New tool helps cracking plants’ chemical code

To thrive, plants produce a number of micronutrients, yet understanding how these substances are produced is not often clear. A group of Dutch scientists developed MEANtools, a digital tool to help uncover how plants produce their natural substances.

MEANtools, short for Metabolite Anticipation tools, represents a leap forward in understanding plant metabolism. Developed by researchers at Maastricht University, this innovative computer program is designed to analyze vast datasets, including plant DNA, gene activity, and chemical composition, to uncover the secrets of how plants produce specialized metabolites. These metabolites are crucial for a plant's survival, enabling it to defend against diseases, attract pollinators, and adapt to harsh environmental conditions.

Traditionally, unraveling these metabolic pathways required extensive prior knowledge and laborious experimentation. MEANtools changes this by autonomously identifying patterns within the data, reducing the reliance on guesswork and minimizing the need for extensive lab work.

How MEANtools Works

MEANtools functions as a computational pipeline, integrating statistical analysis and reaction-rule-based strategies to connect transcripts (RNA sequences produced by genes) to metabolites (the end products of metabolism). It leverages extensive databases like RetroRules, which contains approximately 43,000 reactions annotated with enzymes, and LOTUS, a comprehensive natural product resource, to predict potential biosynthetic pathways. The tool correlates gene expression with metabolite abundance across different samples, pinpointing potential relationships between them. This process involves creating a reaction network where metabolites are nodes, and enzymatic reactions, catalyzed by gene-encoded enzyme families, form the connecting edges. MEANtools then assigns reaction-likelihood scores based on substrate-enzyme associations to predict candidate metabolic pathways.

To validate its capabilities, MEANtools was tested on data from tomatoes, focusing on the production of falcarindiol, a natural compound believed to have antifungal properties. The tool successfully reconstructed key steps in the falcarindiol biosynthetic pathway, correctly anticipating five out of seven transformations of intermediate metabolites, along with the enzymes that catalyze these reactions. Notably, MEANtools also identified new, previously unknown, potential production routes for this compound, demonstrating its ability to generate novel insights. The initial dataset, comprising 11,266 mass features and 20,576 transcripts, was refined to 1,230 mass features and 7,590 transcripts through differential abundance and expression analyses. This highlights the tool's capacity to sift through complex data and identify biologically significant interactions.

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Open source tool

MEANtools is designed to be user-friendly, generating visualizations and supplementary data in easily navigable tables stored in an SQLite database. The software is open source and freely available on GitHub, ensuring accessibility for researchers worldwide. This collaborative approach fosters further development and refinement of the tool, with ongoing efforts focused on improving data processing steps and enhancing gene-metabolite cluster analysis. Ultimately, MEANtools empowers scientists to explore the biosynthetic potential of molecular structures, formulate specific hypotheses about potential pathways, and accelerate the discovery of new plant metabolites and their functions.

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