Literature DB >> 25294044

Testing evolutionary models of senescence: traditional approaches and future directions.

Chloe Robins1, Karen N Conneely.   

Abstract

From an evolutionary perspective, the existence of senescence is a paradox. Why has senescence not been more effectively selected against given its associated decreases in Darwinian fitness? Why does senescence exist and how has it evolved? Three major theories offer explanations: (1) the theory of mutation accumulation suggested by PB Medawar; (2) the theory of antagonistic pleiotropy suggested by GC Williams; and (3) the disposable soma theory suggested by TBL Kirkwood. These three theories differ in the underlying causes of aging that they propose but are not mutually exclusive. This paper compares the specific biological predictions of each theory and discusses the methods and results of previous empirical tests. Lifespan is found to be the most frequently used estimate of senescence in evolutionary investigations. This measurement acts as a proxy for an individual's rate of senescence, but provides no information on an individual's senescent state or "biological age" throughout life. In the future, use of alternative longitudinal measures of senescence may facilitate investigation of previously neglected aspects of evolutionary models, such as intra- and inter-individual heterogeneity in the process of aging. DNA methylation data are newly proposed to measure biological aging and are suggested to be particularly useful for such investigations.

Mesh:

Year:  2014        PMID: 25294044     DOI: 10.1007/s00439-014-1492-7

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  90 in total

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Authors:  D P Shanley; T B Kirkwood
Journal:  Evolution       Date:  2000-06       Impact factor: 3.694

2.  LABORATORY EVOLUTION OF POSTPONED SENESCENCE IN DROSOPHILA MELANOGASTER.

Authors:  Michael R Rose
Journal:  Evolution       Date:  1984-09       Impact factor: 3.694

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Authors:  Brian Charlesworth
Journal:  Evolution       Date:  1990-05       Impact factor: 3.694

Review 4.  Optimality, mutation and the evolution of ageing.

Authors:  L Partridge; N H Barton
Journal:  Nature       Date:  1993-03-25       Impact factor: 49.962

5.  Stochastic model for analysis of longitudinal data on aging and mortality.

Authors:  Anatoli I Yashin; Konstantin G Arbeev; Igor Akushevich; Aliaksandr Kulminski; Lucy Akushevich; Svetlana V Ukraintseva
Journal:  Math Biosci       Date:  2006-12-05       Impact factor: 2.144

6.  Telomeres shorten during ageing of human fibroblasts.

Authors:  C B Harley; A B Futcher; C W Greider
Journal:  Nature       Date:  1990-05-31       Impact factor: 49.962

7.  Genome-wide methylation profiles reveal quantitative views of human aging rates.

Authors:  Gregory Hannum; Justin Guinney; Ling Zhao; Li Zhang; Guy Hughes; SriniVas Sadda; Brandy Klotzle; Marina Bibikova; Jian-Bing Fan; Yuan Gao; Rob Deconde; Menzies Chen; Indika Rajapakse; Stephen Friend; Trey Ideker; Kang Zhang
Journal:  Mol Cell       Date:  2012-11-21       Impact factor: 17.970

8.  Epigenetic predictor of age.

Authors:  Sven Bocklandt; Wen Lin; Mary E Sehl; Francisco J Sánchez; Janet S Sinsheimer; Steve Horvath; Eric Vilain
Journal:  PLoS One       Date:  2011-06-22       Impact factor: 3.240

9.  Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population.

Authors:  Jordana T Bell; Pei-Chien Tsai; Tsun-Po Yang; Ruth Pidsley; James Nisbet; Daniel Glass; Massimo Mangino; Guangju Zhai; Feng Zhang; Ana Valdes; So-Youn Shin; Emma L Dempster; Robin M Murray; Elin Grundberg; Asa K Hedman; Alexandra Nica; Kerrin S Small; Emmanouil T Dermitzakis; Mark I McCarthy; Jonathan Mill; Tim D Spector; Panos Deloukas
Journal:  PLoS Genet       Date:  2012-04-19       Impact factor: 5.917

10.  Mutation accumulation may be a minor force in shaping life history traits.

Authors:  Maciej Jan Dańko; Jan Kozłowski; James Walton Vaupel; Annette Baudisch
Journal:  PLoS One       Date:  2012-04-06       Impact factor: 3.240

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  4 in total

Review 1.  Early-late life trade-offs and the evolution of ageing in the wild.

Authors:  Jean-François Lemaître; Vérane Berger; Christophe Bonenfant; Mathieu Douhard; Marlène Gamelon; Floriane Plard; Jean-Michel Gaillard
Journal:  Proc Biol Sci       Date:  2015-05-07       Impact factor: 5.349

2.  Testing Two Evolutionary Theories of Human Aging with DNA Methylation Data.

Authors:  Chloe Robins; Allan F McRae; Joseph E Powell; Howard W Wiener; Stella Aslibekyan; Elizabeth M Kennedy; Devin M Absher; Donna K Arnett; Grant W Montgomery; Peter M Visscher; David J Cutler; Karen N Conneely
Journal:  Genetics       Date:  2017-08-30       Impact factor: 4.562

3.  Hypothalamic gene transfer of BDNF promotes healthy aging in mice.

Authors:  Travis McMurphy; Wei Huang; Xianglan Liu; Jason J Siu; Nicholas J Queen; Run Xiao; Lei Cao
Journal:  Aging Cell       Date:  2018-12-26       Impact factor: 9.304

4.  Menopause and adipose tissue: miR-19a-3p is sensitive to hormonal replacement.

Authors:  Reeta Kangas; Cristina Morsiani; Grazia Pizza; Catia Lanzarini; Pauliina Aukee; Jaakko Kaprio; Sarianna Sipilä; Claudio Franceschi; Vuokko Kovanen; Eija K Laakkonen; Miriam Capri
Journal:  Oncotarget       Date:  2017-12-18
  4 in total

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