Post-Transcriptional Modification Integration for Ligand-Receptor Cellular Network Inference.

Area of Science:

• Computational biology
• Molecular biology
• Systems biology

Background:

• Cell-cell communication is crucial for tissue homeostasis and disease understanding.
• Existing tools for inferring cellular interactions often overlook post-translational modifications (PTMs).
• PTMs can significantly alter protein function and cellular signaling pathways.

Purpose of the Study:

• To extend the BulkSignalR tool for integrating PTM data into ligand-receptor interaction analysis.
• To enable more accurate prediction of downstream biological pathways.
• To improve the analysis of cell-cell communications by incorporating PTM information.

Main Methods:

• Extension of the BulkSignalR computational tool.
• Integration of diverse post-translational modification (PTM) data.
• Application to both bulk and single-cell transcriptomic/proteomic datasets.
• Validation using two illustrative biological datasets.

Main Results:

• The enhanced tool successfully integrates PTM information into ligand-receptor interaction predictions.
• PTM integration provides deeper insights into biological pathway regulation.
• The new functionality reduces false positive results compared to standard methods.
• The approach is compatible with all PTM types and various data types.

Conclusions:

• Integrating post-translational modifications (PTMs) significantly improves the accuracy of cell-cell communication inference.
• The extended BulkSignalR tool offers a powerful approach for analyzing complex biological signaling.
• This method enhances the understanding of disease mechanisms and tissue homeostasis by revealing PTM-dependent interactions.