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Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems.

Authors :
Fu, Jie
McKinley, Brian
James, Brandon
Chrisler, William
Markillie, Lye Meng
Gaffrey, Matthew J.
Mitchell, Hugh D.
Riaz, Muhammad Rizwan
Marcial, Brenda
Orr, Galya
Swaminathan, Kankshita
Mullet, John
Marshall‐Colon, Amy
Source :
Plant Journal. Jun2024, Vol. 118 Issue 5, p1668-1688. 21p.
Publication Year :
2024

Abstract

SUMMARY: Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc‐lab.shinyapps.io/lcm‐dataset/). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09607412
Volume :
118
Issue :
5
Database :
Academic Search Index
Journal :
Plant Journal
Publication Type :
Academic Journal
Accession number :
177563118
Full Text :
https://doi.org/10.1111/tpj.16690