phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets.

Area of Science:

• Cellular biology
• Systems biology
• Bioinformatics

Background:

• Cell signaling pathways are crucial for cellular functions but existing databases are biased.
• Mass spectrometry-based phosphoproteomics offers unbiased signaling insights but faces signal-to-noise and reproducibility challenges.
• Current methods for extracting signaling signatures from phosphoproteomics data have limitations in balancing bias and interpretability.

Purpose of the Study:

• To introduce phuEGO, a novel computational tool for analyzing cell signaling.
• To improve the interpretation of global phosphoproteomics datasets by identifying active signaling modules.
• To enhance the comparison and integration of data across different experimental contexts.

Main Methods:

• PhuEGO combines up-to-three-layer network propagation with ego network decomposition.
• The method decomposes global phosphoproteomics data into smaller, interpretable networks of active signaling modules.
• It boosts signal-to-noise ratios and enriches networks for functional phosphosites.

Main Results:

• PhuEGO successfully identified common active functions across five SARS-CoV-2 phosphoproteomics datasets.
• The tool pointed to a subnetwork enriched for known COVID-19 targets.
• It demonstrated improved ability to compare and integrate phosphoproteomics data.

Conclusions:

• PhuEGO provides a flexible and effective tool for the functional interpretation of global phosphoproteomics data.
• The method enhances the extraction of meaningful signaling information from noisy biological datasets.
• PhuEGO aids in understanding cellular responses, exemplified by its application to SARS-CoV-2 infection data.