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Summary In plants, the biosynthetic pathways of some specialized metabolites are partitioned into specialized or rare cell types, as exemplified by the monoterpenoid indole alkaloid (MIA) pathway ofCatharanthus roseus(Madagascar Periwinkle), the source of the anticancer compounds vinblastine and vincristine. In the leaf, theC. roseusMIA biosynthetic pathway is partitioned into three cell types with the final known steps of the pathway expressed in the rare cell type termed idioblast. How cell‐type specificity of MIA biosynthesis is achieved is poorly understood.We generated single‐cell multi‐omics data fromC. roseusleaves. Integrating gene expression and chromatin accessibility profiles across single cells, as well as transcription factor (TF)‐binding site profiles, we constructed a cell‐type‐aware gene regulatory network for MIA biosynthesis.We showcased cell‐type‐specific TFs as well as cell‐type‐specificcis‐regulatory elements. Using motif enrichment analysis, co‐expression across cell types, and functional validation approaches, we discovered a novel idioblast‐specific TF (Idioblast MYB1,CrIDM1) that activates expression of late‐stage MIA biosynthetic genes in the idioblast.These analyses not only led to the discovery of the first documented cell‐type‐specific TF that regulates the expression of two idioblast‐specific biosynthetic genes within an idioblast metabolic regulon but also provides insights into cell‐type‐specific metabolic regulation.
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Optimising the use of gene expression data to predict plant metabolic pathway memberships
Summary Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co‐expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts.Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored.Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene‐to‐pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway‐best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality.Our study highlights the need to extensively explore expression‐based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.
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- PAR ID:
- 10449972
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 231
- Issue:
- 1
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 475-489
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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