MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

Abstract

Background Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. Results Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. Conclusion Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

Progressive drought alters the root exudate metabolome and differentially activates metabolic pathways in cotton (Gossypium hirsutum)

“…A total number of 33,870 m/z were identified by FT-ICR MS. After the data was filtered using quality control steps ( Ayala-Ortiz et al., 2023 ), a total of 13,033 m/z remained. After formula assignment, there were 3,985 metabolites with an assigned molecular formula.…”

“…Chemical formulae were assigned based on the following criteria: S/N > 7 and mass measurement error < 1 ppm, taking into consideration the presence of C, H, O, N, S and P and excluding other elements. The data produced by FT-ICR MS (peak masses, peak intensities, and metabolic molecular formula) were then processed through Metabodirect pipeline ( Ayala-Ortiz et al., 2023 ). Peak masses (m/z) < 200 and > 900 were filtered using -m option to target secondary metabolites ( Hsu et al., 2011 ; Park et al., 2013 ).…”

Environment-specific virocell metabolic reprogramming

“…Putative chemical formulas were assigned using Formultitude (previously named Formularity) software [ 36 ]. Gibbs free energy (GFE) was calculated as described previously [ 37 ], with peaks that were assigned a putative molecular formula in all samples. Organic matter (OM) transformation analysis for each individual sample replicate was done via network analysis using MetaNetter [ 38 ].…”

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter

“…Compositional networks are typically analyzed by counting the number of different chemical transformations within a sample (i.e., intrasample ), quantifying their occurrence, and applying downstream statistics (e.g., via comparing the number of each transformation found within all samples). In metabolomics, metabolic networks are frequently used to analyze the metabolic state of organisms since the underlying biochemical reactions are well-known. , However, this perception is likely an oversimplification of the highly dynamic and chemically diverse DOM pool. Major limitations are that the underlying processes are manifold (i.e., microbial production, adsorption), their rates are unknown, and that each process often only acts on a subset of the entire pool of molecules. , As a consequence, compositional networks cannot be considered as static, time-independent networks but should be rather seen as temporal networks.…”

“…In metabolomics, metabolic networks are frequently used to analyze the metabolic state of organisms since the underlying biochemical reactions are well-known. 33,34 However, this perception is likely an oversimplification of the highly dynamic and chemically diverse DOM pool. Major limitations are that the underlying processes are manifold (i.e., microbial production, adsorption), their rates are unknown, and that each process often only acts on a subset of the entire pool of molecules.…”