WAWABILITY July 11–12, 2025 Washington DC. Big ideas. Bold Progress. Global Impact. Powered by TDIforAccess.
WAWABILITY July 11–12, 2025 Washington DC. Big ideas. Bold Progress. Global Impact. Powered by TDIforAccess.

A-S.B.17: Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities

Authors

HUN-REN Institute for Computer Science and Control
Csaba Kerepesi
HUN-REN SZTAKI

Keywords

PPI network, transcriptomics, proteomics, sample-specific network, network analysis
[sponser-meet-now-chat][/sponser-meet-now-chat]
Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understand-ing of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but it varies among metrics. These results shed light to the properties of PPI network estimations and advise caution in interpreting them appropriately.

Please login to see details