Abstract
Metabolome profiling is becoming more commonly used in the study of complex microbial communities and microbiomes; however, to date, little information is available concerning appropriate extraction procedures. We studied the influence of different extraction solvent mixtures on untargeted metabolomics analysis of two continuous culture enrichment communities performing enhanced biological phosphate removal (EBPR), with each enrichment targeting distinct populations of polyphosphate-accumulating organisms (PAOs). We employed one non-polar solvent and up to four polar solvents for extracting metabolites from biomass. In one of the reactor microbial communities, we surveyed both intracellular and extracellular metabolites using the same set of solvents. All samples were analysed using ultra-performance liquid chromatography mass spectrometry (UPLC-MS). UPLC-MS data obtained from polar and non-polar solvents were analysed separately and evaluated using extent of repeatability, overall extraction capacity and the extent of differential abundance between physiological states. Despite both reactors demonstrating the same bioprocess phenotype, the most appropriate extraction method was biomass specific, with methanol: water (50:50 v/v) and methanol: chloroform: water (40:40:20 v/v/v) being chosen as the most appropriate for each of the two different bioreactors, respectively. Our approach provides new data on the influence of solvent choice on the untargeted surveys of the metabolome of PAO enriched EBPR communities and suggests that metabolome extraction methods need to be carefully tailored to the specific complex microbial community under study.
Highlights
Untargeted metabolome profiling is becoming more commonly deployed in the study of complex microbial communities and microbiomes, and this general approach will likely play an increasingly important role in future microbial ecology studies in both host-associated and environmental settings [1].Performing metabolomics on microbial community samples remains challenging [2], in part due to the fact that multiple member species could produce or utilise a given compound [3] and in part due to the high proportion of unannotated mass features that will be detected [4]
In the final part of our analysis, we explore how the use of different solvent types can potentially impact two aspects of biological-level interpretation that are commonly encountered in the literature, by examining (1) which metabolic pathways are implicated by the set of putative compound identification and (2) the number of LCMS features that are differentially abundant between the different physiological states that the microbial community experiences over time, and that are coupled to the excessive phosphorus release and uptake phenomena that are characteristic of the enhanced biological phosphate removal (EBPR) bioprocess
We examined which biological functions were captured by the set of detected LC-MS features by taking the subset of LC-MS features annotated to compounds and examining any Kyoto Encyclopedia of Gene and Genome (KEGG) pathway of which the former are classified as members (Figure 4)
Summary
Performing metabolomics on microbial community samples remains challenging [2], in part due to the fact that multiple member species could produce or utilise a given compound [3] and in part due to the high proportion of unannotated mass features that will be detected [4] Solving these problems will require substantial efforts in the areas of data analysis and interpretation [5,6], new modelling methods [7,8] and large-scale compound annotation [4,9]. Further technical challenges exist due to potential differential extractability among the varied cell types of member species of a microbial community, as well as in the complex biomass morphology that can be encountered, such as flocs or granules [10], and which requires the use of extensive mechanical disruption when performing biomolecular extractions. The choice of an efficient and reproducible metabolite extraction and sample preparation method is extremely important in metabolomics studies, as it is known to influence the observed metabolite features and the subsequent biological interpretation of the data [11]
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