Our experiments were carried out in accordance with Swiss cantonal and national regulations on animal welfare (permission no. 2081). Female NMRI mice (n = 24) were purchased from RCC (Itingen, Switzerland), and housed in groups of 4 in macrolon cages under environmentally-controlled conditions (temperature: ∼25°C; humidity: ∼70%; light-dark cycle: 12–12 h). Mice had free access to commercially available rodent food from Nafag (Gossau, Switzerland) and community tap water supply.

Mice were 5 to 6-week-old at the onset of the experiments and had an average weight of 25.5 g (standard deviation (SD) = 0.9 g). Half of the mice remained uninfected throughout the study and served as controls. The other 12 mice were orally infected with 30 E. caproni metacercariae each (provided by B. Fried; Lafayette College, Easton, PA, United States of America) [20] on designated study day 0, which took place 1 week after arrival of animals to provide sufficient acclimatization time, and hence minimize stress-related impact on the metabolic profiles. Upon dissection of mice at the end of the experiment, however it was found that no infection had been established in 4 animals. Therefore these 4 mice were excluded from any further analysis.

Blood plasma, stool and urine samples were collected over a 33-day time course at 7 distinct sampling points (days 1, 5, 8, 12, 19, 26, and 33 post-infection), representative of different stages in the life of the E. caproni fluke, including acute and chronic infection stages. Collection was carried out between 08:00 and 10:00 hours in order to avoid potential variation of metabolite concentrations due to diurnal fluctuations. Stool and urine samples were collected into Petri dishes by gently rubbing the abdomen of the mice, and were immediately transferred into separate Eppendorf tubes and kept at −40°C. Blood samples (40–50 μl) were collected from the tail tip of each mouse into haematocrit tubes with sodium [Na] heparin-coat. Tubes were placed in a centrifuge (model 1–15, Sigma; Osterode am Harz, Germany) operated at 4,000 g for 4 min in order to separate plasma from red blood cells. The packed cell volume (PCV), i.e., length of red blood cells column in the microcapillary versus total length of blood sample column, was determined and expressed as percentage. Subsequently, the plasma fraction (∼20 μl) was transferred into a separate Eppendorf tube and kept at −40°C. Animals were weighed at each sampling point, using a Mettler balance (model K7T; Greifensee, Switzerland).

Mice were killed 36 days post-infection, using CO₂. The small intestine was removed, and adult worms recovered from the ileum and jejunum and counted. Biological samples and an E. caproni specimen were forwarded to Imperial College London (United Kingdom) on dry ice and stored at −40°C prior to processing for ¹H NMR spectroscopic data acquisition.

Urine samples were prepared with a phosphate buffer (pH 7.4) containing 50% D₂O (Goss Scientific Instruments; Chelmsford, United Kingdom) as a field frequency lock and 0.01% sodium 3-(trimethylsilyl) [2,2,3,3-²H₄] propionate (TSP) (Cambridge Isotope Laboratories Inc.; Andover, MA, United States of America), as a chemical shift reference (δ 0.0). An aliquot of 25 μl of urine was added to 25 μl phosphate buffer. Plasma samples were prepared by adding 30 μl of 0.9% saline made up in 50% D₂O into the Eppendorf tubes containing ∼20 μl of plasma. Because of the limited volumes of urine and plasma, samples were transferred into 1.7 mm diameter micro NMR-tubes (CortecNet; Paris, France) using a micro-syringe.

Stool samples were prepared with the same buffer as for urine but using 90% D₂O to reduce the water content. Two pellets of stool were mashed with 700 μl buffer and sonicated for 30 min to inactivate gut bacteria and achieve biochemical stability in the sample. The samples were then centrifuged at 10,000 g for 2 min, and 550–600 μl of the supernatant was transferred into a new Eppendorf tube and stored at −40°C. Shortly before data acquisition, the stool supernatant was defrosted, centrifuged and transferred into NMR tubes of 5 mm outer diameter.

A tissue extraction was performed on the E. caproni specimen for ¹H NMR spectroscopic analysis. The adult E. caproni fluke was mashed in 1 ml of chloroform with a glass mortar and pestle. A total of 1 ml of methanol and 1 ml of water were added, and this mixture was transferred into a glass tube. Another 0.5 ml of each liquid was used to rinse the mortar and transferred into the same glass tube. The mixture was centrifuged at 2,500 g for 30 min. The aqueous and the chloroform phases were transferred into a new glass tube each, chloroform was evaporated over night and the aqueous phase was lyophilized. Prior to ¹H NMR data acquisition, the powder obtained from the aqueous phase was resolved in 550 μl phosphate buffer (90% D₂O), whereas the dry mass of the chloroform fraction was dissolved in deuterated chloroform (CDCl₃).

¹H NMR spectra from plasma, stool, and urine samples, and the E. caproni extract were recorded on a Bruker DRX 600 NMR spectrometer, operating at 600.13 MHz for proton frequency (Bruker; Rheinstetten, Germany). A Bruker 5 mm triple resonance probe with inverse detection was used, employing a standard NMR 1-dimensional (1D) experiment with pulse sequence [recycle delay (RD)-90°-t₁-90°-t_m-90°-ACQ], setting t₁ to 3 μs, and using a mixing time (t_m) of 150 ms. Water suppression was achieved with irradiation of the water peak during the RD set to 2 s and mixing time. The 90° pulse length was adjusted to ∼10 μs. A total of 256 transients were collected into ∼32,000 data points for each spectrum with a spectral width of 20 ppm. For plasma, two additional pulse programs were applied, namely Carr-Purcell-Meiboom-Gill (CPMG), and diffusion edited spectroscopy [21] to focus on the low and high molecular weight components of the plasma profile, respectively. All free induction decays (FIDs) were multiplied by an exponential function equivalent to a 0.3 Hz line-broadening factor prior to Fourier transformation.

Assignments of the spectral peaks were made from literature values [22]–[25] and confirmed via statistical total correlation spectroscopy (STOCSY) in MATLAB [26] and via standard 2-dimensional (2D) NMR experiments conducted on selected samples, including correlation spectroscopy (COSY), total correlation spectroscopy (TOCSY), and J-resolved NMR spectra [27],[28].

Data processing was as follows. First, spectra were corrected for phase and baseline distortions with an in-house developed MATLAB script. Second, the region containing the water/urea resonances (i.e., δ 4.2–6.3 in urine, δ 4.4–5.2 in plasma, and δ 4.7–5.5 in stool extracts) was excluded. Third, the spectra were normalized over the total sum of the remaining spectral area. Analysis of the spectral data was performed with principal component analysis (PCA) [29], projection to latent structure discriminant analysis (PLS-DA) and orthogonal (O)-PLS-DA [30]. PCA was used to explore any intrinsic similarity between samples. PCA models cannot be over-fitted since no prior information on infection status is included in the model. PLS-DA was then used to apply knowledge of infection status to optimize separation of classes and recovery of candidate biomarkers [30]. O-PLS-DA includes an orthogonal data filter in the PLS-DA and was used to further improve the extraction of infection-related biomarkers by removing the influence of systematic variation not related to infection status. The weight of contribution of the peaks is indicated by the color scale, whereby red symbolizes relatively high correlation with infection and blue indicates relatively low or no correlation. The metabolites which contributed the greatest weight to the O-PLS-DA coefficient plot were identified (Tables 1– 3).

The NMR spectral data were used as the X-matrix and class information (infected or non-infected control) as the Y-matrix to build the O-PLS-DA models. A model consisting of one PLS component and one orthogonal component was generated using 7-fold cross validation.

Finally, in order to more accurately profile the temporal behavior of the discriminatory metabolites characterizing an E. caproni infection, computational integration was performed on selected resonances. Resonances from several of the metabolites in each sample, which showed infection-dependent variations, were integrated using an automated curve fitting program. The relative concentration in relation to the total spectral integral, subsequent to removal of the water resonance, was determined. This was performed in MATLAB using a previously published method [31], and further modified by a colleague (K. Veselkov; Imperial College London, UK). The p-values for the metabolites were assessed using a non-parametric 1-way analysis of variance (Mann-Whitney U) test in MATLAB.

E. caproni-infected mice showed no visible sign of ill-health over the course of the experiment. The mean weight and mean PCV of E. caproni-infected (n = 8) and non-infected control mice (n = 12) did not differ at any of the time points investigated. The PCV values maintained a constant level throughout the experiment (49.6–55.1%). Upon dissection and worm count, an infection was confirmed in 8 out of the 12 mice (average worm count 26.5, SD = 12.0, range: 10–44 worms). The 4 animals with no established infection were excluded from further analyses.

Prior to assessing the metabolic effects of an E. caproni infection, the ¹H NMR spectra of plasma, stool, and urine samples obtained from non-infected mice were characterized and found to be inherently different in composition. All three types of biofluids contained lactate, alanine, glucose, and acetate. Unique to the urine metabolic profile was the presence of hippurate, indoxylsulfate, urocanate, taurine, trimethylamine-N-oxide (TMAO), 2-oxoglutarate, ureidopropanoate, and 2-ketoisocaproate, amongst others (Figure 1A, Figure 2A and Table 1). The plasma spectral profiles were characterized by the predominance of various lipids and lipoprotein fractions, along with resonances from creatine, and several amino and organic acids (Figure 1B, Figure 2B and Table 2). Apart from the standard 1D acquisition, applied on all biofluids, a CPMG and diffusion edited pulse sequence was used in plasma profiling, to represent low and high molecular weight metabolites, respectively. Characteristic metabolic features of the stool extracts were bile acids and short chain fatty acids (SCFAs), such as butyrate, and propionate. In addition, other amino acids, such as tryptophan, lysine, arginine, and glutamine were more visible in stool spectra, compared to urine and plasma (Figure 1C, Figure 2C and Table 3).

In order to establish whether excretory products of the parasite itself were likely to contribute to any of the biofluids analyzed, a standard 1D spectrum of an adult E. caproni was acquired. The spectrum of the parasite differed from the biofluids obtained from the mouse host in content of homocarnosine, histidinol, uridine, pipecolate, and betaine (Figure 1D, Figure 2D), although betaine has been observed in ¹H NMR spectra of rodent urine in previous studies [32]. Tables 1– 4 summarize key metabolites found in urine, plasma, and stool extracts of mice, and in the E. caproni homogenate, respectively.

In both PCA and PLS-DA scores plots of the urinary metabolite profiles, a clear separation of E. caproni-infected and non-infected control mice was already visible 1 day post-infection. This separation was maintained in all later time points except day 5. Metabolic trajectories were constructed for each type of biofluid by taking the mean position in the principal component (PC) scores plot for each group of mice (E. caproni-infected and non-infected controls) separately, and connecting the coordinates chronologically to establish any systematic change in metabolic composition over the time course of the experiment. The control group showed no significant movement over the study duration (data not shown), whereas in the infected group, day 19 was significantly separated from all other days post-infection, and the whole time course of infection showed a shift from the upper left to the lower right quadrant (Figure 3A), whereby days 1 and 5 post-infection differed significantly from the sampling end point (day 33).

The plasma spectra of E. caproni-infected mice showed marked differences at days 1, 12, 26, and 33 post-infection, with the best discriminatory model at day 12 post-infection (goodness of prediction (Q²) according to PCA = 0.97; Q²Y (PLS-DA) = 0.89). Comparing the 3 different pulse programs applied, plasma time trajectories, showed similar behavior. The control trajectories were generally clustered, indicating stability of the metabolite composition over the study period. However, the standard 1D trajectory showed a slight difference between early and late time points (e.g., day 1 was separated in space from days 19 to 33). In contrast, for the E. caproni-infected animals, there was a significant metabolic movement from early (day 1) to intermediate time points post-infection (days 5 and 12) in the first component and finally to late time points (days 26 and 33) in the third component (Figure 3B). This movement pattern was consistent across the datasets acquired by all three pulse programs.

With regard to the ¹H NMR spectra obtained from stool samples, a clear separation was found at day 5 post-infection in both the PCA and PLS-DA scores plot between E. caproni-infected and non-infected control mice, with maximum model fit for the PLS-DA model at day 26 post-infection (Q² = 0.79). At the final time point (day 33), the two groups were metabolically similar; there was no separation between infected and non-infected mice using PCA, and the PLS-DA model revealed a lower, but still significant Q² value than all previous time points. By comparing the time trajectories of the non-infected control with the E. caproni-infected group of mice, the controls were more tightly clustered, but showed a significant movement from day 8 post-infection onwards along PC1. In the 2D time trajectory plot of the E. caproni-infected mice, all time points were tightly clustered with the exception of days 8 and 12 post-infection, which comprised a separate cluster (Figure 3C). Introducing an additional PC brought about clear differentiation of the late time points (days 19, 26, and 33) from day 1 post-infection caused by more subtle systematic variation in metabolic levels.

O-PLS-DA was used to extract information on specific metabolic changes induced by an E. caproni infection over the duration of the study. Changes in urinary, plasma and fecal metabolites are presented in Figures 4– 6 for selected time points and the complete set are summarized in Figure 7. Amongst the most significantly changed urinary metabolites were hippurate (decreased at day 33), 2-ketoisocaproate (decreased from day 8 onwards), trimethylamine (TMA; increased at days 8, 12, 19, and 33), taurine (decreased at days 8, 12, and 19), p-cresol glucuronide (increased at days 8, 12, 19, and 26), mannitol (increased from day 5 onwards), TMAO (increased at days 8, and 12), phenylacetylglycine (increased from day 12 onwards), acetate (decreased at days 8, and 19) and creatine (decreased at days 1, 5, 8, and 12).

Plasma from infected mice showed changes in the relative concentration of acetate (increased at all time points except day 5), creatine (decreased from day 8 onwards), lipids (increased from day 8 onwards), formate (decreased at days 1, 8, 12, and 19, but increased at day 33), lactate (decreased at days 1, 8, 12, 19, and 26), glucose (increased at days 1, and 33, but decreased at days 12, 19, and 26), glycerophosphorylcholine (GPC; decreased at days 12, 26, and 33), choline (decreased at days 1, 12, 26, and 33) and branched chain amino acids (BCAAs; decreased at days 12, 26, and 33).

The changes in stool samples from infected animals included the BCAAs (increased at days 8, and 26), uracil (increased at day 8), butyrate (decreased at days 12, 19, and 26), propionate (decreased at days 12, 19, and 26) and 5-aminovalerate (increased from day 5 onwards).

Figure 8 shows the relative concentration of some of these metabolites both for control (blue) and infected mice (colored according to biofluid). The error bars signify 2 SDs of the mean. According to this 3×3 diagram, the selected plasma and urine metabolites showed a more robust pattern of group separation over time when compared to fecal water extracts. Whereas some overlap was observed in the scores plot relating to the fecal water samples, there was a tendency toward increasing discrimination of urinary metabolites with time over the course of an E. caproni infection, whereas the discrimination became smaller toward the end of the experiment in the selected plasma metabolites.

The time trajectories allow the influence of growth and maturation of either host or parasite to be considered. Since very few of the metabolites observed in the E. caproni fluke appeared in any biofluid, infection-related changes are unlikely to correlate with maturation of the parasite. Additionally, since the urinary time trajectories were very different, comparing the control with the infected group, whereas the control trajectory did not show any significant movement over time; this would indicate that the maturation of the host organism did not markedly influence the metabolic profile over the 33-day time course. Hence, the systemic movement over time observed in the infected group is most likely to be related to the establishment and progression of the E. caproni infection.

In stool and plasma, the time trajectories of the infected animals demonstrated a markedly greater magnitude from the baseline position than the non-infected animals. The greatest differentiation between E. caproni-infected mice and non-infected control animals was found in the PC scores plots based on the urine spectral profiles. The smallest differentiation was observed in the stool. From the scale of the PC scores plot axes the trajectory of the infected group occupied a 1.5 and 50 times larger space for stool and plasma, respectively than the non-infected group, whereas in urine, the control trajectory occupied a 10⁶ times bigger space, compared to the trajectory of infection. The magnitude of infection-induced metabolic disturbance in the urine profile again clearly points to the greater suitability of urine as a diagnostic biological matrix.

The considerable increase in concentration of lipids in the plasma, e.g., fatty acids, triaglycerols, and lipoproteins, reflects the action of the parasite in the hosts gut. In mice harboring a 2-week-old E. caproni-infection, an increased breakdown of membrane lipids in the host intestinal tissue has been observed [35], which is consistent with the present findings of a maximum lipid increase on day 12 post-infection [36]. The excretory products of E. caproni in the intestinal mucosa are primarily free sterols, triaglycerols, and free fatty acids [37], but it is unlikely that the amounts excreted by the parasite make a substantial contribution to the host metabolic profile, given that the total parasite mass to host weight ration is ∼1:300.

Whilst the simple diffusion of lipid micelles into mucosal cells seems unaffected by the parasite, the Na⁺-dependent active transport of amino acids could be impaired as the increase of the BCAAs in stool as the subsequent decrease in plasma supports. Depletion of the carrier molecules at the brush border of the mucosal cells, or a change of the electrochemical gradient for Na⁺ might explain the selective impact on trans-luminal gut transport [38].

The observed decrease of leucine in plasma, in turn, might induce the significant reduction in levels of 2-ketoisocaproate in urine, which is a transamination product of the former [39]. Taurine is mainly conjugated with cholic acid and chenodeoxycholic acid in the liver to form primary bile salts, and is excreted via the urine after deconjugation from the bile salt or it leads into the sulphur- or pyruvate metabolism. Once the taurine conjugated bile salt has transformed the lipids into a micellar form, which is necessary to cross the intestinal wall, taurine is deconjugated by gut bacterial species and reabsorbed into the liver via blood circulation [40]. The decreased levels of excreted taurine in the urine of the infected mice may result from the higher demand for increased lipid digestion, resulting from the action of E. caproni in the gut.

The changes in hippurate, phenylacetylglycine, p-cresol-glucuronide, and TMA in urine, and 5-aminovalerate, and the SCFA levels in stool, are associated with a change in gut microbiotal presence or activity, as all of these metabolites undergo modification via gut microbial species before excretion. For instance, 5-aminovalerate is formed by several different Clostridium species which utilize ornithine and proline as substrates, but to our knowledge, only C. aminovalericum degrades 5-aminovalerate further to form mainly propionate and acetate [41]–[43]. This may imply that the presence of E. caproni in the gut disturbs the microbial balance resulting in depleted or inactivated C. aminovalericum. The formation of p-cresol is likewise known to be performed by a Clostridium subspecies (i.e., C. difficile and C. scatologenes) [44],[45], with the bacterium-specific enzyme p-hydroxyphenylacetate. It is then conceivable that p-cresol is taken up by the bloodstream, bound to serum proteins and glucuronidated in the kidney prior to excretion [46]. Increased excretion of p-cresol-glucuronide might be coupled with a higher activity or higher presence of this bacterial strain as the kidney function does not seem to be impaired in infected animals. PCR analyses on several different Clostridium sub-strains are ongoing and will be discussed in forthcoming publications.

The decrease of the SCFAs in stool may also be indicative of an unbalanced microbiota, as dietary carbohydrates (e.g., starches and fibres) are fermented by colonic bacteria to mainly acetate, propionate, and butyrate. Whilst butyrate serves as main energy source for colonocytes, acetate and propionate pass through the intestinal wall and move via peripheral blood to the liver where they have antagonistic functions on the cholesterol synthesis. Whilst the former increases cholesterol synthesis, the latter was shown to act as an inhibitor. The uptake from colon by the blood system is four times higher in the case of acetate, than propionate, which is a possible explanation for depletion of the SCFA, also reflected by the observed decrease in levels of acetate in both urine and plasma [47],[48].

The increased concentration of TMA and phenylacetylglycine, and the decrease of hippurate in urine, observed at the later time points of our experiment, are concomitant phenomena of the changed gut microbiota [49],[50]. Trimethylammonium compounds like choline and carnitine, which are ingested in the normal diet, are degraded by intestinal bacteria to TMA, and then oxidized in the liver to TMAO in a second step [51],[52]. A microbial shift toward choline degrading bacteria might explain the choline/GPC depletion in plasma and the subsequent increase of TMAO in urine.

An infection with E. caproni induces changes in the concentration of a range of metabolites in urine, plasma, and stool. To be useful as a ‘real’ biomarker, the metabolic candidate must be reproducible, robust, specific and, ideally, easy to measure [53]. From the current analyses an anomalous increase in urinary mannitol was noted in infected animals. Mannitol is likely to derive from the diet, since it is not synthesized by vertebrates. However, the higher amounts of urinary mannitol in E. caproni-infected mice, may reflect the higher intestinal permeability, compared to the control group [54].

To assess the specificity of the biomarkers identified for potential diagnosis of infection, the obtained E. caproni fingerprint was compared to altered metabolite patterns, associated with other parasite-rodent models [16]–[18]. Interestingly, E. caproni seems to alter the gut microbiota in a similar way to the biologically-related blood flukes, i.e., S. mansoni [16], and S. japonicum [17]. Hippurate, phenylacetylglycine, and TMA are modified by several gut microbial species before excretion in urine. In the 3 disease models, hippurate was found to decrease significantly, whereas phenylacetylglycine, p-cresol-glucuronide and TMA showed increased levels in infections with all 3 parasites, which suggests a common trematode-inherent influence on gut microbial composition. The change in 2-ketoisocaproate in urine was unique to the infection with E. caproni and 5-aminovalerate in stool may also deliver an E. caproni-specific marker but, at the time being, cannot be compared to other disease models, as the metabonomic assessment of stool was applied only in the present parasite-rodent model. The metabolic effect of a nematode infection (T. spiralis) in NIH Swiss mice has also been reported by Martin and colleagues [18]. T. spiralis has a similar initial mechanism of pathogenicity and also induces a state of inflammation of the gut before it migrates from the intestine to muscle tissue and induces hypercontractility [55]. While comparison between the study conducted by Martin et al. [18] and the current study revealed a number of biomarkers, which were the same in both models, the directionality of these metabolites was different. For example, a decrease in choline and creatine concentrations was observed in E. caproni-infected mice, whereas the same metabolites were reported to be increased in T. spiralis-infected mice compared to non-infected controls. Furthermore, the lipids (e.g., triaglycerides, saturated and unsaturated fatty acids) which undergo a marked increase in E. caproni-infected animals, showed a significant decrease in the mice infected with T. spiralis.

Future studies evaluating additional laboratory host-parasite models, and applying complementary metabolic profiling methods, such as ultra performance liquid chromatography (UPLC), in combination with mass spectrometry (MS), will help to confirm the specificity of the metabolic perturbations associated with an E. caproni infection. In conclusion, we have shown that metabolic profiling of plasma, urine, and stool delivers a comprehensive fingerprint of an E. caproni infection, composed of general as well as highly specific biomarkers (e.g., 2-ketoisocaproate and 5-aminovalerate). Keeping in mind the long-term objective of developing novel diagnostic assays for trematode-borne diseases, one would emphasize the value of further development, particularly based on the urine profiles.