Applying differential network analysis to longitudinal gene expression in response to perturbations

Area of Science:

• Systems Biology
• Genomics
• Immunology

Background:

• Differential Network (DN) analysis is a key method for interpreting gene expression changes and uncovering biological insights.
• It identifies alterations in gene networks due to external stimuli.
• Applying DN analysis to time-series RNA-sequencing data offers a dynamic view of biological responses.

Purpose of the Study:

• To apply Differential Network (DN) analysis to RNA-sequencing (RNA-seq) time series data.
• To investigate gene expression changes in human saliva post-pneumococcal vaccination (PPSV23) and in primary B cells treated with Rituximab.
• To identify temporal patterns of pathway activation and gene network rewiring.

Main Methods:

• Differential Network (DN) analysis of RNA-sequencing time series data.
• Application to saliva samples after pneumococcal vaccination (PPSV23).
• Application to primary B cells treated ex vivo with Rituximab.

Main Results:

• DN analysis successfully identified biological pathways activated by PPSV23 and targeted by Rituximab.
• Community detection on DNs revealed gene clusters with coordinated temporal behavior.
• Distinct temporal signatures and chronological pathway activation were observed in both saliva and B cell datasets, including early and delayed responses.

Conclusions:

• Differential Network analysis is effective for interpreting dynamic gene expression changes in response to perturbations like vaccination and drug treatment.
• The study highlights the temporal dynamics of biological pathway activation.
• DN analysis provides a robust framework for understanding immune responses and drug mechanisms at a systems level.