Biomarker for distinguishing gram-positive coccus and gram-negative bacillus blood flow infection of sepsis patient

Abstract

The invention relates to the technical field of bioengineering, in particular to a biomarker for distinguishing gram-positive coccus and gram-negative bacillus blood flow infection of sepsis patients. The invention provides a marker capable of distinguishing blood flow infections of different pathogens, Gram-positive coccus and Gram-negative bacillus blood flow infections of sepsis patients are distinguished on the basis of single cell transcriptome sequencing peripheral blood dendritic cell S100A12 expression level, and the infection type of sepsis of the patients can be rapidly identified by utilizing the marker. And a reference basis is provided for clinical early intervention and treatment of sepsis.

Description

Technical Field

[0001] The present invention relates to the field of bioengineering technology, and in particular to a biomarker for distinguishing bloodstream infection caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis. Background Art

[0002] Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. There are nearly 50 million new cases of sepsis worldwide each year, of which about 11 million die, accounting for 20% of all deaths. The prevalence of sepsis in intensive care units (ICUs) is close to 30%, and the mortality rate is 26.7%. About 40% of sepsis patients have pathogens invading the blood circulation, namely bloodstream infection (BSI), and their prognosis is even worse. In addition to the poor short-term prognosis, the long-term prognosis is even more worrying. Bacterial infection is the main factor causing sepsis. The clinical characteristics of sepsis patients infected with Gram-positive cocci (GPC) and Gram-negative bacilli (GNB) are similar, but the pathophysiological complexity of sepsis and the initiation of immunosuppressive reactions make the subgroups of sepsis patients with similar clinical characteristics highly heterogeneous; blood culture is the gold standard for diagnosing sepsis, but it cannot provide reliable results for clinicians before 36 hours. Therefore, the rapid diagnosis of sepsis and the identification of pathogens remain a challenge.

[0003] Single-cell RNA sequencing (scRNA-seq) is an emerging sequencing technology that can comprehensively analyze the whole peripheral blood mononuclear cells of sepsis patients without cellular heterogeneity, and can discover previously unidentified cell subpopulations and differential genes. Dendritic cells (DCs) are professional antigen-presenting cells that play a key role in initiating and regulating immune responses when pathogens invade. However, there is currently a lack of markers to distinguish bloodstream infections caused by different pathogens. Summary of the invention

[0004] The object of the present invention is to provide a biomarker for distinguishing bloodstream infection caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides a biomarker for distinguishing bloodstream infection caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis, wherein the biomarker is S100A12.

[0007] The present invention also provides the use of the biomarker in preparing a bloodstream infection detection kit for distinguishing Gram-positive cocci and Gram-negative bacilli in sepsis patients.

[0008] The present invention also provides a kit for distinguishing bloodstream infection caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis, comprising a reagent for detecting the biomarker.

[0009] Preferably, the reagent is a primer or a probe.

[0010] The present invention also provides a method for screening biomarkers for distinguishing bloodstream infection caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis, comprising the following steps:

[0011] (1) Peripheral blood mononuclear cells were isolated from patients with Gram-positive cocci infection, patients with Gram-negative bacilli bloodstream infection, and healthy subjects. After filtration, single-cell RNA sequencing was performed to obtain the gene expression matrix of peripheral blood mononuclear cells of different patients and analyze the differentially expressed genes of different patients;

[0012] (2) Marker clusters were identified by cluster analysis, and the main cell populations with differentially expressed genes were obtained after manual cell annotation. Cluster annotation was continued according to the Markers gene database to obtain the differentially expressed genes of the cell types with the largest proportion in different patients. After Venn diagram analysis, biomarkers for distinguishing Gram-positive cocci and Gram-negative bacilli bloodstream infections in patients with sepsis were obtained.

[0013] Preferably, the peripheral blood mononuclear cells in step (1) are from peripheral venous blood.

[0014] Preferably, the filtering parameters of step (1) are set as follows: mitochondrial gene ratio: ≤20; number of UMIs identified in a single cell: ≥100; number of genes identified in a single cell: 500-7000.

[0015] Preferably, the marker cluster in step (2) is dimensionally reduced by uniform manifold approximation and projection.

[0016] Preferably, the method for artificial cell annotation in step (2) is: using SingleR automated annotation, cell marker database and related literature to perform artificial cell annotation.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] (1) The present invention uses single-cell RNA sequencing (scRNA-seq) technology to screen markers, which can comprehensively analyze the whole peripheral blood mononuclear cells of sepsis patients without cellular heterogeneity, and can discover previously unidentified cell subpopulations and differentially expressed genes.

[0019] (2) The present invention provides a method for distinguishing Gram-positive cocci and Gram-negative bacilli bloodstream infection in patients with sepsis based on the expression level of S100A12 in peripheral blood dendritic cells based on single-cell transcriptome sequencing. This marker can be used to quickly identify the type of sepsis infection in patients, providing a reference for early clinical intervention and treatment of sepsis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0021] Figure 1 This is a statistical chart of cell type distribution. The horizontal axis represents different samples, the vertical axis represents the corresponding percentage of cell numbers, and different colors represent different cell types.

[0022] Figure 2 t-SNE plot for cell type identification, different colors represent different cell types.

[0023] Figure 3 This is a statistical chart of cell type distribution. The horizontal axis represents different samples, the vertical axis represents the corresponding percentage of cell numbers, and different colors represent different cell types.

[0024] Figure 4 t-SNE plot for cell type identification, different colors represent different cell types.

[0025] Figure 5 The figure shows the percentage of cell types in different patients.

[0026] Figure 6 The percentages of CD1C-CD141- dendritic cells in different groups.

[0027] Figure 7 Differentially expressed genes among different patients.

[0028] Figure 8 This is a graph showing the expression of S100A12 in different patients.

[0029] Fig. 9 It is the ROC curve diagram. DETAILED DESCRIPTION

[0030] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0031] Example 1

[0032] 1. Sample source

[0033] To reduce heterogeneity due to different infection sites and maximize diagnostic clarity, we selected patients with sepsis due to bloodstream infection, and each cohort consisted of one male and one female of similar age. For comparison, we also analyzed specimens from healthy volunteers of similar age.

[0034] The present invention targets subjects with bloodstream infection in the early stage of sepsis. Blood culture is collected from patients who visit the emergency department (ED) or intensive care unit (ICU) of Ningxia Medical University General Hospital within 2 hours and sent for examination. Peripheral venous blood samples (3-5 mL per sample) are collected using sodium heparin blood collection tubes within 2 hours of the blood culture pre-report for single-cell RNA sequencing (scRNAseq) analysis. Each donor sample (including 2 healthy volunteers, 2 sepsis patients with gram-negative bacilli (GPC) bloodstream infection, and 2 sepsis patients with gram-positive cocci (GNB) bloodstream infection) is used as a separate analysis unit.

[0035] Inclusion criteria for the study subjects: The patients were diagnosed with sepsis within 2 hours according to the latest Sepsis 3.0 criteria for sepsis and septic shock developed by the American Society of Critical Care Medicine in 2016.

[0036] Exclusion criteria: ①Age < 18 years old; ②Malignant tumor; ③Acquired immunodeficiency syndrome; ④Autoimmune system disease; ⑤Receiving immunosuppressive treatment; ⑥Withdrawing from the study midway.

[0037] 2. Single-cell RNA sequencing

[0038] Peripheral venous blood was collected from sepsis patients with different bacterial bloodstream infections and healthy volunteers, and PBMCs were isolated. Single-cell RNA sequencing (scRNA-seq) based on the 10× Genomics platform was used to identify differences in cell state composition between the groups and to detect gene expression signatures that distinguish sepsis patients with bloodstream infections caused by different pathogens.

[0039] The obtained cells were subjected to strict quality control and cell screening, and the cell filtration parameters were set as follows:

[0040] ① Mitochondrial gene ratio (%): ≤20. It is generally believed that cell apoptosis and rupture will lead to a high mitochondrial gene ratio, so cells with too high a mitochondrial gene ratio need to be filtered out;

[0041] ②The number of UMIs identified in a single cell: ≥100, filtering the number of transcripts detected in a single cell;

[0042] ③The number of genes identified in a single cell: 500-7000. Under normal circumstances, the number of genes expressed by a cell is within a certain range. Therefore, the number of genes can be used to determine whether the GEMs oil droplet contains more than one cell or whether the cell has ruptured.

[0043] 3. The present invention obtained a total of 6 peripheral blood samples, named A1, A2, B1, B2, C1, C2, namely group A, group B, and control group. A total of 56058 high-quality cells were obtained, and a total of 184542 unique genes were detected in all cells.

[0044] 4. Further, we identified 11 clusters by cluster analysis, reduced the dimension by uniform manifold approximation and projection (UMAP), and clustered and identified four major cell populations, including T cells, B cells, multiple lymphocyte progenitor cells, and bone marrow cells, using SingleR automated annotation combined with cell marker database and related literature for manual cell annotation. We found that the percentage of T cells and multiple lymphocyte progenitor cells in patients with bloodstream infection sepsis (group A + group B) was significantly lower than that in the healthy control group (group C), while the percentage of bone marrow cells was significantly higher than that in the healthy control group (group C) ( Figure 1 and Figure 2 ).

[0045] To further understand the subsets of each major cell population, we used the Seurat R package to further cluster and annotate all T cells and bone marrow cells according to marker genes: T cells were annotated as CD8+T cells and CD4+T cells, and bone marrow cells were annotated as CD1C-CD141- dendritic cells, plasmacytoid dendritic cells, and megakaryocytes.

[0046] Finally, six major cell populations were identified based on the relevant marker genes: CD1C-CD141- dendritic cells, CD8+ T cells, CD4+ T cells, B cells, plasmacytoid dendritic cells, and megakaryocytes. It is worth noting that CD1C-CD141- dendritic cells are the largest cell type, and their percentage in group A is higher than that in group B ( Figure 3 to Figure 6 ).

[0047] 5. The present invention further uses Venn diagram analysis to compare the differentially expressed genes of CD1C-CD141- dendritic cells between the healthy control group and sepsis patients with bloodstream infection of different pathogens (such as Figure 7The results showed that the 19 intersection genes in the Venn diagram results were differentially expressed in the three ACB groups, indicating that these 19 genes were closely related to both G-bacillus and G+ cocci infections. Among them, 13 genes such as S100A12 were highly expressed in group B, with S100A12 being the most significant, indicating that it was more sensitive to G+ cocci infections (Table 19 intersection genes, as shown in Table 1).

[0048] Table 1 Intersection genes

[0049]

[0050]

[0051] 6. ELISA validation

[0052] Peripheral venous blood was collected again from 15 patients with sepsis caused by Gram-negative bacilli (GPC), 15 patients with sepsis caused by Gram-positive cocci (GNB) and 10 healthy volunteers. EDTA or heparin was used as anticoagulant and the blood was centrifuged at 3000 rpm for 15 min at 6°C within 30 minutes after specimen collection. The supernatant was collected and RPL39, SMCHD1, CD177, S100A12, S100A9 and MMP9 were randomly selected from the single-cell RNA sequencing results. Enzyme-linked immunosorbent assay (ELISA) was used to detect their concentrations to verify the single-cell RNA sequencing data. The results showed that the concentrations of CD177, S100A12, and S100A9 in group A were higher than those in group C, and the concentrations of RPL39, CD177, S100A12, S100A9, and MMP9 in group B were higher than those in group C. Only the concentration of S100A12 in group A was higher than that in group B, P < 0.05. Figure 8 ).

[0053] We further analyzed the diagnostic efficiency of S100A12 in differentiating GPC from GNB. We used the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). The results showed that the AUC was 0.746. The sensitivity of S100A12 as a marker was 73.3% and the specificity was 60.0% ( Fig. 9 ).

[0054] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A biomarker for distinguishing Gram-positive cocci from Gram-negative bacilli bloodstream infection in patients with sepsis, characterized in that: The biomarker is S100A12.

2. Use of the biomarker according to claim 1 in the preparation of a bloodstream infection detection kit for distinguishing Gram-positive cocci from Gram-negative bacilli in patients with sepsis.

3. A kit for distinguishing Gram-positive cocci from Gram-negative bacilli in bloodstream infection in patients with sepsis, characterized in that: Comprising a reagent for detecting the biomarker according to claim 1.

4. The kit according to claim 3, characterized in that The reagent is a primer or a probe.

5. A method for screening biomarkers for distinguishing Gram-positive cocci from Gram-negative bacilli bloodstream infection in patients with sepsis, characterized in that: The steps include: (1) Peripheral blood mononuclear cells were isolated from patients with Gram-positive cocci infection, patients with Gram-negative bacilli bloodstream infection, and healthy subjects. After filtration, single-cell RNA sequencing was performed to obtain the gene expression matrix of peripheral blood mononuclear cells of different patients and analyze the differentially expressed genes of different patients; (2) Marker clusters were identified by cluster analysis, and the main cell populations with differentially expressed genes were obtained after manual cell annotation. Cluster annotation was continued according to the Markers gene database to obtain the differentially expressed genes of the cell types with the largest proportion in different patients. After Venn diagram analysis, biomarkers for distinguishing Gram-positive cocci and Gram-negative bacilli bloodstream infections in patients with sepsis were obtained.

6. The screening method according to claim 5, characterized in that The peripheral blood mononuclear cells in step (1) are from peripheral venous blood.

7. The screening method according to claim 5, characterized in that The filtering parameters of step (1) are set as follows: mitochondrial gene ratio: ≤20; number of UMIs identified in a single cell: ≥100; number of genes identified in a single cell: 500-7000.

8. The screening method according to claim 5, characterized in that In step (2), the marker cluster is reduced in dimension by uniform manifold approximation and projection.

9. The screening method according to claim 5, characterized in that The method for artificial cell annotation in step (2) is: using SingleR automated annotation, cell marker database and related literature to perform artificial cell annotation.

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