The complexity of gene expression dynamics revealed by permutation entropy.

Area of Science:

• Systems Biology
• Genomics
• Bioinformatics

Background:

• Living systems exhibit high complexity, a hallmark often overlooked in gene expression analysis.
• Current gene expression studies primarily focus on differential expression, pathways, and regulatory networks.
• This study introduces complexity analysis of temporal gene expression patterns using Permutation Entropy (PE).

Purpose of the Study:

• To investigate the complexity of temporal gene expression patterns using Permutation Entropy (PE).
• To analyze gene expression time series data from a complexity perspective.
• To identify genes with high temporal expression complexity and their regulatory characteristics.

Main Methods:

• Application of Permutation Entropy (PE) as a complexity metric.
• Analysis of abiotic stress response time series data in Arabidopsis thaliana.
• Examination of promoter regions, cis-regulatory motifs, and gene correlation networks.

Main Results:

• Genes involved in stress response and signaling displayed the highest PE complexity, even under non-stress conditions.
• Housekeeping genes showed lower PE complexity.
• Gene expression complexity generally increased upon stress exposure.
• High-complexity genes possess longer upstream intergenic regions and more cis-regulatory motifs, indicating complex regulatory mechanisms.
• High-complexity genes are linked to higher correlation network connectivity.
• Arabidopsis-specific genes exhibited higher PE complexity than genes conserved across plant species.

Conclusions:

• Permutation Entropy (PE) provides a robust method for quantifying temporal gene expression complexity.
• This approach is effective in identifying genes with varying complexity levels.
• PE is a versatile tool applicable to diverse molecular profile data beyond gene expression.