A statistical framework for revealing signaling pathways perturbed by DNA variants.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Inter-individual variation in gene expression is often caused by DNA variants affecting signaling networks.
• Understanding these genetic influences on cellular communication is crucial for disease research.

Purpose of the Study:

• To develop a novel probabilistic approach for identifying specific pathways through which DNA variants perturb cellular signaling networks.
• To provide a statistically robust method for prioritizing hypotheses related to genetic variations and their functional consequences.

Main Methods:

• The PINE (Pathway Information and Network Estimation) procedure integrates biological signaling network knowledge with transcriptional response data.
• A probabilistic framework is employed to systematically analyze the impact of DNA variants on signaling pathways.
• Performance was validated using simulated data and compared against existing methods.

Main Results:

• PINE demonstrated higher accuracy in identifying genetic perturbations compared to current methods when tested on simulated data.
• Application of PINE to immune dendritic cell responses to pathogens revealed statistically significant perturbations in pathogen-sensing signaling networks.
• The analysis suggested novel regulatory mechanisms involving functional DNA variants.

Conclusions:

• PINE offers a statistically rigorous and accurate method for dissecting the functional impact of DNA variants on signaling networks.
• The findings highlight previously unrecognized regulatory mechanisms in immune cell responses to pathogens.
• This approach facilitates hypothesis generation for experimental validation in functional genomics and systems biology.