers, the discrimination effect is 1, so 12 proteins are the optimal combination, that is, the combination that can achieve a discrimination effect of 1); Figure 2 The D in the figure is the combination of these 12 proteins used for identification and the machine learning scores used in other ways. The scores are all higher than 0.8, indicating that the identification effect is very good.

[0034] By collecting blood samples from patients, the 12 blood differential proteins are tested one by one using ELISA technology, or the 12 blood differential proteins are tested simultaneously using protein chip technology. ASD patients can be diagnosed based on the changing trend of protein expression.

[0035] In summary, the present invention provides a group of blood difference protein combinations for use in preparing reagents for detecting ASD, wherein the blood difference protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein. That is, the present invention determines a blood difference protein combination that can be used to detect autism spectrum disorders, and the blood difference protein combination is used to prepare a reagent for detecting autism spectrum disorders, which can improve the objectivity, accuracy and specificity of autism spectrum disorder detection.

[0036] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. Use of a group of blood differential protein combinations in the preparation of reagents for detecting ASD, characterized in that: The blood difference protein combination is a combination of any 10 or more proteins among C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein.

2. The use according to claim 1, characterized in that: The blood difference protein combination is C1QC protein, C3 protein, C4BPA protein, C4BPB protein, C5 protein, C6 protein, C9 protein, CFH protein, CFHR1 protein, CFHR3 protein, FGA protein, and SERPIND1 protein.

3. The use according to claim 1, characterized in that: Blood was collected, and the complement and coagulation pathway proteins in the blood were analyzed by mass spectrometry multiple reaction monitoring proteomics technology. Then, the blood differential protein combination for detecting ASD was confirmed by machine learning methods.

4. The use according to claim 3, characterized in that: Determining the presence of the blood differential protein combination in the blood also includes: using ELISA technology to detect each protein in the blood differential protein combination one by one, and determining the concentration change of the detected blood differential protein.

5. The use according to claim 3, characterized in that: Determining the presence of a combination of blood differential proteins in the blood also includes: using protein chip technology to simultaneously detect the blood differential proteins and determining changes in the concentrations of the detected blood differential proteins.

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