Lifespan estimation in marine turtles using genomic promoter CpG density.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 27 05 2020
accepted: 15 07 2020
entrez: 1 8 2020
pubmed: 1 8 2020
medline: 30 9 2020
Statut: epublish

Résumé

Maximum lifespan for most animal species is difficult to define. This is challenging for wildlife management as it is critical for estimating important aspects of population biology such as mortality rate, population viability, and period of reproductive potential. Recently, it has been shown cytosine-phosphate-guanine (CpG) density is predictive of maximum lifespan in vertebrates. This has made it possible to predict lifespan in long-lived species, which are generally the most intractable. In this study, we use gene promoter CpG density to predict the lifespan of five marine turtle species. Marine turtles are a particularly difficult group for lifespan estimation because of their migratory behaviour, longevity and high juvenile mortality rates, which all restrict individual tracking over their lifespan. Sanger sequencing was used to determine the CpG density in selected promoters. We predicted the lifespans for marine turtle species ranged from 50.4 years (flatback turtle, Natator depressus) to 90.4 years (leatherback turtle, Dermochelys coriacea). These lifespan predictions have broad applications in marine turtle research such as better understanding life cycles and determining population viability.

Identifiants

pubmed: 32735637
doi: 10.1371/journal.pone.0236888
pii: PONE-D-20-15932
pmc: PMC7394378
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0236888

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

J Exp Biol. 2005 May;208(Pt 9):1717-30
pubmed: 15855403
Aging (Albany NY). 2013 Apr;5(4):227-33
pubmed: 23603822
Biol Lett. 2009 Jun 23;5(3):339-42
pubmed: 19324637
PLoS One. 2012;7(12):e51927
pubmed: 23284819
Nucleic Acids Res. 2018 Jan 4;46(D1):D1083-D1090
pubmed: 29121237
PLoS One. 2017 Mar 23;12(3):e0173999
pubmed: 28333937
Aging (Albany NY). 2018 Apr 14;10(4):561-572
pubmed: 29661983
BMC Bioinformatics. 2009 Dec 15;10:421
pubmed: 20003500
PLoS One. 2011 May 11;6(5):e19905
pubmed: 21589942
PLoS One. 2015 Nov 10;10(11):e0141460
pubmed: 26556237
Nucleic Acids Res. 2017 Jan 4;45(D1):D51-D55
pubmed: 27899657
Nat Genet. 2013 Jun;45(6):701-706
pubmed: 23624526
Nucleic Acids Res. 2012 Aug;40(15):e115
pubmed: 22730293
Sci Rep. 2019 Dec 12;9(1):17866
pubmed: 31831772
PLoS One. 2013 Apr 17;8(4):e61082
pubmed: 23613790
J Evol Biol. 2009 Aug;22(8):1770-4
pubmed: 19522730
Nature. 2000 Jun 1;405(6786):529-30
pubmed: 10850701
Sci Rep. 2016 Nov 07;6:36361
pubmed: 27819303

Auteurs

Benjamin Mayne (B)

Environomics Future Science Platform, Indian Oceans Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation, Crawley, Western Australia, Australia.

Anton D Tucker (AD)

Department of Biodiversity, Conservation and Attractions, Marine Science Program, Kensington, Western Australia, Australia.

Oliver Berry (O)

Environomics Future Science Platform, Indian Oceans Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation, Crawley, Western Australia, Australia.

Simon Jarman (S)

School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia.

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Classifications MeSH