Metabolomic Approaches for Microbial Research

Accurate, sensitive and high-throughput research methods are the basis of microbial metabolomics research. The research process of microbial metabolomics usually includes sample preparation, signal acquisition, data processing analysis and biological interpretation.

Sample preparation

To obtain meaningful metabolomics data, microbial metabolomics studies require the use of appropriate sample preparation steps, including rapid sampling, quenching, and extraction of metabolites.

Rapid sampling not only prevents large changes in substrate concentrations but also helps maintain the stability of microbial metabolites. To ensure true information about a sample at a given time, it is often necessary to quench the sample quickly to terminate the metabolic reaction. The ideal quenching technique should quickly quench enzyme activity and maintain cellular or biological integrity.

Liquid nitrogen freezing or perchloric acid inactivation techniques are the main inactivation methods used in plant and animal metabolomics studies. However, this method is not applicable to the treatment of microbial cells, which cannot separate intracellular and extracellular metabolites. For the inactivation of microorganisms, cold methanol and its buffer solutions are more often used. Compared with eukaryotic microorganisms such as yeast and filamentous fungi, inactivation of prokaryotic microorganisms such as bacteria using cold methanol is more likely to cause leakage of intracellular metabolites. Due to the difference in cell wall structure, G- bacteria are more likely to produce leakage of intracellular metabolites than G+ bacteria. How to maintain cell integrity and prevent leakage of intracellular metabolites during inactivation to correctly reflect the physiological state of microbial cells is the key to microbial metabolomics research.

The extraction of metabolites is an important step in microbial metabolomics research. In order to analyze metabolites as a whole, the extraction method should meet the following requirements: 1) capable of extracting the maximum amount of metabolites; 2) unbiased, without excluding molecules with specific physical or chemical properties; 3) without destroying or changing the physical or chemical properties of metabolites. Currently, the commonly used methods for metabolite extraction include cold methanol, hot methanol, perchloric acid or base, chloroform-methanol mixture, and acetonitrile.

Fig. 1 The metabolomic process for determining pathogenic microorganisms and their metabolites (Bamikole et al., 2020).

Detection, analysis and identification of metabolites

Mass spectrometry (MS) is the main platform applied to the study of microbial metabolomics.

GC-MS is a well-developed analytical platform and the first analytical method used in microbial metabolomics research.

GC-MS is capable of analyzing hundreds of compounds (including organic acids, amino acids, glycans, sugar alcohols, aromatic amines and fatty acids, etc.) simultaneously, and is equipped with a standard metabolite library for rapid and accurate qualitative analysis of metabolites. However, derivatization of the sample is required. The two-dimensional GC-MS technique significantly improves the separation and detection sensitivity of complex samples and is effectively applied to microbial metabolomics.

Liquid chromatograph-mass spectrometry (LC-MS) technique is another important analytical platform for the analysis of unstable, volatile and non-polar compounds without the need for derivatization of the sample. Hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) is a high-throughput intracellular metabolomics technique that allows simultaneous analysis of both polar and non-polar metabolites. Its data acquisition and analysis are twice as fast as conventional methods.

Although LC-MS has been applied in many studies, there are still some problems in LC-MS-based microbial metabolomics studies, such as high salt concentration in the medium can inhibit the ionization efficiency of ESI, block the pump, and ultimately affect the validity and reproducibility of quantitative analysis. The capillary electrophoresis- mass spectrometry (CE-MS) technique has the advantages of rapid analysis, low sample requirement, low reagent consumption and relative cheapness.

Data processing and analysis

Pre-processing of the raw data is required to eliminate interfering factors. Data processing generally includes steps such as baseline correction, feature detection, noise filtering, peak alignment, normalization and normalization. Currently, there are a large number of software that can preprocess raw data obtained by MS into 2D data tables, such as MZmine, XCM and METIDEA.

The pre-processed data need to be subjected to multivariate statistical analysis such as PCA and partial least squares discriminant analysis (PLS-DA), from which potentially valid information can be obtained to identify biomarkers and metabolic pathways, etc. Metabolic pathway analysis not only helps to understand the interactions between metabolites, but also enables the exploration of gene expression data to complete functional genomics studies.

Reference

1. Bamikole, O. A., Green, E., et al. (2020). Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review. Food Research International, 110042.

Related Sections

Microbial Metabolomics Service

Application of Microbial Metabolomics