Parasites, animals and infection
C.sinensis metacercariae were collected from Pseudorasbora parva captured in the Songhuajiang River of Heilongjiang Province. The process of collection and preparation is performed as described previously [42]. In brief, the fish were placed in an ice box at 0°C and transported to the laboratory. Next, the fish were washed with tap water, disrupted in a Waring Blender and digested with a pepsin-HCl (0.6%) artificial gastric juice, at 37°C for 12 h. Then, the digested mixture was passed through three sieves with the mesh size of 1000 μm, 300 μm and 106 μm. After centrifugation, mass pure metacercariae were collected and kept in 0.1 M phosphate-buffered saline (PBS, pH 7.4) at 4°C until used.
Wistar rats (150g, 5-6 weeks old, male) were purchased from the Harbin Medical University Laboratory Animal Center. There was a rat in a single animal cage. The rats were housed with an inverse 12 hours day-night cycle with lights on at 8:30pm in a temperature (20±1℃) and humidity (55±5%) controlled room, and fed on standard laboratory food (CS-101; Liaoning Changsheng biotechnology co., Ltd) and water.
All the rats were randomly divided into 3 groups, 4 weeks post infection (wpi), 8 wpi and control groups. Animal model experiment were carried out in the afternoon. In the infection groups, the rats were randomly selected and individually infected orally with 50 metacercariae (n=10 per group). The Kato-Katz method was used to detect C. sinensis eggs from the rat feces [43]. Briefly, KK smears were prepared using 41.7 mg of feces, and the EPG (eggs per gram of feces) was determined by multiplying the number of eggs per smear by 24. Control group rats (n=10) were fed with 50 μl of sterile normal solutio.
At 4 wpi and 8 wpi, the rats were sacrificed under ether anesthesia by cervical dislocation and livers were quickly removed and rinsed with saline solution (0.9% NaCl w/v), and stored at −80°C for analysis. And then livers from control rats were collected at 8 wpi. Inhalation anesthesia (ether anesthesia) reduced animal suffering and increased the success rate of anesthesia. Briefly, every rat was put into a glass anesthesia box containing several ether-soaked cotton balls (2-4ml). After slow breathing, decreased limb tensioning and dull corneal response, the rats were sacrificed by cervical dislocation.
For histology, liver samples were fixed by 4% paraformaldehyde, then dehydrated in a graded series of ethanol and embedded in paraffin wax. Sections were made (5μm) using a microtome, then stained with hematoxylin-eosin (H&E) and examined under an optical microscope (Olympus, Tokyo, Japan).
Chemicals
LC–MS-grade water (H2O), acetonitrile (ACN), methanol (MeOH), 0.1% formic acid (FA) in water and 0.1% FA in ACN were purchased from Honeywell (Muskegon, MI, USA). Ammonium acetate (NH4AC), ammonium hydroxide (NH4OH), ammonium fluoride (NH4F) were purchased from Sigma-Aldrich (St. Louis, MO, USA) and dissolved in LC–MS-grade water prior to use.
Sample collection and preparation
A total of 100 mg liver sample was add 1mL of cold methanol/acetonitrile/H2O (2:2:1,v/v/v). The samples were vortexed for 30 seconds and repeated 5 times. After using MP FastPrep 24 machine (MP Biomedicals, USA) to homogenize the lysate (24×2, 6.0M/S, 60s, twice), the mixture was sonicated for 30 minutes two times at low temperature, and centrifuged by Eppendorf 5430R for 15 min (15,000g, 4 °C), and then dried in a vacuum centrifuge at 1500g overnight. The samples were redissolved in 100 μL acetonitrile/water (1:1, v/v) solvent.
UHPLC-Q-TOF MS analysis
Analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) with a quadrupole time-of-flight mass spectroscopy (AB Sciex Triple TOF 5600) in Shanghai Applied Protein Technology Co., Ltd.
In the HILIC separation process, we utilized a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 µm column (waters, Ireland) to analysis the samples. The mobile phase contained A=25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B= acetonitrile. The gradient was set at 85% B (1 min), reduced to 65% in 11 min, reduced to 40% in 0.1 min (hold on 4 min), increased to 85% in 0.1 min, and then re-balanced in 5 min.
The ESI source situation were used as below: Curtain gas (CUR) as 30, source temperature: 600℃, Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, IonSpray Voltage Floating (ISVF) ± 5500 V. The instrument was set over the m/z range 60-1000 Da, with the accumulation time of TOF MS scan (0.20 s/spectra) in MS analysis. Over the m/z range 25-1000 Da, product ion scan was set at 0.05 s/spectra in auto MS/MS acquisition. With the advantages of high sensitivity mode, information dependent acquisition (IDA) was applied to acquire the product ion scan. The condition was set as below: the collision energy (CE) was set at 35 ± 15 eV; Declustering Potential (DP), 60 V (+) and 60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10.
During mass spectra collection, samples were placed in automatic sampler at 4℃. To monitor the stability and repeatability of analytical system, quality control (QC) samples were prepared by pooling 10 μL of each sample and injected prior to analysis. And then blank and QC samples were injected every 5 samples injections throughout the analytical run.
Data processing
The raw MS data (Wiff format files) were converted to mzXML format using ProteoWizard (http://proteowizard.sourceforge.net/) and processed using R package XCMS (version 3.2). For peak picking, the parameters were set: centWave m/z = 25 ppm, peak width = c (10, 60), prefilter = c (10, 100). For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. R i836 3.1.1 was used to run XCMS and Collection of Algorithms of Metabolite Profile Annotation (CAMERA, 1.42.0). The code was used to perform data processing showed in supporting information. The parameters was loaded on supporting information.The variables with more than 50% of the nonzero measurement values in at least one group were identified. Compound identification of metabolites was performed by comparing the accuracy of m/z values (< 25 ppm), and MS/MS spectra were interpreted with an in house database (Shanghai Applied Protein Technology Co., Ltd.) established with authentic standards.
Statistical analysis
After normalized to total peak intensity, the processed data were uploaded into SIMCA-P (version 14.1, Umetrics, Umea, Sweden). The data were mean-centered and Pareto-scaled before multivariate statistical analysis. PCA, an unsupervised multivariate statistical method, could provide an overview of all observations in data tables, such as groupings, trends, and outliers of the different groups of samples. Then, the supervised partial least-squares discriminant analysis (PLS-DA) were applied to discriminate the different variables between groups. A default seven-fold cross-validation and testing with 200 random permutations were used to avoid the over-fitting of supervised PLS-DA models. The goodness-of-fit parameters for the PLS model, R2 and Q2, were calculated which varied from 0 to 1. The values of R2 and Q2 parameters were used to verify the fitness and predictive ability of the model. The variable importance in the projection (VIP) value was applied to clarify its contribution to the classification in the PLS-DA model. Fold change (FC) was set to be ≥ 1.2 or ≤ 0.8333. Log2 FC based on metabolite abundance was used to demonstrate how the differential abundance of liver metabolites varied at different stages of infection groups.
Heatmaps were employed to describe the unbalanced metabolic profiling among C.sinensis infected and control rats. Euclidean distance algorithm for similarity measure and average linkage clustering algorithm (clustering uses the centroids of the observations) for clustering were selected when performing hierarchical clustering. Based on the deferentially expressed metabolite data (log2-scaled), heatmaps were structured by the MultiExperiment Viewer (MeV) v. 4.9 software (http:// mev.tm4.org/).
The Pathway analysis of the differentially expressed metabolites was depended on KEGG database (http://www.genome.jp/kegg/) and MetaboAnalyst 3.0 (http://www.MetaboAnalyst.ca/). According to our research background and the pathway enrichment results, the corresponding KEGG pathways were extracted. The analyses were applied based on the fisher’ exact test, considering the metabolites as background data sets. And only pathways with FDR-values under a threshold of 0.05 were considered as significant. The identified pathways influenced by C.sinensis infection compared to controls are presented according to p-values from the pathway enrichment analysis (y-axis) and pathway impact values from pathway topology analysis (x-axis), with the most impacted pathways colored in red color.