Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability.

Area of Science:

• Molecular Biology
• Systems Biology
• Bioinformatics

Background:

• Cell function studies often overlook protein modifications and interactions, focusing instead on molecular abundances.
• Previous work established methods for context-specific networks (transcription factor-gene regulatory, kinase-substrate, protein interaction).
• The complex interdependencies between these biological networks remain largely unexplored.

Purpose of the Study:

• To develop a multi-omics framework integrating protein activity within context-specific networks.
• To gain deeper functional insights into molecular phenotypes beyond protein abundance.
• To explore the role of protein complexes and interactions in cellular processes.

Main Methods:

• Developed a multi-omics framework to harness measured or inferred protein activity.
• Applied the framework to context-specific networks.
• Utilized progressively differentiated HeLa cell lines (CCL2 and Kyoto) for analysis.

Main Results:

• The framework provides deeper functional insights compared to analyzing protein abundance alone.
• Demonstrated the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes.
• Explored the impact of protein complexes and interactions on cell doubling time and *Salmonella* Typhimurium infection susceptibility.

Conclusions:

• Protein interaction networks are critical for connecting molecular data to cellular phenotypes.
• The developed multi-omics framework offers a generalizable approach for analyzing complex biological datasets.
• This approach enhances our understanding of cell function and disease mechanisms.