RNA interference
Definition
RNA interference (RNAi) is a conserved biological process in which small RNA molecules, typically 21-23 nucleotides in length, silence gene expression by targeting complementary messenger RNA (mRNA) sequences for degradation or translational repression. The process involves small interfering RNAs (siRNAs) or microRNAs (miRNAs) that are processed by the enzyme Dicer and loaded into the RNA-induced silencing complex (RISC). RNAi serves as both a natural cellular regulatory mechanism and a powerful experimental tool for targeted gene knockdown. This technique has revolutionized functional genomics research by enabling researchers to systematically investigate gene function, validate therapeutic targets, and develop potential RNA-based therapeutics for diseases including cancer, viral infections, and genetic disorders.
Visualize RNA interference in Nodes Bio
Researchers can use Nodes Bio to map RNAi experimental results as gene regulatory networks, connecting miRNAs or siRNAs to their target genes and downstream affected pathways. Network visualization reveals off-target effects, identifies compensatory mechanisms, and illustrates cascading regulatory consequences of gene silencing. Users can integrate RNAi screening data with protein interaction networks to understand how knockdown of specific genes impacts broader cellular systems and identify potential therapeutic intervention points.
Visualization Ideas:
- miRNA-target gene regulatory networks showing direct and indirect silencing effects
- RNAi screening hit networks connecting knocked-down genes to phenotypic outcomes and affected pathways
- Off-target effect maps displaying unintended gene silencing and compensatory regulatory responses
Example Use Case
A cancer research team investigating tumor suppressor pathways uses siRNA libraries to systematically knock down 500 genes in breast cancer cell lines. They measure changes in cell proliferation, apoptosis markers, and gene expression profiles. By visualizing the RNAi screening results in a network context, they discover that silencing three previously uncharacterized genes produces similar phenotypic effects and affects overlapping downstream targets, suggesting these genes function in a common regulatory module. This network-based analysis reveals a novel pathway component that becomes a promising therapeutic target for triple-negative breast cancer.