Use of biomarker in preparation of diagnostic product and therapeutic drug for schistosomiasis japonica

Abstract

The use of a biomarker in the preparation of a diagnostic product and therapeutic drug for Schistosomiasis japonica, wherein the biomarker is uridine or Upase1, which is a key enzyme in uridine metabolism. By means of a large number of studies, changes in metabolites in early and chronic stages of Schistosomiasis japonica infection were analyzed to screen for characteristic metabolites and metabolic pathways. By means of exploring and analyzing abundant region-specific changes of a plurality of metabolites in the liver of mice at different infection stages, it was surprisingly found that Upase1, which is a key enzyme in uridine metabolism, was significantly up-regulated 6 weeks after infection, and the hepatic uridine level was negatively correlated with the abundance of a plurality of lipid-related metabolites. Moreover, verification, and specificity and sensitivity studies confirmed that uridine or Upase1, which is a key enzyme in uridine metabolism, could be used as a characteristic marker molecule for Schistosomiasis japonica infection, and for developing diagnostic products for Schistosomiasis japonica. Further studies have found that in vitro supplementation with uridine can exert an anti-fibrotic role, and uridine can be used as a therapeutic drug for Schistosomiasis japonica.

Description

Application of a biomarker in the preparation of diagnostic products and therapeutic drugs for schistosomiasis japonicus Technical Field

[0001] This invention belongs to the field of biomedical technology. Specifically, it relates to the application of a biomarker in the preparation of diagnostic products and therapeutic drugs for schistosomiasis japonicus. Background Technology

[0002] Schistosomiasis is a parasitic disease prevalent in tropical and subtropical regions, posing a serious threat to human health. The World Health Organization estimates that approximately 779 million people worldwide are at risk of schistosomiasis infection, with over 250 million already infected. The main schistosomiasis parasites that infect humans are *Schistosoma mansoni*, *Schistosoma japonicum*, and *Schistosoma haematobium*, with *Schistosoma japonicum* being prevalent mainly in China. The core damage caused by *Schistosoma japonicum* infection is liver damage and persistent granulomatous reactions caused by the parasite's eggs in the liver. The chronic progression of the disease leads to liver fibrosis and its complications, which is a significant cause of death in schistosomiasis patients. However, the exact molecular mechanisms by which *Schistosoma japonicum* infection causes liver fibrosis remain unclear. Furthermore, the development of clinical treatments depends on a deeper understanding of these mechanisms. Timely diagnostic techniques and the development of therapeutic drugs for *Schistosoma japonicum* are crucial.

[0003] Currently, the diagnosis of schistosomiasis japonicus includes etiological examination, immunological detection, and molecular biological detection. Etiological examination has low specificity and sensitivity, and is not suitable for early diagnosis; while immunological and molecular biological detection methods rely on detection biomarkers.

[0004] Metabolomics is the comprehensive analysis of small-molecule metabolites present in organisms under specific conditions. It is a powerful tool for identifying new drug targets, discovering biomarkers, monitoring diseases, and studying disease pathogenesis. Currently, metabolomics methods are widely used to find new therapeutic targets for parasitic infections (including schistosomiasis). However, due to technological limitations, most current disease metabolomics studies, including schistosomiasis, primarily 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 comprehensive visualization of systemic metabolic reprogramming and its interaction with liver disease remains a significant challenge. The advent of mass spectrometry imaging (MSI) technology has provided 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 disease. This approach helps to describe the metabolic landscape within and around the liver lesion site, offering significant advantages in exploring disease mechanisms. Therefore, using spatial metabolomics methods and in-depth pathway analysis can improve our understanding of disease progression and drug action patterns.

[0005] Currently, there are not enough biomarkers available for the diagnosis of schistosomiasis japonicus, especially for early diagnosis. Finding new diagnostic biomarkers for schistosomiasis japonicus with good specificity and sensitivity, as well as therapeutic targets, is of great significance for the prevention and control of schistosomiasis japonicus. Summary of the Invention

[0006] This invention aims to explore new diagnostic biomarkers for schistosomiasis japonicus with high specificity and sensitivity, as well as to explore new therapeutic targets.

[0007] The first objective of this invention is to provide the application of uridine or the key enzyme Upase1 in the metabolism of uridine in the preparation of diagnostic products for schistosomiasis japonicus.

[0008] A second objective of this invention is to provide the application of uridine or the key enzyme Upase1 in the metabolism of uridine in the preparation of diagnostic products for liver fibrosis caused by Schistosoma japonicum infection.

[0009] A third objective of this invention is to provide the use of uridine or the key enzyme Upase1 in the metabolism of uridine in the preparation and / or screening of drugs for the treatment of schistosomiasis japonicus.

[0010] A fourth objective of this invention is to provide the use of uridine or the key enzyme Upase1 in uridine metabolism in the preparation and / or screening of drugs for the treatment of Schistosoma japonicum-induced liver fibrosis.

[0011] A fifth objective of this invention is to provide the use of uridine in the preparation of a therapeutic agent for schistosomiasis japonicus, including liver fibrosis caused by schistosomiasis japonicus infection.

[0012] The above-mentioned objective of this invention is achieved through the following technical solution:

[0013] This invention utilizes an airflow-assisted desorption electrospray ionization (AFADESI-MSI) platform to investigate changes in liver metabolite abundance during acute and chronic Schistosoma japonicum infection. Negative ion mode analysis revealed the heterogeneous metabolic characteristics of Schistosoma japonicum hepatic egg granulomas. In a chronic Schistosoma japonicum infection model, oral praziquantel (PZQ) treatment significantly improved most metabolic disorders, including fatty acid and pyrimidine metabolism. Unexpectedly, Upase 1, a key enzyme in uridine metabolism, was significantly upregulated 6 weeks post-infection. Hepatic uridine levels were negatively correlated with the abundance of various lipid-related metabolites, and the specificity and sensitivity of hepatic uridine levels for diagnosing Schistosoma japonicum infection were further investigated. Further research revealed that in vitro uridine supplementation inhibited LX-2 cell activation, restored fatty acid metabolic homeostasis through the peroxisome proliferator-activated receptor γ (PPARγ) pathway, and exerted an anti-fibrotic effect. These results indicate that uridine or its key metabolic enzyme Upase 1 can serve as a biomarker for Schistosoma japonicum diagnostic products, and that uridine can be used as or used to prepare therapeutic drugs for Schistosoma japonicum.

[0014] Therefore, this invention claims protection for:

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

[0016] Application of Upase 1, a key enzyme in uridine metabolism, in the preparation of diagnostic products for schistosomiasis japonicus.

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

[0018] Application of Upase 1, a key enzyme in uridine metabolism, in the preparation of diagnostic products for liver fibrosis caused by Schistosoma japonicum infection.

[0019] The use of uridine in the preparation and / or screening of drugs for the treatment of schistosomiasis japonicus.

[0020] Application of Upase 1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for the treatment of schistosomiasis japonicus.

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

[0022] Application of Upase 1, a key enzyme in uridine metabolism, in the preparation and / or screening of drugs for the treatment of liver fibrosis caused by Schistosoma japonicum infection.

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

[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] This invention utilizes an airflow-assisted desorption electrospray ionization (AFADESI-MSI) platform to investigate changes in liver metabolite abundance during acute and chronic Schistosoma japonicum infection. Negative ion mode analysis revealed the heterogeneous metabolic characteristics of Schistosoma japonicum hepatic egg granulomas. In a chronic Schistosoma japonicum infection model, oral praziquantel treatment significantly improved most metabolic disorders, including fatty acid and pyrimidine metabolism. Unexpectedly, it was discovered that Upase 1, a key enzyme in uridine metabolism, was significantly upregulated 6 weeks post-infection, and hepatic uridine levels were negatively correlated with the abundance of various lipid-related metabolites. Validation, specificity, and sensitivity studies have demonstrated that uridine or its key metabolic enzyme Upase 1 can serve as a characteristic biomarker for Schistosoma japonicum infection and can be used to develop diagnostic products for Schistosoma japonicum.

[0027] Further research revealed that in vitro supplementation with uridine inhibits the activation of hepatic stellate cells (LX-2 cells), restores fatty acid metabolic homeostasis through peroxisome proliferator-activated receptor γ (PPARγ) pathway, and exerts an anti-fibrotic effect.

[0028] This invention identifies the role of uridine in LX-2 cells, establishes the regulatory relationship between uridine and lipid metabolism homeostasis, and discovers the impact of schistosomiasis infection on hepatic uridine metabolism and the role of uridine in the pathogenesis and treatment of schistosomiasis-related liver fibrosis. Uric acid can be applied to diagnostic products for schistosomiasis, enriching the diagnostic methods for schistosomiasis and providing new targets and drug options for the treatment of schistosomiasis. Attached Figure Description

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

[0030] Figure 2 shows the AFADESI-MSI images of liver metabolites ((A) Schematic diagram of mass spectrometry imaging workflow. (B) Representative mass spectra of liver samples from each group. A was created using BioRender.com. (C) HE staining of directly adjacent sections for histopathological evaluation using 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 heatmap of differentially expressed metabolites among different samples in the control group and the 6-week group (n=3). (G) Cluster heatmap of differentially expressed metabolites among different samples in the control group and the 12-week group (n=3).

[0031] Figure 3 shows the in-situ visualization of key metabolites and metabolic pathways between the 6-week group and the control group, and between the 12-week group and the control group. ((A) Mass spectra of upregulated ions compared to the control group at 6 weeks. (B) Mass spectra of downregulated ions compared to the control group at 6 weeks. (C) Pathways with significantly different metabolites compared to the control group at 6 weeks (P<0.05). (D) Mass spectra of upregulated ions compared to the control group at 12 weeks. (E) Mass spectra of downregulated ions compared to the control group at 12 weeks. (F) Pathways with significantly different metabolites compared to the control group at 12 weeks (P<0.05)).

[0032] Figure 4 shows visualizations of the metabolomics datasets compared to the control group at 6 weeks and at 12 weeks. (A) PCA score plot comparing 6 weeks to the control group. (B) Ops-DA score plot comparing 6 weeks to the control group. (C) Volcano plot comparing 6 weeks to the control group. (D) PCA score plot comparing 12 weeks to the control group. (E) Ops-DA score plot comparing 12 weeks to the control group. (F) Volcano plot showing differences in metabolites in granulomatous tissue and unaffected tissue of 6-week-old mice infected with Schistosoma japonicum. (G) Bubble plot of the top 16 metabolic pathways compared to the control group at 6 weeks. (H) Bubble plot of the top 20 metabolic pathways compared to the control group at 12 weeks.

[0033] Figure 5 shows the differential metabolites obtained from AFADESI-MSI analysis of granuloma tissues from 6-week-old mice infected with Schistosoma japonicum and uninfected mice ((A) Schematic diagram of local metabolite extraction and analysis of liver tissue 6 weeks post-infection. (B) Heatmap of spatial catabolic metabolomics data. (C) Upregulation of ions in different regions of granuloma tissue compared with unaffected tissue. (D) Downregulation of ions in different regions of granuloma tissue compared with unaffected tissue. (E) Statistical graph of the number of differentially abundant metabolites in granuloma tissue and unaffected tissue. (F) Significant enrichment of pathways with differentially abundant metabolites in granuloma tissue and unaffected tissue (P<0.05). Data are expressed as mean ± standard deviation. Statistical significance is defined as *P<0.05, ***P<0.001, ****P<0.0001, n=5).

[0034] Figure 6 shows the difference in metabolite richness between granuloma tissues from mice infected with Schistosoma japonicum for 12 weeks and uninfected mice, analyzed by AFADESI-MSI ((A) Schematic diagram of local metabolite extraction and analysis of liver tissue 12 weeks post-infection. (B) Heatmap of spatial catabolic metabolomics data. (C) Upregulation of ions in different regions of granuloma tissue compared with unaffected tissue. (D) Downregulation of ions in different regions of granuloma tissue compared with unaffected tissue. (E) Statistical graph of the number of differentially rich metabolites in granuloma tissue and unaffected tissue. (F) Significant enrichment of pathways with differentially rich metabolites between granuloma tissue and unaffected tissue (P<0.05). Data are expressed as mean ± standard deviation. Statistical significance is defined as *P<0.05, ***P<0.001, ****P<0.0001, n=5).

[0035] Figure 7 visualizes the metabolomics datasets of granulomatous tissues and uninfected granulomatous tissues from 6-week and 12-week-old mice infected with *Schistosoma japonicum*. (A) Representative metabolomic profiles of granulomatous tissues from 6-week-old mice infected with *Schistosoma japonicum* and liver tissues from uninfected mice. (B) PCA score plots of granulomatous tissues and uninfected tissues from 6-week-old mice infected with *Schistosoma japonicum*. (C) Ops-DA score plots of granulomatous tissues and uninfected tissues from 6-week-old mice infected with *Schistosoma japonicum*. (D) Volcano plot showing differences in metabolites between granulomatous tissues and uninfected tissues from 6-week-old mice infected with *Schistosoma japonicum*. (E) Representative metabolomic profiles of granulomatous tissues and uninfected liver tissues from 12-week-old mice infected with *Schistosoma japonicum*. (F) PCA score plots of granulomatous tissues and uninfected tissues from 12-week-old mice infected with *Schistosoma japonicum*. (G) Ops-DA score plots of granulomatous tissues and uninfected tissues from 12-week-old mice infected with *Schistosoma japonicum*. (H) Volcano plot showing differences in metabolites between granulomatous tissues and uninfected tissues from 12-week-old mice infected with *Schistosoma japonicum*.

[0036] Figure 8 shows the in-situ visualization and metabolic pathway enrichment of important metabolites in the liver of 6-week-old mice infected with Schistosoma japonicum. (A) In-situ visualization of upregulated and downregulated ions in 6-week-old mice infected with Schistosoma japonicum. (B) Bubble plot of the top 20 metabolic pathways in granuloma tissues of 6-week-old mice infected with Schistosoma japonicum compared with uninfected granuloma tissues.

[0037] Figure 9 shows the in-situ visualization and metabolic pathway enrichment of important metabolites in the liver of 12-week-old mice infected with Schistosoma japonicum. (A) In-situ visualization of upregulated and downregulated ions in 12-week-old mice infected with Schistosoma japonicum. (B) Bubble plot comparing the top 20 metabolic pathways in granuloma tissues of 12-week-old mice infected with Schistosoma japonicum with those in uninfected granuloma tissues.

[0038] Figure 10 shows a visualization of the liver metabolomics dataset after PZQ treatment. (A) PCA score after PZQ treatment. (B) ops-DA score after PZQ treatment. (C) Correlation analysis of differential abundance of metabolites related to PZQ treatment.

[0039] Figure 11 shows the metabolic regulatory effect of praziquantel on liver injury in mice with schistosomiasis japonicus ((A) MS images of key differentially abundant metabolites in situ; (B) heatmap of spatial catabolic metabolomics data; (C) the top 20 enriched pathways of differentially abundant metabolites in KEGG; (D) the top 10 enriched pathways of differentially abundant metabolites in Reactome).

[0040] Figure 12 shows the significant impact of metabolic pathways on uridine metabolism in mice infected with Schistosoma japonicum ((A) Venn diagram showing common differentially abundant metabolites, compared with the control group at 6 weeks and at 12 weeks. (B) Statistical chart of the number of potentially differentially abundant metabolites, compared with the control group at 6 weeks and at 12 weeks. (C) Venn diagram showing common metabolic pathways, compared with the control group at 6 weeks and at 12 weeks. (D) Relative expression level of CAD mRNA, the rate-limiting enzyme in the de novo uridine synthesis pathway, in mouse liver. (E) Relative expression level of HOGA1, a key enzyme in the glyoxylate metabolism 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 glucuronide metabolism pathway, in mouse liver. (H) Relative expression level of Upase, a key enzyme in the uridine metabolism pathway, in mouse liver. (I) Relative expression level of Upase, a key enzyme in the glutamine metabolism pathway, in mouse liver. (J) Relative expression level of IDH, a key enzyme in the tricarboxylic acid cycle, in mouse liver).

[0041] Figure 13 shows the regulation of fatty acid metabolism and cell activation in LX-2 cells by uridine via the PPARγ pathway ((A) Analysis of the abundance of metabolites related to PZQ treatment. (B) Oil Red O staining to detect the effect of uridine intervention on lipid metabolism in in vitro activated LX-2 cells. (C) Western blotting 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 uridine addition on α-SMA protein expression in LX-2 cells. (F) Effect of in vitro uridine addition on PPARγ protein expression in LX-2 cells. (G) Effect of in vitro uridine addition 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 lipid metabolism-related genes in LX-2 cells after uridine intervention).

[0042] Figure 14 shows the relative ionic strength of uridine in the liver of mice in each group in metabolomics; Figure 14 shows the ROC curve of liver uridine for schistosomiasis japonicus (the solid black line is the ROC curve of uridine; the dashed red line is the 50% AUC reference line). Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in this technical field. Unless otherwise specified, the reagents and materials used in the embodiments are commercially available.

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

[0045] 1. Laboratory animals:

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

[0047] 2. Preparation of cercariae

[0048] The *Schistosoma japonicum* strain used in this study was preserved by the Jiangsu Provincial Institute of Parasitic Diseases. Cercariae were obtained from *Oncomelania hupensis* snails infected with *Schistosoma japonicum* in our laboratory for animal experiments.

[0049] 3. Experimental group and infection

[0050] Twenty mice were randomly divided into four groups of five each: an uninfected group (control group), a 6-week infected group (6 weeks), a 12-week infected group (12 weeks), and a PZQ chemotherapy group (PZQ). Mice had free access to food and water and were allowed to acclimate for 1-2 weeks before infection. Mice in the infected group were infected with 15±2 cercariae per mouse via abdominal skin exposure. Five weeks post-infection, five mice were administered PZQ dissolved in sodium carboxymethyl cellulose at a dose of 150 mg / kg / day via gavage for three consecutive days to form the PZQ chemotherapy group. Throughout the study, the survival status of the mice was monitored regularly.

[0051] 4. AFADESI-MSI detection of related metabolites

[0052] 4.1 Liver tissue processing

[0053] Tissue samples immobilized in embedding gel (Cryo-gel cryosection embedding medium) were removed from a -80°C cryogenic freezer, thawed overnight at -20°C, and sectioned (10 μm thick) using a cryostat (Leica CM 1950, Leica Microsystems, Germany). The sections were then fixed onto positively charged desorption plates (Thermo Fisher Scientific, Waltham, USA) and stored at -80°C for subsequent imaging analysis.

[0054] 4.2 Mass Spectrometry Imaging Data Acquisition

[0055] Frozen tissue sections were removed from an ultra-low temperature freezer at -80°C and quickly placed in a vacuum desiccator, where they were kept at room temperature for approximately 30 minutes. Spatial metabolite resolution analysis was performed using an airflow-assisted desorption / electrospray ionization mass spectrometry (AFADESI-MSI) platform (Beijing Victor Technology Co., Ltd., Beijing, China) and a Q-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher Scientific, USA), employing a progressive scan method to acquire distribution maps in the tissue sections. The spray solvent was acetonitrile (MS grade, Thermo Fisher Scientific, USA):water (Watson distilled water, Watson Group) = 8:2 (v / v), at a flow rate of 5 μL / min in negative mode. The delivery gas flow rate 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 delivery tube was 3 mm. The mass resolution was 60,000, the mass range was 70–1000 Da, and the capillary temperature was 350°C. Platform parameters were set as follows: Vx = 0.2 mm / s, Dy = 0.1 mm, and Dt = 7 s. MSI experiments were 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 using the Xcalibur data acquisition and processing system, which set the data acquisition sequence according to sample size, step size, and scan speed. This information was converted using mass spectrometry image analysis software to obtain a two-dimensional spatial intensity distribution map of ions in the liver tissue slices. Ions detected by AFADESI-MSI were annotated using the pySM annotation framework and the internal SmetDB database (Lumingbio, Shanghai, China).

[0056] 4.3 Local Metabolite Extraction

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

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

[0059] 1. Histopathological examination of liver samples

[0060] Small pieces of liver tissue from the right lobe of each mouse were fixed in 4% paraformaldehyde solution for 24 hours. After gradient dehydration, clearing, paraffin embedding, sectioning, drying, dewaxing, rehydration, HE staining (Biosharp, BL700B), and Masson staining (Solarbio, G1340), the sections were placed on glass slides and covered with a glass cover, observed and photographed under an optical microscope.

[0061] 2. Results

[0062] Schistosoma japonicum infection primarily causes liver damage in mice. Acute and chronic Schistosoma japonicum infection mouse models were established, and the model mice were treated with PZQ (Figure 1, A). Compared to the uninfected group, mice in the Schistosoma japonicum infection group showed significantly reduced body weight, activity, and mental status at 6 and 12 weeks (Figure 1, B). Furthermore, liver volume and weight were significantly increased, and liver index was significantly increased (Figure 1, C). Liver samples were subsequently collected for histopathological examination. HE staining revealed significant immune cell infiltration and oogranulomas in the livers of infected mice. The area of ​​oogranulomas was significantly increased in the 12-week group (Figure 1, D). Masson staining showed significant collagen fiber deposition around the oogranulomas, indicating that Schistosoma japonicum infection may lead to liver damage and liver fibrosis with the chronic progression of infection. In addition, PZQ treatment significantly improved the related symptoms in mice. Compared to the infected group, the infected group showed improved mental status, reduced liver index, and less pathological liver damage (Figure 1, BD).

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

[0064] 1. Mass spectrometry analysis

[0065] Same as the experimental content for AFADESI-MSI detection of related metabolites.

[0066] 2. Multivariate statistical analysis

[0067] Multivariate statistical analysis began with unsupervised principal component analysis (PCA) to assess within- and between-group variability. This approach allowed observation of trends in the overall sample distribution, identification of potential dispersions, and evaluation of the stability of the analysis process. Subsequently, supervised orthogonal partial least squares analysis (OPLS-DA) was performed to distinguish overall differences in metabolomic profiles between groups. In OPLS-DA, the overall contribution of each variable to between-group differences was assessed by calculating the importance of projected variables (VIP). The VIP score reflects the importance of the first two principal components of the OPLS-DA model in sample classification. A VIP value greater than 1 indicates that the variable plays a significant role in distinguishing between-group differences. Student's t-test was further used to verify whether metabolites with significant between-group differences were statistically significant. Variables satisfying both VIP > 1 and P < 0.05 were considered potentially significant metabolites.

[0068] 3. UMAP Analysis

[0069] Local structures in the data are identified using the nearest neighbor algorithm, and a weighted graph is constructed, where edge weights represent the probability of a connection between two points. Next, the low-dimensional layout is optimized to maintain the high-dimensional data structure by adjusting hyperparameters such as the number of neighbors and the embedding dimension to configure UMAP. After analysis, the high-dimensional data is reduced to two or three dimensions for easier visualization. The results are validated by comparison with other methods, revealing the spatial distribution characteristics of metabolites and providing intuitive insights for research.

[0070] 4. Metabolic pathway analysis

[0071] KEGG IDs of differentially expressed metabolites were identified for enrichment analysis of metabolic pathways, yielding enrichment results. Metabolic pathways with p-values ​​less than 0.05 were selected using a hypergeometric test as significant enrichment pathways for differentially expressed metabolites. The smaller the p-value, the more significant the variability of the metabolic pathway.

[0072] 5. Results

[0073] Spatial metabolomics was used to investigate liver metabolic changes during the early stage of egg deposition (6 weeks) to better understand the relationship between liver metabolites and disease progression (Figure 2A). First, a representative mass spectrometry sample was compared with the control group. This comparison revealed differences in metabolite peak distribution between the two groups, indicating significant changes in substance distribution (Figure 2B). Principal component analysis was used to analyze the spatial metabolomics results of liver tissues from infected and control mice at 6 weeks. The results showed that both samples fell within the 95% confidence interval, and no abnormal samples were observed (Figure 4A). The data from the two groups were clearly separated on the first principal component, highlighting the significant metabolic differences (Figure 4B).

[0074] To enhance the visual representation of relationships between samples and to showcase the differences in metabolite expression across various samples, hierarchical clustering analysis was performed on all differentially abundant metabolites. The resulting heatmap used a gradient from blue to red to represent metabolite abundance, with deeper red indicating higher abundance (Figure 2, F). The clustering diagram clearly showed significant regionalization of liver metabolite distribution after infection (Figure 2, D), a finding further confirmed by UMAP analysis (Figure 2, E). Furthermore, comparison with HE staining results showed that the metabolite distribution in ovomyogranuloma tissue was significantly different from that in surrounding tissues (Figure 2, C). Further analysis revealed 21 potentially differentially abundant metabolites in the liver tissue of mice infected at 6 weeks gestation under negative ion mode compared to the control group, with 8 metabolites showing increased abundance and 13 showing decreased abundance (Figure 4, C). Mass spectrometry imaging was then performed on these differentially abundant metabolites (Figure 3, A and B). KEGG pathway enrichment analysis was subsequently performed on all differentially abundant metabolites, identifying 16 differential metabolic pathways (Figure 4, G). Of these, six pathways were significantly affected (Figure 3, C). These pathways include glyoxylate and dicarboxylic acid metabolism, the citric acid cycle (tricarboxylic acid cycle), the glucagon signaling pathway, central carbon metabolism in cancer, and ascorbic acid and aldonic acid metabolism.

[0075] Chronic granuloma formation of schistosomiasis eggs plays a crucial role in the development and progression of liver fibrosis in schistosomiasis. We analyzed changes in liver metabolites in mice during chronic infection, particularly at 12 weeks post-infection. Data quality was assessed using mass spectrometry (Figure 2, B), principal component analysis (Figure 4, D), and OPLS-DA modeling (Figure 4, E). These analyses revealed significant differences between the infected and uninfected groups, highlighting significant changes in liver metabolite abundance (Figure 2, G). UMAP analysis effectively clustered metabolites into distinct groups (Figures 2, D and E). Further analysis revealed 25 potentially differentially abundant metabolites in the liver tissue of the 12-week-old infected mice under negative ion mode compared to the control group. Of these, 10 metabolites showed increased abundance, while 15 showed decreased abundance (Figure 4, F). Mass spectra of these differentially abundant metabolites are shown in Figures 3, D and E. Furthermore, KEGG pathway enrichment analysis was performed on all differentially abundant metabolites, identifying several differential metabolic pathways (Figure 4, H). Of these pathways, 10 were significantly enriched (Figure 3, F). These pathways include arachidonic acid metabolism, ascorbic acid and aldonic acid metabolism, pyrimidine metabolism, D-amino acid metabolism, and ABC transporters.

[0076] Example 3: Spatial metabolomics reveals metabolic heterogeneity in liver egg granulomas of mice infected with Schistosoma japonicum.

[0077] 1. Mass spectrometry analysis: Same as in Example 2.

[0078] 2. Multivariate statistical analysis: Same as Example 2.

[0079] 3. Metabolic pathway analysis: Same as in Example 2.

[0080] 4. UMAP analysis: Same as in Example 2.

[0081] 5. Results

[0082] To better understand the metabolic heterogeneity within parasite egg granulomas in the liver, the entire liver of infected mice was divided into two histological regions: granulomatous tissue and unaffected tissue (Figure 5A, Figure 6A). HE images with marked sampling points were then imported into the AFADESI-MSI software MSiReader for image fusion and spatial matching. Subsequently, in-situ AFADESI-MSI spectra were extracted based on the marked sampling points in the HE images.

[0083] The quality of the extracted data was rigorously assessed using mass spectrometry (Figure 7A), principal component analysis (Figure 7B), and OPLS-DA model analysis (Figure 7C). The results consistently demonstrated significant differences in liver metabolomics profiles between granulomatous and unaffected tissues. Furthermore, the liver metabolites from Schistosoma japonicum-infected mice exhibited significant regional distributions, a finding confirmed by the aforementioned UMAP analysis. Analysis of liver metabolites from 6-week-old infected mice identified 32 potential differentially expressed metabolites, with 7 metabolites showing increased abundance and 25 showing decreased abundance (Figure 7D). The abundance of each ion in the samples is illustrated by a heatmap (Figure 5B). A single differentially expressed ion may correspond to multiple potential differentially expressed metabolites (Figure 5E). The mass spectra of the ions corresponding to these differentially expressed metabolites are shown in Figure 8A. Figures 5C and 5D illustrate the relative expression intensity of each ion. In addition, we performed KEGG pathway enrichment analysis on all differentially expressed metabolites, identifying 62 enriched metabolic pathways (Figure 8B). These pathways encompass multiple metabolic aspects, including glucose metabolism, lipid metabolism, amino acid metabolism, and nucleic acid metabolism. Notably, the 23 metabolic pathways exhibit significant differences, including linoleic acid metabolism, butyric acid metabolism, central carbon metabolism in cancer, renal cell carcinoma, and arachidonic acid metabolism (Figure 5, F).

[0084] In the analysis of liver metabolites from mice infected for 12 weeks, we identified 40 potential differentially expressed metabolites compared to unaffected tissues. Of these, 3 metabolites showed increased abundance, while 37 showed decreased abundance (Figure 7, H). The abundance of each ion in the samples is shown in a heatmap (Figure 6, B). A single differentially expressed ion may correspond to multiple potential differentially expressed metabolites (Figure 6, E). Mass spectra of the ions corresponding to these differentially expressed metabolites are shown in Figure 9, A. Figures 6, C and D, show the relative expression intensity of each ion. Furthermore, we performed KEGG pathway enrichment analysis on all differentially expressed metabolites, identifying 60 enriched metabolic pathways (Figure 9, B). These pathways cover multiple metabolic aspects, including glucose metabolism, lipid metabolism, and amino acid metabolism. Notably, KEGG pathway analysis revealed 19 pathways significantly enriched in the differentially expressed metabolites, including unsaturated fatty acid biosynthesis, renal cell carcinoma, arachidonic acid metabolism, central carbon metabolism in cancer, and vascular smooth muscle contraction (Figure 6, F).

[0085] Example 4: Spatial metabolomics revealed changes in liver metabolism in mice infected with Schistosoma japonicum and treated with PZQ.

[0086] 1. Multivariate statistical analysis

[0087] Same as Example 2.

[0088] 2. Between-group analysis

[0089] Experimental data are presented as mean ± standard deviation (SDs). SPSS 22.0 software was used for statistical analysis, and results were compared between groups using independent samples t-tests. One-way ANOVA and Dunnett's test were used to compare the significance of differences among multiple groups, with a significance level set at α = 0.05. P < 0.05 was considered statistically significant (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). GraphPad Prism 9.0 software was used for data visualization and analysis.

[0090] 3. Reactome Analysis

[0091] Spatial distribution information of metabolites in biological tissue samples was obtained using mass spectrometry imaging. The ReactomePA package in R was then used to perform pathway enrichment analysis on differentially expressed metabolites to identify the involved biological pathways. 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 using the Reactome Pathway Browser.

[0092] 4. Results

[0093] First, data quality was assessed using multivariate statistical analysis, exceeding principal component analysis (Figure 10). The results revealed significant differences in liver metabolic characteristics in PZQ-treated mice. In negative ion mode, 27 ions showed significant differences. Compared to the uninfected group, the expression intensity of these ions was either upregulated or downregulated after infection, with varying degrees of reversal observed after PZQ treatment (Figures A and B in Figure 11). Subsequently, 20 regions were randomly selected from each sample, and one-way ANOVA was used to analyze the ion intensity of each region. This analysis revealed specific distributional differences in ion expression intensity among the sample groups. Pathway enrichment analysis of metabolites using KEGG revealed five significantly different metabolic pathways (Figure C in Figure 11). Furthermore, Reactome analysis revealed enrichment of several common metabolic pathways (Figure D in Figure 11). 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-related liver disease and play a role in the anti-schistosomiasis process of PZQ.

[0094] Example 5: Uroside metabolism is a metabolic pathway significantly affected during Schistosoma japonicum infection.

[0095] 1. Reverse transcription-real-time quantitative PCR (RT-qPCR)

[0096] Total RNA was extracted from cells or tissues using RNA-easy Isolation Reagent (Vazyme, R701), and RNA purity (OD260 / OD280 ratio in the range of 1.8–2.2) was verified using a NanoDrop instrument. cDNA was obtained by reverse transcription using a kit (Vazyme, R323-01). RNA levels were assessed using a LightCycler 480 instrument, SYBR qPCR Master Mix (Vazyme, Q711-02), and specified primers (Table S1). After obtaining the threshold cycle (Ct), the relative expression levels of target genes (key enzymes or proteins in various metabolic pathways) were calculated using the 2-ΔΔCt method, with ACTB or β-actin as internal controls.

[0097] The reagents for detecting human gene expression in the target genes are specific primers for COL1A1, COL3A1, α-SMA, PPARγ, PLIN1, CD36, SREBP1c, FASN, UPASE1, and ACTB. These specific primers include upstream and downstream primers as shown in SEQ ID NO:1-20.

[0098] The reagents used to detect mouse gene expression in the target genes are specific primers for Idh1, Idh2, Idh3, Hoga1, Gulo, Ugdh, Upase1, Upase2, Gls1, Gls2, Cad, and β-actin. These specific primers include upstream and downstream primers as shown in SEQ ID NO:20-44.

[0099] Primers were synthesized by Shanghai Bioengineering Co., Ltd., and the relevant primer sequences are detailed in Table 1.

[0100] Table 1. Sequences of primers used for RT-qPCR analysis

[0101] 2. Multivariate statistical analysis

[0102] Same as Example 2.

[0103] 3. Metabolic pathway analysis

[0104] Same as Example 2.

[0105] 4. Results

[0106] Our investigation revealed that multiple metabolites and metabolic pathways were significantly affected in both the early and chronic stages of infection (Figures A and B in Figure 12). 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 respective differential ions are listed in Table 2.

[0107] Table 2. Potentially Differential Metabolites Between the 6-week and 12-week Infection Groups

[0108] These pathways primarily comprise three metabolic pathways: glyoxylate and dicarboxylic acid metabolism, ascorbic acid and aldonic acid metabolism, and pyrimidine metabolism (Figure 12, Figure C). These findings indicate that these three metabolic pathways are closely associated with disease progression. Further examination of the association between these three differentially metabolized pathways and the identified differentially abundant metabolites revealed that glutamine and uridine play central roles in connecting these differentially abundant metabolites. Furthermore, in pathway enrichment analysis of differentially abundant metabolites in the livers of control and 6-week-old infected mice, we also identified pathways associated with ascorbic acid and aldonic acid metabolism. This result confirms our previous metabolomics studies on serum samples. Overall, this analysis highlights the importance of the relationship between metabolites in regulating disease process effects. In addition to the identified differentially metabolites, related metabolic enzymes were validated by RT-qPCR. We found that the key enzyme in uridine metabolism, pyrimidine nucleoside phosphorylase 1 (Upase 1), was significantly upregulated at 6 weeks of infection, while other metabolic enzymes were downregulated. Moreover, the expression of most metabolic enzymes could be reversed by PZQ treatment (Figure 12, Figures D-J). These results suggest that disturbances in uridine metabolism may play an important role in the occurrence and development of liver fibrosis caused by schistosomiasis.

[0109] Example 6: Uric acid regulates fatty acid metabolism reprogramming and cell activation in LX-2 cells via the PPARγ pathway.

[0110] 1. Cell Culture

[0111] LX-2 cells were used in this study. These cells were resuscitated from liquid nitrogen tubes, resuspended in DMEM (HyClone, SH3002201) containing 10% fetal bovine serum (Gibco, 10270106) and 1% penicillin-streptomycin, then transferred to culture flasks and incubated at 37°C in an incubator containing 5% CO2. When the cells reached 80% confluence, they were harvested and used for other cell experiments.

[0112] 2. Cell Experiment Grouping and Processing

[0113] LX-2 cells were cultured to 80% confluence, then collected and seeded into culture dishes. After cell attachment, they were treated with 25 ng / ml TGF-β (Absin; China) for 12 hours to induce LX-2 cell activation. The medium was then replaced with complete medium containing 1 mg / ml uridine, and cultured for another 24 hours. Cells were then collected for subsequent assays. In the recovery assay, after LX-2 cell activation, the medium was replaced with complete medium containing 1 mg / ml uridine (Beyotime, China) and 10 μg / ml PPARγ inhibitor (GW9662, Beyotime, China), and cultured for another 24 hours. Cells were then collected for subsequent assays.

[0114] 3. Oil Red O staining of cells

[0115] Prepare Oil Red O dye stock solution and working solution 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, then wash the cells with double-distilled water until no excess dye remains. Examine the cells under a microscope and take photographs.

[0116] 4. Western blot analysis

[0117] Total protein was extracted from cells, and protein concentration was determined by the BCA method. Protein samples were mixed with 6×SDS protein loading buffer at a 5:1 ratio and boiled at 95-100°C for 10 minutes. 30 μg of protein was loaded into each well and SDS-PAGE was performed. Protein was transferred to a PVDF membrane and blocked with 5% skim milk for 1-2 hours. Primary antibodies (specifically targeting 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 time. Secondary antibody was added, and the membrane was incubated on a shaker at room temperature for 1-2 hours. After thorough washing, protein bands were observed using a Bio-Rad ChemiDoc XRS+ imaging system, and density quantification was performed using ImageJ software.

[0118] 5. Results

[0119] Further correlation analysis of the differentially expressed metabolites after PZQ treatment revealed a negative correlation between uridine and several lipid metabolism-related molecules (Figure 13, A). Lipid homeostasis in hepatic stellate cells is crucial for maintaining cellular resting states. 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 an in vitro TGF-β-induced human hepatic stellate (LX-2) cell activation model. Oil Red O staining showed that α-SMA protein expression was significantly upregulated when activated LX-2 cells lost lipids. Conversely, uridine intervention inhibited cell activation, and intracellular lipid droplet levels were restored (Figure 13, B-G). PPARγ is considered a key factor regulating lipid metabolism. We found that PPARγ was significantly downregulated in activated LX-2 cells, while uridine intervention upregulated it (Figure 13, C and F). Further recovery experiments confirmed that uridine can regulate lipid metabolism homeostasis in LX-2 cells through the PPARγ signaling pathway. The main mechanism may involve upregulating fatty acid transfer and expressing key metabolic enzymes that play an anti-fibrotic role (Figure H and Figure I in Figure 13).

[0120] Example 7: Receiver Operating Curve (ROC) Analysis of Uric acid as a Biomarker for the Detection of Schistosomiasis japonicus

[0121] A good early screening biomarker and product should possess high sensitivity and high specificity. Plotting specificity on the x-axis and sensitivity on the y-axis, connecting the points along these axes yields the Receiver Operating Characteristic (ROC) curve. The area under the ROC curve (AUC) is a typical method for comprehensively evaluating the detection capabilities of early screening products.

[0122] This embodiment uses ROC analysis to study the specificity and sensitivity of uridine as a biomarker for detecting schistosomiasis japonicus.

[0123] 1. ROC Analysis

[0124] Based on the relative ionic strength of uridine obtained from spatial metabolomics analysis, a series of thresholds from minimum to maximum were set. Then, the true positive rate (TPR) and false positive rate (FPR) were calculated for each threshold. TPR measures the model's ability to predict positive samples, while FPR measures the proportion of negative samples incorrectly predicted as positive samples. Finally, all calculated points were plotted on a coordinate system with FPR on the x-axis and TPR on the y-axis, and connected by line segments to form an ROC curve. The area under the ROC curve (AUC) quantifies the model performance; the closer the AUC value is to 1, the stronger the model's ability to distinguish between positive and negative samples.

[0125] 2. Results

[0126] The relative ionic strength of uridine in the liver of mice in each group in metabolomics is shown in Figure 14A. Compared with the control group, the relative uridine strength in the liver of mice infected for 6 weeks and 12 weeks was significantly reduced, suggesting that Schistosoma japonicum infection can lead to a decrease in uridine content in the liver, and the difference in content after PZQ treatment was not statistically significant.

[0127] The ROC analysis results are shown in Figure 14B. The area under the ROC curve for uridine against schistosomiasis japonicus is 1, indicating that uridine can effectively distinguish healthy controls from those infected with schistosomiasis japonicus. This suggests that liver uridine content is an ideal indicator for the detection of schistosomiasis japonicus.

Claims

1. The application of a biomarker in the preparation of diagnostic products for schistosomiasis japonicus, wherein the biomarker is uridine.

2. The application of a biomarker in the preparation of diagnostic products for schistosomiasis japonicus, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

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

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

5. The use of a biomarker in the preparation and / or screening of drugs for the treatment of schistosomiasis japonicus, wherein the biomarker is uridine.

6. The use of a biomarker in the preparation and / or screening of drugs for the treatment of schistosomiasis japonicus, wherein the biomarker is Upase1, a key enzyme in uridine metabolism.

7. The 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. The 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. Application of uridine in the preparation of therapeutic drugs for schistosomiasis japonicus.

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

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