A method, system and apparatus for assessing glomerular aging

Abstract

The application provides a glomerular aging evaluation method, system and device, relates to the technical field of glomerular aging evaluation, and mainly comprises the following steps: based on the low-abundance proteomics result of the plasma sample of an aging mouse, screening core expression proteins related to glomerular aging, and constructing a prediction model for glomerular aging, wherein the prediction model comprises a glomerular aging index; detecting the core expression protein data of a to-be-detected plasma sample, and calculating the glomerular aging index of the to-be-detected plasma sample according to the prediction model. According to the specificity of glomerular aging, the core expression proteins are screened out through the low-abundance proteomics result and other methods, the prediction model is constructed, and then the glomerular aging is simply, reliably and quickly evaluated. The application is helpful for subsequent efficient research and development and evaluation of anti-glomerular aging drugs, and is also helpful for subsequent efficient research and explanation of the role of glomerular aging in CKD progression.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of glomerular aging assessment, and in particular to a glomerular aging assessment method, system and device. BACKGROUND

[0002] Glomerular aging is the main cause of kidney aging. Precise assessment of glomerular aging helps to understand the degree and speed of kidney aging.

[0003] There is significant heterogeneity in aging between different individuals and between different organs of the same individual.

[0004] Existing biomarkers and prediction models generally have low specificity for aging assessment, and there is a lack of assessment methods for single-organ aging, making it more difficult to accurately assess glomerular aging. SUMMARY

[0005] The present application aims to provide a glomerular aging assessment method, system and device to solve at least one of the above technical problems in the prior art.

[0006] In a first aspect, to solve the above technical problems, the present application provides a glomerular aging assessment method, comprising the following steps:

[0007] Step 1: Based on the low-abundance proteomics results of the plasma samples of aging mice, core expression proteins related to glomerular aging are screened out, and a prediction model for glomerular aging is constructed, the prediction model including a glomerular aging index, the specific calculation formula being:

[0008] Glomerular aging index = 0.52*HMGB1 + 0.43*OPN + 0.69*GDF15 - 0.23*GPLD1 - 0.41*STAB1;

[0009] Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteonectin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1;

[0010] Step 2: Detect the core expression protein data of the test plasma sample, calculate the glomerular aging index of the test plasma sample according to the prediction model, and use it to reflect the degree of glomerular aging.

[0011] By the above method, the glomerular aging index of the measured blood plasma sample can be simply, quickly and accurately predicted, which is helpful for subsequent efficient research and development and evaluation of anti-glomerular aging drugs, and is helpful for subsequent efficient research and explanation of the role of glomerular aging in the progression of CKD (chronic kidney disease, which often leads to massive proteinuria, irreversible loss of kidney function, and glomerular sclerosis).

[0012] In a feasible implementation, the step 1 specifically comprises the following steps:

[0013] Step 11, after collecting blood samples of at least 20-week-old mice (male) and at most 8-week-old mice (male) through an anticoagulant blood collection tube containing EDTA (ethylene diamine tetraacetic acid), the blood samples are centrifuged by a centrifuge within 30 minutes, and the supernatant is taken.

[0014] Preferably, the operation parameters of the centrifuge include:

[0015] Centrifugal speed: 3500 rpm;

[0016] Centrifugal temperature: 4°C;

[0017] Centrifugal time: 15 minutes.

[0018] Step 12, the Beads (beads or GST column material) in the high-abundance protein removal enrichment kit are used as pretreatment to enrich low-abundance proteins in the blood samples of each mouse sample; the proteins enriched on the Beads are enzymatically digested by trypsin; and the supernatant is taken to perform desalination on the digested peptides.

[0019] Step 13, the sample obtained in step 12 is identified by LC-MS / MS (liquid chromatography tandem mass spectrometer), and mass spectrum data of each sample is collected by DIA (data-independent acquisition) technology.

[0020] Preferably, before mass spectrometry sampling, each sample is mixed with iRT standard peptides according to a preset volume ratio as an internal control.

[0021] Preferably, the preset volume ratio is 20:1.

[0022] Preferably, the conditions of liquid chromatography-mass spectrometry include:

[0023] Flow rate: 400 nL / min;

[0024] C18 analysis column (model: 15 cm x 75 μm ID, 1.6 μm C18, brand: ionopticks);

[0025] Buffer A is 0.1% FA (formic acid) aqueous solution;

[0026] Buffer B is 0.1% FA (formic acid) / 80% ACN (acetonitrile) / 20% water.

[0027] Step 14, data analysis and processing (median standardization and log2 logarithmic conversion) are performed on the mass spectrometry data to obtain a protein matrix of each sample; the protein matrix includes peptide segments and proteins.

[0028] Preferably, the software for data analysis and processing is Spectronaut Pulsar 18.4 (Biognosys Company); the search sequence file is uniprot-Homo sapiens-9606-2024.2.1.fasta.

[0029] Step 15, principal component analysis (PCA) is performed on the protein matrix according to the aging group and the non-aging group to evaluate the separation degree between the aging group and the non-aging group; the principal component analysis mainly includes: standardizing the original data matrix X, obtaining the covariance matrix, calculating the eigenvalues and corresponding eigenvectors of the covariance matrix, then arranging the eigenvectors into a matrix from top to bottom according to the corresponding eigenvalue size, taking the first K rows to form a matrix P, and performing matrix operation according to the formula Y=PX to obtain the data Y after reduction to K dimensions.

[0030] Step 16, differential analysis is performed on the protein matrix according to the aging group and the non-aging group to obtain differential proteins, including up-regulated expression proteins and down-regulated expression proteins; a matrix expression file of low-abundance proteomics is constructed.

[0031] The differential analysis mainly includes calculating the fold change (FC) and log2(FC), and the specific formula can be:

[0032] log2(FC) = experimental group mean - control group mean;

[0033] and then performing t-test to obtain the P value.

[0034] Preferably, the setting conditions of the differential analysis include: FC≥1.5 or FC≤0.67, and the P value is less than 0.05.

[0035] Step 17, the matrix expression file is taken into the urinary albumin / creatinine ratio (UACR) data of each sample, the gene modules are divided based on weighted gene co-expression network analysis (WGCNA), the association between the gene modules and the urinary albumin phenotype is determined through a pearson correlation algorithm, and the core expression proteins related to the urinary albumin are screened out.

[0036] Preferably, the method for determining the association relationship specifically comprises: calculating the correlation coefficient and p value between the Eigengene in the gene module and the urinary albumin through a pearson correlation algorithm; taking the absolute value of the correlation coefficient greater than or equal to 0.3 and the p value less than 0.05 as a screening threshold, screening out the related gene modules of the urinary albumin; respectively calculating the correlation (Gene Significance, GS) between the expression amount of each gene in the related gene module and the urinary albumin, and the correlation between the expression amount of these genes and the Eigengene in the related gene module, drawing a scatter plot according to the two correlations, for proving that each gene in the related gene module is highly correlated with the urinary albumin phenotype and the Eigengene in the related gene module; based on core gene analysis, selecting the top pre-set number of proteins with the highest connectivity in the related gene module as candidate proteins most related to the urinary albumin, and performing the same protein screening on the aging-related proteins and the kidney intrinsic expression proteins to obtain key candidate proteins; evaluating and pre-testing the key candidate proteins, sorting them in descending order according to the detection frequency and content, and taking the top 5 key candidate proteins as the core expression proteins.

[0037] Step 18, the core expression proteins are subjected to normalization correction processing for keeping the expression value range of the core expression proteins between 0-100, so as to make the different protein expression amounts comparable; based on logistic regression, each core expression protein is given a corresponding weight β, and a calculation formula of the glomerular aging index is obtained.

[0038] The specific calculation formula of the weight β can be:

[0039] β = α * k;

[0040] Wherein, α represents the formula coefficient of each core expression protein obtained by logistic regression; k represents the reciprocal of the sum of the formula coefficients of all core expression proteins.

[0041] In an implementable embodiment, the step 1 further comprises a step 19 of verifying the prediction model by a simple glomerular injury mouse model and clinical samples to ensure the reliability of the prediction model.

[0042] In a second aspect, based on the same inventive concept, the application further provides an evaluation system for glomerular aging, comprising a data receiving module, a data processing module and a result generating module.

[0043] The data receiving module is configured to receive core expression protein data of a to-be-tested plasma sample.

[0044] The data processing module stores a prediction model for glomerular aging, and calculates a glomerular aging index of the to-be-tested plasma sample based on the core expression protein data; the prediction model comprises the glomerular aging index, and the specific calculation formula is:

[0045] Glomerular aging index = 0.52*HMGB1 + 0.43*OPN + 0.69*GDF15 - 0.23*GPLD1 - 0.41*STAB1.

[0046] Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteopontin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylphosphatidylinositol-specific phospholipase D1; and STAB1 represents the expression value of stabilin 1.

[0047] The result generating module is configured to externally output the glomerular aging index.

[0048] In a third aspect, based on the same inventive concept, the application further provides an evaluation device for glomerular aging, comprising a processor, a memory and a bus, the memory stores instructions and data read by the processor, and the processor is configured to call the instructions and data in the memory to execute the evaluation method for glomerular aging as described above, and the bus is connected between the functional components for transmitting information.

[0049] By adopting the above technical solution, the application has the following beneficial effects:

[0050] The evaluation method, system and device for glomerular aging provided by the application can screen core expression proteins by low-abundance proteomics results and other methods according to the specificity of glomerular aging, construct a prediction model, and then simply, reliably and quickly evaluate glomerular aging; the present application is helpful for subsequent efficient research and development and evaluation of anti-glomerular aging drugs, and is also helpful for subsequent efficient research and explanation of the role of glomerular aging in the progression of CKD (chronic kidney disease). BRIEF DESCRIPTION OF DRAWINGS

[0051] The accompanying drawings which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification are illustrated in which:

[0052] Figure 1 A flow chart of a method for evaluating glomerular aging according to an embodiment of the present application;

[0053] Figure 2 A total overview of the number of proteins in the aging group and the non-aging group according to an embodiment of the present application;

[0054] Figure 3 A principal component analysis chart of the aging group and the non-aging group according to an embodiment of the present application;

[0055] Figure 4 A volcano chart of differential analysis according to an embodiment of the present application;

[0056] Figure 5 A color heat map of differential analysis according to an embodiment of the present application;

[0057] Figure 6 A cluster tree chart of WGCNA according to an embodiment of the present application;

[0058] Figure 7 A module-feature relationship chart according to an embodiment of the present application;

[0059] Figure 8 A scatter plot of the black module according to an embodiment of the present application;

[0060] Figure 9 A scatter plot of the midnightblue module according to an embodiment of the present application;

[0061] Figure 10 A scatter plot of the cyan module according to an embodiment of the present application;

[0062] Figure 11 A comparison pie chart according to an embodiment of the present application;

[0063] Figure 12 A column chart of the urine ACR level of each group of mice according to an embodiment of the present application;

[0064] Figure 13 A representative PAS staining column chart of the kidney tissue of each group of mice according to an embodiment of the present application;

[0065] Figure 14A kidney PAS staining quantitative result column chart of each group of mice provided by the embodiment of the present application;

[0066] Figure 15 A kidney podocyte foot process number quantitative result column chart of each group of mice provided by the embodiment of the present application;

[0067] Figure 16 A kidney podocyte foot process width quantitative result column chart of each group of mice provided by the embodiment of the present application;

[0068] Figure 17 A kidney glomerular basement membrane thickness quantitative result column chart of each group of mice provided by the embodiment of the present application;

[0069] Figure 18 A scatter plot color chart of clinical samples provided by the embodiment of the present application: a chart represents a correlation scatter plot between glomerular aging index and age; a chart represents a correlation grouped scatter plot between glomerular aging index and age; a chart represents a correlation scatter plot between glomerular aging index and urine albumin;

[0070] Figure 19 A glomerular aging evaluation system diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0071] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0072] In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0073] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0074] The present invention will be further explained below with reference to specific embodiments.

[0075] It should also be noted that the specific embodiments or implementation methods described below are a series of optimized settings listed by the present invention to further explain the specific content of the invention, and these settings can be combined or used in conjunction with each other.

[0076] Example 1:

[0077] like Figure 1 As shown, this embodiment provides a method for assessing glomerular aging, including the following steps:

[0078] Step 1: Based on the low-abundance proteomics results of plasma samples from aging mice, core expressed proteins related to glomerular aging were screened, and a predictive model for glomerular aging was constructed. This predictive model includes a glomerular aging index, calculated using the following formula:

[0079] Glomerular aging index = 0.52 * HMGB1 + 0.43 * OPN + 0.69 * GDF15 - 0.23 * GPLD1 - 0.41 * STAB1;

[0080] Wherein, HMGB1 represents the expression value of high mobility group B1; OPN represents the expression value of osteopontin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylated phosphatidylinositol-specific phospholipase D1; and STAB1 represents the expression value of stabilizer 1.

[0081] Step 2: Detect the core expressed protein data of the plasma sample to be tested, and calculate the glomerular aging index of the plasma sample to be tested according to the prediction model.

[0082] The above method can be used to predict the glomerular aging index of the tested plasma sample in a simple, fast and accurate manner, which will help to efficiently develop and evaluate anti-glomerular aging drugs and to efficiently study and explain the role of glomerular aging in the progression of CKD (chronic kidney disease).

[0083] Furthermore, step 1 specifically includes the following steps:

[0084] Step 11, after collecting 11 blood samples of 20-week-old mice (male) and 6 blood samples of 8-week-old mice (male) respectively through EDTA (ethylenediaminetetraacetic acid) containing anticoagulant blood collection tubes, centrifuging the blood samples through a centrifuge within 30 minutes, and taking the supernatant.

[0085] Preferably, the operating parameters of the centrifuge include:

[0086] Centrifugal speed: 3500 rpm;

[0087] Centrifugal temperature: 4°C;

[0088] Centrifugal time: 15 minutes.

[0089] Step 12, enrich low-abundance proteins in each mouse sample blood sample by using Beads (beads or GST column material) in the high-abundance protein removal enrichment preparation kit as pretreatment; enrich the proteins on the Beads by trypsin; take the supernatant to desalt the peptides after enzymolysis.

[0090] Step 13, identify the sample obtained in step 12 by LC-MS / MS (liquid chromatography tandem mass spectrometer), and collect mass spectrum data of each sample by DIA (data independent acquisition) technology.

[0091] Preferably, before mass spectrometry sampling, each sample is mixed with iRT standard peptides according to a preset volume ratio as an internal control.

[0092] Preferably, the preset volume ratio is 20:1.

[0093] Preferably, the conditions of liquid chromatography-mass spectrometry include:

[0094] Flow rate: 400 nL / min;

[0095] C18 analysis column (model: 15 cm x 75 μm ID, 1.6 μm C18, brand: ionopticks);

[0096] Buffer A is 0.1% FA aqueous solution;

[0097] Buffer B is 0.1% FA / 80% ACN / 20% water.

[0098] Step 14, data analysis and processing of the mass spectrum data to obtain a protein matrix of each sample; the protein matrix includes peptides (44395) and proteins (5562).

[0099] Preferably, the data analysis and processing software is Spectronaut Pulsar 18.4 (Biognosys); the search sequence file is uniprot-Homo sapiens-9606-2024.2.1.fasta.

[0100] Step 15, as follows Figure 2 As shown, principal component analysis (PCA) was performed on the protein matrix according to the aging group (20 weeks old) and the non-aging group (8 weeks old). The results showed that the separation between the aging group and the non-aging group was good. Figure 3 As shown; the principal component analysis mainly includes: standardizing the original data matrix X, obtaining the covariance matrix, calculating the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, arranging the eigenvectors into a matrix from top to bottom according to the corresponding eigenvalues, taking the first K rows to form a matrix P, and performing matrix operations according to the formula Y=PX to obtain the data Y reduced to K dimensions.

[0101] Step 16: Perform differential analysis on the protein matrix according to the aging group and the non-aging group, obtaining differentially expressed proteins (424), including upregulated proteins (169) and downregulated proteins (255), such as... Figures 4-5 As shown; construct a matrix expression file for low-abundance proteomics;

[0102] The difference analysis mainly includes calculating the fold change (FC) and log2(FC), and the specific formula is as follows:

[0103] log2(FC) = experimental group mean - control group mean;

[0104] The p-value was then obtained through a t-test.

[0105] The conditions for the difference analysis include: the absolute value of FC is greater than 1.5 and the p-value is less than 0.05.

[0106] Step 17: Input the urinary albumin / creatinine ratio (UACR) data of each sample into the matrix expression file, divide the gene modules based on weighted gene co-expression network analysis (WGCNA), and determine the association between gene modules and urinary albumin phenotype using the Pearson correlation algorithm to screen out the core expressed proteins related to urinary albumin.

[0107] like Figures 6-7As shown, the number of the gene modules is 19, from 5485 genes; wherein, the Grey module represents the gene combination which cannot be attributed to any module, and has no reference significance;

[0108] The determination method of the correlation relationship specifically comprises: calculating the correlation coefficient and p value between the Eigengene in the gene module and urine albumin through the pearson correlation algorithm; taking the absolute value of the correlation coefficient greater than or equal to 0.3 and the p value less than 0.05 as a screening threshold, and screening out the relevant gene modules (a total of 3, which are black module, midnight blue module and cyan module) of urine albumin; respectively calculating the correlation (Gene Significance, GS) between the expression amount of each gene in the relevant gene module and urine albumin, and calculating the correlation between the expression amount of these genes and the Eigengene in the relevant gene module, and according to the two correlations, a scatter plot is drawn, as shown in Figures 8-10 As shown, the horizontal axis represents the expression amount (Moddule Membership) of each gene in each gene module, and the vertical axis represents the uncertainty (Gene significance for ulab) of the correlation for the measured value given by the reference laboratory, which is used to prove that each gene in the relevant gene module is highly correlated with the urine albumin phenotype and the Eigengene in the relevant gene module; based on the core gene analysis, the top 50 proteins with the highest connectivity in the relevant gene module are selected as the candidate proteins (146) most related to urine albumin, and the same protein screening is performed on the aging-related proteins and the kidney intrinsic expression proteins, to obtain the key candidate proteins (26), as shown in Figure 11 As shown; the key candidate proteins are evaluated and pre-tested, and are sorted in descending order according to the detection frequency and content, and the top 5 key candidate proteins are taken as the core expression proteins; these 5 core expression proteins mostly have a regulatory effect on inflammation and metabolism (especially lipid metabolism).

[0109] Step 18, the core expression proteins are normalized and corrected, for keeping the expression value range of the core expression proteins between 0-100, so as to make the different protein expression amounts comparable; based on logistic regression, each core expression protein is given a corresponding weight β, to obtain the calculation formula of the glomerular aging index;

[0110] The specific calculation formula of the weight β can be:

[0111] β = α * k;

[0112] Wherein, α represents the formula coefficient of the 5 core expression proteins obtained by logistic regression; k represents the reciprocal of the sum of the formula coefficients of the 5 core expression proteins.

[0113] The calculation formula of the glomerular aging index comprises the product of the expression values of the five core expression proteins and the weight β, and is specifically:

[0114] Glomerular aging index = 0.52*HMGB1 + 0.43*OPN + 0.69*GDF15 - 0.23*GPLD1 - 0.41*STAB1;

[0115] Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteopontin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylphosphatidylinositol-specific phospholipase D1; and STAB1 represents the expression value of stabilin 1.

[0116] Further, the step 1 further comprises a step 19 of verifying the prediction model by a simple glomerular injury mouse model and a clinical sample, so as to ensure the reliability of the prediction model.

[0117] Specifically, the experimental subjects of the simple glomerular injury mouse model include a model group and a control group, and the model group further includes a high (glomerular) aging score group and a low (glomerular) aging score group.

[0118] As shown in Figures 12-17 , the results prove that the samples of the high (glomerular) aging score group have more serious proteinuria, glomerular mesangial matrix expansion, podocyte number reduction, foot process width increase and basement membrane thickening;

[0119] The number of samples in each score group is 6;

[0120] Each sample is calculated according to 15-20 fields of view;

[0121] The symbol "##" in the figure represents that the p value is less than 0.01, and the comparison is made with the control group;

[0122] The symbol "**" in the figure represents that the p value is less than 0.01, and the comparison is made with the high aging score group;

[0123] In Figure 13 , the cell microstructure in the electron microscope under different scales is embodied.

[0124] Specifically, the clinical sample includes 482 plasma samples (including 162 healthy samples and 320 CKD samples) collected by a certain hospital;

[0125] In the healthy samples, as shown in Figure 18 a of FIG. 1, there is a correlation between the glomerular aging index and the age ( = 0.381, p < 0.001);

[0126] In CKD samples, as shown in b of FIG. 2, the correlation is higher (r = 0.443, p < 0.001) than in healthy samples (r = 0.376, p < 0.001). Figure 18 respectively equal to 0.443 and 0.376; p < 0.001).

[0127] At the same time, as shown in c of FIG. 2, there is a correlation between the glomerular aging index and urinary albumin (r = 0.556, p < 0.001). Figure 18 Example Two:

[0128] As shown in FIG. 3, the embodiment provides an evaluation system for glomerular aging, comprising a data receiving module, a data processing module and a result generating module.

[0129] The data receiving module is configured to receive core expression protein data of a to-be-tested plasma sample. Figure 19 The data processing module stores a prediction model for glomerular aging, and calculates a glomerular aging index of the to-be-tested plasma sample based on the core expression protein data; the prediction model comprises the glomerular aging index, and a specific calculation formula is as follows:

[0130] Glomerular aging index = 0.52 * HMGB1 + 0.43 * OPN + 0.69 * GDF15 - 0.23 * GPLD1 - 0.41 * STAB1.

[0131] Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteonectin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylated phosphatidylinositol-specific phospholipase D1; and STAB1 represents the expression value of stabilin 1.

[0132] The result generating module is configured to externally issue the glomerular aging index.

[0133] Example Three:

[0134] The embodiment provides an evaluation device for glomerular aging, comprising a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is configured to call the instructions and data in the memory to execute the evaluation method for glomerular aging as described above, and the bus is connected between each functional component for transmitting information.

[0135]

[0136] The embodiment provides an evaluation device for glomerular aging, comprising a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is configured to call the instructions and data in the memory to execute the evaluation method for glomerular aging as described above, and the bus is connected between each functional component for transmitting information.

[0137] ​​​In yet another implementation form of the present solution, the solution can be implemented by means of an integrated device, which can comprise respective modules for performing the respective or several steps of the above-described embodiments. The modules can be one or more hardware modules specially configured to perform the respective steps, or implemented by a processor configured to perform the respective steps, or stored in a computer-readable medium for implementation by a processor, or by some combination.

[0138] The processor performs the various methods and processes described above. For example, the method embodiments in the present solution can be implemented as a software program that is tangibly embodied in a machine-readable medium, such as a memory. In some embodiments, portions of the software program can be loaded and / or installed via the memory and / or communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps of the above-described methods can be performed. Alternatively, in other embodiments, the processor can be configured to perform one of the above-described methods by any other suitable means, such as by means of firmware.

[0139] The device can be implemented with a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application of the hardware and the overall design constraints. The bus will connect various circuits of the device, including one or more processors, memories, and / or hardware modules. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0140] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like, and can be divided into an address bus, a data bus, a control bus, etc.

[0141] Finally, it should be noted that the above-described embodiments are merely intended to illustrate the technical solutions of the present application, and are not intended to limit the scope of the present application; even though the above-described embodiments have been described in detail, those skilled in the art should understand that they can still modify the technical solutions described in the above-described embodiments, or make equivalent replacements to some or all of the technical features; and such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating glomerular aging, characterized by, The application relates to a method for predicting glomerular aging, and a glomerular aging prediction model. The method comprises the following steps: Step 1: based on the low-abundance proteomics results of the plasma samples of aging mice, core expression proteins related to glomerular aging are screened out, and a prediction model for glomerular aging is constructed, wherein the prediction model comprises a glomerular aging index, and the specific calculation formula is as follows: Glomerular aging index = 0.52*HMGB1 + 0.43*OPN + 0.69*GDF15 - 0.23*GPLD1 - 0.41*STAB1; Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteopontin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylphosphatidylinositol-specific phospholipase D1; and STAB1 represents the expression value of stabilin 1.

2. The evaluation method according to claim 1, characterized in that Step 2: the core expression protein data of the to-be-detected plasma sample is detected, and the glomerular aging index of the to-be-detected plasma sample is calculated according to the prediction model. The step 1 specifically comprises the following steps: Step 11: blood samples of mice aged at least 20 weeks and mice aged at most 8 weeks are collected through an anticoagulation blood collection tube containing ethylenediaminetetraacetic acid, and then the blood samples are centrifuged through a centrifuge within 30 minutes, and the supernatant is taken; Step 12: the Beads in a high-abundance protein removal preparation kit are used to enrich low-abundance proteins in the blood samples of each mouse sample; the proteins enriched on the Beads are enzymatically hydrolyzed through trypsin; and the supernatant is taken to perform desalting operation on the hydrolyzed peptides; Step 13: the sample obtained in the step 12 is identified through a liquid chromatograph-mass spectrometer, and mass spectrometry data of each sample is collected through a data-independent acquisition technology; Step 14: the mass spectrometry data is analyzed and processed to obtain a protein matrix of each sample; the protein matrix comprises peptides and proteins; Step 15: the protein matrix is subjected to principal component analysis according to the aging group and the non-aging group to evaluate the separation degree between the aging group and the non-aging group; Step 16: the protein matrix is subjected to differential analysis according to the aging group and the non-aging group to obtain differential proteins, including up-regulated expression proteins and down-regulated expression proteins; and a low-abundance proteomics matrix expression file is constructed; Step 17: the matrix expression file is taken into the urine albumin creatinine ratio data of each sample, a gene module is divided based on a weighted gene co-expression network analysis, the correlation between the gene module and the urine albumin phenotype is determined through a pearson correlation algorithm, and core expression proteins related to the urine albumin are screened out; 3. The evaluation method according to claim 2, characterized in that Step 18: the core expression proteins are subjected to normalization correction processing, so that the expression value range of the core expression proteins is kept between 0 and 100; and the calculation formula of the glomerular aging index is obtained based on logistic regression and by giving each core expression protein a corresponding weight. The operation parameters of the centrifuge in the step 11 comprise the following: Centrifugal speed: 3500 r / min; Centrifugal temperature: 4 DEG C; Centrifugal time: 15 min.

4. The evaluation method according to claim 2, characterized in that In step 13, before mass spectrometry sampling, each sample is mixed with iRT standard peptide segments as an internal control according to a preset volume ratio.

5. The evaluation method according to claim 4, characterized in that The preset volume ratio is 20:

1.

6. The evaluation method according to claim 2, characterized in that In step 16, the setting conditions for differential analysis include that the absolute value of FC is greater than 1.5 and the P value is less than 0.

05.

7. The evaluation method according to claim 2, characterized in that In step 17, the determination method of the correlation relationship specifically includes: calculating the correlation coefficient and p value between the characteristic genes in the gene module and urinary albumin by the pearson correlation algorithm; taking the absolute value of the correlation coefficient greater than or equal to 0.3 and the p value less than 0.05 as the screening threshold, screening out the correlation gene module with urinary albumin; calculating the correlation between the expression amount of each gene in the correlation gene module and urinary albumin, and the correlation between the expression amount of these genes and the characteristic genes in the correlation gene module, respectively, and drawing a scatter plot according to the two correlations, which is used to prove that each gene in the correlation gene module is highly correlated with both the urinary albumin phenotype and the characteristic genes in the correlation gene module; based on core gene analysis, selecting the top preset number of proteins with the highest connectivity in the correlation gene module as the candidate proteins most related to urinary albumin, and performing the same protein screening on the aging-related proteins and the kidney intrinsic expression proteins to obtain the key candidate proteins; evaluating and pre-testing the key candidate proteins, sorting them from large to small according to the detection frequency and content, and taking the top 5 key candidate proteins as the core expression proteins.

8. The evaluation method according to claim 2, characterized in that Step 1 further includes step 19 of verifying the prediction model by a simple glomerular injury mouse model and clinical samples.

9. An evaluation system of glomerular senescence, characterized by, It comprises a data receiving module, a data processing module and a result generating module. The data receiving module is used to receive the core expression protein data of the plasma sample to be tested. The data processing module stores a prediction model for glomerular aging, and calculates the glomerular aging index of the plasma sample to be tested based on the core expression protein data; the prediction model comprises a glomerular aging index, and the specific calculation formula is: Glomerular aging index = 0.52*HMGB1 + 0.43*OPN + 0.69*GDF15 - 0.23*GPLD1 - 0.41*STAB1; Wherein, HMGB1 represents the expression value of high mobility group protein B1; OPN represents the expression value of osteopontin; GDF15 represents the expression value of growth differentiation factor 15; GPLD1 represents the expression value of glycosylphosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; The result generating module is used to externally issue the glomerular aging index.

10. A device for assessing glomerular aging, characterized in that, It comprises a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute the evaluation method in any one of claims 1-8, and the bus is connected between each functional component for transmitting information.

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