Plant natural products, especially specialized metabolites, play vital roles in plant environmental interactions and ecological adaptation. They are also major sources of medicines, nutrients, food flavors, and industrial materials. Identifying the metabolic enzymes and the biosynthetic pathways underlying the production of specialized metabolites is a critical step for improving crop resistance traits and for producing valuable compounds through synthetic biology approaches. However, due to the high complexity of metabolic networks, the rapid turnover of duplicate genes, and high lineage specificities of specialized metabolites, the identification of plant specialized metabolic genes remains challenging.

Plant researchers are witnessing an era with increasing amounts of plant omics data alongside rapidly-evolving computing power and algorithms. Consequently, the candidate genes responsible for the biosynthesis of specialized metabolites can be identified and validated with higher efficiency, accelerating our understanding of specialized metabolism in plants.

In the past decades, the genes responsible for the biosynthesis of several important plant specialized metabolites have been uncovered, including those in the pathways of artemisinin, paclitaxel, codeine, and physostigmine. However, this knowledge covers the tip of the iceberg, due to the great number of specialized metabolites in plants. Further studies are still needed to decipher the genes responsible for the biosynthesis of diverse plant natural products. This Research Topic will focus on omics data-based and/or computational method-driven approaches that contribute to our understanding of the biosynthesis and evolution of plant natural products.

In this Research Topic, we welcome Original Research, Methods, or Review papers that cover, but are not limited to, the following:

• Computational methods/tools for identifying plant specialized metabolic pathways and/or metabolic gene clusters utilizing omics data, including genome, transcriptome, methylome, metabolome, etc;
• Large scale identification of plant metabolic pathways using omics data-based approaches, such as genome-wide association analysis of metabolic traits, association analysis between transcriptome and metabolome, etc;
• Omics data-based approaches to study metabolic traits involved in plant-environmental interactions, such as insect defense, pathogen resistance, or adaptation to extreme habitats;
• Evolution of specialized metabolic genes/pathways in plants revealed using omics data, such as phylogenomic analysis;
• Machine learning approaches-based prediction of plant specialized metabolic genes,

Please note that studies that only describe omics data without experimental validation or mechanistic insights into biological principles, as well as studies utilizing machine learning-based approaches without experimental validation, will not be considered.

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Frontiers in Plant Science

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