Application of biomarker in preparation of schistosoma japonicum katsurada diagnosis product and treatment drug

Abstract

The invention discloses application of a biomarker in preparation of a schistosoma japonicum katsurada disease diagnosis product and a schistosoma japonicum katsurada disease treatment medicine. The biomarker is uridine or uridine metabolism key enzyme Upse1. According to the invention, through a large amount of research and analysis of changes of metabolites in early and chronic stages of schistosoma japonicum katsurada infection, characteristic metabolites and metabolic pathways are found; exploring and analyzing regional specificity change conditions of abundance of various metabolites in livers of mice with different infection times; surprisingly, a key enzyme Upse1 for uridine metabolism is obviously up-regulated 6 weeks after infection, and the liver uridine level is negatively related to the abundance of various lipid-related metabolites; through verification and specificity and sensitivity research, the uridine or the metabolic key enzyme Upase1 thereof can be used as a characteristic marker molecule of schistosoma japonicum katsurada infection and can be used for developing schistosoma japonicum katsurada diagnosis products. Further research finds that uridine supplemented in vitro can play an anti-fibrosis role, and uridine can be used as a drug for treating schistosoma japonicum katsurada.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to the application of a biomarker in the preparation of a diagnostic product and a therapeutic drug for Schistosoma japonicum. Background Art

[0002] Schistosomiasis is a parasitic disease that is widely prevalent in tropical and subtropical regions and poses a serious threat to human health. The World Health Organization estimates that approximately 779 million people worldwide are at risk of schistosomiasis infection, and more than 250 million people are infected with schistosomiasis. The main schistosomes that infect humans are S. mansoni, S. japonicum, and S. haematobium, of which S. japonicum is mainly prevalent in China. The core damage caused by S. japonicum infection is liver damage and persistent granulomatous reactions caused by liver eggs. The chronic development of the disease leads to liver fibrosis and its complications, and is also an important cause of death in patients with schistosomiasis. However, the exact molecular mechanism of liver fibrosis caused by S. japonicum infection remains unclear. In addition, the development of clinical therapeutic drugs depends on a deeper understanding of these mechanisms. The development of timely diagnostic technology and therapeutic drugs for S. japonicum is essential.

[0003] At present, the diagnosis of Japanese schistosomiasis includes etiological examination, immunological detection and molecular biological detection. The etiological examination has low specificity and sensitivity and is not suitable for early diagnosis; while the immunological detection and molecular biological detection rely on detection markers.

[0004] Metabolomics is a discipline that comprehensively analyzes small molecule metabolites present in an organism under specific conditions. It is a powerful tool for identifying new drug targets, discovering biomarkers, monitoring diseases, and studying disease pathogenesis. Currently, metabolomics methods have been widely used to find new therapeutic targets for parasitic infections, including schistosomiasis. However, due to technical limitations, most current metabolomics studies of diseases, including schistosomiasis, mainly involve serum and urine samples. A common limitation of metabolic analysis of complex biological samples is the failure to consider cellular heterogeneity within organs or tissues. Therefore, achieving a comprehensive visualization of systemic metabolic reprogramming and its interaction with liver disease remains a huge challenge. The emergence of mass spectrometry imaging (MSI) technology provides new possibilities for studying organ-specific heterogeneity and spatial distribution patterns of metabolites in diseased tissues. MSI-based spatial metabolomics facilitates on-site screening of metabolic biomarkers associated with the development of liver lesions. This approach helps to describe in detail the metabolic landscape within the liver lesion site and its surrounding microenvironment, and has obvious advantages in exploring disease mechanisms. Therefore, the use of spatial metabolomics methods and in-depth pathway analysis can improve our understanding of disease progression and drug action modes.

[0005] At present, there are not enough markers that can be used for the diagnosis of schistosomiasis japonicum, especially for early diagnosis. Finding new diagnostic markers and therapeutic targets for schistosomiasis japonicum with good specificity and high sensitivity is of great significance for the prevention and control of schistosomiasis japonicum. Summary of the invention

[0006] The present invention aims to explore new diagnostic markers for schistosomiasis japonicum with good specificity and high sensitivity, as well as to explore new therapeutic targets.

[0007] The first objective of the present invention is to provide the use of uridine or Upase1, a key enzyme in uridine metabolism, in the preparation of a product for diagnosing schistosomiasis japonicum.

[0008] The second objective of the present invention is to provide the use of uridine or Upase1, a key enzyme in uridine metabolism, in the preparation of a diagnostic product for liver fibrosis caused by Schistosoma japonicum infection.

[0009] The third object of the present invention is to provide the use of uridine or Upase1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for treating schistosomiasis japonicum.

[0010] The fourth object of the present invention is to provide the use of uridine or Upase1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for treating Schistosoma japonicum-induced liver fibrosis.

[0011] A fifth object of the present invention is to provide the use of uridine in the preparation of a therapeutic drug for schistosomiasis japonicum, including liver fibrosis caused by schistosomiasis japonicum infection.

[0012] The above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0013] The present invention uses an air flow assisted desorption electrospray ionization (AFADESI-MSI) platform to study the changes in the abundance of liver metabolites during acute and chronic Schistosoma japonicum infection. The heterogeneous metabolic characteristics of Schistosoma japonicum liver egg granulomas were revealed in the negative ion mode analysis. In the chronic infection model of Schistosoma japonicum, oral praziquantel (PZQ) treatment significantly improved most metabolic disorders, including fatty acid and pyrimidine metabolism. It was unexpectedly found that Upase1, a key enzyme in uridine metabolism, was significantly upregulated 6 weeks after infection, and the liver uridine level was negatively correlated with the abundance of multiple lipid-related metabolites, and the specificity and sensitivity of liver uridine levels for diagnosing Schistosoma japonicum infection were further studied. In addition, further studies have found that in vitro supplementation of uridine can inhibit LX-2 cell activation, restore fatty acid metabolism homeostasis through the peroxisome proliferator-activated receptor γ (PPARγ) pathway, and exert an anti-fibrotic effect. The above results show that uridine or its key metabolic enzyme Upase1 can be used as a biomarker for the diagnosis of Schistosoma japonicum, and uridine can be used as or prepared as a therapeutic drug for Schistosoma japonicum.

[0014] Therefore, the present invention claims protection for:

[0015] Application of uridine in the preparation of diagnostic products for schistosomiasis japonicum.

[0016] Application of Upase1, a key enzyme in uridine metabolism, in the preparation of diagnostic products for schistosomiasis japonicum.

[0017] Application of uridine in the preparation of diagnostic products for liver fibrosis caused by Schistosoma japonicum infection.

[0018] Application of Upase1, a key enzyme in uridine metabolism, in the preparation of diagnostic products for liver fibrosis caused by Schistosoma japonicum infection.

[0019] Application of uridine in the preparation and / or screening of drugs for treating schistosomiasis japonicum.

[0020] Application of Upase1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for treating schistosomiasis japonicum.

[0021] Application of uridine in the preparation and / or screening of drugs for treating liver fibrosis caused by Schistosoma japonicum infection.

[0022] Application of Upase1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for treating liver fibrosis caused by Schistosoma japonicum infection.

[0023] Application of uridine in the preparation of therapeutic drugs for schistosomiasis japonicum.

[0024] Application of uridine in the preparation of therapeutic drugs for liver fibrosis caused by Schistosoma japonicum infection.

[0025] The present invention has the following beneficial effects:

[0026] The present invention uses an air flow assisted desorption electrospray ionization (AFADESI-MSI) platform to study the changes in the abundance of liver metabolites during acute and chronic Schistosoma japonicum infection. The heterogeneous metabolic characteristics of Schistosoma japonicum liver egg granulomas were revealed in the negative ion mode analysis. In the chronic infection model of Schistosoma japonicum, oral praziquantel treatment significantly improved most metabolic disorders, including fatty acid and pyrimidine metabolism. It was unexpectedly found that Upase1, a key enzyme in uridine metabolism, was significantly upregulated 6 weeks after infection, and the liver uridine level was negatively correlated with the abundance of multiple lipid-related metabolites; and after verification, specificity, and sensitivity studies, uridine or its key metabolic enzyme Upase1 can be used as a characteristic marker molecule for Schistosoma japonicum infection and can be used to develop diagnostic products for Schistosoma japonicum.

[0027] Further studies found that uridine supplementation in vitro inhibited the activation of hepatic stellate cells (LX-2 cells), restored fatty acid metabolism homeostasis through the peroxisome proliferator-activated receptor γ (PPARγ) pathway, and exerted an anti-fibrotic effect.

[0028] The present invention determines the role of uridine in LX-2 cells, establishes the regulatory relationship between uridine and lipid metabolism homeostasis, and discovers the effect of schistosomiasis japonicum infection on liver uridine metabolism and the role of uridine in the pathogenesis and treatment of schistosomiasis-related liver fibrosis. Uridine can be applied to schistosomiasis japonicum diagnostic products, enriching the diagnostic methods of schistosomiasis japonicum, and providing new targets and drug options for the treatment of schistosomiasis japonicum. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Survival and liver pathology analysis of mice infected with Schistosoma japonicum. ((A) Schematic diagram of the experimental design of the mouse model infected with Schistosoma japonicum. (B) Histogram of mouse liver weight. (C) Mouse liver / body weight ratio. (D) HE and Masson staining of mouse liver tissue (×100). Data are expressed as mean ± standard deviation. Statistical significance is *P<0.05, ***P<0.001, ****P<0.0001, n=5)).

[0030] Figure 2AFADESI-MSI images of liver metabolites ((A) Schematic diagram of mass spectrometry imaging workflow. (B) Representative mass spectra of liver samples in each group. A was created using BioRender.com. (C) HE staining of directly adjacent sections was used for histopathological evaluation by AFADESI-MSI. Scale bar: 1000 μm. (D) Imaging analysis of specific metabolites in different liver tissues. (E) UMAP analysis of specific metabolites in different liver tissues. (F) Cluster heat map of differentially expressed metabolites between different samples in the control group and the 6w group (n=3). (G) Cluster heat map of differentially expressed metabolites between different samples in the control group and the 12w group (n=3)).

[0031] Figure 3 In situ visualization of key metabolites and metabolic pathways between the 6-week group and the control group / 12-week group and the control group ((A) Mass spectrum of up-regulated ions compared with the control group at 6w. (B) Mass spectrum of down-regulated ions compared with the control group at 6w. (C) Pathways with differentially enriched metabolites were significantly enriched compared with the control group at 6w (P<0.05). (D) Mass spectrum of up-regulated ions compared with the control at 12w. (E) Mass spectrum of down-regulated ions compared with the control at 12w. (F) Pathways with differentially enriched metabolites were significantly enriched compared with the control group at 12w (P<0.05)).

[0032] Figure 4 Visualization of metabolomics datasets in the comparison between 6w and control group or 12w and control group. (A) PCA score plot for the comparison between 6w and control group. (B) ops-DA score plot for the comparison between 6w and control group. (C) Volcano plot for the comparison between 6w and control. (D) PCA score plot for the comparison between 12w and control. (E) ops-DA score plot for the comparison between 12w and control group. (F) Volcano plot showing the differences in metabolites between granuloma tissue and unaffected tissue of 6w mice infected with Schistosoma japonicum. (G) Bubble plot of the top 16 metabolic pathways for the comparison between 6w group and control group. (H) Bubble plot of the top 20 metabolic pathways for the comparison between 12w group and control group.

[0033] Figure 5AFADESI-MSI analysis of granuloma tissues from mice infected with Schistosoma japonicum for 6 weeks and granuloma tissues from uninfected mice to obtain differential metabolites ((A) Schematic diagram of local metabolite extraction and analysis of liver tissues 6 weeks after infection. (B) Heat map of spatial decomposition metabolomics data. (C) Up-regulated ions in different regions of granuloma tissues compared with unaffected tissues. (D) Down-regulated ions in different regions of granuloma tissues compared with unaffected tissues. (E) Statistical plot of the number of differentially enriched metabolites in granuloma tissues and unaffected tissues. (F) Significant enrichment of pathways with differentially enriched metabolites between granuloma tissues and unaffected tissues (P<0.05). Data are expressed as mean ± SD. Statistical significance is *P<0.05, ***P<0.001, ****P<0.0001, n=5)).

[0034] Figure 6 Figure 3 AFADESI-MSI analysis of the metabolite richness differences between granuloma tissues of mice infected with Schistosoma japonicum for 12 weeks and granuloma tissues of uninfected mice ((A) Schematic diagram of local metabolite extraction and analysis of liver tissues 12 weeks after infection. (B) Heat map of spatial decomposition metabolomics data. (C) Up-regulated ions in different regions of granuloma tissues compared with unaffected tissues. (D) Down-regulated ions in different regions of granuloma tissues compared with unaffected tissues. (E) Statistical graph of the number of differentially enriched metabolites in granuloma tissues and unaffected tissues. (F) Significant enrichment of pathways with differentially enriched metabolites between granuloma tissues and unaffected tissues (P<0.05). Data are expressed as mean ± SD. Statistical significance is *P<0.05, ***P<0.001, ****P<0.0001, n=5)).

[0035] Figure 7 Visualization of metabolomics datasets of granuloma tissues and uninfected granuloma tissues of mice infected with Schistosoma japonicum for 6 weeks and 12 weeks. (A) Representative mass spectra of granuloma tissues and liver tissues of mice infected with Schistosoma japonicum for 6 weeks and uninfected mice. (B) PCA score plot of granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 6 weeks. (C) Ops-DA score plot of granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 6 weeks. (D) Volcano plot showing the differences in metabolites in granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 6 weeks. (E) Representative mass spectra of granuloma tissues and uninfected liver tissues of mice infected with Schistosoma japonicum for 12 weeks. (F) PCA score plot of granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 12 weeks. (G) Ops-DA score plot of granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 12 weeks. (H) Volcano plot showing the differences in metabolites in granuloma tissues and uninfected tissues of mice infected with Schistosoma japonicum for 12 weeks.

[0036] Figure 8 In situ visualization and metabolic pathway enrichment of important metabolites in the liver of mice infected with Schistosoma japonicum for 6 weeks. (A) In situ visualization of up-regulated and down-regulated ions in mice infected with Schistosoma japonicum for 6 weeks. (B) Bubble diagram of the top 20 metabolic pathways in granuloma tissues of mice infected with Schistosoma japonicum for 6 weeks and those of uninfected granuloma tissues.

[0037] Fig. 9 In situ visualization and metabolic pathway enrichment of important metabolites in the liver of mice infected with Schistosoma japonicum for 12 weeks. (A) In situ visualization of up-regulated and down-regulated ions in mice infected with Schistosoma japonicum for 12 weeks. (B) Comparison of metabolic pathways in granuloma tissues of mice infected with Schistosoma japonicum for 12 weeks and those of uninfected granuloma tissues, with bubble diagrams of the top 20.

[0038] Fig.10 Visualization of liver metabolomics datasets after PZQ treatment. (A) PCA score plot after PZQ treatment. (B) ops-DA score plot after PZQ treatment. (C) Correlation analysis of differential abundance of metabolites associated with PZQ treatment.

[0039] Fig.11 Metabolic regulatory effects of praziquantel on liver damage in mice infected with Schistosoma japonicum ((A) MS image of in situ visualization of key differentially abundant metabolites. (B) Heat map of spatially decomposed metabolomics data. (C) Top 20 enriched pathways of differentially abundant metabolites in KEGG. (D) Top 10 enriched pathways of differentially abundant metabolites in Reactome).

[0040] Fig.12 The metabolism of uridine in mice infected with Schistosoma japonicum is significantly affected by metabolic pathways ((A) Venn diagram showing common differentially abundant metabolites, comparison between 6w and control group and 12w and control group. (B) Statistical diagram of the number of potential differentially abundant metabolites, comparison between 6w and control group and 12w and control group. (C) Venn diagram showing common metabolic pathways, comparison between 6w and control group and 12w and control group. (D) Relative expression level of CAD mRNA, the rate-limiting enzyme in the de novo synthesis of uridine, in mouse liver. (E) Relative expression level of HOGA1, a key enzyme in the glyoxylate metabolic pathway, in mouse liver. (F) Relative expression level of GULO, a key enzyme in the ascorbic acid synthesis pathway, in mouse liver. (G) Relative expression level of UGDH mRNA, a key enzyme in the glucuronic acid metabolic pathway, in mouse liver. (H) Relative expression level of Upase, a key enzyme in the uridine metabolic pathway, in mouse liver. (I) Relative expression level of Upase, a key enzyme in the glutamine metabolic pathway, in mouse liver. (J) Relative expression level of IDH, a key enzyme in the tricarboxylic acid cycle, in mouse liver).

[0041] Fig.13Uridine regulates fatty acid metabolism and cell activation of LX-2 cells through the PPARγ pathway ((A) Analysis of the differential abundance of metabolites associated with PZQ treatment. (B) Oil Red O staining was used to detect the effect of uridine intervention on lipid metabolism of activated LX-2 cells in vitro. (C) Western blotting was used to detect the expression levels of PPARγ and α-SMA in LX-2 cells after uridine intervention. (D) Statistical analysis of lipid droplet area by Oil Red O staining. (E) Effect of in vitro addition of uridine on α-SMA protein expression in LX-2 cells. (F) Effect of in vitro addition of uridine on PPARγ protein expression in LX-2 cells. (G) Effect of in vitro addition of uridine on PPARγ gene expression in LX-2 cells. (H) Effect of PPARγ pathway inhibitors on LX-2 cell activation after uridine intervention. (I) Effect of PPARγ pathway inhibitors on the expression of genes related to lipid metabolism in LX-2 cells after uridine intervention).

[0042] Fig.14 Figure A shows the relative ion intensity of uridine in the liver of each group of mice in metabolomics; Figure B shows the ROC curve of liver uridine for Schistosoma japonicum (the solid black line is the ROC curve of uridine; the dotted red line is the 50% AUC reference line). DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific examples, but the examples do not limit the present invention in any form. Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in the art. Unless otherwise specified, the reagents and materials used in the examples are commercially available.

[0044] The experimental materials and experimental methods involved in the following examples are as follows:

[0045] 1. Experimental Animals:

[0046] Twenty female ICR mice (6 weeks old, weighing 20 ± 2 g) were purchased from Zhejiang Weitonglihua Experimental Animal Technology Co., Ltd. (License No.: SCXK (Zhejiang) 2019-0001). The experimental animals were raised in a barrier environment in the Experimental Animal Center of Jiangsu Institute of Parasitic Diseases (License No.: SYXK (Su) 2017-0050); Ethical Review No.: JIPD-2020-002.

[0047] 2. Preparation of cercariae

[0048] The Schistosoma japonicum (Jiangsu strain) used in this study was preserved by the Jiangsu Institute of Parasitic Diseases. The cercariae were obtained from snails infected with Schistosoma japonicum in our laboratory and used in animal experiments.

[0049] 3. Experimental Groups and Infection

[0050] A total of 20 mice were randomly divided into four groups, 5 mice in each group: uninfected group (control group), 6-week infection group (6w), 12-week infection group (12w) and PZQ chemotherapy group (PZQ). They were free to eat and drink water and adapted for 1-2 weeks before infection. The mice in the infection group were infected with 15±2 cercariae per mouse through abdominal skin exposure. In the fifth week after infection, PZQ was dissolved in sodium carboxymethyl cellulose and administered to 5 mice by gavage for three consecutive days at a dose of 150 mg / kg / day to form the PZQ chemotherapy group. Throughout the study, the survival status of the mice was regularly observed.

[0051] 4. Detection of related metabolites by AFADESI-MSI

[0052] 4.1 Liver tissue processing

[0053] The tissue samples fixed in Cryo-gel were taken out of the -80°C ultra-low temperature freezer, thawed overnight in a -20°C refrigerator, and sliced ​​(10 μm thick) using a cryo-tome (Leica CM 1950, Leica Microsystem, Germany). The slices were fixed on positively charged desorption plates (Thermo Fisher Scientific, Waltham, USA) and stored in an ultra-low temperature freezer at -80°C for subsequent imaging analysis.

[0054] 4.2 Mass spectrometry imaging data acquisition

[0055] The frozen tissue sections were taken out from the ultra-low temperature freezer at -80 °C and quickly placed in a vacuum desiccator and kept at room temperature for about 30 minutes. Spatial metabolite resolution analysis was performed by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) platform (Beijing Victor Technology Co., Ltd., Beijing, China) and Q-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher Scientific, USA), and the distribution map in the tissue sections was obtained by progressive scanning. The spray solvent was acetonitrile (MS grade, Thermo Fisher, USA): water (Watson distilled water (Watson Group)) = 8:2 (v / v), and it was carried out in negative mode at a flow rate of 5 μL / min. The gas flow rate of the transport gas was 45 L / min, the spray voltage was 7 kV, the distance between the sample surface and the sprayer was 3 mm, and the distance between the sprayer and the ion transport tube was 3 mm. The mass resolution was 60,000, the mass range was 70-1000 Da, and the capillary temperature was 350 °C. The platform parameters were set as follows: Vx was 0.2 mm / s, Dy was 0.1 mm, and Dt was 7 s. The MSI experiment was performed at a constant rate of 0.2 mm / s, continuously scanning the slice surface in the x direction and vertically stepping 10 μm in the y direction. Data acquisition was performed by the Xcalibur data acquisition and processing system, which sets the data acquisition sequence according to the sample size, step size, and scanning speed. This information was converted by mass spectrometry image analysis software to obtain the two-dimensional spatial intensity distribution map of ions in liver tissue sections. The ions detected by AFADESI-MSI were annotated by the pySM annotation framework and the in-house SmetDB database (Lumingbio, Shanghai, China).

[0056] 4.3 Local metabolite extraction

[0057] The raw files collected from the 6-week and 12-week sample groups were converted to imzML format and reconstructed using the Cardinal software package after background subtraction. All mass spectrometry images were normalized for each pixel using total ion count normalization. The generated data were matched with high spatial resolution H&E images using MSireader software, and after accurately extracting the coordinate information of the egg granuloma tissue area and the surrounding normal tissue area in each sample, the metabolite information of the pixel points in the area was extracted for comparative analysis.

[0058] Example 1 Mouse survival rate and liver pathology analysis

[0059] 1. Histopathological Examination of Liver Samples

[0060] A small piece of liver tissue from the right lobe of each mouse was fixed in 4% paraformaldehyde solution for 24 hours. After gradient dehydration, clearing, wax immersion, embedding, sectioning, drying, dewaxing, rehydration, HE staining (Biosharp, BL700B) and Masson staining (Solarbio, G1340), the sections were placed on slides and coverslipped, observed and photographed under an optical microscope.

[0061] 2. Results

[0062] Schistosoma japonicum infection mainly causes liver damage in mice. By constructing mouse models of acute and chronic Schistosoma japonicum infection and treating the model mice with PZQ ( Figure 1 Compared with the uninfected group, the body weight, activity and mental state of mice infected with Schistosoma japonicum were significantly reduced at 6 and 12 weeks ( Figure 1 In addition, the liver volume and weight increased significantly, and the liver index increased significantly ( Figure 1 C in the middle). Liver samples were then collected for histopathological examination. HE staining showed obvious immune cell infiltration and egg granulomas in the liver of infected mice. The area of ​​egg granulomas in the 12-week group increased significantly ( Figure 1 (D in the middle). Masson staining showed obvious collagen fiber deposition around the egg granuloma, indicating that Schistosoma japonicum infection may lead to liver damage and liver fibrosis with the chronic development of infection. In addition, PZQ treatment significantly improved the related symptoms of mice. Compared with the infected group, the mental state of the infected group improved, the liver index decreased, and the pathological liver damage was alleviated ( Figure 1 (Fig. BD).

[0063] Example 2 Spatial metabolomics reveals changes in liver metabolism in mice infected with Schistosoma japonicum at different stages

[0064] 1. Mass Spectrometry Analysis

[0065] The same experimental contents as AFADESI-MSI for detecting related metabolites.

[0066] 2. Multivariate statistical analysis

[0067] Multivariate statistical analysis started with unsupervised principal component analysis (PCA), which aims to assess the variability between and within groups within the sample. In this way, the trend of the overall distribution of the samples can be observed, potential scatter points can be identified, and the stability of the analysis process can be evaluated. Subsequently, supervised orthogonal partial least squares analysis (OPLS-DA) was performed to distinguish the overall differences in the metabolic profiles between the groups. In OPLS-DA, the overall contribution of each variable to the differences between groups was evaluated by calculating the projected variable importance (VIP). The VIP score reflects the importance of the first two principal components of the OPLS-DA model in the classification of samples. A VIP value greater than 1 indicates that the variable has a significant effect in distinguishing the differences between groups. The Student's t test was further used to verify whether the differentially enriched metabolites between groups were significant. Variables that simultaneously satisfied VIP>1 and P<0.05 were considered as potential differentially enriched metabolites.

[0068] 3. UMAP Analysis

[0069] The local structure in the data is identified through the nearest neighbor algorithm, and a weighted graph is constructed, where the edge weight represents the probability of connecting two points. Next, the low-dimensional layout is optimized to maintain the high-dimensional data structure, and UMAP is configured by adjusting hyperparameters such as the number of neighbors and the embedding dimension. After performing the analysis, the high-dimensional data is reduced to two or three dimensions for easy visualization. The results are verified by comparing with other methods, revealing the spatial distribution characteristics of metabolites and providing intuitive insights for research.

[0070] 4. Metabolic pathway analysis

[0071] The KEGG IDs of differentially abundant metabolites were identified to perform metabolic pathway enrichment analysis, thereby obtaining the enrichment results of metabolic pathways. Metabolic pathways with a P value less than 0.05 were screened out through the hypergeometric test as significantly enriched pathways of differentially abundant metabolites. The smaller the P value, the more significant the variability of the metabolic pathway.

[0072] 5. Results

[0073] We investigated liver metabolic changes during the early egg deposition period (6 weeks) using spatial metabolomics techniques to better understand the relationship between liver metabolites and disease progression ( Figure 2 First, a representative mass spectrum was selected for comparison with the control group. This comparison revealed differences in metabolite peak distribution between the two groups, indicating that the distribution of substances had changed significantly ( Figure 2 Figure B). Principal component analysis was used to analyze the spatial metabolomics results of liver tissues of mice in the 6-week infection group and the control group. The results showed that both groups of samples fell within the 95% confidence interval, and no abnormal samples were observed ( Figure 4 The two groups of data on the first principal component are clearly separated, emphasizing the significance of the metabolic differences ( Figure 4 (Fig. B).

[0074] To enhance the visual representation of the relationships between samples and the display of metabolite expression differences in various samples, hierarchical clustering analysis was performed on all differentially abundant metabolites. The resulting heat map uses a gradient from blue to red to represent the abundance of metabolites, with darker red indicating higher abundance ( Figure 2 The clustering plot clearly shows the significant regionalization of liver metabolite distribution after infection ( Figure 2 This finding was further confirmed by UMAP analysis ( Figure 2 In addition, comparison with HE staining results showed that the metabolite profile of egg granuloma tissue was significantly different from that of surrounding tissue ( Figure 2 Further analysis revealed that compared with the control group, the liver tissue of mice in the 6-week infection group presented 21 potential differential metabolites in the negative ion mode, of which 8 metabolites increased in abundance and 13 metabolites decreased in abundance ( Figure 4 In addition, mass spectrometry imaging was performed on these differentially abundant metabolites ( Figure 3 Subsequently, KEGG pathway enrichment analysis was performed on all differentially abundant metabolites, and 16 differential metabolic pathways were identified ( Figure 4 Among them, 6 pathways were significantly affected ( Figure 3 These pathways include glyoxylate and dicarboxylic acid metabolism, the citric acid cycle (tricarboxylic acid cycle), glucagon signaling, central carbon metabolism in cancer, and ascorbic acid and aldonic acid metabolism.

[0075] Chronic granuloma formation in worm eggs plays an important role in the development of liver fibrosis in schistosomiasis. We analyzed the changes in liver metabolites of mice during chronic infection, especially 12 weeks after infection. We used mass spectrometry analysis ( Figure 2 Figure B), principal component analysis ( Figure 4 D figure) and OPLS-DA modeling ( Figure 4 Data quality was assessed in Figure 5 (E). These analyses revealed significant differences between the two groups, highlighting significant changes in the abundance of metabolites in the liver compared to the uninfected group ( Figure 2 UMAP analysis effectively clustered the metabolites into different groups ( Figure 2 Further analysis revealed that the liver tissues of mice in the 12-week infection group presented 25 potential differential metabolites in negative ion mode compared with the control group. Among them, the abundance of 10 metabolites increased, while the abundance of 15 metabolites decreased ( Figure 4 The mass spectra of these differentially abundant metabolites are shown in Figure 3Figures D and E in the middle. In addition, KEGG pathway enrichment analysis was performed on all differentially abundant metabolites, and multiple differential metabolic pathways were identified ( Figure 4 Among these pathways, 10 pathways were significantly enriched ( Figure 3 (Fig. F in the middle). These pathways include arachidonic acid metabolism, ascorbic acid and aldonic acid metabolism, pyrimidine metabolism, D-amino acid metabolism and ABC transporters. Example 3 Spatial metabolomics reveals metabolic heterogeneity of Schistosoma japonicum-infected mice liver egg granulomas

[0076] 1. Mass spectrometry analysis: same as Example 2.

[0077] 2. Multivariate statistical analysis: Same as Example 2.

[0078] 3. Metabolic pathway analysis: Same as Example 2.

[0079] 4. UMAP analysis: same as Example 2.

[0080] 5. Results

[0081] To better understand the metabolic heterogeneity within egg granulomas in the liver, we divided the whole liver of infected mice into two tissue regions: granuloma tissue and unaffected tissue ( Figure 5 Figure A, Figure 6 The HE images with marked sampling points were then imported into the AFADESI-MSI software MSiReader program for image fusion and spatial matching. Subsequently, we extracted in situ AFADESI-MSI spectra based on the marked sampling points in the HE images.

[0082] By mass spectrometry ( Figure 7 Figure A), principal component analysis ( Figure 7 Figure B) and OPLS-DA model analysis ( Figure 7 The quality of the extracted data was rigorously evaluated (Figure C). The results consistently showed that there were significant differences in the liver metabolic profiles between granuloma tissues and uninvolved tissues. In addition, the metabolites in the liver tissues of Schistosoma japonicum-infected mice showed significant regional distributions, a finding that was also confirmed by the aforementioned UMAP analysis. By analyzing the metabolites in the livers of mice infected for 6 weeks, we identified 32 potential differential metabolites, of which 7 metabolites increased in abundance, while 25 metabolites decreased in abundance ( Figure 7 The abundance of each ion in the sample is shown in the heat map ( Figure 5 A single differential ion may correspond to multiple potential differential metabolites ( Figure 5 The mass spectra of the ions corresponding to these differentially abundant metabolites are shown in Figure 8 Figure A in the middle. Figure 5Figures C and D show the relative expression intensity of each ion. In addition, we performed KEGG pathway enrichment analysis on all differentially abundant metabolites and identified 62 enriched metabolic pathways ( Figure 8 These pathways cover multiple metabolic aspects including glucose metabolism, lipid metabolism, amino acid metabolism, and nucleic acid metabolism. Notably, 23 metabolic pathways were significantly different, including linoleic acid metabolism, butyrate metabolism, central carbon metabolism in cancer, renal cell carcinoma, and arachidonic acid metabolism ( Figure 5 (Fig. F).

[0083] In the metabolite analysis of livers of 12-week infected mice, we identified 40 potential differential metabolites compared with unaffected tissues. Among them, 3 metabolites were increased in abundance, while 37 metabolites were decreased in abundance ( Figure 7 The abundance of each ion in the sample is shown in the heat map ( Figure 6 A single differential ion may correspond to multiple potential differential metabolites ( Figure 6 The mass spectra of the ions corresponding to these differentially abundant metabolites are shown in Fig. 9 Figure A in the middle. Figure 6 Figures C and D show the relative expression intensity of each ion. In addition, we performed KEGG pathway enrichment analysis on all differentially abundant metabolites and identified 60 enriched metabolic pathways ( Fig. 9 These pathways cover multiple metabolic aspects including glucose metabolism, lipid metabolism, and amino acid metabolism. Notably, KEGG pathway analysis revealed 19 pathways that were significantly enriched in the differentially expressed metabolites, including biosynthesis of unsaturated fatty acids, renal cell carcinoma, arachidonic acid metabolism, central carbon metabolism in cancer, and vascular smooth muscle contraction ( Figure 6 (Fig. F).

[0084] Example 4 Spatial metabolomics reveals changes in liver metabolism in mice infected with Schistosoma japonicum and treated with PZQ

[0085] 1. Multivariate statistical analysis

[0086] Same as Example 2.

[0087] 2. Inter-group analysis

[0088] The experimental data are presented as mean ± standard deviation (SDs). SPSS22.0 software was used for statistical analysis of the data, and the results were compared between the two groups by independent sample t test. One-way analysis of variance (ANOVA) and Dunnett test were used to compare the significance of differences between multiple groups of data, and the significance level was set at α = 0.05, and P < 0.05 was considered to be statistically significant. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001), and GraphPad Prism 9.0 software was used to visualize and analyze the data.

[0089] 3. Reactome analysis

[0090] The spatial distribution information of metabolites in biological tissue samples was obtained by mass spectrometry imaging technology, and the R package ReactomePA was used to perform pathway enrichment analysis on differential metabolites to identify the biological pathways involved. The analysis results include the name of the enriched pathway, the number of metabolites, the enrichment factor, the P value and the FDR value, which are visualized by the Reactome Pathway Browser.

[0091] 4. Results

[0092] First, data quality was assessed using multivariate statistical analysis, beyond principal component analysis ( Fig.10 The results revealed significant differences in the metabolic profiles of the livers of mice treated with PZQ. In negative ion mode, 27 ions showed significant differences. The expression intensities of these ions were either upregulated or downregulated after infection compared with the uninfected group, and varying degrees of reversal were observed after PZQ treatment ( Fig.11 Figures A and B in the figure). Subsequently, 20 regions were randomly selected from each sample, and the ion intensity of each region was analyzed using one-way ANOVA. This analysis revealed unique distribution differences in ion expression intensity between sample groups. Pathway enrichment analysis of metabolites using KEGG revealed five metabolic pathways with significant differences ( Fig.11 In addition, Reactome analysis revealed multiple common metabolic pathways enriched ( Fig.11 The biosynthesis of unsaturated fatty acids, ascorbic acid and aldonic acid metabolism, pyrimidine metabolism, D-amino acid metabolism, and linoleic acid metabolism suggest that metabolic pathways such as unsaturated fatty acids may be involved in the development of schistosomiasis-associated liver disease and play a role in the anti-schistosomiasis effect of PZQ.

[0093] Example 5 Uridine metabolism is a metabolic pathway significantly affected during Schistosoma japonicum infection

[0094] 1. Reverse transcription-real-time quantitative PCR (RT-qPCR)

[0095] Total RNA was extracted from cells or tissues using RNA-easy Isolation Reagent (Vazyme, R701), and the RNA purity was verified using a NanoDrop instrument (OD260 / OD280 ratio in the range of 1.8-2.2). cDNA was obtained by reverse transcription using a kit (Vazyme, R323-01). RNA levels were evaluated using a LightCycler 480 instrument and SYBR qPCR MasterMix (Vazyme, Q711-02) and specified primers (Table S1). After obtaining the threshold cycle (Ct), the relative expression of the target gene was calculated by the 2-ΔΔCt method using ACTB or β-actin as an internal reference. The target gene is the expression gene of the key enzyme or protein in each metabolic pathway.

[0096] The reagents for detecting human gene expression in the target gene are specific primers for COL1A1, COL3A1, α-SMA, PPARγ, PLIN1, CD36, SREBP1c, FASN, UPASE1, and ACTB. The specific primers include upstream and downstream primers as shown in SEQ ID NO: 1-20.

[0097] The reagents for detecting mouse gene expression in the target gene are specific primers for Idh1, Idh2, Idh3, Hoga1, Gulo, Ugdh, Upase1, Upase2, Gls1, Gls2, Cad, and β-actin. The specific primers include upstream and downstream primers as shown in SEQ ID NO: 20-44.

[0098] The primers were synthesized by Shanghai Bioengineering Co., Ltd. The relevant primer sequences are shown in Table 1.

[0099] Table 1 Sequences of primers used in RT-qPCR analysis

[0100]

[0101]

[0102]

[0103] 2. Multivariate statistical analysis

[0104] Same as Example 2.

[0105] 3. Metabolic pathway analysis

[0106] Same as Example 2.

[0107] 4. Results

[0108] Our investigation revealed that multiple metabolites and metabolic pathways were significantly affected during both the early and chronic stages of infection ( Fig.12 (A and B in Figure 2). Thirteen characteristic metabolites and three metabolic pathways associated with disease development may be involved in the chronic process of schistosomiasis. The number and type of potential differential metabolites corresponding to the corresponding differential ions are listed in Table 2.

[0109] Table 2 Potential differential metabolites between the 6-week infection group and the 12-week infection group

[0110]

[0111]

[0112] These pathways mainly include three metabolic pathways: glyoxylate and dicarboxylic acid metabolism, ascorbic acid and aldonic acid metabolism, and pyrimidine metabolism ( Fig.12 (C). These findings suggest that these three metabolic pathways are closely related to disease progression. When further examining the connections between these three differential metabolic pathways and the identified differentially abundant metabolites, we found that glutamine and uridine played a central role in connecting these differentially abundant metabolites. In addition, in the pathway enrichment analysis of differentially abundant metabolites in the livers of control and 6-week infected mice, we also identified pathways related to ascorbic acid and aldonic acid metabolism. This result confirmed our previous metabolomics study of serum samples. Overall, this analysis highlights the importance of the relationship between metabolites in regulating the effects of disease processes. In addition to the identified differential metabolism, the related metabolic enzymes were also validated by RT-qPCR. We found that pyrimidine nucleoside phosphorylase 1 (Upase1), a key enzyme in uridine metabolism, was significantly upregulated at 6 weeks of infection, while other metabolic enzymes were downregulated. In addition, the expression of most metabolic enzymes could be reversed by PZQ treatment ( Fig.12 These results suggest that perturbations in uridine metabolism may play an important role in the development and progression of schistosomiasis-induced liver fibrosis.

[0113] Example 6 Uridine regulates fatty acid metabolism reprogramming and cell activation in LX-2 cells via the PPARγ pathway

[0114] 1. Cell Culture

[0115] LX-2 cells were used in this study. These cells were revived from liquid nitrogen tubes, resuspended in DMEM (HyClone, SH3002201) containing 10% fetal bovine serum (Gibco, 10270106) and 1% penicillin-streptomycin, and then transferred to culture flasks and incubated at 37°C in an incubator containing 5% CO2. When the cells reached 80% confluence, the cells were harvested and other cell experiments were performed.

[0116] 2. Cell Experiment Grouping and Treatment

[0117] LX-2 cells were cultured to 80% confluence, and then the cells were collected and inoculated into culture dishes. After the cells adhered, they were treated with 25ng / ml TGF-β (Absin; China) for 12 hours to induce activation of LX-2 cells. Then, the culture medium was replaced with 1mg / ml uridine, and the culture was continued for 24 hours, and then the cells were collected for subsequent index detection. In the recovery experiment, after LX-2 cells were activated, they were replaced with complete culture medium containing 1mg / ml uridine (Beyotime, China) and 10μg / ml PPARγ inhibitor (GW9662, Beyotime, China), and the culture was continued for 24 hours, and then the cells were collected for subsequent index detection.

[0118] 3. Oil Red O Staining of Cells

[0119] Prepare the Oil Red O dye stock and working solutions in advance. Discard the cell culture medium, wash the cells twice with PBS, add paraformaldehyde, and fix the cells for 30 minutes at room temperature. Discard the fixative, wash the cells twice with double distilled water, and incubate in 60% isopropanol for 5 minutes. Discard the isopropanol, add the pre-prepared Oil Red O working solution, and incubate at 37°C for 15 minutes. Discard the solution, rinse the cells with 60% isopropanol until the intercellular spaces are clear, and then wash the cells with double distilled water until there is no excess dye. Examine the cells under a microscope and take pictures.

[0120] 4. Western Blot Analysis

[0121] Total protein was extracted from cells, and protein concentration was determined by BCA method. Protein samples were mixed with 6×SDS protein loading buffer at a ratio of 5:1 and boiled at 95-100°C for 10 minutes. 30 μg of protein was loaded per well and SDS-PAGE was performed. The protein was transferred to a PVDF membrane and blocked with 5% skim milk powder for 1-2 hours. Primary antibodies (specific for α-SMA, Affinity; PPARγ, Proteintech, ab16502; GAPDH, Absin, abs132004) were added, and the membrane was incubated overnight at 4°C. The blot was then washed three times with TBST for 10 minutes each. Secondary antibodies were added and incubated on a shaker at room temperature for 1-2 hours. After sufficient washing, protein bands were observed with the Bio-Rad ChemiDoc XRS+ imaging system, and optical density quantification was performed by ImageJ software.

[0122] 5. Results

[0123] Further correlation analysis of differentially abundant metabolites after PZQ treatment showed that uridine was negatively correlated with a variety of lipid metabolism-related molecules ( Fig.13 (Figure A in the middle). The homeostasis of lipid metabolism in hepatic stellate cells is essential for maintaining the quiescent state of cells. Therefore, we further explored the relationship between uridine metabolism and lipid metabolism, as well as the role of uridine metabolism in hepatic stellate cell activation, and established a TGF-β-induced human hepatic stellate (LX-2) cell activation model in vitro. Oil red O staining showed that when activated LX-2 cells lost lipids, α-SMA protein expression was significantly upregulated. In contrast, uridine intervention inhibited cell activation, and the level of intracellular lipid droplets was restored ( Fig.13 PPARγ is considered a key factor in regulating lipid metabolism. We found that PPARγ was significantly downregulated in activated LX-2 cells, while uridine intervention upregulated it ( Fig.13 Further response experiments confirmed that uridine can regulate lipid metabolism homeostasis in LX-2 cells through the PPARγ signaling pathway, and the main mechanism may involve upregulating fatty acid transfer and the expression of key metabolic enzymes that play an anti-fibrotic role ( Fig.13 H and I).

[0124] Example 7 Receiver operating curve (ROC) analysis of uridine as a biomarker for the detection of schistosomiasis japonicum

[0125] A good early screening marker and product should have the characteristics of high sensitivity and high specificity. With specificity as the horizontal axis and sensitivity as the vertical axis, the receiver operating characteristic curve (ROC curve) can be drawn by connecting each combination point. The area under the ROC curve AUC is a typical method for comprehensive evaluation of the detection ability of early screening products.

[0126] In this example, the ROC analysis method was used to study the specificity and sensitivity of uridine as a biomarker for detecting Schistosoma japonicum.

[0127] 1. ROC analysis

[0128] Based on the relative ion intensity of uridine obtained from spatial metabolome analysis, a series of thresholds from minimum to maximum are set. Next, the corresponding true positive rate (TPR) and false positive rate (FPR) are calculated for each threshold, where TPR measures the ability of the model to predict positive samples, while FPR measures the proportion of negative samples that the model incorrectly predicts as positive samples. Finally, with FPR as the horizontal coordinate and TPR as the vertical coordinate, all calculated points are plotted in the coordinate system and connected with line segments to form an ROC curve. The model performance can be quantified by analyzing the area under the ROC curve (AUC). The closer the AUC value is to 1, the stronger the model's ability to distinguish between positive and negative samples.

[0129] 2. Results

[0130] The relative ion intensity of uridine in the liver of each group of mice in metabolomics is as follows Fig.14 As shown in Figure A, compared with the control group, the relative intensity of uridine in the liver of mice in the 6-week and 12-week infection groups was significantly reduced, indicating that infection with Schistosoma japonicum can lead to a decrease in uridine content in the liver, while the difference in content after PZQ treatment was not statistically significant.

[0131] The ROC analysis results are as follows Fig.14 As shown in Figure B, the area under the ROC curve of uridine for Schistosoma japonicum is 1, indicating that uridine can effectively distinguish the healthy control group from Schistosoma japonicum infection, suggesting that the content of uridine in the liver is an ideal detection indicator for Schistosoma japonicum.

Claims

1. Use of a biomarker in the preparation of a diagnostic product for schistosomiasis japonicum, wherein the biomarker is uridine.

2. Application of a biomarker in the preparation of a diagnostic product for schistosomiasis japonicum, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

3. Use of a biomarker in the preparation of a diagnostic product for liver fibrosis caused by Schistosoma japonicum infection, wherein the biomarker is uridine.

4. Use of a biomarker in the preparation of a diagnostic product for liver fibrosis caused by Schistosoma japonicum infection, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

5. Use of a biomarker in the preparation and / or screening of drugs for treating schistosomiasis japonicum, wherein the biomarker is uridine.

6. Use of a biomarker in the preparation and / or screening of drugs for treating schistosomiasis japonicum, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

7. Use of a biomarker in the preparation and / or screening of drugs for treating liver fibrosis caused by Schistosoma japonicum infection, wherein the biomarker is uridine.

8. Use of a biomarker in the preparation and / or screening of drugs for treating liver fibrosis caused by Schistosoma japonicum infection, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

9. The use of uridine in the preparation of therapeutic drugs for schistosomiasis japonicum.

10. The use of uridine in the preparation of therapeutic drugs for liver fibrosis caused by Schistosoma japonicum infection.

Images