Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf...

Area of Science:

• Plant Biology
• Genomics
• Systems Biology

Background:

• Time-series transcriptomic data under varying conditions are vital for understanding biological processes.
• Existing methods struggle to analyze the full complexity of 3D transcriptomic data (gene expression, time, condition).

Purpose of the Study:

• To develop a novel computational method for analyzing complex time-series transcriptomic data.
• To identify gene regulatory networks involved in maize leaf development and C4 photosynthesis.

Main Methods:

• Developed a method to analyze 3D transcriptomic data without time-point alignment or inter-condition normalization.
• Applied the method to time-series transcriptomes of developing maize leaves under different light conditions.
• Generated time-ordered gene coexpression networks (TO-GCNs) for regulatory prediction.

Main Results:

• Obtained eight TO-GCNs, enabling prediction of upstream gene regulators.
• Identified a light-independent TO-GCN associated with Kranz anatomy development in C4 plants.
• Validated a regulatory cascade upstream of the SHORTROOT1 gene, a key regulator of Kranz anatomy.
• Discovered regulators of maize C4 enzyme genes and RUBISCO SMALL SUBUNIT2 by comparing maize and rice transcriptomes.

Conclusions:

• The new method effectively handles complex transcriptomic data, providing novel insights into regulatory networks.
• The study elucidates regulatory mechanisms underlying Kranz anatomy development and C4 photosynthesis in maize.
• The findings offer a powerful tool for dissecting gene regulation in complex biological systems.