Introduction

Schistosomiasis is a parasitic disease that is widely prevalent in tropical and subtropical regions and poses a serious health risk to humans [1,2]. The World Health Organization has estimated that approximately 779 million people are at risk of schistosomiasis infection worldwide and that more than 250 million people are infected with schistosomiasis [3,4]. The main schistosomes that infect humans are Schistosoma mansoni, Schistosoma japonicum (S. japonicum), and Schistosoma haematobium, of which S. japonicum is prevalent mainly in China [5,6]. The core damage caused by S. japonicum infection is liver damage and persistent granulomatous reactions caused by eggs in the liver. The chronic development of this disease leads to liver fibrosis and its complications, which are also important causes of death in schistosomiasis patients [7]. However, the precise molecular mechanisms underlying liver fibrosis caused by S. japonicum remain unclear within the scientific community. Additionally, the development of therapeutic drugs in clinical settings relies on a deeper understanding of these mechanisms.

Metabolomics is the comprehensive analysis of small-molecule metabolites present in living organisms under specific conditions and is a powerful tool for identifying new drug targets, discovering biomarkers, surveilling disease, and investigating disease pathogenesis [8]. At present, metabolomics methods have been widely used to find new therapeutic targets for parasitic infections [9]. Nonetheless, owing to technical constraints, most current metabolomics investigations of diseases, including schistosomiasis, have involved the use of primarily on serum and urine samples [10]. A common limitation in the metabolic analysis of complex biological samples is the inability to consider the intercellular heterogeneity within organs or tissues. Consequently, achieving comprehensive visualization of systemic metabolic reprogramming and its interactions with liver diseases remains a formidable challenge [11]. The advent of mass spectrometry imaging (MSI) technology has introduced novel possibilities for studying organ-specific heterogeneity in diseased tissues and the spatial distribution patterns of metabolites [12]. Spatial metabolomics, which is based on MSI, facilitates the onsite screening of metabolic biomarkers associated with the development of liver lesions. This approach facilitates a detailed depiction of the metabolic landscape within liver lesion sites and their surrounding microenvironments, offering a distinct advantage in exploring disease mechanisms [13]. Therefore, the use of spatial metabolomics approaches and in-depth pathway analysis can improve our understanding of disease progression and drug action modes.

Praziquantel (PZQ) is a broad-spectrum antiparasitic drug and is the first choice for the treatment of schistosomiasis [14]. Praziquantel is believed to have three unique pharmacological effects on Schistosoma, namely, stimulation of worm motor activity, spastic contraction of muscle tissue, and the formation of outer skin vesicles [15]. These actions expose the parasite's surface antigens, which enhances the ability of the host immune system to attack them. The effectiveness of PZQ is diminished in mice with depleted T or B cells, highlighting the drug's reliance on the host immune response for optimal efficacy. Additionally, PZQ reduces inflammation around eggs and prevents liver fibrosis. However, the precise mechanisms through which PZQ combats schistosomiasis remain unclear. Schistosomes rely on host nutrients, causing metabolic changes that benefit parasites. Disruption of nutrient intake can harm parasites and mitigate the associated metabolic changes in the host. Understanding liver metabolism following schistosome infection is crucial for identifying key factors involved in nutrient exchange between parasites and hosts. Investigating liver metabolic pathways related to PZQ treatment may reveal new targets for the development of antischistosomiasis drugs.

Uridine is a type of uracil nucleoside that plays a vital role in RNA and DNA biosynthesis, glycogen deposition, protein and lipid glycosylation, and body temperature and circadian rhythm maintenance and is closely associated with many metabolic diseases [16]. The balance of uridine in the body is regulated by uridine phosphorylase 1 (Upase1), which catalyzes the conversion of uridine into uracil [17]. A series of studies have shown that the disruption of uridine homeostasis is related to the occurrence and development of diabetes, neurodegeneration, fatty liver, obesity, and other diseases [16]. A study on liver disease revealed that increasing liver uridine levels through CPBMF 65 (a uridine phosphorylase 1 inhibitor) mitigated CCl4-induced liver fibrosis in mice [18]. In addition, studies have demonstrated that there is crosstalk between uridine metabolism and lipid metabolism [19], and uridine supplementation can prevent tamoxifen-induced nonalcoholic fatty liver disease (NAFLD) and stimulate liver phospholipid biosynthesis [20]. Nevertheless, the role of uridine metabolism in schistosomiasis-induced liver fibrosis remains unknown.

In this study, we used air-flow-assisted desorption electrospray ionization-mass spectrometric imaging (AFADESI-MSI) technology to investigate the variations in metabolite abundance in the liver during acute and chronic S. japonicum infection and the effects of PZQ treatment on the liver metabolism of schistosomiasis-infected mice. We aimed to identify and visualize alterations in liver-related metabolites at a spatially detailed metabolic level and map the distribution patterns of these metabolites within the egg granuloma region. The differentially abundant metabolites and metabolic pathways identified were compared and analyzed, focusing on the mechanism of lipid reprogramming and cell activation induced by the disturbance of uridine metabolism in hepatic stellate cells. This study not only increases our understanding of the metabolic disorders in the liver during the development of schistosomiasis-related liver disease but also highlights the importance of the disturbance of uridine metabolism in the development of liver fibrosis and pinpoints important candidate metabolites for future drug development.

Results

Survival and liver pathological analyses of the mice

S. japonicum infection predominantly leads to liver damage in the host. We constructed mouse models of acute and chronic S. japonicum infection and treated the model mice with PZQ (Fig 1A). Compared with those in the uninfected group, the body weights, activity, and mental states of the mice in the S. japonicum-infected group were significantly lower at 6 and 12 weeks (Fig 1B). Moreover, the liver volume and weight increased significantly, and the liver index increased significantly (Fig 1C). The liver samples were subsequently harvested for histopathology. HE staining revealed obvious infiltration of immune cells and egg granulomas in the livers of infected mice. The area of egg granulomas increased significantly in the 12w group (Fig 1D). Masson staining revealed obvious collagen fibrotic deposition around egg granulomas, suggesting that S. japonicum infection may lead to liver injury and liver fibrosis with the chronic development of infection (Fig 1D). In addition, PZQ treatment significantly ameliorated related symptoms in mice. Compared with those of the infected group, the mental state of the infected group improved, the liver index decreased, and pathological liver injury was alleviated (Fig 1B–1D).

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Fig 1. Survival and liver pathology analyses of mice infected with S. japonicum.

(A) Schematic diagram of the experimental design of the mouse model infected with S. japonicum. The illustration in Fig 1A was drawn by hand using Adobe Illustrator 2021 software. (B) Histogram of the liver weights of the mice. (C) Liver/body weight ratios of the mice. (D) HE and Masson staining of the liver tissue of the mice (×100). The data are presented as the mean ± SD. Statistical significance is shown as ^*P < 0.05, ^***P < 0.001, and ^****P < 0.0001, n = 5.

https://doi.org/10.1371/journal.pntd.0012854.g001

Spatial metabolomics reveals liver metabolic variations in different stages of S. japonicum infection in mice

Liver injury is a prominent pathology following Schistosoma infection. Mature female worms begin laying eggs around 4 to 5 weeks post infection, with eggs deposited in the liver at approximately 6 weeks, leading to the formation of acute egg granulomas and subsequent acute liver injury. Here, we investigated hepatic metabolic changes during the early stage of egg deposition (6 w) via spatial metabolomics techniques to better understand the relationships between hepatic metabolites and disease progression (Fig 2A). We initiated the analysis by selecting a representative mass spectrogram for comparison with the control group. This comparison revealed differences in the peak distributions of metabolites between the two groups, indicating significant variation in substance distribution (Fig 2B). Principal Component Analysis (PCA) was employed to analyze the spatial metabolomics results of liver tissues from the mice in the 6-week infected group and the control group. The results indicated that the samples from both groups fell within the 95% confidence interval, with no abnormal samples observed (S1A Fig). The results demonstrated a significant separation between the two groups of data on the first principal component, emphasizing the substantial metabolic differences (S1B Fig).

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Fig 2. AFADESI-MSI imaging of liver metabolites.

(A) Schematic of the workflow for mass spectrometry imaging. The illustration in Fig 2A was created with BioRender.com. (B) Representative mass spectra of liver samples from each group. A was created with BioRender.com. (C) HE staining of an immediate adjacent section was used for AFADESI-MSI for histopathological assessment. 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) Clustering heatmaps of differentially expressed metabolites between different samples in the control and 6w groups (n = 3). (G) Clustering heatmaps of differentially expressed metabolites between different samples in the control and 12w groups (n = 3).

https://doi.org/10.1371/journal.pntd.0012854.g002

To enhance the visual representation of the relationships between samples and the differences in metabolite expression across various samples, hierarchical clustering was conducted on all differentially abundant metabolites. The resulting heatmap utilized a color gradient from blue to red to depict the abundance of metabolites, with redder shades indicating a greater abundance (Fig 2F). The clustering diagrams distinctly manifested substantial regionalization in the distribution of liver metabolites following infection (Fig 2D), a finding that was further corroborated by UMAP analysis (Fig 2E). In addition, the comparison of HE staining results showed that the metabolic distribution of egg granuloma tissue was significantly different from the surrounding tissue (Fig 2C). Upon further analysis, compared with those in the control group, the liver tissues of the mice in the 6-week infection group presented 21 potential differential metabolites under negative ion mode, with 8 metabolites that increased in abundance and 13 that decreased in abundance (S1C Fig and S2 Table). Moreover, we performed mass spectrometry imaging of these differentially abundant metabolites (Fig 3A and 3B). KEGG pathway enrichment analysis was subsequently conducted for all differentially abundant metabolites, resulting in the identification of 16 differential metabolic pathways (S1G Fig). Among these pathways, 6 were found to be significantly impacted (Fig 3C and S3 Table). These pathways include glyoxylate and dicarboxylate metabolism, the citrate cycle (TCA cycle), the glucagon signaling pathway, central carbon metabolism in cancer, and ascorbate and aldarate metabolism.

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Fig 3. In situ visualization of crucial metabolites and metabolic pathways in the 6w vs. control or 12w vs. control groups.

(A) MS images of upregulated ions in the 6w vs. control comparisons. (B) MS images of downregulated ions in the 6w vs. control comparisons. (C) The significantly (P < 0.05) enriched pathways for differentially abundant metabolites in the 6w vs. control comparisons. (D) MS images of upregulated ions in the 12w vs. control comparison. (E) MS images of downregulated ions in the 12w vs. control comparison. (F) The significantly (P < 0.05) enriched pathways for differentially abundant metabolites in the 12w vs. control comparisons.

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The progression of chronic egg granuloma formation plays a pivotal role in inducing liver fibrosis in schistosomiasis. We also analyzed liver metabolite changes in mice during the chronic infection period, specifically at 12 weeks postinfection. The data quality was assessed via mass spectrometry (Fig 2B), principal component analysis (PCA) (S1D Fig), and OPLS-DA modeling (S1E Fig). Compared with the uninfected group, these analyses revealed significant differences between the two groups, highlighting notable variations in liver metabolite abundance (Fig 2G). UMAP analysis effectively clustered the metabolites into distinct groups (Fig 2D and 2E). Further analysis revealed that, in contrast with those in the control group, the liver tissues of the mice in the 12-week infection group presented 25 potential differential metabolites in negative ion mode. Among these, the abundance of 10 metabolites increased, whereas the abundance of 15 decreased (S1F Fig and S4 Table). The mass spectrograms corresponding to these differentially abundant metabolites are presented in Fig 3D and 3E. Moreover, KEGG pathway enrichment analysis was conducted for all differentially abundant metabolites, leading to the identification of several differential metabolic pathways (S1H Fig). Among these pathways, 10 were significantly enriched (Fig 3F and S5 Table). These pathways included arachidonic acid metabolism, ascorbate and aldarate metabolism, pyrimidine metabolism, D-amino acid metabolism, and ABC transporters, among others.

Spatial metabolomics reveals the metabolic heterogeneity of hepatic egg granulomas in S. japonicum-infected mice

S. japonicum infection is characterized by chronic liver injury, which includes the formation of liver egg granulomas. Metabolic interactions occurring within these granulomas and between granulomas and surrounding normal cells, such as immune cells and stromal cells, have a profound impact on the progression of hepatic injury and the immune response in schistosomiasis. To better understand the metabolic heterogeneity within hepatic egg granulomas, we divided the whole liver of postinfected mice into two tissue regions: granulomatous tissue and unaffected tissue (Figs 4A and 5A). We subsequently imported HE images with labeled sampling points into the AFADESI-MSI software MSiReader program for image fusion and spatial matching. Subsequently, we extracted region-specific in situ AFADESI-MSI spectra in accordance with the labeled sampling points in the HE images.

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Fig 4. Discriminating metabolites were obtained through AFADESI-MSI analysis of the granulomatous tissue and unaffected tissue of 6w mice infected with S. japonicum.

(A) Schematic diagram of the extraction and analysis of local metabolites from liver tissue at 6 weeks after infection. (B) Heatmap of spatially resolved metabolomics data. (C) Upregulation of ions in different regions in the granulomatous tissue vs. unaffected tissue comparisons. (D) Downregulation of ions in different regions in the granulomatous tissue vs. unaffected tissue comparisons. (E) Statistical map of the number of differentially abundant metabolites in the granulomatous tissue and unaffected tissue. (F) The significantly (P < 0.05) enriched pathways for differentially abundant metabolites in the granulomatous tissue vs. unaffected tissue. The data are presented as the mean ± SD. Statistical significance is shown as ^*P < 0.05, ^**P < 0.01, ^***P < 0.001, and ^****P < 0.0001.

https://doi.org/10.1371/journal.pntd.0012854.g004

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Fig 5. Differentially abundant metabolites were obtained through AFADESI-MSI analysis of the granulomatous tissue and unaffected tissue of 12w mice infected with S. japonicum.

(A) Schematic diagram of the extraction and analysis of local metabolites from liver tissue at 12 weeks after infection. (B) Heatmap of spatially resolved metabolomics data. (C) Upregulation of ions in different regions in the granulomatous tissue vs. unaffected tissue comparisons. (D) Downregulation of ions in different regions in the granulomatous tissue vs. unaffected tissue comparisons. (E) Statistical map of the number of differentially abundant metabolites in the granulomatous tissue and unaffected tissue. (F) The significantly (P < 0.05) enriched pathways for differentially abundant metabolites in the granulomatous tissue vs. unaffected tissue. The data are presented as the mean ± SD. Statistical significance is shown as ^**P < 0.01, ^***P < 0.001, and ^****P < 0.0001.

https://doi.org/10.1371/journal.pntd.0012854.g005

The quality of the extracted data was rigorously evaluated via mass spectrometry (S2A and S2E Fig), PCA (S2B and S2F Fig), and OPLS-DA model analysis (S2C and S2G Fig). The results consistently demonstrated significant differences in liver metabolic profiles between granulomatous tissue and unaffected tissue. Furthermore, the liver tissue metabolites of mice infected with S. japonicum exhibited a significant regional distribution, a finding that was also corroborated by UMAP analysis, as described in an earlier section of the results. Through an analysis of liver metabolites in mice infected for 6 weeks, we identified 32 potential differential metabolites in the egg granuloma tissues of the liver in negative ion mode. Among these, the abundance of 7 metabolites was increased, whereas the abundance of 25 were decreased (S2D Fig and S6 Table). The abundance of individual ions in the samples is depicted via a heatmap (Fig 4B). A single differential ion may correspond to multiple potentially differential metabolites (Fig 4E). S6 Table provides details about the ions corresponding to these differentially abundant metabolites, and the mass spectrograms of the ions are displayed in S3A Fig. Fig 4C and 4D depict the relative expression intensity of each ion. Furthermore, we conducted KEGG pathway enrichment analysis for all differentially abundant metabolites, resulting in the identification of 62 enriched metabolic pathways (S3B Fig). These pathways encompass various aspects of metabolism, including glucose metabolism, lipid metabolism, amino acid metabolism and nucleic acid metabolism. Notably, significant differences were observed in 23 metabolic pathways, including pathways such as linoleic acid metabolism, butanoate metabolism, central carbon metabolism in cancer, renal cell carcinoma, and arachidonic acid metabolism (Fig 4F and S7 Table).

In the analysis of liver metabolites from mice infected for 12 weeks, compared with unaffected tissues, we identified 40 potential differential metabolites in the liver egg granuloma tissues under negative ion mode. Among these, the abundance of 3 metabolites were increased, whereas the abundance of 37 were decreased (S2H Fig and S8 Table). The expression of individual ions in the samples is depicted via a heatmap (Fig 5B). A single differential ion may correspond to multiple potentially differential metabolites (Fig 5E). S8 Table provides details about the ions corresponding to these differentially abundant metabolites, and the mass spectrograms of the ions are displayed in S4A Fig. Fig 5C and 5D depict the relative expression intensity of each ion. Additionally, we conducted KEGG pathway enrichment analysis for all differentially abundant metabolites, resulting in the identification of 60 enriched metabolic pathways (S4B Fig). These pathways encompass various aspects of metabolism, 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 (Fig 5F and S9 Table).

Spatial metabolomics reveals changes in liver metabolism in mice infected with S. japonicum and treated with PZQ

Praziquantel (PZQ) is acknowledged as the sole effective medication for schistosomiasis treatment. Our pathological findings indicate that PZQ treatment markedly suppresses the liver egg–granuloma reaction and impedes liver fibrosis progression. The significance of metabolic reprogramming in disease development and drug target screening has attracted increasing attention. Consequently, we further investigated the spatial distribution of liver metabolites linked to PZQ therapy via mass spectrometry imaging. Initially, data quality was assessed via multivariate statistical analysis, extending beyond principal component analysis (S5 Fig). The results revealed substantial disparities in liver metabolism characteristics among the mice treated with PZQ. In negative ion mode, 27 ions exhibited significant differences. Compared with those in the uninfected group, the expression intensities of these ions were either upregulated or downregulated following infection, with varying degrees of reversal observed after PZQ treatment (Fig 6A and 6B). Subsequently, 20 regions were randomly selected from each sample, and one-way ANOVA was employed to analyze the ion intensity in each region. This analysis revealed characteristic distribution differences in ion expression intensity among sample groups. A list of 27 distinct metabolites corresponding to significantly different ions is provided in Table 1. Subsequent pathway enrichment analysis of metabolites via KEGG revealed enrichment of 5 metabolic pathways with significant differences (Fig 6C). In addition, Reactome analysis revealed enrichment of multiple common metabolic pathways (Fig 6D). The biosynthesis of unsaturated fatty acids, ascorbate and aldarate metabolism, pyrimidine metabolism, D-amino acid metabolism, and linoleic acid metabolism suggest the potential involvement of metabolic pathways, such as unsaturated fatty acids, in the development of schistosomiasis-related liver disease and coordination in the antischistosomiasis process of PZQ.

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Table 1. Metabolic regulatory effects of praziquantel on liver injury of schistosomiasis japonica.

https://doi.org/10.1371/journal.pntd.0012854.t001

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Fig 6. Metabolic regulatory effects of praziquantel on liver injury in mice infection with Schistosomiasis japonica.

(A) MS images of situ visualization of crucial differentially abundant metabolites. (B) Heatmap of spatially resolved metabolomics data. (C) The top 20 enriched pathways for differentially abundant metabolites from KEGG. (D) The top 10 enriched pathways for differentially abundant metabolites from Reactome.

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Uridine metabolism is a significantly affected metabolic pathway during S. japonicum infection

To gain deeper insights into the relationship between metabolic disturbances and the disease process, we conducted a more detailed analysis of the identified differentially abundant metabolites. Our investigation revealed that multiple metabolites and metabolic pathways were significantly impacted during both the early and chronic stages of infection (Fig 7A and 7B). Thirteen characteristic metabolites and 3 metabolic pathways related to disease development may be involved in the chronicity of schistosomiasis. The number and types of potential differential metabolites corresponding to the corresponding differential ions are listed in Table 2. This pathway mainly includes three metabolic pathways: glyoxylate and dicarboxylate metabolism, ascorbate and aldarate metabolism, and pyrimidine metabolism (Fig 7C). These findings suggest a close association between these three metabolic pathways and disease progression. Upon further examination of these three differential metabolic pathways in conjunction with the identified differentially abundant metabolites, we found that glutamine and uridine play central roles in connecting these differentially abundant metabolites. Furthermore, in a pathway enrichment analysis of differentially abundant metabolites in the livers of mice in the control and 6-week infection groups, we also identified the pathways associated with ascorbate and aldarate metabolism. This result corroborates our earlier studies on the metabolomics of serum samples [10]. Overall, this analysis highlights the importance of metabolites in the relationships that regulate the effects of disease processes. In addition to the identified differential metabolites, RT‒qPCR verification of related metabolic enzymes was performed. Surprisingly, we found that Upase1, a crucial enzyme of uridine metabolism, was significantly upregulated at 6 weeks of infection, whereas other metabolic enzymes were downregulated. HSCs activation is widely recognized as the central event in the development of schistosomiasis -related liver fibrosis. Consistently, we also observed disruptions in uridine metabolism within the HSCs cell line (S6A and S6B Fig). In addition, the expression of most metabolic enzymes could be reversed by PZQ treatment (Fig 7D–7J). These results suggest that the disturbance of uridine metabolism may play an important role in the occurrence and development of liver fibrosis in schistosomiasis.

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Table 2. Summary of common differential metabolites between the infected group 6w and the infected group 12w, compared with the control group.

https://doi.org/10.1371/journal.pntd.0012854.t002

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Fig 7. Uridine metabolism is significantly affected by metabolic pathways during S. japonicum infection in mice.

(A) Venn diagrams displaying the common differentially abundant metabolites, 6w vs. control and 12w vs. control comparisons. (B) Statistical map of the number of potential differentially abundant metabolites, 6w vs. control and 12w vs. control comparisons. (C) Venn diagrams displaying the common metabolic pathways, 6w vs. control, and 12w vs. control comparisons. (D) Relative expression level of the rate-limiting enzyme CAD mRNA in the de novo synthesis pathway of uridine in the livers of mice. (E) Relative expression levels of HOGA1, a key enzyme in the glyoxylate metabolic pathway, in the livers of mice. (F) The relative expression level of GULO, a key enzyme in the ascorbic acid synthesis pathway, in the livers of the mice. (G) The relative expression level of UGDH mRNA, a key enzyme in the glucuronic acid metabolic pathway, in the livers of the mice. (H) The relative expression level of Upase, a key enzyme in the uridine metabolic pathway, in the livers of the mice. (I) The relative expression level of Upase, a key enzyme in the glutamine metabolic pathway, in the livers of the mice. (J) Relative expression levels of IDH, a key enzyme in the tricarboxylic acid cycle, in the livers of mice. At least three replicate samples were used for each experiment. The data are presented as the mean ± SD. Statistical significance is shown as * P < 0.05, ** P < 0.001, *** P < 0.001, **** P < 0.0001.

https://doi.org/10.1371/journal.pntd.0012854.g007

Uridine regulates fatty acid metabolic reprogramming and cell activation in LX-2 cells through the PPARγ pathway

Upase1 is a key enzyme that catalyzes the metabolism of uridine. Upase1 was significantly upregulated in the livers of mice infected for 6 weeks. The catabolism of uridine in the liver was increased, and its content decreased. The cell experiments demonstrated that Benzylacyclouridine, a Upase1 inhibitor, effectively suppressed the expression of liver fibrosis-related genes in LX-2 cells in vitro, and reduced intracellular uridine levels (S6C–S6E Fig). This findings suggest that regulating of intracellular uridine levels could serve as a promising anti-fibrosis strategy. In addition, further correlation analysis of differentially abundant metabolites identified after PZQ treatment revealed a negative correlation between uridine and various lipid metabolism-related molecules (S5C Fig). The homeostasis of lipid metabolism in hepatic stellate cells is crucial for maintaining the quiescence of cells. To further investigate, we established a TGF-β-induced activation model of LX-2 cells in vitro, and examined the effects of uridine metabolism on lipid metabolism and hepatic stellate cells activation through uridine supplementation. Oil red O staining revealed 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 drops recovered (Fig 8A–8F). PPARγ is considered a key regulatory factor involved in lipid metabolism. We found that PPARγ is significantly downregulated in activated LX-2 cells and upregulated by uridine intervention (Fig 8C, 8D and 8F). Further rescue experiments confirmed that uridine can regulate lipid metabolism homeostasis in LX-2 cells through the PPARγ signaling pathway, and the main mechanism may involve the upregulation of fatty acid transfer and the expression of key metabolic enzymes that play an antifibrotic role (Figs 8G–8I and 9).

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Fig 8. Uridine regulates lipid metabolic reprogramming and cell activation in LX-2 cells through the PPARγ pathway.

(A) The effect of uridine intervention on lipid metabolism in activated LX-2 cells in vitro was addressed using via Oil Red O staining. (B) Statistical analysis of the lipid droplet area was performed using Oil Red O staining. (C) Effects of uridine supplementation in vitro on PPARγ gene expression in LX-2 cells. (D) Western blotting was used to detect the expression levels of PPARγ and α-SMA in LX-2 cells after uridine supplementation. (E) Effects of uridine supplementation in vitro on α-SMA protein expression in LX-2 cells. (F) Effects of uridine supplementation in vitro on PPARγ protein expression in LX-2 cells. (G)Effects of PPARγ inhibitor (WB9662) in vitro on PPARγ gene expression in LX-2 cells. (H) Effects of PPARγ pathway inhibitors on the activation of LX-2 cells after uridine intervention. (I) Effects of PPARγ pathway inhibitors on the expression of genes related to lipid metabolism in LX-2 cells after uridine intervention. At least three replicate samples were used for each experiment. The data are presented as the mean ± SD. Statistical significance is shown as * P < 0.05, ** P < 0.001, *** P < 0.001, **** P < 0.0001.

https://doi.org/10.1371/journal.pntd.0012854.g008

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Fig 9. Summary of the mechanism by which uridine regulates lipid metabolism reprogramming in hepatic stellate cells.

The illustration was drawn by hand using Adobe Illustrator 2021 software.

https://doi.org/10.1371/journal.pntd.0012854.g009

Discussion

In recent years, a growing number of studies have suggested that metabolic disorders involving carbohydrates, lipids, proteins, and hormones occur during the development of diseases, such as those of the lungs, liver, and kidneys, and that correcting these metabolic alterations may provide new strategies for treating disease [21]. The emergence of mass spectrometry imaging has provided researchers with a power tool to delve into the realm of fibro-metabolism [22]. In this study, we introduced an innovative approach by employing AFADESI-MSI to investigate the spatial distribution of liver metabolites in conjunction with the histological features characterizing the process of liver injury in S. japonicum infection. In addition, we conducted further experimental validation of key differentially abundant metabolites. In comparison with conventional metabolomics techniques, the primary advantage of AFADESI-MSI lies in its capacity to address biological questions pertaining to spatial variations with remarkable precision. Notably, this approach allows for the quantification and visual representation of alterations in liver metabolite profiles associated with the granulomatous tissues specific to S. japonicum infection. Such insights are important for advancing our understanding of the genesis and persistence of egg granuloma structures and the progression and treatment of liver fibrosis in the context of schistosomiasis.

The liver, as a pivotal metabolic hub within the body, plays a central role in schistosomiasis-associated pathology [23]. Previous investigations have highlighted the capacity of schistosome infection to disrupt host metabolic processes [10]. This phenomenon was further corroborated in our study through in situ mapping of liver metabolism. We successfully identified 41 and 48 differentially abundant metabolites in the early and chronic stages of schistosome infection, respectively. Notably, 18 of these metabolites exhibited substantial alterations and the same trend in both stages, underscoring their close association with disease progression (Fig 2G). The findings derived from KEGG pathway enrichment analysis revealed distinct impacts on nucleotide metabolism and carbohydrate metabolism in the liver in the early stages of infection. As infection progresses into the chronic phase, lipid metabolism, carbohydrate metabolism, nucleotide metabolism, and amino acid metabolism predominate. Notably, citric acid and isocitric acid were identified as unique differentially abundant metabolites in the early infection stage. These metabolites serve as crucial intermediaries within the TCA cycle and vital precursors for various biosynthetic processes. Their indispensability extends to numerous biological functions, including development, cellular homeostasis, and reproductive processes [24]. Schistosomes heavily rely on the host for essential nutrients, given their highly energy-intensive processes such as development, reproduction, and metabolism. These parasites avidly consume host-provided glucose and lipids to meet their metabolic needs [25]. The influence of schistosomiasis infection on lipid metabolism has been elucidated in prior studies [26]. Our study confirmed these observations, with significant interference detected in various fatty acid metabolism and synthesis pathways during the chronic infection phase. The most pronounced differences pertained to arachidonic acid metabolism and unsaturated fatty acid biosynthesis. The liver plays a vital role in the metabolic production of arachidonic acid, a substrate integral to the generation of numerous eicosanoid proinflammatory mediators [27]. Changes in arachidonic acid levels can mediate hepatocyte injury through diverse mechanisms, including neutrophil and macrophage activation, free radical production, and membrane lipid peroxidation [28]. The substantial alterations in various eicosanoic acids, represented by 15-HETE, in our results further support the notion of host liver damage induced by schistosome infection. Furthermore, our findings highlighted a significant downregulation of hepatic glutamine levels during chronic infection, establishing a pivotal link between multiple enriched metabolic pathways. Previous studies have extensively examined the bioactive roles of glutamine, particularly its ability to modulate immune cell functions [29]. For example, glutamine deprivation has been shown to inhibit T-cell proliferation and cytokine production, whereas T-cell activation is associated with increased glutamine metabolism [30,31]. Considering that chronic schistosomiasis encompasses a T-cell-mediated delayed-type hypersensitivity reaction [32], the decrease in glutamine levels suggests an elevated state of metabolic depletion, which is consistent with an immune-related disease state. Recent research has also implicated glutamine in liver injury [33,34], highlighting the need for further exploration of its role in schistosomiasis-associated liver fibrosis.

The chronicity of Schistosomiasis japonica infection is primarily attributed to the formation of egg granulomas within the liver [35]. Modern immunological studies have proposed that metabolic reprogramming plays a critical role in granuloma formation [36]. Our findings are consistent with this perspective, as they highlight the high metabolic heterogeneity exhibited by schistosome infection-induced granulomatous tissues. Owing to the structural complexity of egg granulomas, the identified differentially abundant metabolites and associated metabolic pathways exhibit a wide array of variations and complexities. Generally, dysregulation of linoleic acid metabolism, butanoate metabolism, and central carbon metabolism is prominently observed in the early stages of granuloma formation. During this phase, schistosome eggs tend to absorb essential nutrients from the host environment to support their development, encompassing the acquisition of energy resources and structural components required for synthesizing cell membranes [37,38]. Notably, our data revealed substantial alterations in various substances linked to the TCA cycle in proximity to early-stage eggs, possibly providing the energetic resources necessary for egg maturation. Additionally, since parasites are incapable of synthesizing lipids and rely on acquiring them from the surrounding environment [37], our results demonstrate a unique distribution of fatty acids within egg granulomatous tissue, offering further evidence of disrupted energy metabolism. During the acute inflammatory response induced by eggs, many immune cells accumulate around the eggs, indicating that metabolic reprogramming of egg granuloma tissues plays an important role in immune cell phenotype regulation. This phenomenon parallels observations made in the field of tumor immunology. In the chronic phase of infection, as eggs undergo starvation and calcification, the number of immune cells, particularly M2-type macrophages and type 2 helper T cells, increases [39,40]. Various studies have identified metabolic reprogramming as a defining characteristic of immune cells. M2-type macrophages, for example, tend to rely on mitochondrial oxidative phosphorylation metabolism to generate ATP, thereby supporting their survival and facilitating tissue repair. Their metabolic features include fatty acid oxidation, augmented arginine metabolic pathways, and activation of the tricarboxylic acid cycle [41,42]. Our study corroborates these observations, as we also detected reprogramming of these metabolic pathways in the liver with egg granulomas during chronic infection. These pathways involve the biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, butanoate metabolism, linoleic acid metabolism, the citric acid cycle, central carbon metabolism, and arginine biosynthesis. Furthermore, metabolites have been recognized as regulators of T-cell phenotypes [43–45], with arachidonic acid, for example, exerting immunomodulatory effects by influencing the synthesis of prostaglandins, which primarily serve as anti-inflammatory molecules and inhibit the differentiation of Th1 cells [46]. Collectively, these changes in the metabolic profile tend to promote immune cell polarization, which is conducive to the persistence of granulomas. Remarkably, recent research has revealed that schistosome eggs can directly influence hepatic metabolic reprogramming independently of immune cells, particularly in the context of hepatic glycolipid metabolism [38]. Consequently, the specific mechanisms governing the establishment and maintenance of metabolic reprogramming within schistosome egg granulomas warrant further investigation. In conclusion, our data provide a robust dataset for explaining the course of Schistosoma infection-induced liver disease from a metabolic perspective.

Schistosoma survive by obtaining nutrients from the host, including proteins, glucose, lipids, and amino acids, through the digestive tract or the outer membrane [47,48]. Changes in liver metabolism during schistosomiasis infection have been discussed, and praziquantel, the drug of choice for the treatment of schistosomiasis, has been found to significantly alleviate most metabolic disorders in the liver after treatment. Lipid metabolism, including unsaturated fatty acid anabolism and arachidonic acid metabolism, was altered significantly. Many studies have shown that fatty acids are essential for schistosoma development and oviposition, mainly for maintaining oviposition and resisting host immune biosynthesis [49,50]. We found that multiple unsaturated fatty acids in the liver were reversed by PZQ treatment. Unsaturated fatty acids, which are believed to have anti-inflammatory effects, can promote the proliferation and differentiation of immune cells and enhance the function of the immune system [51,52]. These findings also suggest that PZQ therapy may regulate the differentiation of immune cells through the metabolic reprogramming of unsaturated fatty acids, thus playing an antischistosomiasis role, which indirectly provides a possible explanation for the poor efficacy of PZQ in immunodeficient mice. Notably, arachidonic acid (a fatty acid) has been considered an important target of PZQ in the treatment of schistosomiasis in previous studies [53]. In this study, we also observed significant changes in the liver metabolism of arachidonic acid before and after PZQ treatment. The parasite lacks raw materials to synthesize arachidonic acid and is dependent on the host. These findings also highlight the role of the arachidonic acid metabolic pathway in the treatment of PZQ. In addition, we also observed that carbohydrate, nucleotide, and amino acid metabolic pathways are related to PZQ therapy and that related metabolites may be involved in the process of PZQ resistance in schistosomiasis. Overall, we elucidated the potential effects of PZQ treatment on the metabolic interaction between Schistosoma and the host liver, and praziquantel may exert antischistosomiasis effects mainly by affecting liver fat metabolism and regulating parasite nutrient uptake and the immune microenvironment balance.

Notably, our study revealed a shared disruption in glyoxylate and dicarboxylate metabolism, ascorbate and aldarate metabolism, and pyrimidine metabolism during both the early and chronic phases of schistosomiasis. The metabolism of glyoxylate and dicarboxylate is closely linked to oxidation processes within organisms, contributing to the tricarboxylic acid (TCA) cycle that fuels energy metabolism. This pathway can perturb the activity of key enzymes, thereby resulting in abnormal levels of various reactants and products associated with the TCA cycle [54]. These findings offer additional insights into the influence of Schistosoma infection on energy metabolism. Within the ascorbate and aldarate metabolism pathway, a notable metabolite is D-glucuronic acid, which can undergo a series of enzymatic conversions to produce ascorbic acid. Ascorbic acid has been identified for its capacity to suppress inflammatory responses [55]. Although the human body lacks the enzyme system to synthesize ascorbic acid endogenously, our findings indirectly suggest that artificial supplementation with ascorbic acid may mitigate schistosomiasis-induced liver injury. The pyrimidine metabolism pathway is categorized under nucleotide metabolism and has garnered increased attention in relation to hepatic fibrosis in various studies [18,56]. Uridine, a pivotal component of this pathway, serves as a potential mediator of hepatoprotection [19]. Notably, our investigation revealed a substantial reduction in uridine levels within schistosome-infected liver tissues and revealed that Upase1 expression was significantly upregulated at 6 weeks of infection. This finding underscores the intricate interplay between pyrimidine metabolism and schistosomiasis-associated liver fibrosis.

The disturbance of uridine metabolism is associated with the progression of many diseases, and exogenous uridine supplementation is considered a potential strategy for the treatment of diseases [16]. We found that in vitro uridine supplementation significantly inhibited the activation of LX-2 cells, which was also consistent with the findings of several previous studies [57]. Spatial metabolomics analysis revealed that the content of uridine in the livers of schistosomiasis-infected mice was correlated with many lipid-related metabolites. We also observed that in vitro uridine supplementation increased the expression of lipid metabolism-related genes and the key pathway molecule PPARγ. PPARγ is a major transcriptional regulator of lipid formation and, together with other transcription factors, such as SREBP-1, induces adipocyte-specific gene expression [58]. In HSCs, normal expression of PPARγ may be necessary to maintain the quiescence of HSCs and is involved in the transdifferentiation of HSCs from lipose-storing quiescent cells to activated myofibrocytes. In quiescent HSCs, retinol-rich lipid droplets (LDs), which contain large amounts of lipids, are stored in the cytoplasm. Several studies have shown that the reduction in lipids in HSCs and the reduction in lipid-forming signals help maintain their activation state. The results of the cell oil red O staining confirmed these findings. Activated LX-2 cells lost lipids, and in vitro supplementation with uridine restored the number of intracellular lipid droplets. However, some studies have shown that uridine supplementation can reduce the formation of lipid droplets in hepatocytes and inhibit nonalcoholic fatty liver disease [59]. Overall, we identified the role of uridine metabolism in hepatic stellate cells, established the regulatory relationship between uridine metabolism and lipid metabolism homeostasis, and highlighted the influence of schistosomiasis infection on hepatic uridine metabolism and the potential role of uridine metabolism in the pathogenesis and treatment of liver fibrosis in schistosomiasis.

Conclusions

In summary, we employed AFADESI-MSI to visualize the abundance of metabolites in the livers of mice infected with schistosomiasis. The identification of 41 and 48 metabolites associated with S. japonicum-induced liver injury at early and chronic infection stages, respectively, provides a comprehensive visual description of the overall metabolic heterogeneity of the liver and reveals the heterogeneous abundance of related metabolites in the egg granuloma region. In addition, PZQ treatment significantly reversed most metabolic disorders, especially those related to fatty acid metabolism and pyrimidine pathways. The key findings are that uridine metabolism affects cell activation by influencing fatty acid metabolism reprogramming in hepatic stellate cells and that uridine metabolism is involved in the progression of liver fibrosis. Our study provides key insights into the molecular pathology of schistosomiasis-related liver disease and the role of uridine metabolism in this disease, highlighting the potential of spatial metabolomics techniques in investigating therapeutic targets for the development of new liver disease treatments.

Methods and methods

Supporting information

Acknowledgments

We thank Shanghai Lu Ming Biotech Co., Ltd. (Shanghai, China) for the AFADESI spatial-resolved metabolomics used in this study. We also acknowledge their Ruizhi Li and Zhenyu Xu for their important suggestions and valuable technical support.