Advances in analyzing and engineering plant metabolic diversity

The immense diversity of the members of the kingdom Plantae, so far comprising more than 400,000 known species (Guiry, 2024), is reflected not only in their morphological and genetic variability but also in their metabolic complexity. Plant metabolic networks are dynamic and expanding systems that evolve in a lineage-specific manner and produce hundreds of thousands of structurally diverse metabolites, which can be broadly categorized into general (or core/primary) and specialized (or secondary) metabolites. General metabolites, such as carbohydrates, amino acids, and lipids, are essential for fundamental physiological processes like growth, development, and reproduction. In contrast, specialized metabolites like alkaloids, flavonoids, terpenoids, and other phenolic compounds often have an impact at different levels beyond central carbon metabolism—from allosteric regulation of proteins, subcellular organization, and intercellular interactions to organismal phenotypes, phylogeographic/interspecies diversification, biotic/abiotic interactions, and ecosystem maintenance (Weng et al., 2021; Ono and Murata, 2023).

The chemical diversity of plant metabolites constitutes a vast and largely untapped phytochemical space with significant potential for applications across multiple fields. In medicine, plant-derived compounds have been a cornerstone of drug discovery for centuries. For example, alkaloids, like morphine and quinine, and terpenoids, such as paclitaxel, have revolutionized the treatment of pain, malaria, and cancer, respectively (Newman and Cragg, 2020; Atanasov et al., 2021). In agriculture, phytochemicals are increasingly recognized for their contribution to plant defense against pests and diseases, reducing the need for synthetic pesticides while promoting sustainable farming practices and food security (Sousa et al., 2021). Beyond medicine and agriculture, plant metabolites hold promise for applications in biotechnology and industrial processes. For instance, terpenoids and phenolic compounds are being investigated for their potential as biofuels, bioplastics, and natural food preservatives (Mewalal et al., 2017). Even though less than 10% of plant species have been thoroughly investigated for their chemical composition (Li and Vederas, 2009), it is estimated that plants produce over a million compounds, although pinpointing a specific number is challenging because of the heterogeneity of metabolite databases available (Wang et al., 2016; Nguyen-Vo et al., 2020; Hawkins et al., 2021). Recent estimates suggest that the total number of unique structures across the entire plant kingdom likely spans into the millions to tens of millions (Engler Hart et al., 2025), indicating that over 99% of the phytochemical space remains unexplored and highlighting its vast and largely untapped potential.

To capture this broad range of metabolite diversity and function, a variety of techniques are used, such as specialized protocols for metabolite extraction, mass spectrometry, computational metabolomics including compound annotation, cheminformatics, bioassays, chemotaxonomy, phylogenomics, ancestral state reconstruction, and chemical ecology. As the revolutions in genomics, big data, and artificial intelligence (AI) have taken hold, there is an increasing need to develop high-throughput alternatives for the above techniques and to leverage AI to address outstanding roadblocks. Similarly, there is a greater impetus to combine different “parts” (e.g., enzymes, regulators, transporters) for metabolic engineering and reconstruction of complex metabolic pathways. Nevertheless, as just one example of persisting challenges, the comparison of metabolomic datasets and experiments remains difficult, caused by differences in extraction and analytic methods. Furthermore, standards for the collection, preservation, and sharing of metabolomic data are only slowly evolving (Alseekh et al., 2021; Genesiska et al., 2024), and the ecological roles of many metabolites are still poorly understood (Kessler and Kalske, 2018), limiting our ability to harness their full potential.

In this special issue, we highlight a sampling of these approaches, providing more insights into plant metabolic diversity. A summary of the articles in these issues is provided below, broadly divided into novel bench techniques, data applications, and AI techniques. We anticipate that the applications of these methods will boost our understanding of the arsenal of chemical responses plants deploy to respond to their natural environments and will help efforts to breed/engineer these chemical traits into crops.

K.J.T. and G.D.M. initiated this special issue, and S.D.S. and F.R. contributed to its development. All authors contributed to editorial duties for the manuscripts included in this special issue. All authors contributed text for the manuscript, and K.J.T. combined those contributions and led the writing and editing. All authors approved the final version of the manuscript.