Mining for Active Molecules in Probiotic Supernatant by Combining Non-Targeted Metabolomics and Immunoregulation Testing

Abstract

Chronic respiratory diseases such as asthma are highly prevalent in industrialized countries. As cases are expected to rise, there is a growing demand for alternative therapies. Our recent research on the potential benefits of probiotics suggests that they could prevent and reduce the symptoms of many diseases by modulating the host immune system with secreted metabolites. This article presents the first steps of the research that led us to identify the immunoregulatory bioactivity of the amino acid d-Trp reported in our previous study. Here we analyzed the cell culture metabolic footprinting of 25 commercially available probiotic strains to associate metabolic pathway activity information with their respective immune modulatory activity observed in vitro. Crude probiotic supernatant samples were processed in three different ways prior to untargeted analysis in positive and negative ionization mode by direct infusion ESI-FT-ICR-MS: protein precipitation and solid phase extraction (SPE) using HLB and CN-E sorbent cartridges. The data obtained were submitted to multivariate statistical analyses to distinguish supernatant samples into the bioactive and non-bioactive group. Pathway analysis using discriminant molecular features showed an overrepresentation of the tryptophan metabolic pathway for the bioactive supernatant class, suggesting that molecules taking part in that pathway may be involved in the immunomodulatory activity observed in vitro. This work showcases the potential of metabolomics to drive product development and novel bioactive compound discovery out of complex biological samples in a top-down manner.

Keywords: ESI[±] FT-ICR-MS; bioactive compounds; cell culture supernatant; metabolic footprinting; probiotics; solid-phase extraction; tryptophan pathway; untargeted metabolomics.

Figure 1

Schematic representation of two approaches to new bioactive compound discovery. (Left panel) Traditional bioassay-guided fractionation is time and labor intensive due to its step-by-step separation using chromatographic techniques followed by biological activity assessments. It removes most of the interfering matrix compounds but may lead to losses of active compounds. (Right panel) Holistic metabolomics approach deals with complexity, requires high level of expertise in instrumentation and data analysis, but offers scientists a broader picture of the biological system in study [19].

Figure 2

Unsupervised PCA scores plots obtained from the data matrix of samples of crude probiotic supernatants (A,B), HLB-SPE extracts (C,D), and CN-E-SPE extracts (E,F). The left side displays results obtained from FT-ICR-MS analyses in ESI positive mode and the right side in ESI negative mode. Only models D and F showed a group separation trend with predictive relevance.

Figure 3

Supervised OPLS-DA scores plots. Models C, D and F are robust and have a high classi-fication power. Discriminant molecular features (m/z) observed in these three models were submitted to metabolic pathway analysis. The left side displays results obtained from FT-ICR-MS analyses in ESI positive mode (A,C,E) and the right side in ESI negative mode (B,D,F).

Figure 4

Description of the most populated KEGG metabolic pathways and their respective number of hits. Pathway analysis was originated from the most discriminant mass features of each class derived from the OPLS-DA models that showed robust classification power.