title: Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging.
abstract: 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.
authors.affiliation: Department of Molecular Epidemiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands; The Delft Bioinformatics Lab, Delft University of Technology, PO Box 5031, 2600 GA, Delft, The Netherlands.
authors.forename: Erik B
authors.initials: EB
authors.lastname: van den Akker
authors.forename: Willemijn M
authors.initials: WM
authors.lastname: Passtoors
authors.forename: Rick
authors.initials: R
authors.lastname: Jansen
authors.forename: Erik W
authors.initials: EW
authors.lastname: van Zwet
authors.forename: Jelle J
authors.initials: JJ
authors.lastname: Goeman
authors.forename: Marc
authors.initials: M
authors.lastname: Hulsman
authors.forename: Valur
authors.initials: V
authors.lastname: Emilsson
authors.forename: Markus
authors.initials: M
authors.lastname: Perola
authors.forename: Gonneke
authors.initials: G
authors.lastname: Willemsen
authors.forename: Brenda W J H
authors.initials: BW
authors.lastname: Penninx
authors.forename: Bas T
authors.initials: BT
authors.lastname: Heijmans
authors.forename: Andrea B
authors.initials: AB
authors.lastname: Maier
authors.forename: Dorret I
authors.initials: DI
authors.lastname: Boomsma
authors.forename: Joost N
authors.initials: JN
authors.lastname: Kok
authors.forename: Pieternella E
authors.initials: PE
authors.lastname: Slagboom
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
authors.forename: Marian
authors.initials: M
authors.lastname: Beekman
mesh_terms: D000369:Aged, 80 and over; D000375:Aging / Q000097:blood* / Q000235:genetics*; D015415:Biomarkers / Q000097:blood*; D018797:Cell Cycle Proteins / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D006801:Humans; D008213:Lymphocyte Activation / Q000235:genetics; D018655:Lymphocyte Count; D018832:Molecular Chaperones; D060066:Protein Interaction Maps / Q000235:genetics*; D015203:Reproducibility of Results; D016019:Survival Analysis; D013601:T-Lymphocytes / Q000276:immunology; D059467:Transcriptome / Q000235:genetics*
chemical_list: C491641:ASF1A protein, human; D015415:Biomarkers; D018797:Cell Cycle Proteins; D018832:Molecular Chaperones
keywords: aging; blood transcriptomics; meta-analysis; network-based analysis; protein-protein interactions