Using knowledge graphs to infer gene expression in plants.

Area of Science:

• Plant biology
• Bioinformatics
• Genomics

Background:

• Climate change impacts ecosystems and agriculture, necessitating adaptation strategies.
• Understanding genotype-environment-phenotype (G×E×P) dynamics across species is crucial for resilience.
• Gene regulatory networks are vital for predicting phenotype, and knowledge transfer between species can accelerate this understanding.

Purpose of the Study:

• To develop a knowledge graph (KG) integrating gene expression, molecular interactions, and functions.
• To investigate genotype-environment-phenotype dynamics using homology inference for cross-species knowledge application.
• To assess the role of cis and trans-regulatory elements in predicting homologous gene expression patterns under drought stress.

Main Methods:

• Constructed a KG using data from Planteome and EMBL-EBI Expression Atlas.
• Analyzed gene expression data from *Arabidopsis thaliana* and *Populus trichocarpa* under drought conditions.
• Utilized graph queries to identify homologous genes and analyzed their cis-regulatory regions.

Main Results:

• Identified 16 homologous gene pairs between *A. thaliana* and *P. trichocarpa* with varying expression patterns under drought.
• Homologous genes with similar expression responses to drought exhibited conserved cis-regulatory regions and trans-element interactions.
• Homologous genes with opposing expression patterns showed divergence in regulatory elements.

Conclusions:

• Homology inference for predicting gene expression and phenotype requires careful integration of cis and trans-regulatory information.
• Knowledge graphs can facilitate cross-species comparisons of gene regulatory networks.
• This approach aids in understanding and predicting plant responses to environmental changes like drought.