Secondary Metabolic Pathway-Targeted Metabolomics

Abstract

This chapter provides step-by-step methods for building secondary metabolic pathway-targeted molecular networks to assess microbial natural product biosynthesis at a systems level and to aid in downstream natural product discovery efforts. Methods described include high-resolution mass spectrometry (HRMS)-based comparative metabolomics, pathway-targeted tandem MS (MS/MS) molecular networking, and isotopic labeling for the elucidation of natural products encoded by orphan biosynthetic pathways. The metabolomics network workflow covers the following six points: (1) method development, (2) bacterial culture growth and organic extraction, (3) HRMS data acquisition and analysis, (4) pathway-targeted MS/MS data acquisition, (5) mass spectral network building, and (6) network enhancement. This chapter opens with a discussion on the practical considerations of natural product extraction, chromatographic processing, and enhanced detection of the analytes of interest within complex organic mixtures using liquid chromatography (LC)-HRMS. Next, we discuss the utilization of a chemometric platform, focusing on Agilent Mass Profiler Professional software, to run MS-based differential analysis between sample groups and controls to acquire a unique set of molecular features that are dependent on the presence of a secondary metabolic pathway. Using this unique list of molecular features, the chapter then details targeted MS/MS acquisition for subsequent pathway-dependent network clustering through the online Global Natural Products Social Molecular Networking (GnPS) platform. Genetic information, ionization intensities, isotopic labeling, and additional experimental data can be mapped onto the pathway-dependent network, facilitating systems biosynthesis analyses. The finished product will provide a working molecular network to assess experimental perturbations and guide novel natural product discoveries.

Keywords: Chemical signaling; Comparative metabolomics; High-resolution mass spectrometry; Isotopic labeling; Molecular networking; Natural product discovery; Nonribosomal peptide biosynthesis; Secondary metabolism.

Fig. 1

Untargeted versus pathway-targeted mass spectral data collection. Under untargeted MS/MS fragmentation modes, abundant ions are preferentially selected for fragmentation (highlighted with stars), and depending on inclusion and exclusion parameters, many of the less abundant ions go undetected in MS/MS data. Under pathway-targeted fragmentation modes, only pathway-dependent masses are selected (highlighted in red and by stars), providing higher fragmentation coverage of the secondary metabolic pathway ions of interest for subsequent molecular networking

Fig. 2

Workflow overview for pathway-targeted molecular networking

Fig. 3

Pathway-targeted mass spectral networking. (a) An untargeted auto MS /MS approach leads to a network with a much larger number of total ion masses (nodes) with often more limited coverage of less abundant pathway-dependent ions. As highlighted in the Venn Diagram, there are a large number of entities associated with controls (media components, primary metabolites, and contaminants). (b) By removing all entities detected in any control sample, targeted MS/MS can be performed to access higher MS/MS fragmentation coverage for “pathway-dependent” molecular features (MOFs) associated with the presence of the gene cluster of interest (highlighted in Venn Diagram)

Fig. 4

Find compounds by molecular feature. As described in Table 1 and detailed in the chapter, the different settings (tabs) can be modified to optimize for the in silico extraction of compounds of interests

Fig. 5

MS convert settings. MS convert program is used to convert mass spectral data files to CEF files for analyses in Mass Profiler Professional. Selection settings are highlighted (red ovals). Once the filter is selected, click “Add,” then select “Start”

Fig. 6

Molecular network feature enhancement. (a) Features, such as average ionization intensities and other experimental data, can be used to enhance the network in Cytoscape to help guide pathway and secondary metabolite characterization. These graphical illustrations allow for the quick identification of metabolic “bottlenecks” in secondary metabolism, such as for a pathway mutant. (b) Ion masses (or molecular features, nodes) incorporating labeled amino acid (e.g., methionine (Met) or cysteine (Cys)), as determined by HRMS, are color coded on the network map. Figure was adapted from Vizcaino and Crawford [6]

Fig. 7

Schematic for bacterial culture extraction