Metacercariae of C. sinensis were collected from muscular tissues of naturally infected Pseudorasbora parva in the endemic area of Qiqihar, Heilongjiang Province, China. The muscular tissue was digested with artificial digestive juice (1% pepsin-hydrochloric acid; Aladdin, China), and viable metacercariae were identified and collected via microscopy (CX23, Olympus, Japan). Japanese White rabbits (n = 18; weighing 2000–2250 g) were purchased from Changchun Yisi Experimental Animal Biotech Co. (SCXK-2016–0004) and randomly allocated into two groups: C. sinensis-infected group (500 metacercariae/rabbit; n = 9) and control group [treated with 500 μl phosphate-buffered saline (PBS); n = 9]. The rabbits were housed in separate cages at a controlled temperature (22 ± 2 °C; 12-h light/dark cycle) and were provided with water and pellet feed ad libitum. Plasma samples were collected from the ear veins of the rabbits after weeks 1, 2, 3, 4, 5, 6, 7, 9, and 11. The samples were stored at –80 °C after inactivation with liquid nitrogen. All animal studies were performed in accordance with the Guide for the Care and Use of Laboratory Animals (1996).
Fecal samples of the rabbits in the treated group were observed under the microscope to confirm C. sinensis infection. Subsequently, the rabbits were anesthetized using isoflurane and killed by injecting air through ear vein at 77 days post-infection. Following the rapid isolation of liver in an aseptic environment, adult parasites were collected from the liver and bile duct and identified through internal transcribed spacer (ITS) sequencing (forward primer: 5ʹ- GTA GGT GAA CCT GCG GAA GGA TCA TT -3ʹ; reverse primer: 5ʹ- TTA GTT TCT TTT CCT CCG CT -3ʹ). The liver tissue was then cut into small pieces, rinsed with saline solution (0.9% NaCl w/v), stored in 10% neutral buffered formalin for 1 week, dehydrated in ethanol, and embedded in paraffin wax. Five-micron paraffin sections were stained with hematoxylin and eosin (H&E) and Masson’s trichrome staining and examined under a light microscope (CX23, Olympus, Japan).
Detection of biochemical indices
An automatic biochemical analyzer (AU5800, Beckman Coulter, USA) was used to detect total protein (TP), albumin (ALB), globulin (GLB), total bile acid (TBA), ALT, AST, GGT, lactate dehydrogenase (LDH), cholesterol (CHOL), triglyceride (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), blood urea nitrogen (BUN), creatinine (CREA), uric acid (UA), glucose (GLU), prealbumin (PA), and cholinesterase (CHE) content in the samples.
Plasma samples were thawed at 4 ℃ on ice, and 100 μl of sample was taken in an EP tube and extracted with 400 μl of extraction solvent (Vmethanol:Vacetonitrile = 1:1) containing internal standard (2-chloro-l-phenylalanine, 2 μg/ml). This was vortex-mixed for 30 s and sonicated for 10 min (incubated in ice water), and the proteins were precipitated at − 20 ℃ for 1 h; 500 μl liquid was centrifuged at 12,000 rpm for 15 min at 4 ℃, and 425 μl supernatant was transferred into EP tubes. The supernatant was dried in a vacuum concentrator without heating, reconstituted with 100 μl extraction solvent (Vacetonitrile:Vwater = 1:1), vortex mixed for 30 s, sonicated for 10 min (4 ℃ water bath), and centrifuged at 12,000 rpm for 15 min at 4 ℃. Finally, 60 μl of the supernatant was used for ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry analysis (UHPLC-QTOF-MS). For monitoring the performance of data acquisition, 11 quality control (QC) samples were prepared using 10 μl from each sample.
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis
LC-MS/MS assay was performed using a UHPLC system (1290, Agilent Technologies) with an UPLC BEH Amide column (1.7 μm, 2.1 × 100 mm, Waters) connected to a Triple TOF 6600® system (Q-TOF, AB SCIEX). The mobile phase consisting of 25 mM NH4CH3CO2 and 25 mM NH4OH in water (pH = 9.75) (A) and acetonitrile (B) was carried out with elution gradient as follows: 0 min, 95% B; 0.5 min, 95% B; 7 min, 65% B; 8 min, 40% B; 9 min, 40% B; 9.1 min, 95% B; 12 min, 95% B, delivered at 0.5 ml/min. The injection volume was 1.5 μl. The Triple TOF mass spectrometer was used to acquire MS/MS spectra on an information-dependent acquisition basis during an the LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB SCIEX) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, 12 precursor ions having intensity > 100 were chosen for fragmentation at a collision energy of 30 V (15 MS/MS events with product ion accumulation time of 50 ms each). Electrospray ionization (ESI) source conditions were set as follows: ion source gas 1 at 60 Psi, ion source gas 2 at 60 Psi, curtain gas at 35 Psi, source temperature at 650 ℃, and ion spray voltage floating at 5000 V in positive and − 4000 V in negative modes, respectively.
Identification of the metabolites and data analysis
MS raw data (.wiff) files were converted to the mzXML format using ProteoWizard and processed by the R package XCMS (version 3.2). The preprocessing results generated a data matrix that consisted of the retention time, mass-to-charge ratio (m/z) values, and peak intensity. The R package CAMERA was used for peak annotation after XCMS data processing. In-house MS2 database was developed to identify metabolites assisted by Biotree Biotech Co., Ltd. (Shanghai, China).
The resultant data set was uploaded to the SIMCA (version 14.1, Umetrics, Umea, Sweden) for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The data for both negative and positive ion modes were mean-centered and scaled using Unit-Variance (for PCA) or Pareto (for OPLS-DA) before multivariate statistical analysis. Differential metabolites were identified by variable importance in projection (VIP) values > 1 in OPLS-DA model and Student’s t-test on the normalized peak areas (P < 0.05). To check the distinctions in metabolic state of the C. sinensis-infected group in different infection periods, log2 transformation was performed for cluster analysis, and heat maps were generated using Multiple Experiment Viewer 4.9.0 (http://mev.tm4.org/). Metabolite pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/kegg/) database and MetaboAnalyst (http://www.metaboanalyst.ca/).
Detection of candidate biomarkers
To identify the potential biochemical indices and metabolites for clinical diagnosis, serum samples of patients were collected from C. sinensis-endemic areas in Jilin Province, China. Patients having chronic diseases (such as diabetes and high blood pressure) and other hepatic diseases (not caused by C. sinensis) were excluded. Samples from 8 healthy people and 22 with C. sinensis infection were collected. Biochemical indices were detected using an automatic biochemical analyzer (AU5800 Beckman Coulter, USA), and the targeted metabolites were detected by internal standard method. Metabolite extraction and detection were based on the previous literature . Six standards included glycodeoxycholic acid (CAS: 360–65-6, Macklin, China), xanthine (CAS: 69-89-6, Macklin, China), hypoxanthine (CAS: 68-94-0, Acmec, China), d-glucuronate (CAS: 12-3-6556, Sigma-Aldrich, St Louis, MO), l-pipecolic acid (CAS: 3105–95-1, Macklin, China), and 3-methylglutaric acid (CAS: 626-51-7, Macklin, China). Additionally, two internal standards were used: naptalam (CAS: 132-66-1, Sigma-Aldrich, St Louis, MO) and tinidazole (CAS: 19387-91-8, Macklin, China). The primary and secondary MS data were collected under MRM mode of Analyst 1.7.0 software of the AB SCIEX 4500 mass spectrometer (AB SCIEX).
Correlation between candidate biochemical indices and metabolites
The correlation between candidate biochemical indices and metabolites was analyzed using Spearman’s correlation coefficient (https://hiplot.com.cn/) analysis. Binary logistic regression and receiver-operating characteristic (ROC) curve analyses were performed to identify biomarkers for clonorchiasis. The area under the curve (AUC) was used to assess the diagnostic accuracy: 0.8 < AUC < 0.9 as good and 0.9 < AUC ≤ 1.0 as excellent.
SPSS Statistics software version 22 (IBM, Armonk, NY, USA) was used for statistical analysis of the data, and Prism version 8.0.1 (GraphPad Software, San Diego, CA) was used for generating plots. Statistical analysis of every biochemical index and metabolite analyzed during each infection period was performed using unpaired two-tailed Student’s t-test (*P < 0.05, **P < 0.01, ***P < 0.001) between C. sinensis-infected and control groups. Results are expressed as mean ± SEM.