Method, system and device for evaluating glomerular senescence

Abstract

The invention provides a glomerular senescence evaluation method, system and device, and relates to the technical field of glomerular senescence evaluation.The evaluation method mainly comprises the steps that based on a low-abundance proteomics result of an aged mouse plasma sample, core expression protein related to glomerular senescence is screened out, a prediction model aiming at glomerular senescence is constructed, and the prediction model is used for predicting the glomerular senescence; the prediction model comprises a glomerular senescence index; detecting core expression protein data of a to-be-detected plasma sample, and calculating the glomerular senescence index of the to-be-detected plasma sample according to the prediction model. According to the scheme, aiming at the specificity of glomerular senescence, core expression protein can be screened out and a prediction model can be constructed through methods such as low-abundance proteomics results, so that the glomerular senescence can be evaluated simply, conveniently, reliably and quickly; the scheme is helpful for follow-up efficient research and evaluation of anti-glomerular senescence drugs, and is helpful for follow-up efficient research and explanation of the effect of glomerular senescence in CKD progress.

Description

Technical Field

[0001] The present invention relates to the technical field of glomerular aging assessment, and in particular to a method, system and device for assessing glomerular aging. Background Art

[0002] Glomerular aging is the main cause of kidney aging. Accurately assessing glomerular aging helps understand the extent and speed of kidney aging.

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

[0004] Existing biomarkers and prediction models generally have low specificity for aging assessment, and lack assessment methods for single organ aging, making it even more difficult to accurately assess glomerular aging. Summary of the invention

[0005] The object of the present invention is to provide a method, system and device for evaluating glomerular aging to solve at least one of the above-mentioned technical problems existing in the prior art.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for evaluating glomerular aging, comprising the following steps: Step 1: Based on the low-abundance proteomics results of aging mouse plasma samples, core expressed proteins related to glomerular aging were screened out, and a prediction model for glomerular aging was constructed. The prediction model included a glomerular aging index, and the specific calculation formula was: Glomerular aging index = 0.52*HMGB1+0.43*OPN+0.69*GDF15- 0.23*GPLD1-0.41*STAB1; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; Step 2: Detect the core protein expression 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 to reflect the degree of glomerular aging.

[0007] Through the above method, the glomerular aging index of the tested plasma sample can be predicted simply, quickly and accurately, which will help the subsequent efficient development and evaluation of anti-glomerular aging drugs, and help the 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 renal function, glomerular sclerosis and other consequences).

[0008] In a feasible implementation manner, the step 1 specifically includes the following steps: Step 11: Collect blood samples from mice (male) at least 20 weeks old and mice (male) at most 8 weeks old using anticoagulant blood collection tubes containing EDTA (ethylenediaminetetraacetic acid), centrifuge the blood samples within 30 minutes, and collect the supernatant.

[0009] Preferably, the operating parameters of the centrifuge include: Centrifugal speed: 3500 rpm; Centrifugation temperature: 4°C; Centrifugation time: 15 minutes.

[0010] Step 12: Use the beads (beads or GST column materials) in the high-abundance protein enrichment preparation kit as a pre-treatment to enrich the low-abundance proteins in the blood samples of each mouse sample; use trypsin to enzymatically hydrolyze the proteins enriched on the beads; take the supernatant and desalt the enzymatically hydrolyzed peptides.

[0011] Step 13: Identify the samples obtained in step 12 by LC-MS / MS (liquid chromatography tandem mass spectrometry), and collect mass spectrometry data of each sample by DIA (data independent acquisition) technology.

[0012] Preferably, before mass spectrometry injection, each sample is mixed with an iRT standard peptide at a preset volume ratio as an internal control.

[0013] Preferably, the preset volume ratio is 20:1.

[0014] Preferably, the conditions for LC-MS include: Flow rate: 400 nL / min; C18 analytical column (model: 15 cm × 75 μm ID, 1.6 μm C18, brand: ionoptics); Buffer A is 0.1% FA (formic acid) aqueous solution; Buffer B is 0.1% FA (formic acid) / 80% ACN (acetonitrile) / 20% water.

[0015] Step 14: Perform data analysis and processing (median normalization and log2 logarithmic transformation) on the mass spectrometry data to obtain a protein matrix of each sample; the protein matrix includes peptides and proteins.

[0016] Preferably, the software for data analysis and processing is Spectronaut Pulsar 18.4 (Biognosys); the search sequence file is uniprot-Homo sapiens-9606-2024.2.1.fasta.

[0017] 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 degree of separation between the aging group and the non-aging group; the principal component analysis mainly includes: standardizing the original data matrix X, finding the covariance matrix, calculating the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, and then 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 after reducing to K dimensions.

[0018] Step 16: Perform differential analysis on the protein matrix according to the aging group and the non-aging group to obtain differentially expressed proteins, including up-regulated expression proteins and down-regulated expression proteins; construct a matrix expression file for low-abundance proteomics; The difference analysis mainly includes calculating the fold change (FC) and log2 (FC), and the specific formula can be: log2(FC) = mean of experimental group - mean of control group; Then the P value was obtained by t-test.

[0019] Preferably, the setting conditions for the difference analysis include: FC≥1.5 or FC≤0.67, and the P value is less than 0.05.

[0020] Step 17, the matrix expression file is brought into the urine albumin / creatinine ratio (UACR) data of each sample, and the gene modules are divided based on weighted gene co-expression network analysis (WGCNA); the association between the gene module and the urine albumin phenotype is determined by the Pearson correlation algorithm, and the core expression proteins related to urine albumin are screened out.

[0021] Preferably, the method for determining the association relationship specifically includes: calculating the correlation coefficient and p-value between the characteristic gene (Eigengene) in the gene module and urine albumin by using 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, and screening out the gene module related to urine albumin; 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 characteristic gene (Eigengene) in the relevant gene module, and drawing a scatter plot based on these two correlations to prove that each gene in the relevant gene module is highly correlated with both the urine albumin phenotype and the characteristic gene in the relevant gene module; based on the core gene analysis, selecting the first preset number of proteins with the highest connectivity in the relevant gene module as the candidate proteins most correlated with urine albumin, and then performing the same protein screening with aging-related proteins and kidney intrinsic expression proteins to obtain key candidate proteins; evaluating and pre-experimenting 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.

[0022] Step 18, normalizing and correcting the core expression proteins to keep the expression value range of the core expression proteins between 0 and 100 so that the expression levels of different proteins are comparable; based on logistic regression, assigning each core expression protein a corresponding weight β to obtain a calculation formula for the glomerular aging index; The specific calculation formula of the weight β can be: β = α * k; Among them, 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.

[0023] In a feasible embodiment, the step 1 further includes step 19, performing correlation verification on the prediction model through a simple glomerular injury mouse model and clinical samples to ensure the reliability of the prediction model.

[0024] In the second aspect, based on the same inventive concept, the present application also provides a system for evaluating glomerular aging, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the core expressed 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 includes the 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; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; The result generating module is used to send out the glomerular aging index.

[0025] On the third aspect, based on the same inventive concept, the present application also provides a device for evaluating glomerular aging, including a processor, a memory and a bus, wherein 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 glomerular aging evaluation method as described above, and the bus connects the functional components for transmitting information.

[0026] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a method, system and device for evaluating glomerular aging, which can target the specificity of glomerular aging, screen out core expressed proteins through low-abundance proteomics results and other methods, build a prediction model, and then evaluate glomerular aging simply, reliably and quickly; this scheme is conducive to the subsequent efficient development and evaluation of anti-glomerular aging drugs, and is conducive to the subsequent efficient research and explanation of the role of glomerular aging in the progression of CKD (chronic kidney disease). BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flow chart of a method for evaluating glomerular aging provided by an embodiment of the present invention; Figure 2 An overview of the protein quantity in the aging group and the non-aging group provided in the embodiments of the present invention; Figure 3 The principal component analysis diagram of the aging group and the non-aging group provided in the embodiment of the present invention; Figure 4 A volcano color map for difference analysis provided by an embodiment of the present invention; Figure 5 A color heat map of the difference analysis provided by an embodiment of the present invention; Figure 6 A clustering tree color map of WGCNA provided in an embodiment of the present invention; Figure 7 A color diagram of the module-feature relationship provided for an embodiment of the present invention; Figure 8 A scatter plot of the black module provided by an embodiment of the present invention; Fig. 9 A scatter plot of the midnightblue module provided by an embodiment of the present invention; Fig.10 A scatter plot of the cyan module provided by an embodiment of the present invention; Fig.11 A comparison pie chart provided for an embodiment of the present invention; Fig.12 A bar graph showing the urine ACR levels of each group of mice provided in the embodiments of the present invention; Fig.13 Representative PAS staining histograms of kidney tissue of each group of mice provided in the embodiments of the present invention; Fig.14 A bar graph showing the quantitative results of PAS staining of the kidneys of each group of mice provided in the embodiments of the present invention; Fig.15 A bar graph showing the quantitative results of the number of foot processes of kidney podocytes in each group of mice provided in the embodiments of the present invention; Fig.16 A bar graph showing the quantitative results of the width of the foot process of the kidney podocytes of each group of mice provided in the embodiments of the present invention; Fig.17 A bar graph showing the quantitative results of the thickness of the glomerular basement membrane of the kidneys of each group of mice provided in the embodiments of the present invention; Fig.18 The scatter plots of clinical samples provided in the embodiments of the present invention are as follows: Figure a represents a scatter plot of the correlation between the glomerular aging index and age; Figure b represents a scatter plot of the correlation between the glomerular aging index and age by grouping; Figure c represents a scatter plot of the correlation between the glomerular aging index and urine albumin; Fig.19 A diagram of a system for evaluating glomerular aging provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0031] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0032] The present invention is further explained below in conjunction with specific implementation modes.

[0033] It should also be noted that the following specific embodiments or specific implementations are a series of optimized settings listed in the present invention to further explain the specific content of the invention, and these settings can be used in combination or in association with each other.

[0034] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for evaluating glomerular aging, comprising the following steps: Step 1: Based on the low-abundance proteomics results of aging mouse plasma samples, core expressed proteins related to glomerular aging were screened out, and a prediction model for glomerular aging was constructed. The prediction model included a glomerular aging index, and the specific calculation formula was: Glomerular aging index = 0.52*HMGB1+0.43*OPN+0.69*GDF15- 0.23*GPLD1-0.41*STAB1; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; Step 2: Detect the core protein expression 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.

[0035] Through the above method, the glomerular aging index of the tested plasma sample can be predicted simply, quickly and accurately, which will help the subsequent efficient development and evaluation of anti-glomerular aging drugs, and will help the subsequent efficient research and explanation of the role of glomerular aging in the progression of CKD (chronic kidney disease).

[0036] Furthermore, the step 1 specifically includes the following steps: Step 11, collect 11 blood samples from 20-week-old mice (male) and 6 blood samples from 8-week-old mice (male) using anticoagulant blood collection tubes containing EDTA (ethylenediaminetetraacetic acid), centrifuge the blood samples within 30 minutes, and take the supernatant.

[0037] Preferably, the operating parameters of the centrifuge include: Centrifugal speed: 3500 rpm; Centrifugation temperature: 4°C; Centrifugation time: 15 minutes.

[0038] Step 12: Use the beads (beads or GST column materials) in the high-abundance protein enrichment preparation kit as a pre-treatment to enrich the low-abundance proteins in the blood samples of each mouse sample; use trypsin to enzymatically hydrolyze the proteins enriched on the beads; take the supernatant and desalt the enzymatically hydrolyzed peptides.

[0039] Step 13: Identify the samples obtained in step 12 by LC-MS / MS (liquid chromatography tandem mass spectrometry), and collect mass spectrometry data of each sample by DIA (data independent acquisition) technology.

[0040] Preferably, before mass spectrometry injection, each sample is mixed with an iRT standard peptide at a preset volume ratio as an internal control.

[0041] Preferably, the preset volume ratio is 20:1.

[0042] Preferably, the conditions for LC-MS include: Flow rate: 400 nL / min; C18 analytical column (model: 15 cm × 75 μm ID, 1.6 μm C18, brand: ionoptics); Buffer A is 0.1% FA aqueous solution; Buffer B is 0.1% FA / 80% ACN / 20% water.

[0043] Step 14: Perform data analysis on the mass spectrometry data to obtain a protein matrix of each sample; the protein matrix includes peptides (44,395) and proteins (5,562).

[0044] Preferably, the software for data analysis and processing is Spectronaut Pulsar 18.4 (Biognosys); the search sequence file is uniprot-Homo sapiens-9606-2024.2.1.fasta.

[0045] Step 15: Figure 2 As shown in Figure 2, 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). It was evaluated that the separation between the aging group and the non-aging group was good, as shown in Figure 2. Figure 3 As shown; the principal component analysis mainly includes: standardizing the original data matrix X, finding the covariance matrix, calculating the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, and then 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 after being reduced to K dimensions.

[0046] Step 16: Perform differential analysis on the protein matrix according to the aging group and the non-aging group, and obtain differentially expressed proteins (424), including up-regulated proteins (169) and down-regulated proteins (255), such as Figure 4-5 As shown; construct a matrix expression file for low-abundance proteomics; The difference analysis mainly includes calculating the fold change (FC) and log2 (FC), and the specific formula can be: log2(FC) = mean of experimental group - mean of control group; Then the P value was obtained by t-test; The setting 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.

[0047] Step 17, the matrix expression file is brought into the urine albumin / creatinine ratio (UACR) data of each sample, and the gene modules are divided based on weighted gene co-expression network analysis (WGCNA); the association between the gene module and the urine albumin phenotype is determined by the Pearson correlation algorithm, and the core expression proteins related to urine albumin are screened out; like Figure 6-7 As shown, there are 19 gene modules from 5485 genes; the Grey module represents a gene combination that cannot be assigned to any module and has no reference significance; The method for determining the association relationship specifically includes: calculating the correlation coefficient and p-value between the characteristic gene (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 the screening threshold, screening out the gene modules related to urine albumin (a total of 3, namely black module, midnightblue module and cyan module); calculating the correlation (Gene Significance, GS) between the expression level of each gene in the related gene module and urine albumin, and calculating the correlation between the expression levels of these genes and the characteristic gene (Eigengene) in the related gene module, and drawing a scatter plot based on these two correlations, such as Figure 8-10 As shown in the figure, the horizontal axis represents the expression level of each gene in each gene module (Moddule Membership), and the vertical axis represents the uncertainty of the correlation for the measured value given by the reference laboratory (Gene significance for ulab), which is used to prove that each gene in the relevant gene module is highly correlated with the urinary albumin phenotype and the characteristic genes in the relevant gene module; based on the core gene analysis, the top 50 proteins with the highest connectivity in the relevant gene module were selected as the candidate proteins most correlated with urinary albumin (146), and then the same protein screening was performed with aging-related proteins and kidney-specific expression proteins to obtain key candidate proteins (26), as shown in the figure. Fig.11 As shown; the key candidate proteins were evaluated and pre-experimented, and ranked from high to low according to detection frequency and content, and the top 5 key candidate proteins were selected as core expression proteins; most of these 5 core expression proteins also have regulatory effects on inflammation and metabolism (especially lipid metabolism).

[0048] Step 18, normalizing and correcting the core expression proteins to keep the expression value range of the core expression proteins between 0 and 100 so that the expression levels of different proteins are comparable; based on logistic regression, assigning each core expression protein a corresponding weight β to obtain a calculation formula for the glomerular aging index; The specific calculation formula of the weight β can be: β = α * k; Among them, α represents the formula coefficient of the five core expression proteins obtained by logistic regression; k represents the reciprocal of the sum of the formula coefficients of the five core expression proteins; The calculation formula of the glomerular aging index includes the product of the expression values ​​of 5 core expressed proteins and their weights β, specifically: Glomerular aging index = 0.52*HMGB1+0.43*OPN+0.69*GDF15- 0.23*GPLD1-0.41*STAB1; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilizer 1.

[0049] Furthermore, the step 1 also includes step 19, performing correlation verification on the prediction model through a simple glomerular injury mouse model and clinical samples to ensure the reliability of the prediction model.

[0050] Specifically, the experimental subjects of the simple glomerular injury mouse model include a model group and a control group, and the model group also includes a high (glomerular) aging score group and a low (glomerular) aging score group; like Figure 12-17 As shown, the results demonstrated that samples in the high (glomerular) senescence score group had more severe proteinuria, mesangial matrix expansion, decreased podocyte number, increased foot process width, and basement membrane thickening; The number of samples in each scoring group is 6; Each sample was calculated based on 15-20 fields of view; The symbol “##” in the figure indicates p value < 0.01, compared with the control group; The symbol “**” in the figure indicates a p value < 0.01, compared with the high senescence score group; exist Fig.13 The figure shows the cell microstructure under an electron microscope at different scales.

[0051] Specifically, clinical samples included 482 plasma samples collected from a hospital (including 162 healthy samples and 320 CKD samples); In the healthy samples, Fig.18 As shown in Figure a, there is a correlation between the glomerular aging index and age ( =0.381, p<0.001); In the CKD samples, e.g. Fig.18 As shown in Figure b, this correlation is even higher ( =0.443 and 0.376, respectively; p < 0.001); At the same time, if Fig.18 As shown in Figure c, there is a correlation between glomerular aging index and urinary albumin ( =0.556, p<0.001).

[0052] Embodiment 2: like Fig.19 As shown, this embodiment provides a system for evaluating glomerular aging, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the core expressed 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 includes the 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; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; The result generating module is used to send out the glomerular aging index.

[0053] Embodiment three: The present embodiment provides a device for evaluating glomerular aging, including 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 method for evaluating glomerular aging as described above. The bus connects the functional components for transmitting information.

[0054] In another embodiment, the present solution can be implemented by an integrated device, which may include corresponding modules for performing each or several steps in the above-mentioned embodiments. The module may be one or more hardware modules specially configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination.

[0055] The processor performs the various methods and processes described above. For example, the method implementation in the present solution can be implemented as a software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be executed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (for example, by means of firmware).

[0056] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus connects various circuits including one or more processors, memories, and / or hardware modules together. The bus can also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0057] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these 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 invention.

Claims

1. A method for evaluating glomerular aging, characterized in that: include: Step 1: Based on the low-abundance proteomics results of aging mouse plasma samples, core expressed proteins related to glomerular aging were screened out, and a prediction model for glomerular aging was constructed. The prediction model included a glomerular aging index, and the specific calculation formula was: Glomerular aging index = 0.52*HMGB1+0.43*OPN+0.69*GDF15- 0.23*GPLD1-0.41*STAB1; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; Step 2: Detect the core protein expression 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.

2. The evaluation method according to claim 1, characterized in that: The step 1 specifically includes: Step 11, collecting blood samples from mice at least 20 weeks old and mice at most 8 weeks old using anticoagulant blood collection tubes containing ethylenediaminetetraacetic acid, centrifuging the blood samples using a centrifuge within 30 minutes, and taking the supernatant; Step 12, using beads in the high-abundance protein enrichment preparation kit as a pre-treatment to enrich low-abundance proteins in the blood samples of each mouse sample; using trypsin to enzymatically hydrolyze the proteins enriched on the beads; taking the supernatant and performing a desalting operation on the enzymatically hydrolyzed peptides; Step 13, identifying the samples obtained in step 12 by liquid chromatography tandem mass spectrometry, and collecting mass spectrum data of each sample by data independent acquisition technology; Step 14: performing data analysis on the mass spectrometry data to obtain a protein matrix of each sample; the protein matrix includes peptides and proteins; Step 15, performing principal component analysis on the protein matrix according to the aging group and the non-aging group to evaluate the degree of separation between the aging group and the non-aging group; Step 16: Perform differential analysis on the protein matrix according to the aging group and the non-aging group to obtain differentially expressed proteins, including up-regulated and down-regulated proteins; construct a matrix expression file for low-abundance proteomics; Step 17, the matrix expression file is brought into the urine albumin-creatinine ratio data of each sample, and the gene modules are divided based on the weighted gene co-expression network analysis; the association between the gene module and the urine albumin phenotype is determined by the Pearson correlation algorithm, and the core expression proteins related to urine albumin are screened out; Step 18: normalize and correct the core expressed proteins to keep the expression value range of the core expressed proteins between 0 and 100; assign corresponding weights to each core expressed protein based on logistic regression to obtain the calculation formula of the glomerular aging index.

3. The evaluation method according to claim 2, characterized in that: The operating parameters of the centrifuge in step 11 include: Centrifugal speed: 3500 rpm; Centrifugation temperature: 4°C; Centrifugation time: 15 minutes.

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

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: The setting conditions for the difference analysis in step 16 include: 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: The method for determining the association relationship in step 17 specifically includes: calculating the correlation coefficient and p-value between the characteristic genes 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 the screening threshold, and screening out the gene module related to urine albumin; calculating the correlation between the expression amount of each gene in the related gene module and urine albumin, and calculating the correlation between the expression amount of these genes and the characteristic genes in the related gene module, and drawing a scatter plot based on the two correlations to prove that each gene in the related gene module is highly correlated with both the urine albumin phenotype and the characteristic genes in the related gene module; based on the core gene analysis, selecting the first preset number of proteins with the highest connectivity in the related gene module as the candidate proteins most correlated with urine albumin, and then performing the same protein screening with aging-related proteins and kidney intrinsic expression proteins to obtain key candidate proteins; evaluating and pre-experimenting 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: The step 1 also includes step 19, verifying the correlation of the prediction model using a simple glomerular injury mouse model and clinical samples.

9. A system for evaluating glomerular aging, characterized in that: It includes a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the core expressed 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 includes the 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; Among them, 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 glycosylated phosphatidylinositol-specific phospholipase D1; STAB1 represents the expression value of stabilin 1; The result generating module is used to send out the glomerular aging index.

10. A device for evaluating glomerular aging, characterized in that: It includes a processor, a memory and a bus, wherein 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 as described in any one of claims 1 to 8, and the bus connects the functional components for transmitting information.

Images