CN119147763A

CN119147763A - Application of reagent for detecting AAGN marker F2 protein

Info

Publication number: CN119147763A
Application number: CN202411175038.2A
Authority: CN
Inventors: 陈丽萌; 张硕; 陈欣
Original assignee: Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Current assignee: Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Priority date: 2023-09-05
Filing date: 2023-09-05
Publication date: 2024-12-17
Also published as: CN117368482B; CN117368482A; CN119001117A

Abstract

The invention relates to the technical field of nephritis medicine, and discloses application of a reagent for detecting AAGN marker F2 protein. The reagent for detecting AAGN markers is used for preparing a medical device for identifying AAGN disease activity, and the prepared medical device can detect the content or the expression level of AAGN markers so as to accurately identify AAGN disease activity, thereby providing effective basis for knowing AAGN malignancy degree, AAGN prognosis of patients and guiding clinical diagnosis and treatment.

Description

Application of reagent for detecting AAGN marker F2 protein

The invention is a divisional application of 2023, 9, 5, 202311140261.9 and entitled "application of reagent for detecting AAGN marker and method for identifying AAGN disease activity".

Technical Field

The invention relates to the technical field of nephritis medicine, in particular to application of a reagent for detecting AAGN marker F2 protein.

Background

The incidence of ANCA-related vasculitis (AAV) kidney involvement in patients with polyangiitis Granulomatosis (GPA) is about 70% and in patients with Microscopic Polyangiitis (MPA) is almost 100%. Kidney involvement is often manifested as ANCA-associated glomerulonephritis (AAGN), generally characterized by rapidly progressing glomerulonephritis. Patients who are predominantly MPO-ANCA positive may undergo a process of steadily worsening renal function, often with irreparable renal lesions when initially present.

Although the pathogenic role of anti-neutrophil cytoplasmic antibodies (ANCAs) in AAV has been demonstrated in animal experiments, there are limitations in accurately judging disease activity. Meta analysis showed that the rising or persisting presence of ANCA in remission had little predictive effect on future disease recurrence. Only 64% of AAV patients have a change in continuous ANCA titers correlated in time with a change in disease state. Even in clinical practice, some active aristolochic acid kidney disease (AAN) patients were found to exhibit low levels of ANCAs, while other disease-modifying patients had a sustained rise of ANCAs.

Accordingly, there is a need to provide a new ANCA-related marker of glomerulonephritis to provide more accurate information about kidney involvement and disease activity.

Disclosure of Invention

The invention aims to provide a method capable of accurately and simply identifying AAGN disease activities so as to guide AAGN clinical treatment.

To achieve the above object, a first aspect of the present invention provides a use of an agent for detecting AAGN marker selected from at least one of CFD protein, F2 protein, FGA protein, PLG protein in the preparation of a medical device for recognizing AAGN disease activity.

A second aspect of the invention provides a method of identifying AAGN disease activities, the method comprising:

Detecting the content or the expression level of AAGN markers in the sample;

The AAGN marker is selected from at least one of CFD protein, F2 protein, FGA protein and PLG protein.

In a third aspect, the invention provides the use of a CFD protein inhibitor in the manufacture of a medicament for the treatment AAGN.

The invention provides a novel AAGN marker, and prepares a medical appliance capable of identifying AAGN disease activity through a reagent capable of detecting AAGN marker, and the medical appliance is used for detecting the content or the expression level of AAGN marker so as to accurately identify AAGN disease activity, thereby providing effective basis for knowing AAGN malignancy degree, knowing AAGN patient prognosis and guiding clinical diagnosis and treatment.

Additional features and advantages of the invention will be set forth in the detailed description which follows.

Drawings

FIG. 1 is a volcanic diagram of a differentially expressed gene provided by the present invention;

FIG. 2 is a heat map of differentially expressed genes provided by the present invention;

FIG. 3 is a KEGG enrichment pathway diagram provided by the present invention;

FIG. 4 is a graph showing ELISA detection results of CFD proteins provided by the invention;

FIG. 5 is a graph of ELISA detection results of F2 protein provided by the invention;

FIG. 6 is a graph of ELISA detection results of FGA protein provided by the invention;

FIG. 7 is a graph showing ELISA detection results of PLG proteins provided by the present invention;

FIG. 8 is a graph of AUC of CFD protein prediction AAGN of disease activity provided by the present invention;

FIG. 9 is a graph of AUC of the F2 protein predicted AAGN disease activity provided by the present invention;

FIG. 10 is a graph of AUC of FGA protein predictions AAGN for disease activity provided by the present invention;

FIG. 11 is a graph of AUC of PLG protein predictive AAGN disease activity provided by the present invention;

FIG. 12 is a graph showing the AUC of the F2 protein in combination with CFD protein for predicting AAGN disease activity.

Detailed Description

The endpoints and any values of the ranges disclosed herein are not limited to the precise range or value, and are understood to encompass values approaching those ranges or values. For numerical ranges, one or more new numerical ranges may be found between the endpoints of each range, between the endpoint of each range and the individual point value, and between the individual point value, in combination with each other, and are to be considered as specifically disclosed herein.

In the invention, CFD protein is abbreviated as complement factor D protein, F2 protein is abbreviated as recombinant human coagulation factor II protein, FGA protein is abbreviated as fibrinogen alpha chain protein, and PLG protein is abbreviated as plasminogen protein.

As previously described, a first aspect of the invention provides the use of an agent for detecting a AAGN marker selected from at least one of CFD protein, F2 protein, FGA protein, PLG protein in the manufacture of a medical device for identifying AAGN disease activity.

Preferably, the method for detecting AAGN markers is an enzyme-linked immunosorbent assay.

The specific operation of the enzyme-linked immunosorbent assay for detecting AAGN markers is not particularly limited, and a person skilled in the art can select the method according to known techniques to achieve an excellent detection result. The following of the present invention illustratively provides a method of detecting AAGN markers, which should not be construed as limiting the invention.

Preferably, the reagent for detecting AAGN marker is an antibody against the AAGN marker.

Those skilled in the art will be able to prepare a corresponding medical device according to the AAGN markers disclosed in the present invention, according to the known techniques in the field of medical device preparation, the medical device containing a reagent capable of detecting the AAGN marker, and the medical device may be an ELISA cartridge or the like.

As previously described, a second aspect of the invention provides a method of identifying AAGN disease activities, the method comprising:

Detecting the content or the expression level of AAGN markers in the sample;

Preferably, the AAGN marker is a combination of CFD protein and F2 protein. The inventors of the present invention found that when CFD protein and F2 protein were used as AAGN markers at the same time, there was higher accuracy.

Preferably, the method further comprises the step of providing said sample with a CFD protein content or expression level of greater than 65.0ng/mL, a AAGN disease activity sensitivity of 83.3% and a specificity of 81.1%. That is, when the amount or expression of CFD protein in the sample is greater than 65.0ng/mL, and the sensitivity of CFD protein to AAGN disease activities is 83.3% and the specificity is 81.1%, the presence of AAGN disease activities is indicated.

Preferably, the method further comprises the step of determining that the content or the expression amount of the F2 protein in the sample is more than 580.2ng/mL, the sensitivity of the F2 protein to AAGN disease activity is 71.4%, and the specificity is 97.1%. That is, when the amount or expression of F2 protein in the sample is greater than 580.2ng/mL and the sensitivity of F2 protein to AAGN disease activity is 71.4% and the specificity is 97.1%, AAGN disease activity is indicated.

According to a preferred embodiment, the method further comprises the step of providing said sample with a content or expression of said FGA protein of greater than 14.4ng/mL and a sensitivity of 78.6% and a specificity of 82.4% for said FGA protein to AAGN disease events. That is, when the amount or expression of FGA protein in the sample is greater than 14.4ng/mL and the sensitivity of FGA protein to AAGN disease activity is 78.6% and the specificity is 82.4%, AAGN disease activity is indicated.

According to another preferred embodiment, the method further comprises the step of providing said sample with a PLG protein content or expression level of greater than 36.4ng/mL, and a PLG protein sensitivity to AAGN disease events of 57.1% and a specificity of 97.1%. That is, when the content or expression amount of PLG protein in the sample is greater than 36.4ng/mL, and the sensitivity of PLG protein to AAGN disease activity is 57.1%, the specificity is 97.1%, the presence of AAGN disease activity is indicated.

Preferably, the sample is urine.

As previously described, a third aspect of the invention provides the use of a CFD protein inhibitor in the manufacture of a medicament for the treatment AAGN.

The invention will be described in detail below by way of examples.

In the examples below, all the raw materials used are commercially available, unless otherwise specified.

In the examples below, unless otherwise specified, TFA each represents trifluoroacetic acid, ACN each represents acetonitrile, and FA each represents formic acid.

Example 1

This example is a data independent tandem mass spectrometry (DIA) proteomic analysis of AAV patient urine samples

Urine samples of 40 patients diagnosed with AAV in beijing co-ordination hospital at month 5 of 2022 to 4 of 2023 were collected for DIA proteomic analysis, wherein 20 cases of AAV renal involvement (AAGN), including 16 cases of AAGN active period (Ra), 4 cases of AAGN remission period (Rr), 20 cases of AAV non-renal involvement (Nr), including 10 cases of active period, 10 cases of remission period, and specific analytical detection procedures were as follows:

1a) Preparation of samples

After centrifugation of the urine samples to remove cell debris, urine protein was precipitated with acetone pre-chilled at-20 ℃, and the urine protein concentration was determined using BCA protein assay kit (ThermoScientific, brandhburg, NJ, USA), with specific assay procedures referred to the instructions within the kit.

Pretreatment of urine protein was performed according to the instructions in the protein pretreatment kit (OSFP 00018X, peptide-prone Micro), specifically as follows:

Taking 30 mu g of protein from each sample, adding 20 mu L of reagent O and 30 mu L of reagent A, blowing and mixing uniformly, then adding 1.2 mu L of reagent B, mixing uniformly, heating at 95 ℃ for 5min, returning the sample to room temperature after heating, adding 2 mu L of reagent C and 5 mu L of reagent D, mixing uniformly, and carrying out enzymolysis at 37 ℃ for 2h. After the enzymolysis is finished, adding 3 mu L of reagent E, uniformly mixing, stopping enzymolysis reaction, centrifuging 17000g for 1min, sucking the supernatant, concentrating the supernatant by adopting a vacuum freezing centrifugal concentrator, and carrying out subsequent desalting on the obtained dry powder, wherein the method comprises the following steps of:

Desalting column activation, namely adding 100 mu L of methanol into a desalting column, and centrifuging for 1min at 700 g;

de-salting column for removing impurities 100. Mu.L of a Condition Buffer (Condition Buffer,70v% ACN+0.2v% TFA in water) was added to the de-salting column, and 700g was centrifuged for 1min;

Desalting column equilibration 100. Mu.L of Wash Buffer (Wash Buffer,0.2v% TFA in water) was added to the desalting column and centrifuged at 700g for 1min;

Loading the sample, namely dissolving the dry powder sample by using 100 mu L of heavy suspension buffer solution (ResuspendBuffer, 1v%TFA aqueous solution), loading the dry powder sample onto a desalting column, centrifuging for 1min at 700g, and repeating the operation once;

Desalting column washing 100. Mu.L of washing Buffer (Wash Buffer,0.2v% TFA in water) was added to the desalting column, and the column was centrifuged at 700g for 1min, and the operation was repeated once;

Sample Elution 30. Mu.L of eluent (solution Buffer,90v% ACN+0.2v% TFA in water) was added to the desalting column, centrifuged at 700g for 1min, and eluted twice to give a final sample volume of 60. Mu.L, and the sample was concentrated by a vacuum freeze centrifugation concentrator, and the resulting dry powder was dissolved in 20. Mu.L of 0.1v% FA in water for subsequent analysis, wherein a Quality Control (QC) sample was prepared by mixing 4. Mu.L of each sample.

1B) DIRECT DIA proteomic analysis

LC-MS/MS analysis was performed using an EASY-nLC1200UHPLC liquid chromatograph and a Q-ExactiveHF mass spectrometer (thermo scientific, rockwell, IL, USA), specifically as follows:

The peptide sample obtained in step 1 a) was subjected to a pre-column (75. Mu.m.times.2cm, 3. Mu.m, thermo scientific) followed by a gradient elution through a reverse phase analytical column (75. Mu.m.times.500 mm, 2. Mu.m, monotech) with mobile phase A being 20% acetonitrile in water and mobile phase B being 0.1% acetonitrile formate in water, with an elution gradient of 5% -30% mobile phase B (flow rate=600 nL/min; elution time 60 min).

The parameters of mass spectrum scanning are set as follows, full scanning resolution is 120000m/z, scanning range is 350-1250m/z, AGC target value is 3e6, injection time is not more than 50ms, parameters of secondary mass spectrum are set as follows, resolution is 30000, AGC target value is 1e6, and maximum injection time is automatic.

Wherein, during the analysis, a QC sample is inserted after every 10 samples for quality control.

1C) DIRECT DIA proteomic data analysis

The mass spectrum raw data obtained in step 1 b) was searched for by default using Spectronaut PulsarX software (Biognosys, AG, schlieren, switzerland). All results were filtered according to a Q cutoff of 0.01 (i.e., false positive rate (FDR) of 1%), and at least two unique peptide fragments were identified for each protein. The missing value of the protein abundance is filled up by adopting a kNN method.

1D) Differentially expressed genes (differentiallyexpressedgenes, DEGs) analysis Differentially Expressed Genes (DEGs) were analyzed using the limma package of R software, as follows:

The corrected P value <0.05, the absolute value of Fold Change (FC) was >1.5, resulting in a volcanic plot of the differentially expressed gene shown in fig. 1 and a thermal plot of the differentially expressed gene shown in fig. 2.

As can be seen from the volcanic plot of FIG. 1, there were significant differences between AAV kidney involvement and AAV non-kidney involvement, and 353 DEGs were identified (absolute value of log2 (FC) >1.5; p-value <0.05 after correction), with 314 up-regulated expressed genes and 39 down-regulated expressed genes.

As can be seen from the heat map of fig. 2, there was a clear difference between AAV kidney involvement and AAV non-kidney involvement, with 4 cases of AAV kidney involvement in remission with protein profiles that were similar to those of AAV non-kidney involvement. Further subpopulation analysis, there were significant differences in heat map protein expression profiles between AAV kidney involvement active and AAV kidney involvement remission.

1E) Pathway enrichment analysis

Using clusterProfiler software package of R software, functional enrichment analysis was performed on the differentially expressed genes based on GO and KEGG databases, resulting in KEGG enrichment pathway maps as shown in fig. 3.

In the GO analysis, three categories were identified, biological Processes (BP), cellular Components (CC), and Molecular Functions (MF), and used to study biological processes related to differentially expressed genes, where BP analysis suggested that humoral immune response, complement activation, and platelet degranulation were the first three major biological processes, CC analysis suggested that blood microparticles, extracellular matrix, and vesicles were the first three major biological processes, MF analysis suggested that peptide inhibitor activity and endopeptidase inhibitor/modulator activity were important.

As can be seen from the enrichment pathway diagram of fig. 3, the complement thrombotic pathway and cholesterol metabolism are significantly activated in AAV kidney affected patients in KEGG analysis.

Example 2

This example was used to further determine the correlation of the above differentially expressed genes with AAGN

Screening the target differentially expressed genes in table 1 by weighted gene co-expression network analysis (WGCNA), minimum absolute shrinkage and selection operator (LASSO) and random forest analysis, specifically as follows:

2a) Weighted gene co-expression network analysis

Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression network in the proteomics cohort. Soft threshold power values and adjacencies are calculated using pick Soft Threshold functions in WGCNA software packages. By quantifying the saliency modules, the correlation between the saliency modules and AAGN is evaluated, and key modules related to AAGN are established. The overlap between the differentially expressed genes and the genes within the key module was studied with emphasis to determine the key genes.

2B) Machine learning analysis of key genes

Performing Lasso analysis on the key genes determined in step 2 a) using the glmnet package of R software and setting penalty parameters for 10-fold cross-validation to evaluate high-dimensional data, and at the same time classifying the differentially expressed genes using the random forest package of R software to identify central genes and determining the optimal number of variables by calculating the average error rate of candidate central genes.

The intersection of the two machine learning algorithms is the characteristic gene (namely the target differential expression gene) related to the activity degree of the AAV patient.

TABLE 1

Reference numerals	Gene shorthand	Gene Chinese
			1	OGN	Osteoinductive factor
2	SERPINF1	Serine protease inhibitors
			3	CFD	Complement factor D
4	SOD1	Copper-zinc superoxide dismutase
			5	F2	Recombinant human coagulation factor II
6	FGA	Fibrinogen alpha chain
			7	CA1	Recombinant human carbonic anhydrase 1
8	PLG	Plasminogen (P)

Example 3

This example was used to further verify the correlation of part of the target differentially expressed genes with AAGN

Urine samples were collected from 50 patients diagnosed with AAV in the beijing co-hospital at 5 months 2022 to 4 months 2023, of which 35 cases of AAV kidney involvement (AAGN) included 14 cases of AAGN active period (Ra), 21 cases of AAGN remission period (Rr), and 15 cases of AAV non-kidney involvement (Nr). The expression level of a part of the target differential expression gene protein was measured by enzyme-linked immunosorbent assay (ELISA), the results are shown in FIGS. 4 to 7, and the feasibility of predicting AAGN active phase of the target differential expression gene protein was evaluated by AUC curve, the results are shown in FIGS. 8 to 12.

As can be seen from fig. 4 to 7, the levels of F2 protein, CFD protein, FGA protein and PLG protein in urine of AAGN active patients were significantly higher than AAGN remission and AAV non-kidney affected patients (P < 0.05).

As can be seen from FIGS. 8 to 12, for patients in AAGN active phase, the urine had an AUC of >65.0ng/mL, a sensitivity of 83.3%, a specificity of 81.1%, an AUC of >580.2ng/mL of F2 protein of 0.899, a sensitivity of 71.4%, a specificity of 97.1%, an AUC of >14.4ng/mL of FGA protein of 0.851, a sensitivity of 78.6%, a specificity of 82.4%, an AUC of >36.4ng/mL of PLG protein of 0.796, a sensitivity of 57.1%, a specificity of 97.1%, and an AUC of up to 0.992 (P < 0.001), a sensitivity of 75.0% and a specificity of 97.1% when F2 protein and CFD protein are combined.

The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited thereto. Within the scope of the technical idea of the invention, a number of simple variants of the technical solution of the invention are possible, including combinations of the individual technical features in any other suitable way, which simple variants and combinations should likewise be regarded as being disclosed by the invention, all falling within the scope of protection of the invention.

Claims

1. Use of a reagent for detecting an AAGN marker in the preparation of a medical device for identifying AAGN disease activity, wherein the AAGN marker is F2 protein.

2. The use according to claim 1, wherein the method for detecting the AAGN marker is enzyme-linked immunosorbent assay.

3. The use according to claim 1 or 2, wherein the reagent for detecting the AAGN marker is an antibody against the AAGN marker.