CN115044665A

CN115044665A - Application of ARG1 in preparation of sepsis diagnosis, severity judgment or prognosis evaluation reagent or kit

Info

Publication number: CN115044665A
Application number: CN202210661083.3A
Authority: CN
Inventors: 张景翔; 王彦; 许维恒; 张俊平
Original assignee: Second Military Medical University SMMU
Current assignee: Second Military Medical University SMMU
Priority date: 2022-06-08
Filing date: 2022-06-13
Publication date: 2022-09-13

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

Application of ARG1 in preparation of sepsis diagnosis, severity judgment or prognosis evaluation reagent or kit

Technical Field

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

Background

Sepsis is very common clinically, but its mechanism is extremely complex. Sepsis develops as the host's response to pathogen infection is deregulated and further leads to acute multiple organ failure of the lungs, liver, kidneys, etc. and death of the patient. The Incidence of Sepsis is very high, accumulating about 3150 thousands of Sepsis cases globally in 2006-2016, and 1940 thousands of severe Sepsis cases (C Fleischmann, A Schorg and N K Adhikari, et al.Association of Global incorporation and Mortality of Hospital-Treated Sepsis.). The mortality rate for Sepsis is also very high, with a 30-day average mortality rate of 24.4% and an average mortality rate of 32.2% over 90 days, being one of the most major causes of death worldwide (M Bauer, H Gerlach and T Vogelmann, et al mortalities in Sepsis and Septic Shock in Europe, North America and Australia Between 2009 and 2019-resources from a Systematic Review and Meta-analysis. Crit Care,2020,24(1): p.239.). Sepsis is therefore a very critical public health problem that greatly affects the quality of life and survival of hospitalized patients, especially in intensive care units.

Biomarkers are biochemical indicators of changes in the structure or function of marker systems, organs, tissues, cells and subcellular cells, and can be used in disease diagnosis and to determine disease stage. Molecular level Biomarkers can stratify Sepsis patients more quickly and better, thus helping Sepsis patients get timely treatment (S Gibot, M C stone and R noise, et al. combination Biomarkers to diagnosis separation in the clinical il patent. am J rapid Crit Care Med,2012,186(1): p.65-71.).

The emergence and development of bioinformatics provides a new approach to finding sepsis biomarkers. In recent decades, the development of gene chip (DNAmicroarray) and RNA high-throughput Sequencing (RNA-Sequencing) technology has led to the development of new fields of Bioinformatics and the generation of massive amounts of data (Z Chen, Y Lin and J Gao, et al.identification of Key Cancer Genes for color Cancer by biological informatics. Oncol Lett,2019,18(6): p.6583-6593.). These data have prompted the emergence of methods such as Weighted Correlation Network Analysis (WGCNA), Protein-Protein Interaction Network (PPI Network) model construction, and mRNA-miRNA Network construction. These methods provide very valuable clues for finding biomarkers. For example, Chinese patent application No. CN202011059286.2 entitled "method for screening disease markers by using bioinformatics" and application thereof, application publication No. CN114333991A discloses a method for screening biomarkers by using bioinformatics on keloid and other diseases and application thereof.

The ARG1 gene is a gene encoding Arginase 1(Arginase 1), and the protein encoded by the gene catalyzes the hydrolysis of arginine to ornithine and urea. Immune dysfunction is one of the important features of sepsis, while arginase-related metabolism is a key regulator of innate and adaptive immune responses (Markus Munder, Faustino molinedo, Jero Calafat et al. arginine I. connective expressed in human venulocytes and particulate in viral 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 three subtypes of the protein are shown in NP-001231367.1, NP-000036.2 and NP-001355949.1.

At present, no literature report on the aspect of using ARG1 as a sepsis biomarker exists at home and abroad.

Disclosure of Invention

The invention is carried out by the research, aims to provide a diagnosis and prognosis marker of sepsis, and also aims to provide a new application of ARG1, namely an application in preparing a sepsis diagnosis, severity judgment or prognosis evaluation kit.

To find a gene characteristic of peripheral blood of patients with sepsis as a biomarker. The invention carries out screening of differential expression genes by downloading and analyzing data sets of sepsis patients and healthy controls, then PPI network analysis and WGCNA analysis are respectively used for the differential expression genes, and the intersection of the two analysis results is taken to obtain ARG 1. And then analyzing the value of the ARG1 in the aspects of accurate diagnosis, severity judgment, prognosis prediction and the like of the sepsis, thereby finding that the ARG1 is suitable for serving as a biomarker for diagnosis and treatment of the sepsis.

Specifically, the invention provides the following technical scheme:

in a first aspect of the present invention, there is provided a method for finding biomarkers, comprising the following steps:

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

(2) These expression data were analyzed for differential expression of genes using limmarr package.

(3) Based on the expression profile of each differentially expressed gene, a gene with a higher degree of significance (i.e., closer correlation with clinical characteristics) was identified using a Weighted co-expression network analysis (WGCNA) algorithm.

(4) Constructing a Protein-Protein Interaction Network (Protein-Protein Interaction Network) model, and screening out proteins which have more interactions with other proteins from the proteins corresponding to the differential expression genes, wherein the genes are called key genes.

(5) And (4) taking intersection of the genes screened out by the PPI network in the step (4) and the identified genes with higher significance of the WGCNA in the step (3) to obtain a key biomarker.

In a second aspect of the invention, a scheme for investigating the performance of a sepsis peripheral blood biomarker is provided, which specifically comprises the following steps:

(1) and (3) whether the expression level of the biomarker in the peripheral blood of the sepsis patient is obviously higher than that of a healthy control group or not is examined.

(2) And (3) investigating whether the expression level of the biomarker in peripheral blood of patients with severe sepsis or lethal sepsis is obviously higher than that of the patients with ordinary sepsis.

(3) The expression level of the biomarker in the peripheral blood of patients with septic shock is considered to be significantly higher than that of patients with other types of shock.

(4) It was examined whether the expression level of the biomarker in the peripheral blood of sepsis patients who did not respond to subsequent supportive care was significantly higher than that of the corresponding patients.

(5) And (3) whether the expression of the biomarker in the peripheral blood of the sepsis modeling mouse is obviously higher than that of a sham operation group mouse or not is examined.

In a third aspect of the invention, the application of the ARG1 as a sepsis marker is provided, in particular the application as a biomarker in the aspects of accurate diagnosis, severity judgment, prognosis evaluation and the like, and more particularly the application in the preparation of a sepsis diagnosis, severity judgment or prognosis evaluation reagent or kit.

In the present invention, the ARG1 refers to a gene and its corresponding protein. The gene is located on the 6 th chromosome of human, the specific position is 6q23.2, the number of exons is 8, and the Ensembl ID is ENSG 00000118520. The amino acid sequences of three subtypes of the corresponding proteins are shown in NP-001231367.1, NP-000036.2 and NP-001355949.1.

In the present invention, sepsis is a life-threatening multiple organ dysfunction disease due to a dysregulated host response to infection. The biomarker refers to a biochemical marker that can mark changes or changes that may occur in the structure or function of systems, organs, tissues, cells and subcellular cells.

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

The experimental results show that compared with healthy people, ARG1 is remarkably highly expressed in the peripheral blood of patients with sepsis; the transcription abundance in peripheral blood of severe or lethal sepsis patients is obviously higher than that of common sepsis patients, and the severity of sepsis disease is judged by the expression level of the ARG1 gene; the expression level in septic shock (a severe sepsis) is significantly higher than in non-septic shock. Given that septic shock has similar symptoms to non-septic shock, ARG1 has important value as a biomarker for distinguishing between these two types of conditions in clinical practice.

Preferably, the diagnostic or prognostic assessment agent is an agent for detecting the amount of expression of ARG1 in a biological sample; the kit comprises a reagent for detecting the ARG1 content in the biological sample.

Further, the reagent for detecting the content of ARG1 in the biological sample is selected from the group consisting of: PCR primers with detection specificity for the ARG1 gene, or antibodies that specifically bind to the ARG1 protein. Wherein, the PCR primer with detection specificity to the ARG1 gene is shown as 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, respectively; (SEQ ID NO. 3);

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

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

In a fourth aspect of the invention, there is provided a sepsis diagnostic, severity determining or prognosis assessment kit comprising reagents for detecting the amount of ARG1 in a biological sample.

Preferably, the kit consists of a reverse transcription system, a primer system and an amplification system, wherein the primer system comprises a PCR primer shown as SEQ ID No. 1-4.

In a fifth aspect of the present invention, there is provided a method for diagnosing, determining the severity or evaluating the prognosis of sepsis using the above kit, comprising:

A. centrifuging a blood sample to be detected at normal temperature to obtain plasma, extracting total RNA in the plasma and determining the purity of the plasma;

B. carrying out reverse transcription on the total RNA in the step A to obtain cDNA;

C. and (3) carrying out quantitative detection on the copy number of the ARG1 by adopting real-time fluorescent quantitative PCR, wherein primers used in the detection process are shown as SEQ ID NO. 1-4.

Data were processed using SPSS16.0 and expressed as mean ± standard deviation.

Compared with the prior art, the invention has the following beneficial effects:

first, as for the method, the present invention provides a biomarker identification method capable of rapidly finding a biomarker through bioinformatics analysis using a public, free data set. Meanwhile, the invention provides a scheme for inspecting the performance of the sepsis peripheral blood biomarker, and the performance of the biomarker in the aspects of accurate sepsis diagnosis, severity judgment, prognosis prediction and the like can be quickly inspected and verified by using a bioinformatics data set and an experimental mouse. In addition, the ARG1 can be used as a multifunctional biomarker of sepsis, which is beneficial to accurate diagnosis of the disease condition of patients with sepsis and quick prediction of prognosis, thereby being beneficial to quick stratification and timely treatment of patients with sepsis.

Secondly, in terms of technology, the detection of the ARG1 is essentially a quantitative PCR detection of blood genome, has the characteristics of simple operation, sensitive detection, good specificity, high repeatability and the like, and is increasingly applied to clinical examination technology nowadays. The basic detection method adopted by the invention is quantitative PCR, the technology is proved to be a high-sensitivity and high-accuracy detection method in modern experimental diagnostics, the test technology is mature, and the standard curve quantitative method in the technology is adopted, so that the nucleic acid molecules in various samples can be accurately quantified.

Thirdly, in terms of effects, the ARG1 index related by the invention is remarkably and highly expressed in peripheral blood of sepsis patients, the transcription abundance in peripheral blood of severe or lethal sepsis patients is remarkably higher than that of common sepsis patients, the expression level in septic shock is remarkably higher than that of non-septic shock, the difference has statistical significance (P <0.05), and the clinical reference value and the credibility are higher. Not only can the severity of the sepsis condition be judged by the expression level of the ARG1 gene, but also the method has important value for distinguishing septic shock and non-septic shock with similar symptoms.

Fourthly, as for the detection mode, the detection result can be obtained only by collecting the blood of the detector, the operation is simple, the wound is not generated, and the acceptance degree of the patient is high.

Drawings

FIG. 1 is a graph showing the division of differentially expressed genes in peripheral blood of sepsis patients into 4 modules (gene groups) each in texture according to the expression pattern of the genes using WGCNA algorithm

And

gray indicates loose genes that could not be classified into any module.

Figure 2 is the correlation coefficient for each gene module with clinical diagnosis (sepsis or control), where the correlation coefficient is shown in squares, the greater the absolute value of the correlation coefficient, the darker the module color. The correlation is positive, except that Module 1(Module 1) is negative.

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

Figure 4 is a table of genes that are common to the PPI network screening and WGCNA analysis results. The results show that ARG1 is the only gene in common between the two results.

Figure 5 shows high expression of ARG1 in peripheral blood of sepsis patients: six adult datasets, namely GSE95233, GSE134347, GSE154918, GSE13015, GSE60424 and GSE131761, and four child datasets, namely GSE8121, GSE26378, GSE26440 and GSE145227, are used respectively to illustrate the high expression of ARG1 in the peripheral blood of patients with sepsis. The box plot for each data set shows that ARG1 appears to be significantly highly expressed in peripheral blood in both adult and pediatric patients. An ROC curve is arranged below each box type graph, the area under the curve of the ROC curve is equal to or close to 1, and the ARG1 is proved to have good sensitivity and specificity and has potential as a biomarker. Statistical differences (P <0.001) were observed between sepsis and control groups.

FIG. 6 shows that ARG1 is expressed at a significantly higher level in septic shock than in non-septic shock: the expression level of ARG1 in septic shock (a severe sepsis) was confirmed to be significantly higher than in non-septic shock by box plot using the two data sets GSE131411 and GSE131761, respectively. Given that septic shock has similar symptoms to non-septic shock, ARG1 has important value as a biomarker for distinguishing between these two types of conditions in clinical practice. Statistical differences (P <0.001) between septic shock and non-septic shock groups; indicates that there is a statistical difference (P <0.05) between septic shock and non-septic shock groups.

FIG. 7 shows that the transcriptional abundance of the ARG1 gene in peripheral blood of severe or lethal sepsis patients is significantly higher than that of the common sepsis patients: the two data sets of GSE63042 and GSE154918 are respectively used to confirm that the transcriptional abundance of the ARG1 gene in the peripheral blood of severe or lethal sepsis patients is obviously higher than that of the common sepsis patients by drawing a box-type diagram. This indicates that the expression level of ARG1 gene is helpful for determining the severity of sepsis. Indicates that there is a statistical difference (P <0.001) between the normal sepsis group and the septic shock group; indicates that there was a statistical difference between the normal sepsis and the severe or lethal sepsis group (P < 0.05).

Figure 8 shows that ARG1 is significantly higher in peripheral blood of sepsis patients who did not respond to subsequent treatment than in patients who responded: using the data set GSE110487, the fact that ARG1 is remarkably higher in peripheral blood of sepsis patients who do not respond to subsequent treatment than in patients who respond is proved by drawing a box-type graph shows that the expression level of ARG1 is helpful for predicting whether sepsis patients respond to subsequent supportive treatment or not, and the good performance of the ARG 3578 as a biomarker is reflected. Indicates that there was a statistical difference (P <0.01) between sepsis patients who did not respond to subsequent treatment and sepsis patients who responded to subsequent treatment.

FIG. 9 is a method using real-time fluorescent quantitative PCR, and experimentally confirmed that the expression level of ARG1 in the peripheral blood of septic mice is significantly higher than that of the mice in the sham operation group. Statistical differences (P <0.01) were observed in sepsis and sham mice.

Detailed Description

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

The C57BL/6 mice used in the present invention were purchased from Shanghai Spiker laboratory animals, Inc.

The reagents and starting materials used in the present invention are commercially available or can be prepared according to literature procedures. The following examples are given with respect to analytical methods and experimental methods without specifying specific conditions, generally according to conventional conditions, or according to conditions recommended by the manufacturer.

Example 1: screening of peripheral blood differential expression gene of sepsis patient

Four gene expression profile data sets are found in a GEO database (online address: https:// www.ncbi.nlm.nih.gov/GEO /), the numbers of the four gene expression profile data sets are GSE28750, GSE57065, GSE65682 and GSE69528 respectively, and each data set comprises a sepsis patient peripheral blood sample (an experimental group) and a healthy person peripheral blood sample (a control group). The gene expression profiles for each data set are downloaded.

After downloading the R4.1.2 version from the R language homepage (online address: www.r-project. org) on the Microsoft Windows 1064 bit operating system, the default installation is performed as prompted. After the integrated development environment RStudio homepage (online address: www.rstudio.com /) downloads the RStudio 1.3.1073 version, installation is performed according to default prompts. The limmarr module was installed using the BiocMarager:: install ("limma") command. Then the module is used for carrying out differential expression analysis on the four gene expression profile data sets respectively, and log Fold Change (FC) >1 or < -1 and adjusted Pvalue <0.05 are used as screening standards to obtain up-regulated and down-regulated differential expression genes of each data set.

Wenn plots were generated using the online tool Venny 2.1.0 (online address: https:// bioinfogp. cnb. csic. es/tools/Venny/index. ht ml) to look for common up-and down-regulating differentially expressed genes in the four datasets, a detailed list of differentially expressed genes is shown in Table 1:

TABLE 1 Sepsis Up-and Down-Regulation differentially expressed Gene List

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

In the RStudio integrated development environment, the R package of WGCNA was installed using the BiocMarager:: install ("WGCNA") command. For each dataset, the goodsamples genems function was used to remove samples with a high number of missing genes and indeed a high number of genes. Next, the samples were cluster analyzed using the hclust function and sepsis or healthy control samples that were too outlier were removed. Next, the soft threshold is adjusted by using a pickSoftThreshold function and a softConnectivity function, so that the connection between genes in the subsequent network is approximately subjected to non-scale distribution, and the network has more biological significance. Then generating a gene expression correlation matrix, converting the gene expression correlation matrix into an adjacent matrix, and further converting the adjacent matrix into a topological overlapping matrix to obtain a co-expression network. Then, hierarchical clustering analysis of genes is carried out, genes with similar expression patterns are divided into the same module by using a dynamic cutting tree algorithm, and Pearson correlation coefficient (Pearson correlation coefficient) between each module and clinical diagnosis (sepsis or healthy control) is calculated. Finally, the pearson correlation coefficient between the expression pattern of each Gene and the clinical diagnosis in the most relevant modules was calculated as the degree of Significance of the Gene (Gene signature, GS).

The modular division of genes is shown in FIG. 1, and the Pearson correlation coefficient of each module with clinical characteristics is shown in FIG. 2. The genes with higher significance are shown in table 2.

TABLE 2 15 genes most closely related to clinical characteristics (highest significance) obtained using WGCNA analysis

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

Opening a STRING database (online address: https:// STRING-db. org /), introducing the screened differentially expressed gene list of the sepsis patients into the database, and constructing a Protein-Protein Interaction Network (PPI Network). The network was visualized using Cytoscape software (version: 3.8.2). The MCODE insert in Cytoscape was then used to identify proteins that have more interactions with other proteins. These proteins are more likely to be at the core position in the pathogenic process of sepsis and their corresponding genes are therefore more critical.

The protein-protein interaction network is shown in FIG. 3, and the list of the names of the corresponding genes of proteins that interact more with other proteins is shown in Table 3.

TABLE 3 summary of the more interactive proteins identified by PPI network model

Example 4: search of common genes in PPI network screening and WGCNA analysis results

Wehn plots were generated using the online tool Venny 2.1.0 (online address: https:// bioinfogp. cnb. csic. es/tools/Venny/index. ht ml) to find the corresponding genes in the protein obtained by PPI network screening, and whether there was an intersection with the genes obtained by WGCNA analysis.

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

Example 5: performance study of ARG1 in differentiating sepsis from healthy controls

The R package for ggplot2 was installed using the BiocMarager: "install (" ggplot2") command. The R-package was used to generate a histogram of the relative expression levels of the genes in the data set.

The R package for the pROC was installed using the BiocMarager:: install ("pROC") command. The R-packet is used to generate Receiver Operating Characteristics (ROC) plots for the subjects in each dataset and to calculate the Area Under the Curve (Area Under Curve).

The data sets used above include adult data sets GSE95233, GSE134347, GSE154918, GSE13015, GSE60424, GSE131761 and child data sets GSE8121, GSE26378, GSE26440 and GSE 145227. Sepsis patients and healthy human controls or control group patients were included in each data set.

The results are shown in FIG. 5, and the transcriptional abundance of the ARG1 gene in the peripheral blood of sepsis patients is shown to be significantly higher than that of the control group, the area under the ROC curve is also equal to or close to 1, and the ARG1 is proved to have relatively good sensitivity and specificity and have potential as a biomarker.

Example 6: performance Studies of ARG1 in differentiating sepsis from symptom-like disease

Both data sets GSE131411 and GSE131761 contain samples of peripheral blood from patients with septic shock as well as non-septic shock. The invention proceeds to use the limma R package to generate histograms of the relative expression of ARG1 in the two data sets.

The results are shown in FIG. 6, which shows that the transcriptional abundance of ARG1 gene in peripheral blood of septic shock patients is significantly greater than that of 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 in clinical practice for distinguishing between these two types of conditions.

Example 7: performance review of ARG1 in differentiating severity of sepsis

Both GSE63042 and GSE154918 data sets contain peripheral blood samples from patients with common sepsis as well as patients with severe or fatal sepsis. The present invention proceeds to use the limmarr package to generate histograms of the relative expression of ARG1 in both datasets.

The results are shown in fig. 7, which shows that the transcriptional abundance of the ARG1 gene in peripheral blood of severe or lethal sepsis patients is significantly greater than that of common sepsis patients. This demonstrates that the level of ARG1 in peripheral blood helps to determine the severity of the sepsis condition, thereby facilitating screening patients for severe sepsis for more targeted treatment.

Example 8: study of ARG1 Performance in predicting whether there is a corresponding aspect to sepsis supportive therapy

GSE110487 is the only data set that can be found to analyze whether there is a response to sepsis supportive therapy. In sampling this data set, the patient is first subjected to a blood test at the time of entry into the intensive care unit, while his blood sample is sent for sequencing. The next few days then recorded whether each sepsis patient had responded to subsequent supportive therapy. Responders and non-responders did not differ significantly in blood test results such as infection type, inflammatory cycle markers or leukocyte and lymphocyte counts when they entered the ICU for treatment (Barcella M, Bollen P B, Braga D, et al. identification of a transcriptional profile associated with an infection of an organic function in a therapeutic short patient after early therapy [ J ] crack Care,2018,22(1): 312.). As shown in figure 8, however, the level of ARG1 was significantly higher in whole blood of sepsis patients who did not respond to subsequent treatment compared to sepsis patients who responded. This demonstrates that the level of ARG1 in the peripheral blood of sepsis patients is helpful in predicting whether sepsis patients will respond to subsequent supportive treatment, indicating its good potential as a biomarker.

Example 9: animal experiments prove that ARG1 is highly expressed in sepsis peripheral blood

The experimental group constructed a sepsis model by Cecum Ligation and Puncture (CLP) experiment using C57BL/6 mice with Sham surgery (Sham) as a control group. After the operation was performed for 24 hours, peripheral blood of the mouse was taken, RNA was extracted using an RNAStart 200 kit (Shanghai Feijie Biotech Co., Ltd.), reverse transcription was performed using a reverse transcriptase mixture (next Saint Biotech (Shanghai) Co., Ltd.) according to the instructions, and then reverse transcription was performed using a reverse transcriptase mixture

qPCR SYBR Green Master Mix (No Rox) (St. Seawa Biotech (Shanghai) Co., Ltd.) A real-time fluorescent quantitative PCR experiment was performed with GAPDH as an internal reference gene. The primer sequence is as follows:

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).

See table 4 for amplification conditions:

TABLE 4 summary of amplification conditions

The results are shown in FIG. 9, which shows that ARG1 has a significantly higher gene expression level in the peripheral blood of sepsis mice modeled by CLP than that of Sham (Sham) mice. This demonstrates the performance of ARG1 as a biomarker for sepsis from an experimental level.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the invention is not limited thereto, and that various changes and modifications may be made without departing from the spirit of the invention, and the scope of the appended claims is to be accorded the full scope of the invention.

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Claims

Use of ARG1 in the manufacture of a sepsis diagnostic, severity assessment or prognosis assessment reagent or kit.
2. The use of claim 1, wherein the severity determination reagent or kit is a reagent or kit for determining whether severe or fatal sepsis is present, or a reagent or kit for further determining whether septic shock is present.
3. The use of claim 1, wherein ARG1 refers to the ARG1 gene and the corresponding ARG1 protein.
4. The use according to claim 3, wherein the reagent is a reagent for detecting the expression level of ARG1 in a biological sample; the kit comprises a reagent for detecting the ARG1 content in the biological sample.
5. The use according to claim 4, wherein the reagent for detecting the amount of ARG1 in the biological sample is selected from the group consisting of: PCR primers with detection specificity for the ARG1 gene, or antibodies that specifically bind to the ARG1 protein.
6. The use of claim 5, wherein the PCR primers with detection specificity for the ARG1 gene are shown in SEQ ID No. 1-4.
7. The use of claim 4, wherein the biological sample is obtained from peripheral blood of the subject.
8. A sepsis diagnostic, severity determining or prognostic assessment kit comprising reagents for detecting the amount of ARG1 in a biological sample.
9. The kit according to claim 8, wherein the kit comprises a reverse transcription system, a primer system and an amplification system, and the primer system comprises PCR primers shown as SEQ ID No. 1-4.