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

Pierre Giroux1, Morgan Maillard1, Jacques Colinge1

  • 1IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Montpellier, France; Université de Montpellier, Montpellier, France; ICM, Institut régional du Cancer de Montpellier, Montpellier, France.

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Summary
This summary is machine-generated.

We developed a new computational tool extension to integrate post-translational modifications (PTMs) into cell-cell communication analysis. This approach enhances understanding of biological pathways and reduces false positives in ligand-receptor interaction predictions.

Keywords:
ligand–receptor interactionspost-translational modificationsproteomics data integration

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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.