bioRxivPlant BiologyPreprint
Single-cell multi-omics enabled discovery of alkaloid biosynthetic pathway genes in the medical plant Catharanthus roseus
C. Li, J. C. Wood, A. H. Vu, J. P. Hamilton, C. E. Rodriguez Lopez, R. M. E. Payne, D. A. Serna Guerrero, K. Yamamoto, B. Vaillancourt, L. Caputi, S. E. O'Connor, C. R. Buell
Abstract
Advances in omics technologies now permit generation of highly contiguous genome assemblies, detection of transcripts and metabolites at the level of single cells, and high-resolution determination of gene regulatory features including 3-dimensional chromatin interactions. Using a complementary, multi-omics approach, we interrogated the monoterpene indole alkaloid (MIA) biosynthetic pathway in Catharanthus roseus, a source of leading anti-cancer drugs. We identified not only new clusters of genes involved in MIA biosynthesis on the eight C. roseus chromosomes but also rampant gene duplication including paralogs of MIA pathway genes. Clustering was not limited to the linear genome and through chromatin interaction data, MIA pathway genes were shown to be present within the same topologically associated domain, permitting identification of a secologanin transporter. Single cell RNA-sequencing revealed exquisite and sequential cell-type specific partitioning of the leaf MIA biosynthetic pathway that, when coupled with a newly developed single cell metabolomics approach, permitted identification of a reductase that yields the bis-indole alkaloid anhydrovinblastine. Last, we revealed cell-type specific expression in the root MIA pathway that is conferred in part by neo- and sub-functionalization of paralogous MIA pathway genes. This study highlights how a suite of omic approaches, including single cell gene expression and metabolomics, can efficiently link sequence with function in complex, specialized metabolic pathways of plants.
- organism: plant (other)
- assay: scRNA-seq
- assay: metabolomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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