A multi-omics integrative gene network of pear (Pyrus)

Area of Science:

• Plant biology
• Genomics
• Molecular genetics

Background:

• Multi-omics data offers insights into plant development, but complete gene regulatory networks are challenging to map.
• Understanding gene networks is crucial for deciphering plant growth and developmental processes.

Purpose of the Study:

• To construct an integrative multi-omics gene network for pear fruit development.
• To predict and validate gene regulatory relationships influencing fruit quality traits.
• To explore the evolutionary dynamics of duplicated genes in regulatory networks.

Main Methods:

• Integrated 3D genomics, transcriptomics, transcription factor (TF) binding, chromatin accessibility, protein structure, and proteomics data.
• Developed a large-scale integrative network with over 45,678 elements and 3.15 million edges.
• Applied machine learning models for predicting fruit trait-related genes and utilized molecular experiments for validation.

Main Results:

• The integrative network successfully predicted interactors of PbrII5 and identified novel sugar metabolism-related genes.
• Validated functions of newly predicted genes through overexpression in pear fruit.
• Observed significant regulatory network divergence in duplicated genes, with neofunctionalization as a key evolutionary process.

Conclusions:

• The developed multi-omics network provides a powerful tool for understanding pear fruit development and quality formation.
• The study establishes a foundation for discovering novel regulatory mechanisms in fruit development.
• A public database, pearGRN, was created to support further research on complex gene regulatory relationships.