1H-NMR analysis of feces: new possibilities in the helminthes infections research
© The Author(s). 2017
Received: 20 May 2016
Accepted: 28 March 2017
Published: 17 April 2017
Analysis of the stool samples is an essential part of routine diagnostics of the helminthes infections. However, the standard methods such Kato and Kato-Katz utilize only a fraction of the information available. Here we present a method based on the nuclear magnetic resonance spectroscopy (NMR) which could be auxiliary to the standard procedures by evaluating the complex metabolic profiles (or phenotypes) of the samples.
The samples were collected over the period of June-July 2015, frozen at −20 °C at the site of collection and transferred within four hours for the permanent storage at −80 °C. Fecal metabolites were extracted by mixing aliquots of about 100 mg thawed stool material with 0.5 mL phosphate buffer saline, followed by the homogenization and centrifugations steps. All NMR data were recorded using a Bruker 600 MHz AVANCE II spectrometer equipped with a 5 mm triple resonance inverse cryoprobe and a z-gradient system.
Here we report an optimized method for NMR based metabolic profiling/phenotyping of the stools samples. Overall, 62 metabolites were annotated in the pool sample using the 2D NMR spectra and the Bruker Biorefcode database. The compounds cover a wide range of the metabolome including amino acids and their derivatives, short chain fatty acids (SCFAs), carboxylic acids and their derivatives, amines, carbohydrates, purines, alcohols and others. An exploratory analysis of the metabolic profiles reveals no strong trends associated with the infection status of the patients. However, using the penalized regression as a variable selection method we succeeded in finding a subset of eleven variables which enables to discriminate the patients on basis of their infections status.
A simple method for metabolic profiling/phenotyping of the stools samples is reported and tested on a pilot opisthorchiasis cohort. To our knowledge this is the first report of a NMR-based feces analysis in the context of the helminthic infections.
Analysis of stool samples is an essential part of routine diagnostics of the helminthes infections. For years, despite a consistent background of criticism and occasional new developments, the direct smear and Kato-Katz techniques remain the gold standard diagnostic tests for schistosomiasis, opisthorchiasis and the soil-transmitted helminthiasis . However, here we introduce a method based on nuclear magnetic resonance spectroscopy (NMR), which could be auxiliary to the standard methodologies. In contrast to the Kato and Kato-Katz tests which use only the eggs count as a measure, we examine the complex metabolic profile of the sample. In other words we are applying the metabolomics approach. Metabolomics is a discipline studying the metabolome - a totality of the metabolites that can be measured in a biological sample. The metabolites are defined as the end products and the intermediates of the metabolism. In the clinical setting the metabolomics studies are commonly based on the analysis of the body fluids. Urine and blood (serum or plasma) are being the most common sample types due to the minimally invasive procedures of sample collection. Feces as a material for metabolomics studies has only recently started to gain the deserved attention [2, 3]. Over recent years few metabolomics studies in such areas as e.g. dietary interventions , inflammatory bowel disease [5, 6] and colorectal cancer  have been published.
Indeed, the fecal masses are the physiological product of the gastrointestinal tract, one of the key metabolic systems of the human body. Thus, it is logical to assume that their composition should reflect current metabolic status of the digestive tract or its metabolic phenotype . The human gut represents a complex ecosystem and harbors gut bacteria outnumbering the cells in our organism  and the analysis of the fecal masses or/and their derivatives (e.g. extracts or fecal waters) offers the most direct access to the physiological processes controlling the gastrointestinal system homeostasis, gut bacteria-host interactions and interaction between the hosts and parasitic helminthes. For example, the helminth infections are often accompanied by such symptoms as diarrhea, abdominal pain and blood in the stool. The given examples represent the extreme cases, but they provide a clear illustration of the parasite’s ability changing the metabolic homeostasis of the host and the host’s digestive system in particular. This, in turn, makes metabolic analysis of the fecal masses an interesting, non-invasive way to monitor such changes.
Here we present a simple NMR based metabolomics workflow for the analysis of fecal samples. For this pilot study we used stool samples of patients diagnosed with opisthorchiasis and a group of matched controls. Opisthorchiasis is parasitic disease caused by trematodes belonging to the family Opisthorchiidae (Opisthorchis felineus, Opisthorchis viverrini) . According to WHO there are about 17 million infected people and approximately 112 million people exposed or at risk of infection. The workflow presented here is only a proof of principle, but it can be easily scaled, tuned towards a quantitative analysis and implemented into other case studies or in future routine screening without fundamental modification of the sample collection or the exiting diagnostic routines.
Characteristics of participants
Opisthorchiasis (n = 16)
Control (n = 14)
Fecal metabolites extraction
Fecal metabolites were extracted as described elsewhere  with some minor modifications. Briefly, the aliquots of about 100 mg thawed stool material were mixed with 0.5 mL phosphate buffer saline (1.9 mM Na2HPO4, 8.1 mM NaH2PO4, 150 mM NaCl, pH 7.4; Sigma-Aldrich, Germany) containing 10% deuterated water (D2O 99.8%; Cortecnet, France) and 0.05 mM sodium 3-trimethylsilyl-propionate-d 4 (TMSP-2,2,3,3-d 4; Cambridge Isotope Laboratories Inc., UK) as chemical shift reference. The mixtures were homogenized by bead beating with zirconium oxide beads of 1 mm diameter for 30 s at 4 °C in a Bullet Blender 24 (Next Advance Inc., USA). The fecal slurry was then centrifuged at 16100×g for 15 min at 4 °C. Supernatants were collected and centrifugation was repeated. Finally, the resulting fecal extracts were transferred to a 96 well plate (Bruker, Germany) and 190 μL of each sample was transferred to a 3 mm NMR tube in SampleJet 96 tube rack (Bruker, Germany) using 215 Gilson liquid handler. The samples were then placed in a SampleJet system and kept cooled at 6 °C while queued for NMR measurements.
Alternative protocols for fecal extraction, as described elsewhere [5, 12, 13] were also applied using technical replicates and the same equipment and chemicals described above. For filtration we used the Whatman filters with 0.2 μm diameter pores (GE Healthcare, UK). An ultracentrifugation step with filtration was also tested using Amicon Ultra cellulose centrifugal filters with a cut-off MW of 3000 Da (Millipore Ireland, Ltd). The filters were washed with doubly distilled water before use and tested for impurities and presence of additives using a blank PBS buffer sample and acquisition of NMR spectra with the same parameters as those used for fecal extracts measurements (see below).
All NMR data were recorded using a Bruker 600 MHz AVANCE II spectrometer equipped with a 5 mm triple resonance inverse cryoprobe and a z-gradient system. The temperature of the samples was controlled at 27 °C during measurement. Prior to data acquisition, tuning and matching of the probe head followed by shimming and proton pulse calibration were performed automatically for each sample. One-dimensional (1D) 1H NMR spectra were recorded using the first increment of a NOESY pulse sequence with presaturation (γB1 = 50 Hz) for water suppression during a relaxation delay of 4 s and a mixing time of 10 ms [14, 15] 64 scans of 65,536 points covering 12,335 Hz were recorded and zero filled to 65,536 complex points prior to Fourier transformation, an exponential window function was applied with a line-broadening factor of 1.0 Hz. The spectra were automatically phase and baseline corrected and referenced to the internal standard (TMSP; δ 0.0 ppm).
After tube filling, 30 μL from the leftovers of each sample were combined to form a pool sample mix. The pool sample was aliquoted and used for acquisition of two-dimensional (2D) NMR spectra to aid the assignment of fecal metabolites. The set of 2D experiments included a J-resolve (J-res), 1H-1H correlation spectroscopy (COSY), 1H-1H total correlation spectroscopy (TOCSY), 1H-13C heteronuclear single quantum correlation (HSQC) and 1H-13C heteronuclear multiple bond correlation spectroscopy (HMBC) using the standard parameters implemented in Topspin 3.0 (Bruker Biospin, Germany).
NMR data processing
NMR data were further processed using in house routines written in Matlab 2014a (The Mathworks, Inc., USA) and Python 2.7 (Python Software Foundation, www.python.org). Briefly, the obtained 1H spectra were re-evaluated for incorrect baselines and corrected using a polynomial fit of degree 5. The spectral region from 0.5 to 9.7 ppm was binned using an in-house algorithm for adaptive intelligent binning, which is based on the original paper of De Meyer et al. . Initial bin width was set to 0.02 ppm and final variable bins sizes were calculated based on the peaks position and width in the spectra. The spectral region with the residual water peak (4.5 – 5.1 ppm) was excluded from the data. The final data consisted of 429 bins that were normalized by the Probabilistic Quotients Normalization method  to correct for dilution differences from sample to sample. Data were first normalized to unit total area and subsequently, the variables of each sample were divided by those of a reference sample, in this case the median spectrum. Each sample was subsequently scaled by its median quotient, which represents the most probable dilution factor. Finally, the normalized data was autoscaled prior to statistical analysis.
All the analysis was performed in the R statistical software environment (http://www.r-project.org/, R version 3.2.3.). Exploratory data analysis was performed using the package “pcaMethods” . Variable selection was performed with the “glmnet” package . For data visualization the “ggplot2”, “GGally” and “gridExtra” packages were used.
Optimization of the sample preparation
Exploratory analysis of the data
Variable selection and validation of the selected subset
The selected variable assignments and corresponding p-values
Spectral region (ppm)
Threonine and Lactate
Phenylalanine / Fructose
Tyrosine / Ornithine / Putrescine
Threonine and Lactate
Ornithine / Unknown
Here we present an analytical workflow for 1H-NMR analysis of feces with special emphasis on application in the field of the helminthes infections. The described procedure resulted in rich spectra where 62 metabolites are annotated (Fig. 2). Using our set of the samples selection we were able to dissect a subset of the metabolites (Fig. 4) which may be discriminative for the infections status. This subset includes such common constituents of human biofluids as threonine, asparagine, lactate and hypoxanthine. Asparagine is higher in the samples of the control patients while the other selected compounds have higher levels in the infected samples. The limited number of samples is a clear limitation of this study and therefore we restrain ourselves from the discussion of the possible physiological models based on the selected markers or the attempts to deconvolute the metabolic profiles into the infection predictive patterns. On the other hand the proposed method clearly stresses out the potential for a new window of information that can be used in such case studies. In principle, the fact that a subset of the discriminative metabolites can be dissected gives a clear illustration of the method’s potential. A combination of a simple, commonly accepted diagnostic method and such advanced analytical method as NMR provides a powerful research tool which enables the collection of a wealth of information without interference or in parallel with the routine diagnostics or epidemiological studies. Taking advantage of the robustness and quantitative nature of this technology, obtaining the metabolic profiles of fecal material is rather straightforward and provides both an insight into biochemistry/physiology of the host-pathogen interaction and the possibility of accessing the morbidity and eventually play an auxiliary role in the diagnostics. The main limitations of this approach arise mainly from the absence of standard procedures in stool collection rather than the technology itself. However, taking into account the increasing interest in using the NMR (as well as mass spectrometry) based metabolomics approaches in fecal samples, we envisage that more established routines and practices in sample collection will be developed in the near future which will reveal the underlying potential of this type of analysis.
In summary, a simple method for metabolic profiling/phenotyping of the stools samples is reported and tested on a pilot opisthorchiasis cohort. To our knowledge this is the first report of a NMR-based feces analysis in the context of the helminthic infections. With this study, an attempt was made to extend a conventional way of the stool analysis adding an extra dimension which can be used for metabolic phenotyping of the patients, in depth exploration of the host-parasite interaction and search for metabolic morbidity or/and infection markers. To extend and take full advantage of the possibilities offered by NMR based metabolic profiling much larger cohorts than the one used in this study are needed, preferably, even collected in the different endemic areas. With this report however, we provide a simple proof of concept aiming to introduce a well-established technology in the field of infectious diseases and fecal material analysis and with this, trigger future studies in this direction.
1H-1H correlation spectroscopy
Heteronuclear multiple bond correlation spectroscopy
Nuclear magnetic resonance
Principal Component Analysis
Partial Least Squares Discriminant Analysis
standard operating procedures
1H-1H total correlation spectroscopy
This study was supported by the Tomsk State University Academic D.I. Mendeleev Fund Program (grant № 188.8.131.525). We thank Dr. Eugenie Semichev for facilitation of the sample collection.
Availability of data and materials
The data are included in the manuscript. The raw NMR spectra can be made available upon request.
SK, YHC and OAM conceived the study, its design and planning; SK and DK coordinated the study; DK and ND participated in the NMR data generation, managing the clinical material and drafting the text; IVS coordinated parasitological part and participated in the drafting of the manuscript; SK, HKK and OAM performed the data analysis; SK and OAM wrote the final version of the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The study was reviewed and approved by the local ethics committee of the Siberian State Medical University (Tomsk, Russia).
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