The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein-protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10(-38)) enriched for 'T-cell activation' marking age-associated shifts in lymphocyte blood cell counts (R(2) = 0.603; P = 1.9 × 10(-10)). Adjusting the analysis in the compendium for the 'T-cell activation' module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for 'Translational elongation', 'Cytolysis' and 'DNA metabolic process'. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.
Bibliographical note© 2013 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
- Aged, 80 and over
- Cell Cycle Proteins/genetics
- Databases, Genetic
- Gene Expression Regulation
- Lymphocyte Activation/genetics
- Lymphocyte Count
- Protein Interaction Maps/genetics
- Reproducibility of Results
- Survival Analysis