Inference of the Dynamic Aging-related Biological Subnetwork via Network Propagation

TL;DR

This paper introduces a network propagation-based method to infer dynamic, aging-related biological subnetworks from gene expression and protein interaction data, revealing proteins with significant age-related network changes.

Contribution

It presents a novel application of network propagation to infer dynamic aging-related subnetworks, improving prediction quality over existing static methods.

Findings

Dynamic subnetworks better capture aging processes.

Proteins with significant network position changes are potential aging biomarkers.

Validated predictions through functional enrichment and literature review.

Abstract

Gene expression (GE) data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.) However, GE analyses ignore physical interactions between gene products, i.e., proteins. Since proteins function by interacting with each other, and since biological networks (BNs) capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP) to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related) subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP.