CN115044665A - Application of ARG1 in the preparation of sepsis diagnosis, severity judgment or prognosis assessment reagents or kits - Google Patents

Abstract

The invention relates to the technical field of biological detection, provides application of ARG1 as a sepsis biomarker, and particularly provides application of ARG1 in preparation of a sepsis diagnosis, severity judgment or prognosis evaluation reagent or kit. The invention is analyzed and screened and experimentally verified by bioinformatics, the expression level of ARG1 in the peripheral blood of patients with sepsis is obviously higher than that of a healthy control group, the expression level in the peripheral blood of patients with severe sepsis or lethal sepsis is obviously higher than that of common patients with sepsis, the expression level in the peripheral blood of patients with septic shock is obviously higher than that of patients with other types of shock, and the expression level in the peripheral blood of patients with sepsis, which does not respond to subsequent supportive treatment, is obviously higher than that of corresponding patients, thereby indicating that ARG1 can be used as a biomarker for accurate diagnosis, severity judgment and prognosis prediction of sepsis.

Description

ARG1 is used in the preparation of sepsis diagnosis, severity judgment or prognosis assessment reagents or tests Application in kits

technical field

The invention relates to the technical field of biological detection, relates to the application of ARG1 as a sepsis biomarker, and particularly relates to the application of ARG1 in the preparation of a sepsis diagnosis, severity judgment or prognosis assessment reagent or kit.

Background technique

Sepsis is very common clinically, but its mechanism is extremely complex. Sepsis develops as a dysregulated host response to pathogen infection and further leads to acute multiorgan failure of the lungs, liver, kidneys, etc., as well as patient death. The incidence of sepsis is very high. From 2006 to 2016, there were about 31.5 million cases of sepsis and 19.4 million cases of severe sepsis in the world (C Fleischmann, A Scherag and N K Adhikari, et al. Assessment of Global Incidence and Mortality of Hospital-Treated Sepsis.). Sepsis also has a very high mortality rate, with an average 30-day mortality rate of 24.4% and a 90-day average mortality rate of 32.2%, making it one of the leading causes of death worldwide (M Bauer, H Gerlach and T Vogelmann, et al. al. Mortality in Sepsis and SepticShock in Europe, North America and Australia Between 2009 and 2019-Results from a Systematic Review and Meta-Analysis. Crit Care, 2020, 24(1):p.239.). Therefore, sepsis is a very critical and important public health problem, which greatly affects the quality of life and survival of hospitalized patients, especially those in intensive care units.

Biomarkers refer to biochemical indicators that mark changes in system, organ, tissue, cell and subcellular structure or function, and can be used for disease diagnosis and disease staging. Molecular-level biomarkers can allow for faster and better stratification of patients with sepsis, thereby helping patients with sepsis receive timely treatment (S Gibot, M C Bene and R Noel, et al. Combination Biomarkers to Diagnose Sepsis in the Critically IllPatient. Am J Respir Crit Care Med, 2012, 186(1):p.65-71.).

The emergence and development of bioinformatics provides new avenues for finding biomarkers of sepsis. The continuous development of technologies such as DNA Microarray and RNA-Sequencing in recent decades has led to the continuous development of new fields in bioinformatics and generated massive amounts of data (Z Chen, Y Lin and J Gao, et al. Identification of Key Candidate Genes for Colorectal Cancer by Bioinformatics Analysis. Oncol Lett, 2019, 18(6): p. 6583-6593.). These data prompted the emergence of methods such as Weighted Correlation Network Analysis (WGCNA), constructing Protein-Protein Interaction Network (PPI Network) models, and constructing mRNA-miRNA networks. These methods provide very valuable clues for finding biomarkers. For example, Chinese patent application CN202011059286.2, the name of the invention is "a method for screening disease markers using bioinformatics and its application", application publication number: CN114333991A, discloses a kind of keloid and other diseases, using biological information Methods for screening biomarkers and their applications.

The ARG1 gene is a gene encoding arginase 1 (Arginase 1), and the protein encoded by this gene catalyzes the hydrolysis of arginine into ornithine and urea. Immune dysfunction is one of the important features of sepsis, and arginase-related metabolism is a key regulator of innate and adaptive immune responses (Markus Munder, Faustino Mollinedo, JeroCalafat et al. Arginase I is constitutively expressed in human granulocytes and participates in fungicidal activity. Blood. 2005 Mar 15; 105(6): 2549-56.), suggesting that ARG1 may be associated with sepsis. The Ensembl ID of the human ARG1 gene is ENSG00000118520, and the amino acid sequences of the three isoforms of the protein are shown in NP_001231367.1, NP_000036.2 and NP_001355949.1.

At present, there is no literature report on ARG1 as a sepsis biomarker at home and abroad.

SUMMARY OF THE INVENTION

The present invention relies on the above-mentioned research to provide diagnostic and prognostic markers for sepsis, and the present invention also aims to provide new uses of ARG1, that is, in the preparation of sepsis diagnosis, severity judgment or prognosis assessment kits application.

In order to find the characteristic genes in the peripheral blood of sepsis patients as biomarkers. In the present invention, the data sets of sepsis patients and healthy controls are downloaded and analyzed, and the differentially expressed genes are screened, and then PPI network analysis and WGCNA analysis are respectively used for the differentially expressed genes, and the two analysis results are obtained by intersecting the two analysis results to obtain ARG1. Then, the value of ARG1 in the precise diagnosis, severity judgment and prognosis prediction of sepsis was analyzed, and it was found that ARG1 was suitable as a biomarker for the diagnosis and treatment of sepsis.

Specifically, the present invention provides the following technical solutions:

A first aspect of the present invention provides a method for finding biomarkers, which specifically includes the following steps:

(1) Download the gene expression profiling data of sepsis patients and healthy controls from the Gene Expression Omnibus database (online address: https://www.ncbi.nlm.nih.gov/geo/).

(2) The differential expression analysis of genes was performed using the limmaR package for these expression data.

(3) According to the expression profile of each differentially expressed gene, the weighted co-expression network analysis (WGCNA) algorithm was used to identify genes with a high degree of significance (ie, closely related to clinical characteristics).

(4) Constructing a protein-protein interaction network model. Among the proteins corresponding to the differentially expressed genes, the proteins that have more interactions with other proteins are screened out, and their genes are called key genes.

(5) The genes screened by the PPI network in step (4) are intersected with the genes with higher significance identified by WGCNA in step (3) to obtain key biomarkers.

The second aspect of the present invention provides a scheme for investigating the performance of peripheral blood biomarkers of sepsis, which specifically includes the following steps:

(1) To investigate whether the expression level of this biomarker in peripheral blood of patients with sepsis is significantly higher than that of healthy controls.

(2) To investigate whether the expression level of this biomarker in the peripheral blood of patients with severe sepsis or fatal sepsis is significantly higher than that of ordinary patients with sepsis.

(3) The expression level of this biomarker in peripheral blood of patients with septic shock was significantly higher than that of patients with other types of shock.

(4) To investigate whether the expression level of this biomarker in the peripheral blood of sepsis patients who did not respond to follow-up supportive treatment was significantly higher than that of the corresponding patients.

(5) To investigate whether the expression of this biomarker in the peripheral blood of the sepsis model mice is significantly higher than that of the sham-operated mice.

The third aspect of the present invention provides the application of ARG1 as a marker of sepsis, specifically the application of ARG1 as a biomarker in the aspects of accurate diagnosis, severity judgment and prognosis evaluation, more specifically in the preparation of sepsis diagnosis, Severity judgment or the use of prognostic assessment reagents or kits.

In the present invention, the ARG1 refers to a gene and its corresponding protein. The gene is located on human chromosome 6, the specific position is 6q23.2, the number of exons is 8, and the Ensembl ID is ENSG00000118520. See NP_001231367.1, NP_000036.2 and NP_001355949.1 for the amino acid sequences of the three isoforms of the corresponding proteins.

In the present invention, sepsis is a life-threatening multiple organ dysfunction disease caused by a dysregulated host response to infection. The biomarkers refer to biochemical indicators that can mark changes in system, organ, tissue, cell and subcellular structure or function or possible changes.

Preferably, the above-mentioned severity judging reagent or kit is a reagent or kit for judging whether it is severe or fatal sepsis, or a reagent or kit for further judging whether it is septic shock.

The experimental results showed that compared with healthy people, ARG1 was significantly expressed in the peripheral blood of patients with sepsis; the transcriptional abundance in the peripheral blood of patients with severe or fatal sepsis was significantly higher than that of patients with ordinary sepsis. The expression level of s is helpful to judge the severity of sepsis; the expression level in septic shock (a severe form of sepsis) is significantly higher than that in non-septic shock. Given that septic shock and non-septic shock have similar symptoms, in clinical practice, ARG1 is of great value as a biomarker to differentiate these two conditions.

Preferably, the diagnostic or prognostic assessment reagent is a reagent for detecting ARG1 expression in a biological sample; the kit includes a reagent for detecting ARG1 content in a biological sample.

Further, the reagent for detecting ARG1 content in the biological sample is selected from: PCR primers with detection specificity for ARG1 gene, or antibodies that specifically bind to ARG1 protein. Among them, PCR primers with detection specificity for ARG1 gene are shown in SEQ ID NO. 1-4.

ARG1-F primer: TCACCTGAGCTTTGATGTCGA (SEQ ID NO. 1);

ARG1-R primer: TGAAAGGAGCCCTGTCTTGTA (SEQ ID NO. 2);

GAPDH-F primer: TCACCATCTTCCAGGAGCGAGAC; (SEQ ID NO. 3);

GAPDH-R primer: AGACACCAGTAGACTCCACGACATAC (SEQ ID NO. 4).

Further, the biological sample is obtained from the peripheral blood of the subject.

A fourth aspect of the present invention provides a sepsis diagnosis, severity judgment or prognosis assessment kit, which contains a reagent for detecting ARG1 content in a biological sample.

Preferably, the kit is composed of a reverse transcription system, a primer system and an amplification system, and the primer system includes PCR primers as shown in SEQ ID NO. 1-4.

A fifth aspect of the present invention provides a method for diagnosing sepsis, judging the severity or evaluating the prognosis using the above-mentioned kit, as follows:

A. Centrifuge the blood sample to be tested at room temperature to obtain plasma, extract the total RNA in the plasma and determine the plasma purity;

B. The total RNA in step A is reverse transcribed to obtain cDNA;

C. Quantitatively detect the copy number of ARG1 by real-time fluorescence quantitative PCR, and the primers used in the detection process are shown in SEQ ID NO. 1-4.

All data were processed by SPSS 16.0 and expressed as mean ± standard deviation.

Compared with the prior art, the beneficial effects of the present invention are as follows:

First, in terms of methods, the present invention provides a method for identifying biomarkers, which can quickly find biomarkers through bioinformatics analysis using open and free data sets. At the same time, the present invention provides a scheme for examining the performance of biomarkers in peripheral blood of sepsis, which can use bioinformatics data sets and experimental mice to relatively quickly examine and verify the use of a biomarker in accurate diagnosis of sepsis, Severity judgment and prognosis prediction. In addition, the present invention finds that ARG1 can be used as a "multifunctional" biomarker of sepsis, which is conducive to the accurate diagnosis of the condition of patients with sepsis and the rapid prediction of prognosis, thereby facilitating the rapid stratification and rapid prediction of patients with sepsis. Treat in time.

Secondly, in terms of technology, the detection of ARG1 is essentially a quantitative PCR detection of blood genome, which has the characteristics of simple operation, sensitive detection, good specificity and high repeatability, and has been increasingly used in clinical testing. in technology. The basic detection method adopted in the present invention is quantitative PCR, which has been proved to be a high-sensitivity and high-accuracy detection method in modern laboratory diagnostics. The standard curve quantification method can accurately quantify nucleic acid molecules in various samples.

Third, in terms of effect, the index ARG1 involved in the present invention is significantly highly expressed in the peripheral blood of patients with sepsis, and the transcriptional abundance in the peripheral blood of patients with severe or fatal sepsis is significantly higher than that in patients with ordinary sepsis , the expression level in septic shock was significantly higher than that in non-septic shock, and the difference was statistically significant (P<0.05), and its clinical reference value and reliability were high. The expression of ARG1 gene can not only help to judge the severity of sepsis, but also has important value in distinguishing septic shock and non-septic shock with similar symptoms.

Fourth, as far as the detection method is concerned, the test results can be obtained only by collecting the blood of the tester. The operation is simple, non-invasive and highly acceptable to patients.

Description of drawings

以及

表示，灰色是没能划分进任何模块的散在基因。Figure 1 uses the WGCNA algorithm to divide the differentially expressed genes in the peripheral blood of patients with sepsis into 4 modules (gene groups) according to their gene expression patterns.

as well as

Indicates that the gray are scattered genes that could not be classified into any modules.

Figure 2 is the correlation coefficient between each gene module and clinical diagnosis (sepsis or control), wherein the correlation coefficient is displayed in the square, and the greater the absolute value of the correlation coefficient, the darker the color of the module. Except for Module 1, which is a negative correlation, the rest are all positive correlations.

Figure 3 is a protein-protein interaction network composed of proteins corresponding to differentially expressed genes in sepsis, in which dark gray represents up-regulated genes and light gray represents down-regulated genes.

Figure 4 is to find common genes in the results of PPI network screening and WGCNA analysis. The results showed that ARG1 was the only shared gene in both results.

Figure 5 shows that ARG1 is highly expressed in the peripheral blood of patients with sepsis: six adult datasets GSE95233, GSE134347, GSE154918, GSE13015, GSE60424, GSE131761 and four pediatric datasets GSE8121, GSE26378, GSE26440, GSE145227 were used, respectively. This indicates that ARG1 is highly expressed in the peripheral blood of patients with sepsis. The boxplots corresponding to each dataset show that ARG1 is significantly overexpressed in peripheral blood in both adult and pediatric patients. There is a ROC curve below each box plot, and the area under the curve of the ROC curve is also equal to or close to 1, which proves that ARG1 has relatively good sensitivity and specificity, and has the potential as a biomarker. *** indicates that there is a statistical difference between the sepsis group and the control group (P<0.001).

Figure 6 shows that the expression level of ARG1 in septic shock is significantly higher than that in non-septic shock: using two datasets GSE131411 and GSE131761, respectively Symptoms) were significantly higher than those in non-septic shock. Given that septic shock and non-septic shock have similar symptoms, in clinical practice, ARG1 is of great value as a biomarker to differentiate these two conditions. *** means there is a statistical difference between the septic shock group and the non-septic shock group (P<0.001); * means there is a statistical difference between the septic shock group and the non-septic shock group (P<0.05) ).

Figure 7 shows that the transcriptional abundance of ARG1 gene in the peripheral blood of patients with severe or lethal sepsis is significantly higher than that of patients with common sepsis: using two datasets GSE63042 and GSE154918, respectively, the ARG1 gene was confirmed by drawing box plots The transcript abundance in peripheral blood of severe or fatal sepsis patients was significantly higher than that of common sepsis patients. This shows that the expression of ARG1 gene is helpful to judge the severity of sepsis. *** indicates that there is a statistical difference between the common sepsis group and the septic shock group (P<0.001); * indicates that there is a statistical difference between the common sepsis group and the severe or fatal sepsis group ( P<0.05).

Figure 8 shows that ARG1 is significantly higher in the peripheral blood of septic patients who do not respond to subsequent treatment than in patients who do respond: Using the GSE110487 dataset, it was confirmed that ARG1 was not involved in septic patients who did not respond to subsequent treatment by drawing a box plot. The peripheral blood of patients with sepsis was significantly higher than that of patients who responded, indicating that the expression level of ARG1 can help predict whether patients with sepsis will respond to subsequent supportive treatment, reflecting its good performance as a biomarker. ** Indicates that there is a statistical difference between sepsis patients who do not respond to subsequent treatment and sepsis patients who respond to subsequent treatment (P<0.01).

Figure 9 shows the method using real-time fluorescence quantitative PCR, which confirms from the experimental level that the expression level of ARG1 in the peripheral blood of septic mice is significantly higher than that of sham-operated mice. *** indicates that there is a statistical difference between the septic mice and the sham-operated mice (P<0.01).

Detailed ways

The present invention will now be described in detail with reference to the embodiments and the accompanying drawings, but the implementation of the present invention is not limited thereto.

The C57BL/6 mice used in the present invention were purchased from Shanghai Slack Laboratory Animal Co., Ltd.

The reagents and raw materials used in the present invention are commercially available or can be prepared according to literature methods. In the following examples, the analytical methods and experimental methods without specific conditions are usually in accordance with conventional conditions, or in accordance with the conditions suggested by the manufacturer.

Example 1: Screening of differentially expressed genes in peripheral blood of patients with sepsis

The present invention has found four gene expression profile data sets in the GEO database (online address: https://www.ncbi.nlm.nih.gov/geo/), the numbers are GSE28750, GSE57065, GSE65682, GSE69528, each Each dataset contains peripheral blood samples from patients with sepsis (experimental group) and peripheral blood samples from healthy people (control group). Download gene expression profiles for each dataset.

On Microsoft Windows 1064-bit operating system, after downloading the R 4.1.2 version from the R language homepage (online address: www.r-project.org), follow the prompts for default installation. After downloading the RStudio 1.3.1073 version from the RStudio homepage of the integrated development environment (online address: www.rstudio.com/), follow the default prompts to install it. Install the limmaR module using the BiocManager::install("limma") command. Then use this module to perform differential expression analysis on the above four gene expression profiling datasets respectively, with log Fold Change (FC)>1 or <-1 and adjustedPvalue<0.05 as the screening criteria to obtain the up-regulated and down-regulated values of each dataset. differentially expressed genes.

Venn diagrams were generated using the online tool Venny 2.1.0 (online address: https://bioinfogp.cnb.csic.es/tools/venny/index.html) to find up- and down-regulated differential expressions common to the four datasets Genes, a detailed list of differentially expressed genes is shown in Table 1:

Table 1 List of up-regulated and down-regulated differentially expressed genes in sepsis

Example 2: Gene-weighted co-expression network analysis of differentially expressed genes in patients with sepsis

In the RStudio IDE, use the BiocManager::install("WGCNA") command to install the WGCNA R package. For each dataset, use the goodSamplesGenesMS function to remove genes with a large number of missing samples and samples with a really large number of genes. Next, the samples were clustered using the hclust function and the sepsis or healthy control samples that were too outliers were removed. Next, use the pickSoftThreshold function and the softConnectivity function to adjust the soft threshold, so that the connections between genes in the subsequent network approximately obey a scale-free distribution, thus making the network more biologically meaningful. Then a gene expression correlation matrix is generated, converted into an adjacency matrix, and further converted into a topological overlap matrix to obtain a co-expression network. Then perform hierarchical clustering analysis of genes, use dynamic cutting tree algorithm, divide genes with similar expression patterns into the same module, and calculate the Pearson correlation coefficient between each module and clinical diagnosis (sepsis or healthy controls) ( Pearson correlation coefficient). Finally, the Pearson correlation coefficient between the expression pattern of each gene and the clinical diagnosis in the most relevant module was calculated as the degree of significance (GeneSignificance, GS) of the gene.

The module division of genes is shown in Figure 1, and the Pearson correlation coefficient between each module and clinical characteristics is shown in Figure 2. The genes with a higher degree of significance are shown in Table 2.

Table 2 The 15 genes most closely related to clinical characteristics (highest degree of significance) obtained by WGCNA analysis

Example 3: Construction of protein-protein interaction network of differentially expressed genes in sepsis patients

Open the STRING database (online address: https://string-db.org/), import the differentially expressed gene list of the patients with sepsis screened above into the database, and construct a protein-protein interaction network (Protein-Protein Interaction Network). Interaction Network, PPI Network). The network was visualized using Cytoscape software (version: 3.8.2). Proteins with more interactions with other proteins were then identified using the MCODE plugin in Cytoscape. These proteins are more likely to be at the core of the pathogenesis of sepsis, so their corresponding genes are more critical.

The protein-protein interaction network is shown in Figure 3, and the corresponding gene names of proteins that have more interactions with other proteins are shown in Table 3.

Table 3 Summary of proteins with more interactions identified by the PPI network model

Example 4: Search for common genes in PPI network screening and WGCNA analysis results

Use the online tool Venny 2.1.0 (online address: https://bioinfogp.cnb.csic.es/tools/venny/index.html) to generate a Venn diagram to find the corresponding genes of the proteins screened in the PPI network, Whether there is an intersection with the genes analyzed by WGCNA.

The results are shown in Figure 4, indicating that the ARG1 gene is a common gene in the results obtained by the two bioinformatics methods.

Example 5: Performance of ARG1 in distinguishing sepsis from healthy controls

Use the BiocManager::install("ggplot2") command to install the R package for ggplot2. Use this R package to generate a histogram of the relative expression of genes in the dataset.

Use the BiocManager::install("pROC") command to install the pROC R package. This R package was used to generate Receiver Operating Characteristic (ROC) plots for each dataset and to calculate the Area Under Curve.

The datasets used above include adult datasets GSE95233, GSE134347, GSE154918, GSE13015, GSE60424, GSE131761, and children's datasets GSE8121, GSE26378, GSE26440, and GSE145227. Patients with sepsis and healthy controls or control patients were included in each dataset.

The results are shown in Figure 5, showing that the ARG1 gene transcriptional abundance in the peripheral blood of sepsis patients was significantly higher than that of the control group, and the area under the curve of the ROC curve was also equal to or close to 1, proving that ARG1 has relatively good sensitivity and specificity, have potential as biomarkers.

Example 6: Performance of ARG1 in distinguishing sepsis from symptom-like diseases

Both datasets GSE131411 and GSE131761 contain peripheral blood samples from septic shock as well as non-septic shock patients. The present invention continues to use the limma R package to generate a histogram of the relative expression levels of ARG1 in these two data sets.

The results are shown in Fig. 6, showing that the transcriptional abundance of ARG1 gene in peripheral blood of patients with septic shock is significantly greater than that in non-septic shock. Since septic shock is a severe form of sepsis and has similar symptoms to non-septic shock, ARG1 is of great value as a biomarker to differentiate these two conditions in clinical practice.

Example 7: Performance of ARG1 in differentiating sepsis severity

Both datasets GSE63042 and GSE154918 contain peripheral blood samples from patients with common sepsis as well as patients with severe or fatal sepsis. The present invention continues to use the limmaR package to generate a histogram of the relative expression levels of ARG1 in these two datasets.

The results are shown in Figure 7, showing that the transcriptional abundance of ARG1 gene in the peripheral blood of severe or fatal sepsis patients was significantly greater than that of common sepsis patients. This shows that the level of ARG1 in peripheral blood can help to judge the severity of sepsis, so as to facilitate the screening of patients with severe sepsis and give more targeted treatment.

Example 8: The performance of ARG1 in predicting whether there is a response to supportive treatment of sepsis

GSE110487 is the only dataset that could be found to analyze whether there is a corresponding dataset for sepsis supportive care. When this dataset was sampled, patients first underwent blood tests upon admission to the intensive care unit, and their blood samples were sent for sequencing. Each septic patient's response to follow-up supportive care was then recorded over the next few days. Responders and non-responders did not differ significantly in type of infection, inflammatory cycle markers, or blood test results such as white blood cell and lymphocyte counts when admitted to the ICU (Barcella M, Bollen P B, Braga D, et al. Identification of a transcriptome profile associated with improvement of organ function inseptic shock patients after early supportive therapy[J].Crit Care,2018,22(1):312.). However, as shown in Figure 8, ARG1 levels in whole blood were significantly higher in sepsis patients who did not respond to subsequent treatment compared with responsive sepsis patients. This indicates that the level of ARG1 in peripheral blood of patients with sepsis can help predict whether patients with sepsis will respond to subsequent supportive treatment, reflecting its good potential as a biomarker.

Example 9: Animal experiments confirm that ARG1 is highly expressed in the peripheral blood of sepsis

qPCR SYBRGreen Master Mix(No Rox)(翌圣生物科技(上海)股份有限公司)以GAPDH为内参基因进行实时荧光定量PCR实验。引物序列为：Using C57BL/6 mice, the experimental group established a sepsis model by cecum ligation and puncture (Cecum Ligation and Puncture, CLP) experiment, and the sham operation (Sham) was used as the control group. Twenty-four hours after the operation, the peripheral blood of mice was collected, RNA was extracted with RNAFast200 kit (Shanghai Feijie Biotechnology Co., Ltd.) perform reverse transcription, then use

qPCR SYBRGreen Master Mix (No Rox) (Yisheng Biotechnology (Shanghai) Co., Ltd.) used GAPDH as the internal reference gene for real-time quantitative PCR experiments. The primer sequences are:

ARG1-F primer: TCACCTGAGCTTTGATGTCGA (SEQ ID NO. 1)

ARG1-R primer: TGAAAGGAGCCCTGTCTTGTA (SEQ ID NO. 2)

GAPDH-F primer: TCACCATCTTCCAGGAGCGAGAC (SEQ ID NO. 3)

GAPDH-R primer: AGACACCAGTAGACTCCACGACATAC (SEQ ID NO. 4).

The amplification conditions are shown in Table 4:

Table 4 Summary of amplification conditions

The results are shown in FIG. 9 , showing that the gene expression level of ARG1 in the peripheral blood of the septic mice modeled by CLP was significantly higher than that of the sham-operated group (Sham group) mice. This experimentally confirms the performance of ARG1 as a sepsis biomarker.

The preferred embodiments of the present invention have been specifically described above, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalents without departing from the spirit of the present invention. Modifications or substitutions, these equivalent modifications or substitutions are all included within the scope defined by the claims of the present application.

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Claims (9)

1. Application of ARG1 in the preparation of reagents or kits for sepsis diagnosis, severity judgment or prognosis assessment. 2. application according to claim 1, is characterized in that, described severity judgment reagent or test kit is the reagent or test kit of judging whether it is severe or lethal sepsis, or for further judging whether it is septic shock reagents or kits. 3. The application according to claim 1, wherein ARG1 refers to ARG1 gene and corresponding ARG1 protein. 4 . The application according to claim 3 , wherein the reagent is a reagent for detecting ARG1 expression in a biological sample; and the kit comprises a reagent for detecting ARG1 content in a biological sample. 5 . 5 . The application according to claim 4 , wherein the reagent for detecting ARG1 content in a biological sample is selected from: PCR primers with detection specificity for ARG1 gene, or antibodies that specifically bind to ARG1 protein. 6 . 6 . The application according to claim 5 , wherein the PCR primers with detection specificity for the ARG1 gene are shown in SEQ ID NO. 1-4. 7 . 7. The use according to claim 4, wherein the biological sample is obtained from the peripheral blood of a subject. 8. A kit for diagnosis, severity judgment or prognosis assessment of sepsis, characterized in that the kit comprises a reagent for detecting ARG1 content in a biological sample. 9 . The kit according to claim 8 , wherein the kit is composed of a reverse transcription system, a primer system and an amplification system, and the primer system includes as shown in SEQ ID NO. 1-4 9 . PCR primers shown.

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