ViralLink

ViralLink reconstructs and analyzes signaling networks to trace signal transduction from intracellular viral proteins, including SARS-CoV-2, through their human binding partners to transcription factors and downstream gene expression, providing mechanistic insight into viral effects on intracellular signaling.

Key Features:

• Network Reconstruction and Analysis: Reconstructs networks representing the flow of signals initiated by viral proteins through cellular pathways to gene regulation.
• Transcriptomics Integration: Accepts transcriptomics counts data as input to associate changes in gene expression with network-derived signaling paths.
• Modularity and Customization: Provides a modular design that supports customization for different viral contexts and user-specified a priori interactions.
• Cell-Type Specific Analysis: Enables analysis of cell-type specific responses, demonstrated for bronchial/tracheal epithelial cells infected with SARS-CoV-2.
• Integration of Molecular Interaction and Expression Data: Combines molecular interaction data with gene expression datasets to link viral-host interactions to downstream transcriptional effects.

Scientific Applications:

• Mechanistic interpretation of viral-host interactions: Elucidates how viral proteins perturb host intracellular signaling and affect transcriptional regulation, exemplified by SARS-CoV-2.
• Target identification: Supports identification of proteins and pathways that may serve as potential drug targets downstream of viral-host interactions.
• Explaining cell-type and clinical variation: Helps explain cell-type specific responses and diverse clinical manifestations by mapping signaling to transcriptional outcomes.
• Comparative transcriptomics analysis: Facilitates applying network analysis across multiple transcriptomics datasets and cell types to uncover common and divergent mechanisms of pathogenesis.

Methodology:

Reconstructs signaling networks by tracing signal transduction from intracellular viral proteins through human binding partners to transcription factors and downstream gene expression, integrates transcriptomics counts data as input, and implements a modular pipeline with customizable scripts in R and Python.