Application of PRDX3 as marker of non-alcoholic fatty liver disease

Abstract

The invention relates to the technical field of biology, and discloses application of PRDX3 as a non-alcoholic fatty liver disease marker. Specifically, the invention provides an innovative non-alcoholic fatty liver diagnosis method and a therapeutic drug effect evaluation technology, which have the characteristics of simple operation, low cost and high diagnosis accuracy. By measuring the change of the PRDX3 concentration, the existence of the non-alcoholic fatty liver disease can be accurately identified, the effect of a therapeutic drug can be evaluated, and powerful assistance is provided for clinical diagnosis and treatment. In addition, the invention further discloses detection of PRDX3 combined with CD80 and CD206, the accuracy and convenience of diagnosis and evaluation can be further enhanced, and remarkable practical application potential is shown.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and more particularly, to the application of PRDX3 as a biomarker for non-alcoholic fatty liver disease. Background Art

[0002] Non-alcoholic fatty liver disease is a chronic liver disease closely related to metabolic syndrome, and its global incidence is increasing year by year, posing a serious threat to human health. Traditional diagnostic methods mainly rely on imaging and pathological examinations, but these methods often have problems such as complex operation, high cost, and insufficient diagnostic accuracy. Therefore, it is particularly important to find an efficient, accurate, and economical diagnostic method for non-alcoholic fatty liver disease.

[0003] As a peroxidase, PRDX3 has been found to be closely related to the occurrence and development of various diseases in recent years. However, regarding the specific mechanism of action and application value of PRDX3 in non-alcoholic fatty liver disease, there is currently a lack of in-depth research and clear conclusions. Summary of the Invention

[0004] In view of this, the present invention proposes an application of PRDX3 in non-alcoholic fatty liver disease, aiming to provide new ideas and methods for the diagnosis and treatment of non-alcoholic fatty liver disease by deeply studying the function and mechanism of action of PRDX3 in non-alcoholic fatty liver disease.

[0005] The present invention proposes an application of PRDX3 or a reagent for detecting PRDX3 in the preparation of a reagent or kit for diagnosing fatty liver.

[0006] Preferably, the fatty liver is non-alcoholic fatty liver.

[0007] The present invention also proposes an application of PRDX3 or a reagent for detecting PRDX3 in the preparation of a reagent or kit for predicting and / or judging the therapeutic effect of a therapeutic drug for fatty liver.

[0008] Preferably, the steps for predicting and / or judging the therapeutic effect of a therapeutic drug for fatty liver include:

[0009] Measuring the concentration of PRDX3 in the peripheral blood of a subject before treatment, and measuring the concentration of PRDX3 in the peripheral blood of the subject at different time points during treatment;

[0010] When the concentration of PRDX3 in the peripheral blood of the subject shows a decreasing trend during treatment, it indicates that the therapeutic effect of the therapeutic drug for fatty liver is effective.

[0011] Preferably, the steps for predicting and / or judging the therapeutic effect of a therapeutic drug for fatty liver further include:

[0012] Measure the concentrations of CD80 and CD206 in the peripheral blood of the subject before treatment, and measure the concentrations of CD80 and CD206 in the peripheral blood of the subject at different time points during treatment;

[0013] When the concentrations of PRDX3 and CD80 in the peripheral blood of the subject show a decreasing trend during treatment, and the concentration of CD206 shows an increasing trend during treatment, it indicates that the therapeutic effect of the fatty liver treatment drug is effective.

[0014] Preferably, the fatty liver is non-alcoholic fatty liver.

[0015] The present invention also provides a kit for predicting and / or diagnosing fatty liver, and the kit includes a reagent for detecting PRDX3.

[0016] Preferably, the kit further includes reagents for detecting CD80 and CD206.

[0017] The present invention also provides the use of PRDX3, CD80 and CD206 as non-alcoholic fatty liver markers.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] A new method for diagnosing non-alcoholic fatty liver and a means for evaluating the therapeutic effect of treatment drugs are provided, which have the advantages of simple operation, low cost, high diagnostic accuracy, etc. By detecting the change in the concentration of PRDX3, the presence of non-alcoholic fatty liver and the therapeutic effect of treatment drugs can be effectively judged, providing strong support for clinical diagnosis and treatment. At the same time, the present invention also provides a method for further improving the accuracy and convenience of diagnosis and evaluation by detecting PRDX3, CD80 and CD206, which has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0021] Figure 1 It is a quality control chart of the number of single-cell sequences, the number of genes, and the mitochondrial ratio before filtration, where the vertical axis represents the values of each index;

[0022] Figure 2 It is a scatter plot of the mitochondrial ratio and the number of sequences of single cells before quality control (A), and a scatter plot of the number of sequences and the number of genes (B);

[0023] Figure 3It is a quality control chart of the number of single-cell sequences, the number of genes, and the mitochondrial proportion after filtration (the vertical axis represents the values of each index);

[0024] Figure 4 It is a scatter plot of the relationship between the mitochondrial proportion and the number of sequences of single cells after quality control (A), and a scatter plot of the relationship between the number of sequences and the number of genes (B);

[0025] Figure 5 It is a characteristic variance chart of 3000 highly variable genes;

[0026] Figure 6 It is a result chart of principal component analysis;

[0027] Figure 7 It is a UMAP clustering chart of different Clusters;

[0028] Figure 8 It is a marker bubble chart for annotation;

[0029] Figure 9 It is a UMAP chart of the annotation results of all cells;

[0030] Figure 10 Stacked chart of cell proportions;

[0031] Figure 11 It is a TSNE chart of Kupffer cell subsets (A), a display of the annotation results of Kupffer cell subsets (B), a marker bubble chart for annotating Kupffer cell subsets (C), and a cell proportion chart of Kupffer cell subsets (D);

[0032] Figure 12 For endothelial cells

[0033] Marker bubble chart for annotating subsets;

[0034] Figure 13 For endothelial cells

[0035] UMAP chart of the annotation results of endothelial cell subsets;

[0036] Figure 14 It shows a stacked chart of the proportions of endothelial cell subsets, which reflects the relative frequencies of different endothelial cell subsets in the sample;

[0037] Figure 15 It is a cell communication intensity bubble chart;

[0038] Figure 16 It is the immunofluorescence of PRDX3 and M1 / M2 polarization markers in mouse liver;

[0039] Figure 17 It is the immunofluorescence of PRDX3 in liver organoids. Specific implementation mode

[0040] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0041] Example 1

[0042] I. Data acquisition and analysis

[0043] The present invention selects the GSE129516 dataset in the GEO database, which covers the single-cell transcriptome data of 3 groups of non-alcoholic fatty liver disease (NAFLD) mice and 3 groups of control mice.

[0044] 1. Reading and quality control of single-cell data

[0045] In this embodiment, the Seurat package (version 4.1.0) of R language was used to preprocess the single-cell RNA sequencing (scRNA-seq) data of 3 NAFLD mice and 3 control mice in the GSE129516 dataset. First, the 6 sample datasets were integrated, and the batch effect was eliminated by the co-expression component analysis (CCA) method. Subsequently, the number of genes (nFeature), the number of sequences (nCounts), and the percentage of mitochondrial genes in the integrated dataset were shown by violin plots.

[0046] The number of cell genes, expression levels, and mitochondrial gene contents before filtering were compared, and the relationship between them was described by a dot density plot. The relevant results are as Figure 1-2 shown.

[0047] Furthermore, in this embodiment, a quality control program is executed on the integrated data, and this program involves two main parameters: (1) the number of genes and the number of sequences detected in each cell. These two criteria ensure the gene expression abundance and diversity of each cell in the analysis. The higher this value, the more stringent the degree of cell expression. Usually, relatively loose thresholds are adopted, and the minimum thresholds for both indicators are 100, while there is no upper limit. (2) If the proportion of mitochondrial genes detected in a cell is too high, it implies that the cell may have been in a "dying" state before lysis, and its nuclear gene expression may tend to be biased towards pathway genes related to cell activity (such as apoptosis). If such cells are not excluded, the analysis results may lead to incorrect conclusions due to poor cell state. The threshold we set is: nFeature < 7500 (see Figure 3 ). After quality control, the number of cells is 33062, and the number of genes is 19349.

[0048] A comparative analysis was performed on the number of genes, expression levels, and mitochondrial content of the filtered cells, and their relationships were revealed through dot density plots, and the results are as Figure 4 shown.

[0049] 2. Identification of highly variable genes in single-cell RNA sequencing

[0050] After performing standardization processing on the data, the present invention is committed to identifying genes that show significant differences in expression among cells. The expression patterns of all genes in the sample are revealed through a feature variance plot (see Figure 5 ), where the abscissa represents the average expression level of each gene in all cells, and the ordinate reflects the variance of the expression levels of each gene. Figure 5 Each point in

[0051] corresponds to a specific gene.

[0052] After completing the data normalization process, this embodiment performs principal component analysis (PCA). PCA is a multivariate statistical analysis method, and its core concept is dimensionality reduction, that is, multiple variables are synthesized into a smaller number of variables through linear transformation to retain the main features of the data set. In the field of high-throughput sequencing of transcriptomes, gene expression levels are used as variables, and through PCA analysis, representative samples and key genes can be screened out. The analysis results are as Figure 6 shown, and this embodiment selects the first 25 principal components for subsequent in-depth analysis.

[0053] 4. UMAP dimensionality reduction technology

[0054] This example uses the UMAP dimensionality reduction technique based on the first 25 principal components, precisely identifies neighboring cells through the FindNeighbors function, then finely clusters the cells using the FindClusters function, and finally identifies 27 cell clusters. Subsequently, clustering analysis is performed using the UMAP technique, and the relevant results are shown in Figure 7 .

[0055] 5. Single-cell RNA sequencing cell annotation

[0056] This invention uses the marker genes provided by the cellmarker2.0 database, reveals the expression patterns of cell marker genes in different cell populations using bubble charts, and thus achieves precise annotation of cell populations. The marker genes applied are detailed in Figure 8 . The annotation results are highly consistent with previous studies, and eight major cell types are successfully identified, including astrocytes, B cells, dendritic cells, endothelial cells, fibroblasts, hepatocytes, Kupffer cells, and T / NK cells, etc. (see Figure 9 ), and finally presented in the form of a stacked bar chart of cell proportions (see Figure 10 ).

[0057] 6. Subpopulation analysis of Kupffer cells

[0058] Numerous previous studies have clearly pointed out that Kupffer cells polarize in different directions during the process of non-alcoholic fatty liver disease (NAFLD) (PMID: 31610032, PMID: 36521495, PMID: 32562600), and then differentiate into M1-type Kupffer cells with pro-inflammatory functions and M2-type Kupffer cells with immunomodulatory functions. M1-type Kupffer cells have pro-inflammatory functions, while M2-type Kupffer cells have immunomodulatory functions. Based on this, this invention performed subpopulation analysis on Kupffer cells in NAFLD according to traditional M1 and M2 polarization markers. The results are as Figure 11 shown. Kupffer cells are divided into three main subpopulations, namely CD80+ Kupffer cells, CD206+ Kupffer cells, and S100A4+ Kupffer cells. Among them, CD206+ Kupffer cells express classical M2 polarization markers such as CD206, MRC1, etc.; S100A4+ Kupffer cells weakly express common M1 and M2 markers, so they are defined as S100A4+ Kupffer cells in the M0 state. It is worth noting that in NAFLD samples, the proportion of CD80+ Kupffer cells highly expressing M1 pro-inflammatory related markers is significantly up-regulated, and these cells express various pro-inflammatory factors such as CD80, TNF, IL1B, etc.

[0059] As is well known, when interleukin IL1B and tumor necrosis factor TNF are continuously upregulated, it may lead to non-alcoholic steatohepatitis (NASH), and further lead to liver fibrosis, damaging the immune homeostasis of the liver, thus resulting in the pathological development of NASH and ultimately liver failure (PMID: 36521495). Therefore, a deep understanding of the heterogeneity of CD80+ Kupffer cells is crucial for revealing the mechanism of NAFLD disease progression. In the present invention, genes specifically upregulated in CD80+ Kupffer cells were screened, and GO-KEGG enrichment analysis was performed. The results are as Figure 11 shown. The results showed that a large number of inflammation-related pathways were significantly enriched, especially the NF-kappa B signaling pathway and the TNF signaling pathway, with the highest enrichment scores, which further confirmed that CD80+ Kupffer cells have a strong pro-inflammatory function.

[0060] 7. Endothelial cell subsets

[0061] In the present invention, we performed in-depth annotation analysis on the Endothelial cells in the aforementioned annotation results. The markers used are as Figure 12 shown. After analysis, we successfully identified three endothelial cell subsets, namely Apoa2+ EC, C1qb+ EC, and Col4a1+ EC.

[0062] Figure 13 Shown as the UMAP graph of the subset annotation results; Figure 14 Shown as the stacked graph of the cell subset proportions, which reflects the relative frequencies of different subsets in the samples. Specifically, the Apoa2+ EC subset expresses genes related to lipid metabolism and glucose metabolism (such as Apoa2, Dpm3, etc.). It is worth noting that this cell subset dominates in control mice but is significantly reduced in the non-alcoholic fatty liver disease (NAFLD) model. Previous studies have pointed out that Apoa2 is closely related to the regulation of blood lipids and cholesterol (PMID: 30500605). The Col4a1+ EC subset expresses a large number of genes related to vascular survival. In addition, as the endothelial cells specifically present in non-alcoholic fatty liver disease, the Col4a1+ EC subset highly expresses a variety of inflammatory factors (including C1qb, C1qa, C1qc, etc.).

[0063] 8. Cell communication

[0064] To deeply explore the communication mechanisms among different cell subsets, the present invention uses the R package CellChat 1.6.1 to infer cell-cell communication relationships. The operating logic of this method is based on constructing a detailed database of signal molecule interactions, which comprehensively considers the known structures of ligand-receptor interactions, including multimeric ligand-receptor complexes, soluble agonists and antagonists, as well as stimulatory and inhibitory membrane-bound coreceptors. CellChat applies the mass action model and combines differential expression analysis and statistical tests to infer signal communication related to cell states in specific scRNA-seq data. In addition, CellChat provides various visualization outputs and quantitatively characterizes and compares cell-cell communication through social network analysis tools, pattern recognition methods, and various learning methods.

[0065] The research results are shown in the figure. CellChat calculates the communication probability at the signal pathway level by aggregating the communication probabilities of all ligand-receptor interactions related to each signal pathway. Given the complexity of the cell communication network, the present invention shows the signals emitted by each subset. By adjusting the parameter edge.weight.max, we can control and compare the weights of the edges in different networks. As shown in Network x, the edge weight reflects the strength of the relationship between nodes. The cell communication intensities of different pathways are shown as Figure 15 shown.

[0066] II. In vivo verification experiment

[0067] The present invention selects 20 6-week-old C57 / B6 mice and randomly divides them into a control group and a non-alcoholic fatty liver disease (NAFLD) model group induced by a high-fat diet, with 10 mice in each group. The mice in the control group are fed a standard diet, while the model group is fed a high-fat diet for 16 weeks to construct a NAFLD model. At the 16th week of the experiment, 6 mice are randomly selected from each group and euthanized by inhaling carbon dioxide, and then liver tissues are extracted. After the extracted liver tissues are washed, they are fixed with 10% paraformaldehyde and subjected to paraffin embedding. Pathological immunofluorescence staining analysis of PRDX3, CD80, and CD206 is performed on the liver tissues. The research results show that compared with the control group, the expression levels of PRDX3 and CD80 in the livers of mice in the NAFLD model group are significantly increased, while the expression level of CD206 is significantly decreased. For detailed results, see Figure 16 .

[0068] IV. Liver organoid verification experiment

[0069] In this invention, a mouse liver organoid model was constructed, and a non-alcoholic fatty liver disease model was successfully induced by sodium palmitate combined with insulin. Detection was carried out using immunofluorescence staining technology. The results showed that, compared with the normal control group, the expression level of peroxisome proliferator-activated receptor γ coactivator 1α (PRDX3) was significantly increased in the non-alcoholic fatty liver disease (NAFLD) group (see Figure 17 ).

[0070] V. Summary

[0071] In this example, through a series of rigorous data analysis processes, the differences between non-alcoholic fatty liver disease (NAFLD) mice and control mice at the single-cell transcriptome level were revealed. First, through the preprocessing and quality control of the GSE129516 dataset in the GEO database, the validity and reliability of the data were ensured. Subsequently, through steps such as the identification of highly variable genes, principal component analysis, UMAP dimensionality reduction technology, and cell annotation, the characteristics of cell populations in the samples were deeply analyzed. In particular, this example conducted a detailed analysis of the Kupffer cell subset and identified subsets with different polarization characteristics. In addition, in-depth research was also carried out on the endothelial cell subset, revealing its potential role in NAFLD. Cell communication analysis further revealed the interaction mechanisms between different cell subsets. In the in vivo validation experiment, by constructing an NAFLD mouse model, the expression changes of molecules such as PRDX3, CD80, and CD206 in NAFLD were verified. In the liver organoid validation experiment, an NAFLD model was successfully induced, and a significant increase in the expression level of PRDX3 was observed. These findings not only provide a new perspective for in-depth understanding of the pathogenesis of NAFLD but also provide potential targets for the development of effective treatment strategies for NAFLD. In summary, the research results of this example bring important inspirations to the research and treatment fields of NAFLD.

[0072] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. Use of PRDX3 or a reagent for detecting PRDX3 in the preparation of a reagent or kit for diagnosing fatty liver.

2. The application according to claim 1, characterized in that, The fatty liver is non-alcoholic fatty liver.

3. Use of PRDX3 or a reagent for detecting PRDX3 in the preparation of a reagent or kit for predicting and / or judging the therapeutic effect of a fatty liver treatment drug.

4. The application according to claim 3, wherein The steps for predicting and / or judging the therapeutic effect of a fatty liver treatment drug include: Measuring the concentration of PRDX3 in the peripheral blood of a subject before treatment, and measuring the concentration of PRDX3 in the peripheral blood of the subject at different time points during treatment; When the concentration of PRDX3 in the peripheral blood of the subject shows a decreasing trend during treatment, it indicates that the therapeutic effect of the fatty liver treatment drug is effective.

5. The application according to claim 4, characterized in that, The steps for predicting and / or judging the therapeutic effect of a fatty liver treatment drug further include: Measuring the concentrations of CD80 and CD206 in the peripheral blood of the subject before treatment, and measuring the concentrations of CD80 and CD206 in the peripheral blood of the subject at different time points during treatment; When the concentrations of PRDX3 and CD80 in the peripheral blood of the subject show a decreasing trend during treatment, and at the same time the concentration of CD206 shows an increasing trend during treatment, it indicates that the therapeutic effect of the fatty liver treatment drug is effective.

6. The application according to any one of claims 3 to 5, characterized in that, The fatty liver is non-alcoholic fatty liver.

7. A kit for predicting and / or diagnosing fatty liver, characterized in that, The kit includes a reagent for detecting PRDX3.

8. The kit for predicting and / or judging fatty liver according to claim 7, characterized in that, The kit further includes reagents for detecting CD80 and CD206.

9. Use of PRDX3, CD80 and CD206 as non-alcoholic fatty liver markers.

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