Architecture of the human regulatory network derived from ENCODE data

Area of Science:

• Genomics
• Systems Biology
• Molecular Biology

Background:

• Gene regulation relies on transcription factors binding DNA in specific combinations.
• Understanding the human transcriptional regulatory network is crucial for deciphering cellular function and disease.

Purpose of the Study:

• To map the genomic binding of 119 human transcription factors.
• To analyze the combinatorial binding patterns and network properties of these factors.

Main Methods:

• Conducted over 450 experiments to determine transcription factor genomic binding information.
• Organized binding data into a hierarchical network and integrated it with other genomic data, like microRNA regulation.

Main Results:

• Identified context-specific combinatorial binding of transcription factors, differing near and far from genes.
• Revealed a hierarchical network structure where top-level factors influence expression and middle-level factors mitigate bottlenecks.
• Discovered enriched network motifs, such as noise-buffering feed-forward loops.
• Found that highly connected network components are under stronger selection and show allele-specific activity.

Conclusions:

• The human transcriptional regulatory network exhibits complex, context-dependent organization.
• This detailed regulatory map provides a foundation for interpreting personal genomes and understanding human biology and disease.