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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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Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment
Abstract Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introducemuMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.
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- Award ID(s):
- 1759949
- PAR ID:
- 10233493
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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