Multi-omics reveals the impact of Clonorchis sinensis infection on mouse gut microbiota, metabolomics and transcriptomics

Abstract

Background

Clonorchiasis is a globally significant zoonotic disease. The complex interplay among gut microbiota, metabolomics, and host transcriptomics is increasingly recognized as a crucial factor in maintaining health. However, the impacts of Clonorchis sinensis (C. sinensis) infection on these interactions remain unclear.

Objective

This study investigates the relationships and pathogenic mechanisms of C. sinensis infection using a BALB/c mouse model infected for 2–15 week post-infection (wpi).

Methods

Fecal samples were collected at multiple time points to profile gut microbiota dynamics, while simultaneously detecting alterations in the ileal tissue transcriptome and fecal metabolome at 5 wpi.

Results

Gut microbiota analysis revealed that C. sinensis infection disrupted microbial homeostasis, significantly altering the Firmicutes/Bacteroidetes (F/B) ratio, with the most pronounced effects observed at 5 wpi. The impact on microbiota increased during the larval-to-adult transition (2–5 wpi) and diminished in the later adult stage (8–15 wpi). Transcriptomic analysis at 5 wpi revealed substantial dysregulation of immune- and metabolism-related genes. Functional enrichment analyses identified key GO terms of complement activation and immune response, and KEGG pathways of chemokine signaling and Th1/Th2 cell differentiation. Concurrent metabolomic profiling revealed significant changes in metabolites, including PC(18:0/0:0), 2-LysoPC, and LysoPC(18:0/0:0), enriched in multiple lipid metabolism pathways. Multi-omics correlation analysis demonstrated strong associations between specific bacterial genera (e.g., Lachnoclostridium, Turicibacter, Dubosiella and Marvinbryantia) and lipid metabolism, as well as metabolites and genes linked to the Lands cycle, suggesting these genera as keystone bridging microbial-immune-metabolic crosstalk.

Conclusion

This study elucidates the dynamic changes in gut microbiota and multi-omics interactions during C. sinensis infection, providing a foundation for further mechanistic research and potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-04531-1.

Introduction

Clonorchiasis, caused by infection with Clonorchis sinensis (C. sinensis), is a significant public health issue, primarily transmitted through the consumption of raw or undercooked freshwater fish containing metacercariae. This disease is endemic to East Asia, particularly in regions such as China, South Korea, and northern Vietnam [1, 2]. Adult C. sinensis worms predominantly reside in the bile ducts of the host, where they disrupt digestive function and induce chronic inflammation [3]. Prolonged infection can lead to severe hepatobiliary pathologies, including liver fibrosis, cirrhosis, and even liver cancer [1, 3]. During its parasitic lifecycle, C. sinensis releases excretory-secretory products (CsESPs), which interact with host tissues, triggering inflammatory responses and abnormal cell proliferation. These interactions are critical in modulating the host’s immune response [4, 5].

The gut microbiota, a complex and dynamic microbial ecosystem, plays a pivotal role in maintaining intestinal and systemic health [6]. Comprising approximately 10¹⁴ microorganisms, the gut microbiota population increases progressively from the esophagus to the colon [7]. These microorganisms serve as a protective barrier against pathogens and produce essential metabolites, such as short-chain fatty acids (SCFAs), which contribute to gut homeostasis and overall health [8, 9]. Emerging evidence suggests that parasitic infections, including those caused by Toxoplasma gondii and Trichinella spiralis, significantly alter the composition and diversity of the host’s gut microbiota [10, 11]. Our previous research has shown that early infection with C. sinensis can lead to changes in the gut microbial structure of BALB/c mice and significantly affect the expression levels of genes related to immunity, and circadian rhythm [12]. Other reports have confirmed that C. sinensis can significantly alter the composition of gut microbiota in humans, C57BL/6 mice, and hamsters [13–15]. Changes in gut microbiota are closely associated with disruptions in host metabolic and transcriptomic activities, underscoring the intricate interplay among gut microbiota, metabolites, and gene expression [16–18]. However, the specific mechanisms underlying these interactions remain poorly understood.

Gut barrier dysfunction and microbial dysbiosis are implicated in the pathogenesis of various diseases, including liver cirrhosis, liver cancer, inflammatory bowel disease, and metabolic disorders [1, 19]. C. sinensis infection not only damages the hepatobiliary system but also significantly alters the host’s gut microbiota and genes [1, 14, 20]. Despite these findings, the specific effects of C. sinensis infection on the host’s intestinal microbiota, metabolome, and transcriptome remain unclear. It takes about one month for C. sinensis juvenile to develop into adults in the hepatic bile ducts of definitive host, and then they parasitize chronically under suitable conditions [3]. Therefore, this study aims to investigate the temporal effects of C. sinensis infection on the gut microbiota of mice and to explore changes in gut metabolites and transcriptomes during peak microbial disruption. Additionally, the relationships among the microbiome, metabolome, and transcriptome were analyzed to gain a more profound understanding of the mechanisms underlying the effects of C. sinensis infection on the intestine. The findings will provide valuable insights into the impact of C. sinensis infection on gut microbiota and molecular events, offering a foundation for further exploration of the regulatory mechanisms of parasitic infections on host health.

Methods

Parasite preparation

Viable C. sinensis metacercariae were isolated from naturally infected Pseudorasbora parva collected in Hengzhou City, Guangxi Zhuang Autonomous Region, China. The isolation procedure adhered to established protocols [21]: fish tissues were digested in a 0.8% pepsin solution (0.2% HCl) at 37 °C with continuous shaking (150 rpm) overnight. The digested mixture was subsequently filtered through 60–80 mesh sieves and rinsed 3–4 times with water. For experimental infection, live C. sinensis metacercariae were identified under an optical microscope based on established morphological keys and specific criteria: a pale brownish-yellow, translucent, ellipsoidal cyst (average size 0.138 mm × 0.15 mm) with a double-layered wall, containing a coiled, motile larva with a distinct dark excretory bladder. The selected metacercariae were then preserved in PBS at 4 °C.

Animal experimentation

Six-week-old female BALB/c mice (Hunan SJA Laboratory Animal Co., Ltd.) were housed under controlled conditions (25 ± 2 °C, 12 h light/dark cycle) with ad libitum access to a standard diet (standard chow main ingredients: corn, soybean meal, flour, bran, calcium hydrogen phosphate, fish meal, stone powder, sodium chloride, vitamin premix, trace element premix, etc.), and were raised under specific pathogen-free (SPF) conditions. Mice were randomly assigned to 10 groups (n = 5 per group), categorized by five time points post-infection: 2 weeks (2 w), 5 weeks (5 w), 8 weeks (8 w), 10 weeks (10 w), and 15 weeks (15 w). Each time point comprised two groups: an experimental group and a control group. Each mouse in the experimental group was orally infected with 60 live metacercariae, while the control mice received an equivalent volume of PBS (200 µL). At designated time points, mice were euthanized with CO₂, and ileal tissues along with fecal samples were collected, flash-frozen in liquid nitrogen, and stored at −80 °C for subsequent analyses.

Gut microbiota analyses

Fecal samples from the 2, 5, 8, 10 and 15-week groups mice (n = 5 per group) were subjected to 16S rRNA gene sequencing. Initially, genomic DNA was extracted from the feces following the instructions provided by the EZNA^® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The purity of the extracted DNA was assessed using a NanoDrop2000 (Thermo Fisher Scientific, Waltham, USA), and its integrity was verified through agarose gel electrophoresis. Subsequently, the V3-V4 region of the 16S rRNA was PCR amplified using Quantifluor™ (Promega, Lyon, France) with the following primers: 338F: 5’-ACTCCTACGGGAGGCAGCAG-3’ and 806R: 5’-GGACTACHVGGGTWTCTAAT-3’. The amplification products were visualized using 2% agarose gel electrophoresis and purified using the AxyPrep DNA Gel Extraction Kit (Axygen, USA), and gel electrophoresis images were presented in Supplementary Material 1. The analysis included the community richness indices (Ace and Chao), and the community diversity indices (Shannon and Simpson), and principal co-ordinates analysis (PCoA analysis), community composition analysis, and differential genus analysis. The Ace, Chao, Shannon, Simpson indices were assessed using the Student’s t -test and adjusted for the false discovery rate (FDR). PCoA used the Bray-Curtis distance algorithm to compute the distances between samples, while Adonis was employed to evaluate differences between groups. Group differences were analyzed using the Wilcoxon rank-sum test with FDR correction. The above analyses were conducted on the Majorbio online cloud platform (https://cloud.majorbio.com/) and computed using R-3.3.1. Subsequently, ileal tissue and fecal samples from the time point when the microbiota structure changed most significantly (5 wpi) were used for further transcriptome and metabolome analyses, respectively.

Gut transcriptomic analyses

To assess the gene expression profile of the mouse gut, total RNA was extracted from the ileal tissue of mice at 5 wpi (randomly selected, n = 3 per group) using MJzol reagent (Invitrogen, MA, USA). Sequencing was performed on the Illumina NovaSeq 6000 platform (San Diego, CA, USA). Following quality control, gene/transcript read counts were obtained through expression level analysis. Differentially expressed genes (DEGs) were identified using DESeq2 software, and hierarchical clustering was performed using Euclidean distance and average linkage methods. Finally, DEGs enrichment analysis was conducted using Goatools and KOBAS software, including Gene Ontology (GO, http://geneontology.org/) term analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) pathway enrichment analysis. The above omics data were all calculated using R-3.3.1.

Gut metabolomic analyses

Fecal samples from the 5 week groups mice (n = 5 per group) were prepared for gut metabolite detection using non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). The following steps were carried out by Majorio Bio-Pharmm Technology Co., Ltd. (Shanghai, China). The reference method was as follows [22]: in brief, metabolites were extracted after grinding the samples, and the supernatant was analyzed post-centrifugation. Quality control (QC) samples were prepared by mixing equal volumes of metabolites from all samples. Analysis was performed using an UHLC-Q Active HF-X system (Thermo, Massachusetts, USA). Samples were separated using an HSS T3 column (Waters, Milford, USA) and then detected by mass spectrometry. Both positive and negative ion scanning modes were used for collecting mass spectrometry signals. The parameters used were as follows: spray voltages of 3.5 and − 3.5 kV, a scan range of 70–1050 m/z, normalized collision energies of 20–40-60 V, and resolutions of 60,000 for the primary mass spectrometry and 7500 for the secondary mass spectrometry. Data collection was performed in DDA mode. Subsequently, the raw metabolite data were processed using Progenesis QI (Waters Corporation, Milford, USA), and a data matrix was obtained through a series of methods for further differential metabolites (DMs) analysis, Principal component analysis (PCA), Venn analysis, VIP analysis, ROC analysis and KEGG pathway analysis. The above omics data were all calculated using R-3.3.1.

Correlation analyses of gut microbiota and metabolomics

To elucidate relationships between gut microbiomics and metabolomics, Spearman correlation analysis was used to reveal the correlation between the gut microbiome and the DMs enriched in significant KEGG pathways. Procrustes analysis was employed to assess the variability and consistency between different pathways and their relationships with DMs. Correlation heatmaps and network analyses were used to observe the degree of correlation between differential bacteria and DMs, and network analysis utilizing Pearson correlation to show cases where the absolute value of the correlation coefficient was greater than 0.6 and the P < 0.05. Correlation analysis was calculated using the Python package scipy.stats version 1.0.0.

Correlation analyses of gut metabolomics and transcriptomics

To better understand the relationship between the gut metabolomics and genes, Spearman correlation analysis was used to reveal the correlation between the gut DMs and DEGs enriched in significant KEGG pathways. Procrustes analysis was employed to assess the variability and consistency between the DMs and DEGs. Correlation heatmaps and network analyses were used to observe the magnitude of the correlation between DMs and DEGs, with the network analysis showing cases where the absolute value of the correlation coefficient was greater than 0.6 and the P < 0.05. Correlation analysis was calculated using the Python package scipy.stats version 1.0.0.

Statistical analyses

For microbiota data, the Ace, Chao, Shannon, and Simpson indices were assessed using the Student’s t -test and adjusted for the false discovery rate (FDR). PCoA used the Bray-Curtis distance algorithm to compute the distances between samples, while Adonis was employed to evaluate differences between groups. Group differences were analyzed using the Wilcoxon rank-sum test with FDR correction. For transcriptomic data, DEGs were conducted with DESeq2 software, applying criteria of P < 0.05 and |FC| ≥ 2.0. GO and KEGG pathway analyses utilized Fisher’s exact test, with significance determined by P < 0.05. For metabolomic data, differences in metabolites between two groups were detected using an unpaired Student’s t-test, defining significant metabolites as VIP > 1, |FC| ≥ 1.0, and P < 0.05. KEGG analysis utilized Relative-Betweenness centrality and BH correction for enrichment analysis, with significance based on P < 0.05. The correlation network between gut microbiome and metabolomics was examined using Euclidean distance and Spearman correlation (|r| >0.6, P < 0.05). The correlation network between gut metabolomics and transcriptomics was analyzed using Euclidean distance and Spearman/Pearson correlation (|r| >0.6, P < 0.05). The P of the correlation analysis were all corrected using BH. Correlation analysis was calculated using the Python package scipy.stats version 1.0.0.

Results

Dynamic alterations in gut microbiota diversity and composition during C. sinensis infection

Longitudinal analysis of gut microbiota revealed reductions in α-diversity indices of Shannon, Ace, and Chao, and an elevation of the Simpson index across infection time points (2–15 weeks) (Fig. 1A-E, Supplementary Fig. 1). β-diversity analysis (PCoA) demonstrated progressive divergence between infected and control groups, with maximal separation observed at 5 wpi (P = 0.011, Fig. 1F-J). Phylum-level compositional analysis identified Firmicutes and Bacteroidetes as dominant taxa in both groups. However, C. sinensis infection induced an increase in Firmicutes abundance and a concomitant decrease in Bacteroidetes, resulting in a heightened Firmicutes/Bacteroidetes (F/B) ratio in infected mice across all time points. Notably, the infection-to-control F/B ratio (I/C_[F/B]) peaked at 5 weeks (I/C_[F/B] = 5.909, Fig. 2A-E). Among the top 10 abundant genera, those belonging to Firmicutes phylum exhibited higher abundance. For instance, the levels of Lactobacillus were increased at all infected time points. The abundance of unclassified_f_Lachnospiraceae increased at 5 and 8 weeks, and Bacillus showed increased abundance at 2, 10 and 15 weeks (Fig. 2F-J). However, the levels of norank_f_Muribaculaceae decreased at 2, 5, 8 and 15 weeks, and Alloprevotella abundance declined at 2, 10 and 15 weeks, while the abundance of Odoribacter increased at all the infected time points except week 8 and 15, all of which belong to Bacteroidetes phylum. In addition, Lactococcus abundance increased at 2 wpi, while Turicibacter abundance decreased at 5 weeks (Fig. 2F, G). The Circos plots illustrated the dominant species composition ratio for each sample at different time points, as well as the distribution ratio of each dominant species across different samples (Fig. 2K-O). The above results indicate that C. sinensis infection impacts the diversity and abundance of gut microbiota in mice, resulting in an increase in the F/B ratio.

Fig. 1.

Temporal dynamics of gut microbiota diversity in mice induced by C. sinensis. A-E Shannon diversity indices at 2-, 5-, 8-, 10-, and 15-wpi. F-J PCoA plots at corresponding time points

Fig. 2.

Temporal shifts in gut microbiome composition during C. sinensis infection. A-E Phylum-level relative abundance profiles (Firmicutes vs. Bacteroidetes dominance) at 2-, 5-, 8-, 10-, and 15-wpi. F-J Genus-level relative abundance of the top 10 taxa at each time point. K-O Circos plots illustrating sample composition at corresponding time points

Differences in gut microbiota of mice after infection with C. sinensis at different time points

To assess the significance level of the differences in bacterial richness, abundance analyses were conducted for each microbial community group. At 2, 5, 8, 10, and 15 wpi, 7, 18, 5, 3 and 7 different bacteria were detected, respectively (P < 0.05, Fig. 3A-E). Compared to the control group, at 2 weeks of infection, the abundance of several harmful or opportunistic pathogens including Candidatus_Saccharimonas, Desulfovibrio, Monoglobus, and Gemella significantly increased, while the abundance of norank_f_norank_o_Clostridia_UCG-014 and Paraprevotella significantly decreased (Fig. 3A). At 5 wpi, the impact on beneficial bacteria became even more pronounced, with significant reductions in the abundance of several beneficial bacteria, including norank_f_Muribaculaceae, Turicibacter, Faecalibaculum, Bifidobacterium, and Gordonibacter. In contrast, the levels of unclassified_f_Lachnospiraceae, Lachnospiraceae_UCG-006, and Lachnoclostridium exhibited notable increases in abundance (Fig. 3B). After 8, 10 and 15 weeks, the number of differential bacteria decreased. By week 8, the abundance of unclassified_c_Bacilli, Mycoplasma, and unclassified_f_Erysipelotrichaceae significantly decreased, whereas the abundance of Anaeroplasma increased (Fig. 3C). At 10 weeks of infection, the abundance of Monoglobus and Akkermansia significantly increased, whereas Desulfovibrio abundance significantly decreased (Fig. 3D), indicating a gradual restoration of gut microbiota balance. After 15 weeks of infection, the abundance of pathogenic bacteria Streptococcus and Monoglobus significantly increased, but a significant decrease in the abundance of Alloprevotella Parasutterella, and Colidextribacter was also observed (Fig. 3E). These results indicate that the impact of C. sinensis infection on the gut microbiota of mice was most significant at 5 wpi and subsequently moderated.

Fig. 3.

Temporal shifts in differential gut microbiota abundance during C. sinensis infection. Differences in gut microbiota at 2 weeks (A), 5 weeks (B), 8 weeks (C), 10 weeks (D), and 15 weeks (E) post-infection. P < 0.01 and P < 0.05 were marked ** and *, respectively

DEGs in mouse intestine induced by C. sinensis infection

PCA analysis of ileal tissue transcriptome data at 5 wpi revealed distinct separation between the C. sinensis-infected and control groups, with samples within the infected group showing closer clustering (Fig. 4A). A total of 1109 DEGs were identified, comprising 371 significantly up-regulated and 738 significantly down-regulated genes (Table S1). Specifically, genes such as Ugt1a6b, Cyp3a11, Itgax, Lpcat2 and Alox12 were significantly down-regulated, whereas Pla2g4c, Hsp90b1, Bicd1 and Ccar1 were significantly up-regulated (|FC| ≥ 2, P < 0.05, Fig. 4B). A clustering heatmap further confirmed significant differences in DEG expression levels between the two groups (Fig. 4C). GO enrichment analysis of DEGs primarily focused on immune-related biological processes (BPs), including complement activation, immune responses, and positive regulation of lymphocyte activation (Fig. 4D). Key genes involved in these GO terms included Ighv14-3, Ighv1-77, Ighv5-9, Ighv1-5, Ighv1-34 and Ighv10-3. For KEGG pathway enrichment, the major KEGG pathways were chemokine signaling pathway, hematopoietic cell lineage, MicroRNAs in cancer, hedgehog signaling pathway, VEGF signaling pathway, Th1 and Th2 cell differentiation, along with cytokine-cytokine receptor interaction (Fig. 4E). Genes critical to these pathways included Cxcl14, Arrb1, Kdr, Pla2g4c, Cd3g and Cd3e.

Fig. 4.

Clustering and enrichment analysis of DEGs in ileal tissues of mice at 5 weeks post C. sinensis infection. A PCA analysis of the infection group and the control group. B Volcano plot of DEGs. C Clustering heatmap of DEGs. D GO enrichment analysis of DEGs. E KEGG enrichment analysis of DEGs

DMs in mouse intestine induced by C. sinensis infection

Results from PCA, PLS-DA (Negative: R²X(cum) = 0.291, R²Y(cum) = 0.918, Q²(cum) = 0.923. Positive: R²X(cum) = 0.269, R²Y(cum) = 0.951, Q²(cum) = 0.923) and OPLS-DA (Negative: R²X(cum) = 0.291, R²Y(cum) = 0.918, Q²(cum)=−0.0992. Positive: R²X(cum) = 0.269, R²Y(cum) = 0.951, Q²(cum) = 0. 199) models demonstrated distinct separation between the infection and control group samples, highlighting differences in metabolite profiles (Supplementary Fig. 2). A total of 1999 and 2018 metabolites were detected in the control group and the infection group, respectively (Supplementary Fig. 3). Among these, 55 DMs were identified, including 29 that were up-regulated and 26 that were down-regulated (Table S2). The representative up-regulated/down-regulated metabolites were illustrated in the Volcano plot (Fig. 5A). Cluster analysis indicated significant differences in the expression patterns of DMs between the two groups (Fig. 5B). VIP analysis revealed 16 DMs that significantly contributed to the grouping (VIP > 1, P < 0.05). The top 5 metabolites were 3-hydroxyvalerylcarnitine, D-Mannose 6-Phosphate, deoxycytidine, 2-Lysophosphatidylcholine (2-LysoPC) and triterpenoid (Fig. 5C). In addition, the ROC analysis indicated that both all DMs (AUC = 0.9626) and those important DMs from VIP (AUC = 0.9253) can effectively distinguish between the C. sinensis-infected group and the control group (Fig. 5D). Notably, the important DMs of 2-LysoPC and PC(14:1(9Z)/20:0) exhibited high accuracy in differentiating C. sinensis infection (AUC > 0.9), and PC(18:0/0:0), Allochenodeoxycholic acid (ACDCA), and LysoPC(18:0/0:0) demonstrated moderate accuracy (0.7 < AUC < 0.9, Fig. 5E). KEGG enrichment analysis confirmed that C. sinensis infection significantly impacted metabolic pathways, including choline metabolism in cancer, vitamin digestion and absorption, alpha-Linolenic acid metabolism, and glycerophospholipid metabolism (Fig. 5E). The DMs involved in these KEGG enrichment pathways included PC(14:1(9Z)/20:0), PC(18:0/0:0), 2-LysoPC, LysoPC(18:0/0:0), and riboflavin (Fig. 5F).

Fig. 5.

Differential metabolite profiling in mouse feces at 5 weeks post-C. sinensis infection. A Volcano plot of DMs. B Hierarchical clustering heatmap of DMs. C VIP plots of DMs. D ROC analysis of all DMs, along with those filtered through VIP analysis. E ROC analysis of key DMs. F KEGG pathway enrichment analysis of the DMs between infection group and control group. P < 0.01 and P < 0.05 were marked ** and *, respectively

Correlation analysis of gut microbiota and metabolomics after C. sinensis infection

To investigate the relationship between DMs enriched in key KEGG pathways and significantly different bacteria, correlation analyses were performed. Procrustes analysis demonstrated the overall expression trends of different bacteria and DMs were consistent across groups, with stronger consistency observed in post-infection samples (Fig. 6A-D). The correlation analysis revealed that in the choline metabolism in cancer pathway, PC(14:1(9Z)/20:0) was significantly negatively correlated with Lachnoclostridium, unclassified_f_Lachnospiraceae, and Marvinbryantia (P < 0.01), while it was significantly positively correlated with Turicibacter and norank_f_Erysipelotrichaceae (P < 0.05). Additionally, PC(18:0/0:0), 2-LysoPC, and LysoPC(18:0/0:0) were significantly positively correlated with Lachnoclostridium (P < 0.05), and significantly negatively correlated with Dubosiella and Turicibacter (P < 0.05, Fig. 6E). In both the pathways of vitamin digestion and absorption and biosynthesis of cofactors, pantothenic acid and riboflavin were significantly negatively correlated with Marvinbryantia, Lachnoclostridium, and unclassified_f_Lachnospiraceae (P < 0.05). Riboflavin exhibited significant correlations with more differential bacteria such as Lachnospiraceae_UCG-006 and Faecalibaculum (Fig. 6F, G). In the biosynthesis of cofactors pathway, D-Mannose 6-Phosphate was significantly negatively correlated with Clostridium_sensu_stricto_1 and Parabacteroides (P < 0.05), and positively correlated with unclassified_f_Erysipelotrichaceae, Blautia, and Lachnospiraceae_UCG-006 (P < 0.05, Fig. 6G). In the ABC transporters pathway, deoxycytidine was significantly negatively correlated with unclassified_f_Rikenellaceae (P < 0.05), and positively correlated with Blautia and Marvinbryantia (Fig. 6H). The network analysis highlighted significant associations between DMs (e.g., LysoPC(18:0/0:0), PC(18:0/0:0), riboflavin) and beneficial bacterial genera such as Lachnoclostridium, Faecalibaculum, and unclassified_f_Lachnospiraceae (|correlation coefficient| ≥ 0.6, P < 0.05, Fig. 6I-L). These findings suggest potential functional links between gut microbiota shifts and metabolic dysregulation during C. sinensis infection.

Fig. 6.

Correlation analysis between gut microbiota and metabolomics. Procrustes analysis of DMs and differential bacterial genera in the pathways of choline metabolism in cancer (A), vitamin digestion and absorption (B), biosynthesis of cofactors (C), and ABC transporters (D). Correlation heatmap plots for the pathways of choline metabolism in cancer (E), vitamin digestion and absorption (F), biosynthesis of cofactors (G), and ABC transporters (H). Interaction network analysis for the pathways of choline metabolism in cancer (I), vitamin digestion and absorption (J), biosynthesis of cofactors (K), and ABC transporters (L)

Correlation analysis of gut metabolomics and transcriptomics after C. sinensis infection

To explore the relationship between DEGs and important DMs of VIP within enriched KEGG pathways, correlation analysis was conducted. Procrustes analysis revealed an overall consistency between DEGs and important DMs across four KEGG pathways: Th1 and Th2 cell differentiation, cytokine-cytokine receptor interaction, MAPK signaling pathway, and Hedgehog signaling pathway (Fig. 7A-D). In the Th1 and Th2 cell differentiation pathway, immune-related molecules Cd3g and Cd3e exhibited significant positive correlations with the metabolites triterpenoid and riboflavin, while Cd3g showed a significant negative correlation with D-Mannose 6-Phosphate, PC(18:0/0:0) and 2-LysoPC (P < 0.05, Fig. 7E). Within the cytokine-cytokine receptor interaction pathway, the chemokine receptor Cx3cr1 exhibited strong associations with lipid metabolites, particularly PC(18:0/0:0), LysoPC(18:0/0:0), PC(14:1(9Z)/20:0) and 2-LysoPC (P < 0.001). Meanwhile, chemokines Cxcl14 and Ccl5 showed similar correlations with important DMs, exhibiting significant negative correlations with PC(18:0/0:0) and 2-LysoPC (P < 0.05, Fig. 7F). In the MAPK signaling pathway, the genes Pla2g4c and Arrb1 were significantly associated with most important DMs. Pla2g4c exhibited significant positive correlations with PC(18:0/0:0), 2-LysoPC, LysoPC(18:0/0:0) and cholylglutamine (P < 0.01), and significant negative correlation with PC(14:1(9Z)/20:0) and riboflavin (P < 0.05), while Arrb1 displayed an opposing correlation pattern (Fig. 7G). In the Hedgehog signaling pathway, the genes Arrb1 and Hhip were important genes involved in it, with Hhip showing a significant positive correlation only with nopalinic acid (P < 0.01, Fig. 7H). The network analysis illustrated DEGs within the four pathways were closely related to lipid metabolites and vitamins, such as PC(18:0/0:0), 2-LysoPC, LysoPC(18:0/0:0) and riboflavin (|correlation coefficient| ≥ 0.6, P < 0.05, Fig. 7I-L).

Fig. 7.

Correlation analysis between gut metabolomics and transcriptomics. Procrustes analysis of DMs and DEGs in the pathways of Th1 and Th2 cell differentiation (A), cytokine-cytokine receptor interaction (B), MAPK signaling pathway (C), and hedgehog signaling pathway (D). Correlation heatmap plots for the pathways of Th1 and Th2 cell differentiation (E), cytokine-cytokine receptor interaction (F), MAPK signaling pathway (G), and hedgehog signaling pathway (H). Interaction network analysis for the pathways of Th1 and Th2 cell differentiation (I), cytokine-cytokine receptor interaction (J), MAPK signaling pathway (K), and hedgehog signaling pathway (L)

Discussion

Following C. sinensis infection, the complex host-parasite interaction induces dynamic perturbations across multiple biological systems [23]. Approximately one month post-infection, adult worms colonize the bile ducts and continuously secrete CsESPs that interact with hepatic tissues [5, 24]. This interaction initiates bidirectional enterohepatic crosstalk: liver-derived substances circulate to the intestine, disrupting gut microbial homeostasis and subsequently exacerbating hepatic dysfunction [25]. As a crucial metabolic regulator, the gut microbiota plays a pivotal role in this pathological process [26]. In this study, a murine model of C. sinensis infection was established and monitored from 2 to 15 wpi. Longitudinal analysis revealed progressive decreases in both α- and β-diversity indices, with maximal microbial disturbance at 5 wpi. Notably, the infection group exhibited elevated F/B ratios across multiple timepoints, peaking at 5 wpi and gradually declining after 8 wpi, which is consistent with our team’s previous observation of F/B ratio fluctuations in early-stage C. sinensis infection [12]. As a well-recognized indicator of gut ecosystem imbalance, the elevated F/B ratio has been extensively linked to pathological states including metabolic dysregulation, chronic inflammation, immune impairment, and obesity-related disorders [27–30]. Taxonomic analysis showed that Firmicutes dominated among the top 10 most abundant genera. Consistent with Kim et al. [14], we observed increased abundance of Lactobacillus (a Firmicutes genus) across infection stages, accompanied by decreased abundance of norank_f_Muribaculaceae (a Bacteroidetes genus). Collectively, these results demonstrate that C. sinensis infection significantly reduces gut microbiota diversity and richness, elevates the F/B ratio, and ultimately induces microbial dysbiosis.

At 2 wpi, we observed a significant increase in opportunistic pathogens, including Candidatus_Saccharimonas [31], pro-inflammatory Desulfovibrio [32], and inflammation/obesity- related Monoglobus [33, 34]. Concurrently, beneficial probiotic bacteria, particularly norank_f_norank_o_Clostridia_UCG-014, significantly decreased, which is detrimental to intestinal barrier repair and inflammatory lesion alleviation [35]. Microbial imbalance peaked at 5 wpi, characterized by a dramatic depletion of beneficial genera like norank_f_Muribaculaceae, Turicibacter, and Faecalibaculum, which are key producers of SCFAs and play established roles in immune modulation, energy utilization, and intestinal barrier maintenance [36–38]. This period also showed a significant reduction in anti-inflammatory Bifidobacterium, corroborating findings by Xu et al. [13], alongside an increase in opportunistic pathogens such as unclassified_f_Lachnospiraceae, which is positively correlated with obesity [39], and Lachnospiraceae_UCG-006, associated with intestinal inflammation, diabetes, and lipid metabolism [40, 41]. By 8 wpi, beneficial bacteria, including unclassified_c_Bacilli and SCFA-producing unclassified_f_Erysipelotrichaceae [42, 43], significantly declined, although pathogenic Mycoplasma abundance decreased [44]. Simultaneously, Anaeroplasma, a potential new probiotic for chronic inflammation [45], increased. Notably, by 10 wpi, the gut microbiota showed signs of recovery, indicated by rising Akkermansia levels, which enhance intestinal barrier function and reduce inflammation [46], alongside decreased pro-inflammatory Desulfovibrio. However, Monoglobus, a harmful bacterium that disrupts intestinal barrier integrity and triggers systemic inflammation [47], remained elevated through week 15. Additionally, SCFA-producing Alloprevotella, probiotic Colidextribacter, and inflammation-associated Parasutterella showed marked declines [48–50]. This temporal analysis demonstrates a continuous, phase-dependent modulation of gut microbiota by C. sinensis infection. The larval stage induced pathogenic proliferation and probiotic depletion, reaching maximal dysbiosis at week 5 post-infection, followed by a gradual but incomplete recovery of microbial ecosystem in the subsequent weeks.

Our gut transcriptomic profiling revealed significant enrichment of immune-related GO terms, including complement activation, immune response-activating cell surface receptor, and positive regulation of lymphocyte activation. The DEGs involved, such as Ighv14-3, Ighv1-5, Ighv1-34, Ighv1-72, Ighv1-77, Ighv5-9, Ighv3-1 and Ighv10-3, were all down-regulated. These genes encode immunoglobulin heavy chain variable regions, which are crucial for adaptive immune responses [51]. KEGG pathway analysis demonstrated significant enrichment in immuno-inflammatory pathways, including chemokine signaling pathway, Th1 and Th2 cell differentiation, cytokine-cytokine receptor interaction, and MAPK signaling pathway, as well as tumor-related pathways such as MicroRNAs in cancer, hedgehog signaling pathway, VEGF signaling pathway, and basal cell carcinoma. The transcriptome exhibited substantial suppression of key immune mediators by C. sinensis, including chemokines Ccl5, Cxcl14, chemokines receptor Cx3cr1, T-cell markers Cd3g, Cd3e, inflammation-associated enzyme Alox12, and immunoregulatory factor Itgax [52–57]. Simultaneously, we observed dysregulation of cancer-associated genes, such as downregulation of oncogene Plau and tumor suppressor Hhip, alongside the upregulation of poor prognostic indicator Hsp90b1 [58–60]. These transcriptomic results indicate that C. sinensis infection primarily negatively regulates the immune and inflammatory systems of the host intestine, and causes abnormal expression of tumor-related genes and pathway enrichment. This suggests possible overlap between immune regulation and cancer-related pathways, potentially contributing to the immunomodulatory effects reported in colitis models [61, 62].

Furthermore, C. sinensis infection induced significant alterations in multiple metabolism-associated genes. For instance, Ugt1a6b was significantly down-regulated. This gene encodes a critical enzyme involved in the detoxification of intestinal xenobiotics [63]. The genes Pla2g4c and Lpcat2, which encode key enzymes in the Lands cycle, also exhibited significant alterations [64]. This cycle is essential for lipid metabolism and the regulation of immune responses and inflammation, particularly in mediating the interconversion of phosphatidylcholine (PC) and lysophosphatidylcholine (LysoPC), as well as arachidonic acid (AA) metabolism [65, 66]. Specifically, the Lands cycle can modulate the host’s immune and inflammatory responses. For example, LysoPC, as a signaling molecule, plays a role in regulating the cellular immune response and the progression of chronic diseases [67]. By interfering with the normal immune response through these regulatory mechanisms, it may help the parasites evade the host’s immune surveillance. Consistent with these enzymatic changes, fecal metabolomics revealed dysregulated lipid species, including up-regulated PC(18:0/0:0), 2-LysoPC and LysoPC(18:0/0:0), alongside down-regulated PC(14:1(9Z)/20:0). These DMs were enriched in the lipid metabolic pathways, including glycerophospholipid metabolism, choline metabolism in cancer, and AA metabolism. ROC curve analysis identified PC(18:0/0:0) and 2-LysoPC were candidate metabolic biomarkers worthy of further validation in C. sinense infection. Of particular note, LysoPC(18:0) and related subspecies are established proinflammatory mediators in infectious pathologies [68]. Additional metabolic disruptions included a significant downregulation of the pivotal bile acid metabolism gene Cyp3a11, which may alter bile acid pool composition [69]. Moreover, the reduced riboflavin (vitamin B2) levels concurrent with a depletion of the Bacteroidetes family norank_f_Muribaculaceae, the primary microbial riboflavin producer, were observed [70]. Collectively, these findings demonstrate that C. sinensis infection disrupts intestinal lipid homeostasis, bile acid metabolism, and vitamin biosynthesis through coordinated transcriptional reprogramming and microbiota alterations.

Integrated multi-omics analysis revealed intricate interactions among gut microbiota, metabolites, and host transcriptome, forming a complex regulatory network. Our findings demonstrated that C. sinensis infection transcriptionally modulated intestinal lipid metabolism through the Lands cycle, particularly affecting the expression of PC and LysoPC. Further correlation analysis indicated notable correlations between specific gut bacteria and these lipid metabolites. For instance, Lachnoclostridium displayed inverse correlations with PC(14:1(9Z)/20:0) but positive correlations with 2-LysoPC, PC(18:0/0:0) and LysoPC(18:0/0:0). Conversely, Turicibacter and Dubosiella exhibited opposing correlation patterns. It is well known that the interplays among gut microbiota, metabolomics, and transcriptomics are crucial for host health [71]. The negative correlation between Lachnoclostridium and riboflavin further implies that this bacterium may play an important role in regulating host vitamin metabolism. Additionally, prior reports have associated Turicibacter and Dubosiella with various metabolic diseases, such as obesity and diabetes, which influence the host’s lipid metabolism [72, 73]. Marvinbryantia and unclassified_f_Lachnospiraceae showed significant negative correlations with PC(14:1(9Z)/20:0), along with significant positive correlations with PC(18:0/0:0) and LysoPC(18:0/0:0). They also exhibited a significant negative correlation with riboflavin, indicating their potential roles in regulating host lipid and vitamin metabolism. As SCFA-producing Lachnospiraceae members unclassified_f_Lachnospiraceae and Marvinbryantia likely influence host lipid metabolism and immune homeostasis [74, 75]. The Lands cycle enzyme Pla2g4c showed a strong negative correlation with PC(14:1(9Z)/20:0) and positive correlations with 2-LysoPC, PC(18:0/0:0) and LysoPC(18:0/0:0), confirming its central role in lipid remodeling. Intriguingly, PC(14:1(9Z)/20:0) was positively correlated with intestinal barrier gene Arrb1 and immune-associated receptor Cx3cr1 [76, 77], suggesting dual roles in mucosal immunity and barrier function. Furthermore, 2-LysoPC and PC(18:0/0:0) displayed significant negative correlations with immune-related genes Cd3g, Cx3cr1, Ccl5 and Cxcl14 [52–55], indicating potential immunosuppressive effects. The positive correlation of riboflavin with immune genes Cd3g, Cd3e, Cx3cr1 and Arrb1, coupled with its negative correlation with Pla2g4c, indicates its potential role in immune stimulation and lipid metabolic regulation. These comprehensive analyses reveal that C. sinensis infection disrupts intestinal lipid-immune crosstalk through coordinated microbial-metabolite-transcriptome interactions, suggesting potential impairment of mucosal immune regulation and barrier function.

Conclusion

In summary, C. sinensis infection caused time-dependent alterations in gut microbiota, with the most pronounced dysbiosis and F/B ratio imbalance emerging at 5 wpi. Crucially, significant proliferation of pathogenic bacteria (e.g., Candidatus_Saccharimonas and Monoglobus) concurrent with depletion of beneficial genera (e.g., norank_f_Muribaculaceae, Turicibacter and Bifidobacterium) was observed. Transcriptomic profiling revealed dysregulated immune pathways, including complement activation, cytokine signaling, and MAPK cascades, along with immune-related genes (e.g., Hhip and Hsp90b1), indicating compromised intestinal immunity. Metabolomic analysis highlighted the remodeling of lipid metabolism, particularly in the Lands cycle, with altered levels of PC and LysoPC, and identified potential biomarkers (PC(14:1(9Z)/20:0, 2-LysoPC). Integrated multi-omics analysis revealed strong correlations between specific bacterial genera (Lachnoclostridium, Turicibacter, Dubosiella, Marvinbryantia, and unclassified_f_Lachnospiraceae) and metabolites of the Lands cycle. These findings enhance the understanding of the interplay between host gut genes and microbiota under C. sinensis infection (Fig. 8). However, mechanisms underlying key DEGs, DMs, and their interactions with the gut microbiota warrant further exploration.

Fig. 8.

The effects of C. sinensis infection on the gut microbiota, metabolome, and transcriptome of mice. C. sinensis infection at different time points led to gut microbiota dysbiosis in mice, especially at 5 wpi. Moreover, at 5 wpi, it also resulted in the dysregulation of gut immune pathways and changes in lipid, vitamin, and bile acid metabolism. Meanwhile, some important bacterial genera (such as Lachnoclostridium) were found to be significantly correlated with the metabolites of the Lands cycle. Created with BioRender.com

Abbreviations

16S rRNA

16S ribosomal RNA

AA

Arachidonic acid

BPs

Biological processes

C. sinensis

Clonorchis sinensis

CsESPs

C. sinensis releases excretory-secretory products

DEGs

Differentially expressed genes

DMs

Differential metabolites

F/B

Firmicutes/Bacteroidetes

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

LC-MS/MS

Liquid chromatography-tandem mass spectrometry

LysoPC

Lysophosphatidylcholine

PCA

Principal component analysis

PC

Phosphatidylcholine

QC

Quality control

SCFAs

Short-chain fatty acids

SPF

Specific pathogen-free

Wpi

Weeks post-infection

Authors’ contributions

XD, MF, and XF performed data curation, Writing–original draft, methodology, and investigation. SL, YJ, YW, DL and QL performed methodology, investigation, and validation. TZ performed data curation, project administration, and supervision. ZT, ZW and MF performed conceptualization, data curation, funding acquisition, project administration, supervision, and Writing–review and editing.

Funding

This work was supported by Guangxi Natural Science Foundation (Grant No. 2023GXNSFAA026201 and 2025GXNSFAA069826), the Natural Science Foundation of China (Grant No. 31900681 and 82360410), First-class discipline innovation-driven talent program of Guangxi Medical University (Grant No. 2023-23), and Guangxi University of Chinese Medicine Youth Innovation Research Team Project (Grant No. 2023TD001).

Data availability

The raw data has been deposited to NCBI database under the accession number PRJNA1245126, PRJNA1245106, PRJNA1244241, PRJNA1235996, PRJNA1235437 and PRJNA1234877.

Declarations

Ethics approval and consent to participate

All animal experiments were conducted in strict accordance with the guidelines for the Care and Use of Laboratory Animals in China and were approved by approved by the ethical committee for animal research of Guangxi Medical University (Approval No. 202304012).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.