Erik B. van den Akker1,2, Willemijn M. Passtoors1, Rick Jansen3,4, Erik W. van Zwet5, Jelle J. Goeman5, Marc Hulsman2, Valur Emilsson6, Markus Perola7, Gonneke Willemsen8, Brenda W.J.H. Penninx3,4, Bas Heijmans1, Andrea B. Maier9, Dorret I. Boomsma8,4, Joost N. Kok10,1, P. Eline Slagboom1,11, Marcel J. T. Reinders2, Marian Beekman1,11
1Department of Molecular Epidemiology Leiden University Medical Center PO Box 9600 2300 RC Leiden The Netherlands
2The Delft Bioinformatics Lab Delft University of Technology PO Box 5031 2600 GA Delft The Netherlands
3Department of Psychiatry VU University Medical Center Neuroscience Campus Amsterdam VU University Medical Center A.J. Ernststraat 1187 1081 HL Amsterdam The Netherlands
4EMGO Institute for Health and Care Research Neuroscience Campus Amsterdam Van der Boechorststraat 7 1081 BT Amsterdam The Netherlands
5Department of Medical Statistics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands
6Icelandic Heart Association Holtasmari 1 IS‐201 Kópavogur Iceland
7National Institute for Health and Welfare, PO Box 30, 00271 Helsinki, Finland
8Department of Biological Psychology VU University Van der Boechorststraat 7 1081 BT Amsterdam The Netherlands
9Section of Gerontology and Geriatrics Department of Internal Medicine VU University Medical Center De Boelelaan 1117 1007 MB Amsterdam The Netherlands
10Department of Algorithms Leiden Institute of Advanced Computer Science University of Leiden Niels Bohrweg 1 2333 CA Leiden The Netherlands
11Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands
Tóm tắt
SummaryThe 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 (R2 = 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.
