Biomarker combination for diagnosis of immune depletion type bone and soft tissue infection and application of biomarker combination

Abstract

The invention relates to a biomarker combination for diagnosis of immune depletion type bone and soft tissue infection and application of the biomarker combination, the biomarker combination is cystatin C and granzyme K, when cystatin Cgt; when the concentration is 0.15 mg / L and the grazyme K is 30-70 pg / mL, or when the Cst3 + Arg-1 + macrophages account for the total quantity gt of the macrophages; when the content of the Gzmk is 15% and the content of the Gzmk + Tigit + CD8 + T cells accounts for 10-35% of the content of the CD8 + T cells, the bone and soft tissue infection immune microenvironment of the subject is in a depleted state. Compared with the prior art, the built biomarker combination exceeds a traditional binary diagnosis mode for judging whether infection exists or not, and whether the bone and soft tissue infection local immune microenvironment is in a depletion state or not can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a biomarker combination for diagnosing immune-exhaustion bone and soft tissue infections and applications thereof. Background Art

[0002] Bone and soft tissue infectious diseases such as osteomyelitis, suppurative arthritis, muscle and soft tissue infection, implant infection, infected nonunion, bone defects, etc. are catastrophic diseases in orthopedics. Their diagnosis and treatment have long been in a clinical dilemma of high failure rate, high recurrence and high disability.

[0003] The clinical manifestations and pathological features of bone and soft tissue infections are complex and diverse, and the efficacy of standardized anti-infective treatments exhibits significant heterogeneity. Abnormalities in the immune regulatory network may be a key factor contributing to this heterogeneity, manifested in differences in the immune microenvironment between the different clinical and pathological phenotypes of bone and soft tissue infections. Establishing molecular classifications of immune-related diseases and personalized diagnosis and treatment plans based on immune regulatory networks has been well established in the field of oncology. However, there are currently no reports on molecular classifications of bone and soft tissue infectious diseases based on immune regulatory characteristics, either domestically or internationally.

[0004] Currently, the clinical diagnosis of bone and soft tissue infections primarily relies on symptoms and signs, blood and tissue fluid inflammatory markers, bacteriological cultures, and imaging results. Diagnostic markers have evolved from the most traditional white blood cell count and neutrophil ratio, C-reactive protein, erythrocyte sedimentation rate, and procalcitonin, to α-defensins, neutrophil gelatinase-associated lipocalin, IL-6, and further to PCR and mNGS. Despite the continuous advancement of markers, clinical diagnosis remains based on a binary "presence or absence" of pathogens, making it difficult to identify the immune heterogeneity of different bone and soft tissue infection cases and, consequently, to provide more precise treatment. Summary of the Invention

[0005] The purpose of the present invention is to break through the binary diagnostic model of bone and soft tissue infection, namely the presence or absence of infection, and to establish a close connection between immune-related disease molecules and the diagnosis and prognosis evaluation of bone and soft tissue infection, thereby providing a biomarker combination for the diagnosis of immune-depletion bone and soft tissue infection and its application.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] One of the technical solutions of the present invention is to provide an application of a biomarker combination in the preparation of a kit for diagnosing immune-depletion bone and soft tissue infections, wherein the biomarker combination is cystatin C and granzyme K.

[0008] In some embodiments, the bone and soft tissue infection includes osteomyelitis, septic arthritis, muscle and soft tissue infection, orthopedic implant infection, infected nonunion, and bone defect.

[0009] In some embodiments, the kit includes any one or both of the following reagents a) or b):

[0010] a) Reagents for detecting the expression levels of cystatin C and granzyme K;

[0011] b) Reagents for detecting the number of immune cells where cystatin C and granzyme K are located.

[0012] In some embodiments, when cystatin C>0.15 mg / L and granzyme K is between 30 and 70 pg / mL, the immune microenvironment of bone and soft tissue infection in the subject is in an exhausted state.

[0013] In some embodiments, the immune cell where cystatin C is located is Cst3 + Arg-1 + Macrophages, the immune cells where the granzyme K is located are Gzmk + Tigit + CD8 + T cells.

[0014] In some embodiments, when Cst3 + Arg-1 + Macrophages account for >15% of the total macrophage population and Gzmk + Tigit + CD8 + CD8 T cells + When the number of T cells is 10-35%, the subject's bone and soft tissue infection immune microenvironment is in an exhausted state.

[0015] In some embodiments, the kit further comprises an ex vivo tissue sample from a subject.

[0016] In some embodiments, the sample comprises joint fluid, synovial tissue, bone marrow, or muscle.

[0017] The second technical solution of the present invention is to provide a kit for diagnosing immune-depletion bone and soft tissue infections, which includes a detection reagent for the expression level of the biomarker combination cystatin C and granzyme K and / or a detection reagent for the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located.

[0018] A third technical solution of the present invention is to provide a system for diagnosing immune-depletion bone and soft tissue infections, comprising any one or both of the following 1) or 2):

[0019] 1) A first data input module for obtaining the expression levels of cystatin C and granzyme K;

[0020] a first analysis module, connected to the data input module, for diagnosing immune-depletion bone and soft tissue infection using statistical methods or artificial intelligence methods based on the expression levels of cystatin C and granzyme K;

[0021] 2) A second data input module, used to obtain the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located;

[0022] The second analysis module is connected to the data input module and is used to diagnose immune-depletion bone and soft tissue infection using statistical methods or artificial intelligence methods based on the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located.

[0023] In some specific embodiments, the artificial intelligence method is a machine learning method, and the machine learning method is selected from one of logistic regression, decision tree, random forest, support vector machine, naive Bayes, K nearest neighbor and neural network.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] (1) The cystatin C and granzyme K biomarker combination established in the present invention goes beyond the traditional binary diagnosis model of whether infection is present or not. It can detect the values ​​of these two markers in tissues such as joint fluid: the expression level of cystatin C is greater than 0.15 mg / L and the expression level of granzyme K is between 30 and 70 pg / mL, or by detecting the proportion of specific cell subsets: Cst3 + Arg-1 + Macrophages account for >15% of total macrophages and Gzmk + Tigit + CD8 + CD8 T cells + The T content is 10-35%, which can accurately assess whether the local immune microenvironment of bone and soft tissue infection is in a state of exhaustion. It can also provide early warning signals for clinical cases of severe bone and soft tissue infection with potential poor prognosis, helping to achieve risk stratification management.

[0026] (2) The cystatin C and granzyme K biomarker combination provided by the present invention can also specifically identify the pathological state of increased proportion of functionally exhausted macrophages and T lymphocytes in the microenvironment of bone and soft tissue infection, providing a molecular basis for the clinical formulation of targeted immune regulation treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 These are the four molecular classifications of bone and soft tissue infection diseases of the present invention and their corresponding characteristic immune cells.

[0028] Figure 2 A is the expression of Cst3 and Gzmk in the CM4 type immune exhaustion type of the present invention, Figure 2 B-C show the differences in expression of Cst3 and Gzmk between CM4 immune-depleted type and healthy control group.

[0029] Figure 3 To analyze the expression of Cst3 and Gzmk and characteristic immune cells in CM4 immune-depleted type and control group based on transcriptome and protein levels.

[0030] Figure 4 The diagnostic value of cystatin C and granzyme K as a marker combination. DETAILED DESCRIPTION

[0031] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0032] In the following examples, unless otherwise specified, raw materials or processing techniques are conventional commercially available raw materials or conventional processing techniques in the art.

[0033] Example 1 Screening of biomarkers

[0034] 397 samples from patients with bone and soft tissue infections (including 122 cases of osteomyelitis, 96 cases of suppurative arthritis, 112 cases of muscle and soft tissue infection, and 67 cases of implant infection) and 120 samples from animals with bone and soft tissue infection (including 37 mouse osteomyelitis models, 21 cases of mouse suppurative arthritis, 40 cases of mouse muscle and soft tissue infection, and 22 cases of mouse implant infection) were collected. The bone marrow, joint fluid, synovium, muscle and other tissues of the infected area were isolated and single-cell RNA sequencing was performed. The sequencing data were first analyzed by differential gene and enrichment pathway analysis, cell type and cell subcluster analysis to explore the regulatory characteristics of immune cells. The results are as follows Figure 1As shown in A, the cell types of the sample include B cells, red blood cells, T & NK cells, basophils, bone marrow cells, and neutrophils.

[0035] Further combined with the Consensus Cluster algorithm, a molecular classification of bone and soft tissue infection was proposed based on unsupervised clustering, such as Figure 1 As shown in Figure B, these four types are based on the characteristics of cell subclusters that play a key role in immune regulation in the local infection focus, namely CM1 type immune activation type (containing characteristic immune cells such as Neu_05_CXCL10, Mph_02_S100A9, Mph_09_TREM2, DC_02_CCL3, NK_07-CCR9), CM2 type myeloid enriched immunosuppressive type (containing Mph_04_ARG1, Neu_02_LCN2, NK_05_NSG2, CD4T_02_HMGB2, DC_01_C D52 and other characteristic immune cells), CM3 type matrix-enriched immune-persistent type (Neu_01_S100A11, Mph_07_INHBA, Neu_06_CTSB, Ec_05_CXCL12, Neu_11_LRG1, Mph_01_LARS2 and other characteristic immune cells), CM4 type immune-exhausted type (Mph_05_CST3, CD8T_04_GZMK, NK_02_PTMS, Fb_06_COL3A1, CD4T_05_FOXP3 and other characteristic immune cells).

[0036] Based on the analysis of immune cell subset characteristics and clinical prognosis data, CM4 immune exhaustion type has two significant characteristics: on the one hand, based on the transcriptome level, Cst3 + Arg-1 + Macrophages (Mph_05_CST3) and Gzmk + Tigit + CD8 + Key immune cell subsets such as T cells (CD8T_04_GZMK) showed characteristic enrichment and functional exhaustion in the infection microenvironment; on the other hand, the treatment failure rate of patients with this subtype was as high as 45%, significantly higher than the overall average level of the disease (P < 0.01), and the clinical efficacy outcome was the worst. Within 20 months after surgery, only 55% of patients had no recurrence of infection. Figure 1 As shown in C. It can be seen that CM4 immune exhaustion type belongs to the subtype of refractory bone and soft tissue infection.

[0037] Through a systematic comparative analysis of the differential gene expression profiles between different molecular subtypes, it was found that there was a group of characteristic differentially expressed genes in the samples of the immune exhaustion phenotype (CM4 subtype), namely, Cst3 and Gzmk were the most significantly expressed, such as Figure 2 As shown in A.

[0038] Cst3 gene, gene ID: 13010, encodes the protein cystatin C (cysteine ​​protease inhibitor C).

[0039] Gzmk gene, gene ID: 14945, encodes the protein granzyme K.

[0040] Therefore, cystatin C and granzyme K were used as a biomarker combination for the diagnosis of immune-depleting bone and soft tissue infections.

[0041] Example 2 is verified based on clinical samples

[0042] Samples were collected from 13 cases of immune-depleting bone and soft tissue infections and 15 healthy controls. Peripheral bone marrow and soft tissue specimens from both cases and healthy controls were mechanically dissociated and enzymatically digested to obtain single-cell suspensions. Single-cell transcriptome sequencing was performed using the 10x Genomics platform for library construction and high-throughput sequencing. The data were subjected to standard quality control, clustering, immune subset annotation, and exhaustion marker expression analysis. Protein levels were measured using multicolor flow cytometry, staining for immune exhaustion markers and functional effector molecules in T cells and macrophages. Data were collected and statistically analyzed for the proportions and phenotypic characteristics of each immune cell subset.

[0043] Based on single-cell transcriptome data, it was found that Figure 2 Violin plots of gene expression shown in B and 2C show that the expression of Cst3 and Gzmk in the immune-depleted infection group was significantly stronger than that in the healthy control group.

[0044] ELISA data analysis based on peripheral blood, such as Figure 3 As shown in A and B, the levels of cystatin C and granzyme K were significantly increased in the immune-depleted infection group, with average values ​​of 0.217 mg / L and 56.3 pg / mL, respectively, which were significantly higher than the average values ​​of 0.079 mg / L and 23.7 pg / mL in the control group.

[0045] Based on flow cytometric data analysis of tissues surrounding infected lesions, such as Figure 3 As shown in C and D, the Cst + Arg + Gzmk in macrophages and granzyme K + Tight + CD8 + The proportion of T cells in the infected group increased significantly. + Arg +The average proportion of macrophages in total macrophages was 20.07%, while Gzmk + Tight + CD8 + CD8 T cells + The average proportion of T cells was 26.07%, which was significantly higher than 6.51% and 12.17% of the healthy control group, respectively.

[0046] Based on flow cytometric data analysis of tissues surrounding infected lesions, such as Figure 3 As shown in E and F, in the immune-depleted infection group, Cst + Arg + Macrophages and Gzmk + Tight + CD8 + The expression of T cell functional effector molecules TNF-α and GZMB was significantly reduced, and the killing ability was reduced, which is consistent with the characteristics of immune exhaustion.

[0047] Example 3 is verified by biological experiments

[0048] To verify the clinical relevance of the above findings, a stable and reproducible CM4 subtype animal model was successfully constructed based on the pathophysiological mechanism.

[0049] The CM4 subtype animal model was constructed as follows:

[0050] C57BL / 6 mice (6 weeks old, female) were anesthetized with sodium pentobarbital. After local depilation of the knee joint, an incision was made along the medial side of the patella including the knee joint surface using a scalpel. A 1 mL syringe needle was used to puncture the proximal tibia to reach the medullary cavity (until bright red bone marrow blood gushed out). 100 μL of methicillin-resistant Staphylococcus aureus (MRSA43300) suspension (inoculation concentration of 1×10^9 CFU / mL) was injected along the hole. Sterile titanium alloy wire was then implanted, and the joint cavity was sutured layer by layer to complete the model.

[0051] The control group consisted of healthy mice.

[0052] Key parameters of the CM4 model include: 1) a precisely controlled high-dose pathogen (methicillin-resistant Staphylococcus aureus MRSA43300) inoculum (1×10^9 CFU / mL) to simulate the microbial load of severe clinical infection; 2) the use of titanium alloy wire as an implant to promote bacterial adhesion and formation of a stable biofilm; and 3) long-term infection progression monitoring (≥90 days) to ensure the full development of the immune exhaustion phenotype.

[0053] The lesion local flow cytometry analysis of the control group and CM4 subtype animal model revealed that the immune exhausted cells Cst + Arg+ Macrophages and Gzmk + Tight + CD8 + The proportion of T cells was significantly increased compared with the control group, with significant differences, indicating that the CM4 subtype animal model was successfully constructed.

[0054] Based on the paired data of the diagnostic results of the cystatin C and granzyme K marker combination and its actual diagnostic results, the ROC curve was drawn for analysis, which showed that the diagnostic power AUC reached 0.932, the sensitivity reached 90.1%, and the specificity reached 87.4%, proving that the diagnostic value of the diagnostic marker combination based on cystatin C and granzyme K for potential infection typing is high. Figure 4 shown.

[0055] In summary, this paper proposes a biomarker combination of cystatin C and granzyme K for the differential diagnosis of immune-depleting bone and soft tissue infections. This combination was developed through deep mining and analysis of single-cell RNA sequencing data to investigate the immunophenotype associated with poor prognosis in bone and soft tissue infections—immune-depleting infections. This biomarker combination was determined by systematically analyzing the distribution and functional status of immune cell lineages within the local microenvironment of immune-depleting bone and soft tissue infections, screening for the characteristic expression of cystatin C and granzyme K, and ultimately validating the combination.

[0056] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention, without departing from the scope of the present invention, should be within the scope of protection of the present invention.

Claims

1. Use of a biomarker combination in the preparation of a kit for diagnosing immune-depleting bone and soft tissue infections, characterized in that: The biomarker combination is cystatin C and granzyme K.

2. The use according to claim 1, characterized in that The bone and soft tissue infections include osteomyelitis, suppurative arthritis, muscle and soft tissue infection, orthopedic implant infection, infected nonunion, and bone defect.

3. The use according to claim 1, characterized in that The kit includes any one or two of the following reagents a) or b): a) Reagents for detecting the expression levels of cystatin C and granzyme K; b) Reagents for detecting the number of immune cells where cystatin C and granzyme K are located.

4. The use according to claim 3, characterized in that When cystatin C>0.15mg / L and granzyme K is between 30 and 70pg / mL, the subject's bone and soft tissue infection immune microenvironment is in a state of exhaustion.

5. The use according to claim 3, characterized in that The immune cell where cystatin C is located is Cst3 + Arg-1 + Macrophages, the immune cells where the granzyme K is located are Gzmk + Tigit + CD8 + T cells.

6. The use according to claim 5, characterized in that When Cst3 + Arg-1 + Macrophages account for >15% of the total macrophage population and Gzmk + Tigit + CD8 + CD8 T cells + When the number of T cells is 10-35%, the subject's bone and soft tissue infection immune microenvironment is in an exhausted state.

7. The use according to claim 1, characterized in that The kit also includes a tissue sample isolated from a subject.

8. The use according to claim 7, characterized in that The samples include joint fluid, synovial tissue, bone marrow, and muscle.

9. A kit for diagnosing immune-depleting bone and soft tissue infections, characterized in that: The kit includes a detection reagent for the expression level of the biomarker combination cystatin C and granzyme K and / or a detection reagent for the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located.

10. A system for diagnosing immune-depleting bone and soft tissue infections, characterized in that: Including any one or both of the following 1) or 2): 1) A first data input module for obtaining the expression levels of cystatin C and granzyme K; a first analysis module, connected to the data input module, for diagnosing immune-depletion bone and soft tissue infection using statistical methods or artificial intelligence methods based on the expression levels of cystatin C and granzyme K; 2) A second data input module, used to obtain the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located; The second analysis module is connected to the data input module and is used to diagnose immune-depletion bone and soft tissue infection using statistical methods or artificial intelligence methods based on the number of immune cells where cystatin C is located and the number of immune cells where granzyme K is located.

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