MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments

Abstract

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well. IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method-MetFish-using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Keywords: exometabolomics; extreme environments; hypersaline; mass spectrometry; microbial communities.

FIG 1

Overview of the MetFish method. (a) MetFish reagents and associated derivatization reactions. (b) General workflow of the MetFish method.

FIG 2

Validation of the MetFish method using amino acids. (a) Tandem mass spectra from analysis of a mixture of unlabeled (black spectrum) and ¹³C and ¹⁵N uniformly labeled glycine (red spectrum), both derivatized with dansyl chloride. The m/z of each fragment peak is listed, and the mass shifts due to the isotopic labels are indicated. (b) Overlayed extracted ion chromatograms (EICs) of amino acids in neat solution analyzed by nanocapillary liquid chromatography-tandem mass spectrometry (LC-MS/MS) without chemical tagging (upper chromatogram); total ion chromatogram from analysis of amino acids in 2 M MgSO₄ analyzed by nanocapillary LC-MS/MS without chemical tagging (middle chromatogram); EICs of amino acids in 2 M MgSO₄, derivatized using dansyl chloride, followed by extraction with organic solvent, and analyzed by nanocapillary LC-MS/MS with dansylation chemical tagging (lower chromatogram). The y axes for all plots have been normalized to the highest intensity peaks in each.

FIG 3

MetFish is applicable to measuring metabolites with a broad range of functional groups in challenging sample matrices. Shown are extracted ion chromatograms with the transitions obtained in selected reaction monitoring (SRM) mode for metabolite quantification from application of MetFish in measurement of (a) amine metabolites, (b) carboxyl metabolites, (c) carbonyl metabolites, (d) hydroxyl metabolites as sugars, and (e) hydroxyl metabolites as alcohols. In all cases, MetFish was deployed in situ in metabolite-salt mixtures containing 2 M MgSO₄.

FIG 4

Application of MetFish in quantification of proteinogenic amino acids in representative microbial communities. (a) Quantification of amino acids during phototrophic microbial community succession with various nitrogen amendments (data shown are normalized average amino acid concentrations from analysis of 3 biological replicate succession experiments). (b) Exo- and endometabolomics analysis of amino acids in soil, using a high-salt wash to increase recovery due to possible disruption of nonspecific binding to soil particles (data shown are mean ± standard deviation from analysis of 3 replicate soil samples).

FIG 5

Untargeted metabolomics using MetFish. (a) Workflow for untargeted metabolomics analysis using MetFish. (b) Global amine tag-based metabolite profile of a produced fluid sample. The size of the circle is proportional to the ion intensity, and putatively identified metabolites are labeled.

FIG 6

Targeted metabolomics analysis using MetFish in injected and produced fluids from hydraulic fracturing. (a) Quantification of 37 metabolites identified by targeted MetFish analysis. (b) Comparison of metabolite levels in the input material and spent fracking fluid. Data shown are from replicate analysis (n = 3) of each fluid sample. Heatmaps were generated using MetaboAnalyst 3.0 (67), using Pearson’s distance measure and average linkage for the clustering algorithm.