Plant Alkaloid Biosynthesis

Background and thematic area

Plant natural products (PNPs), or specialised metabolites, provide resilience against environmental and biological stresses. They are also valuable for humanity, contributing to pharmaceuticals, flavours and agrichemicals. Identifying the genes and enzymes that plants use to make PNPs leads to new biotechnologies that enhance crops, improve health, and replace fossil-fuel derived synthetic chemistry (10.3389/fchem.2020.596479). This boosts the bioeconomy and contributes to developing a Sustainable Planet.

Core research problem

Typically, PNPs are identified using metabolomics, and candidate biosynthetic genes are identified using transcriptomics (10.1016/j.copbio.2024.103147). However, few computational methods integrate this data into a holistic multi-omics analysis, and fewer tools include chemical logic. Yet, such methods could massively accelerate plant natural product pathway discovery. An exception is MEANtools (10.1371/journal.pbio.3003307) which is capable of integrating transcriptomics, metabolomics and reaction predictions to identify gene candidates. This tool could be useful across multiple PNP pathways, but should be expanded to accommodate spatial omics data, a new frontier in the field. The Daphniphyllum alkaloids (DA) are a class of PNP with remarkable structural complexity and diversity, but their bioactivities remain untapped due to limited access (10.1111/nph.19814). Understanding DA biosynthesis would enable access to a new chemical space with potential applications. However, identifying gene candidates in these complex, spatially distributed pathways requires state-of-the-art omics integration.

Aim

To develop integrative approaches for the multi-omics discovery of PNP biosynthesis, using DAs as a case study.

Objectives:

• Collate extensive multi-omic data of DAs into a single resource (genomics, metabolomics, transcriptomics, spatial transcriptomics)
• Obtain additional spatial data for DA biosynthesis (single-nuclear-RNAseq and spatial metabolomics)
• Adapt MEANtools to incorporate spatial datasets
• Employ MeanTools to predict gene candidates for DA biosynthesis
• Validate candidate prediction using experimental biochemistry
• Dr Singh travel to UK to conduct training in multi-omics and MeanTools
• Dr Lichman to travel to the Netherlands to explore further grants and collaborations.

Outputs and Impact:

Immediate

revealing key genes and enzymes involved in DA biosynthesis, enabling future reconstruction of DAs using engineering biology approaches, unlocking this compound class for screening and applications. The project will also lead to the further development of MeanTools, establishing its utility as a key tool for plant natural product discovery.

Sustained

we will develop a new collaborative framework for plant biology research and data-analysis across York and Maastricht, which will lead to further projects between the individuals and institutes.