1H-NMR analysis of feces: new possibilities in the helminthes infections research

Abstract

Abstract Background 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. Method 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. Results 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. Conclusions 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.

Keywords

• Nuclear Magnetic Resonance
• Partial Little Square Discriminant Analysis
• Nuclear Magnetic Resonance Data
• Principal Component Analysis Model
• Partial Little Square Discriminant Analysis Model