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Comprehensive map of age-associated splicing changes across human tissues and their contributions to age-associated diseases.
Wang, Kun; Wu, Di; Zhang, Haoyue; Das, Avinash; Basu, Mahashweta; Malin, Justin; Cao, Kan; Hannenhalli, Sridhar.
Affiliation
  • Wang K; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, USA.
  • Wu D; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA.
  • Zhang H; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA.
  • Das A; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA.
  • Basu M; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, USA.
  • Malin J; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, USA.
  • Cao K; Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Hannenhalli S; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA. kcao@umd.edu.
Sci Rep ; 8(1): 10929, 2018 Jul 19.
Article in En | MEDLINE | ID: mdl-30026530
ABSTRACT
Alternative splicing contributes to phenotypic diversity at multiple biological scales, and its dysregulation is implicated in both ageing and age-associated diseases in human. Cross-tissue variability in splicing further complicates its links to age-associated phenotypes and elucidating these links requires a comprehensive map of age-associated splicing changes across multiple tissues. Here, we generate such a map by analyzing ~8500 RNA-seq samples across 48 tissues in 544 individuals. Employing a stringent model controlling for multiple confounders, we identify 49,869 tissue-specific age-associated splicing events of 7 distinct types. We find that genome-wide splicing profile is a better predictor of biological age than the gene and transcript expression profiles, and furthermore, age-associated splicing provides additional independent contribution to age-associated complex diseases. We show that the age-associated splicing changes may be explained, in part, by concomitant age-associated changes of the upstream splicing factors. Finally, we show that our splicing-based model of age can successfully predict the relative ages of cells in 8 of the 10 paired longitudinal data as well as in 2 sets of cell passage data. Our study presents the first systematic investigation of age-associated splicing changes across tissues, and further strengthening the links between age-associated splicing and age-associated diseases.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aging / RNA / Gene Expression Profiling Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male Language: En Journal: Sci Rep Year: 2018 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aging / RNA / Gene Expression Profiling Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male Language: En Journal: Sci Rep Year: 2018 Document type: Article Affiliation country: United States