A biomarker for distinguishing Gram-positive cocci from Gram-negative bacilli in bloodstream infections in patients with sepsis

Abstract

The present invention relates to the field of bioengineering technology, and in particular to a biomarker for distinguishing Gram-positive cocci from Gram-negative bacilli in bloodstream infections in patients with sepsis. The present invention provides a biomarker that can distinguish bloodstream infections caused by different pathogens. Based on single-cell transcriptome sequencing of S100A12 expression levels in peripheral blood dendritic cells, this biomarker can be used to rapidly identify the type of sepsis infection in patients, providing a reference for early clinical 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 infections caused by Gram-positive cocci and Gram-negative bacilli in patients with sepsis. Background Art

[0002] Sepsis is a dysregulated host response to infection that leads to life-threatening organ dysfunction. Globally, nearly 50 million new cases of sepsis occur annually, with approximately 11 million deaths, accounting for 20% of all deaths. The prevalence of sepsis in intensive care units (ICUs) approaches 30%, with a mortality rate of 26.7%. Approximately 40% of sepsis patients experience pathogen invasion of the bloodstream, known as bloodstream infection (BSI), which worsens the prognosis. In addition to the poor short-term prognosis, the long-term prognosis is even more concerning. Bacterial infection is the most common cause of sepsis. While clinical features of sepsis caused by Gram-positive cocci (GPCs) and Gram-negative bacilli (GNBs) are similar, the pathophysiological complexity of sepsis and the initiation of immunosuppression result in high heterogeneity among subgroups of sepsis patients with similar clinical features. Blood culture, the gold standard for diagnosing sepsis, does not provide reliable results to clinicians before 36 hours. Therefore, rapid diagnosis of sepsis and identification of the pathogen remain challenges.

[0003] Single-cell RNA sequencing (scRNA-seq) is an emerging sequencing technology that can comprehensively analyze whole peripheral blood mononuclear cells (PBMCs) from patients with sepsis, eliminating cellular heterogeneity and revealing previously unidentified cell subsets and differentially expressed genes. Dendritic cells (DCs) are professional antigen-presenting cells that play a key role in initiating and regulating immune responses to pathogen invasion. However, biomarkers that distinguish between different pathogens in bloodstream infection are currently lacking. Summary of the Invention

[0004] The present invention aims to provide a biomarker for distinguishing Gram-positive cocci from Gram-negative bacilli bloodstream infections 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 from 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 Gram-positive cocci and Gram-negative bacilli bloodstream infection in patients with sepsis, comprising the following steps:

[0011] (1) Isolate peripheral blood mononuclear cells from patients with Gram-positive cocci infection, Gram-negative bacilli bloodstream infection, and healthy subjects. After filtration, perform single-cell RNA sequencing to obtain the gene expression matrix of peripheral blood mononuclear cells of different patients and analyze the differentially expressed genes in 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 based on 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 derived 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 reduced in dimension 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 relevant 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 excluding cell 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 infections in patients with sepsis based on the expression level of S100A12 in peripheral blood dendritic cells by 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0021] Figure 1 This is a statistical diagram 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, with different colors representing different cell types.

[0023] Figure 3 This is a statistical diagram 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, with different colors representing different cell types.

[0025] Figure 5 Figure 2 shows the proportion 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 Figure 2 shows the expression of S100A12 in different patients.

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

[0030] The technical solutions provided by the present invention are described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention. Example 1

[0031] 1. Sample Source

[0032] To reduce heterogeneity due to different infection sites and maximize diagnostic clarity, we selected patients with sepsis and bloodstream infection, with each cohort consisting of one male and one female of similar age. For comparison, we also analyzed specimens from healthy volunteers of similar age.

[0033] This method targets subjects with bloodstream infection in the early stages of sepsis. Blood cultures are collected from patients who present to the emergency department (ED) or intensive care unit (ICU) of Ningxia Medical University General Hospital within 2 hours and sent for testing. 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 two healthy volunteers, two patients with bloodstream infection caused by Gram-negative bacilli (GPC) and two patients with bloodstream infection caused by Gram-positive cocci (GNB)) serves as a separate analysis unit.

[0034] Inclusion criteria for the study subjects: 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.

[0035] Exclusion criteria: ① Age < 18 years old; ② Malignant tumor; ③ Acquired immunodeficiency syndrome; ④ Autoimmune system disease; ⑤ Receiving immunosuppressive treatment; ⑥ Withdrawal from the study midway.

[0036] 2. Single-cell RNA sequencing

[0037] Peripheral venous blood was collected from patients with sepsis and healthy volunteers with different bacterial bloodstream infections, and PBMCs were isolated. Single-cell RNA sequencing (scRNA-seq) using the 10× Genomics platform was used to identify differences in cell state composition between the groups and to detect gene expression signatures that distinguished patients with sepsis due to bloodstream infections caused by different pathogens.

[0038] The obtained cells were subjected to strict quality control and cell screening, and the cell filtration parameters were set as follows:

[0039] 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 an excessively high mitochondrial gene ratio need to be filtered out;

[0040] ② The number of UMIs identified in a single cell: ≥100, filtering the number of transcripts detected in a single cell;

[0041] ③ 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 a GEMs oil droplet contains more than one cell or whether a cell has ruptured.

[0042] 3. The present invention obtained a total of 6 peripheral blood samples, named A1, A2, B1, B2, C1, and C2, namely Group A, Group B, and Control. A total of 56,058 high-quality cells were obtained, and a total of 184,542 unique genes were detected in all cells.

[0043] 4. We further identified 11 clusters using cluster analysis, performed dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP), and used SingleR automated annotation combined with cell marker databases and relevant literature for manual cell annotation. We then clustered and identified four major cell populations, including T cells, B cells, multiple lymphocyte progenitor cells, and bone marrow cells. We found that the percentages of T cells and multiple lymphocyte progenitor cells in patients with bloodstream infection sepsis (Group A + Group B) were significantly lower than those 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 ).

[0044] To further understand the subpopulations 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.

[0045] 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 ( Figures 3 to 6 ).

[0046] 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 caused by 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 when compared with each other, indicating that these 19 genes are closely related to both Gram-negative and Gram-positive cocci infections. Among them, 13 genes, including S100A12, were highly expressed in group B, with S100A12 being the most significant, indicating greater sensitivity to Gram-positive cocci infections (Table 1).

[0047] Table 1 Intersection genes

[0048]

[0049] 6. ELISA validation

[0050] 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 an anticoagulant. The samples were centrifuged at 3000 rpm for 15 minutes at 6°C within 30 minutes after collection. The supernatant was collected and RPL39, SMCHD1, CD177, S100A12, S100A9, and MMP9 were randomly selected from the single-cell RNA sequencing results. The 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. Figure 8 ).

[0051] 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. As a marker, S100A12 had a sensitivity of 73.3% and a specificity of 60.0% ( Figure 9 ).

[0052] 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Use of a reagent for detecting biomarkers in the preparation of a bloodstream infection detection kit for distinguishing Gram-positive cocci from Gram-negative bacilli in patients with sepsis, characterized in that: The biomarker is S100A12.

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