Blomqvist, L. & Sten, I. Reproductive Biology of the Snow Leopard. Panthera Books, London (1982).
Kirkwood, T. B. & Austad, S. N. Why do we age?. Nature 408, 233–238 (2000).
Google Scholar
Zhao, M., Klaassen, C. A. J., Lisovski, S. & Klaassen, M. The adequacy of aging techniques in vertebrates for rapid estimation of population mortality rates from age distributions. Ecol. Evol. 9, 1394–1402 (2019).
Google Scholar
Oli, M. K. & Dobson, F. S. The relative importance of life-history variables to population growth rate in mammals: Cole’s prediction revisited. Am. Nat. 161, 422–440 (2003).
Google Scholar
Mori, A. Analysis of population changes by measurement of body weight in the Koshima troop of Japanese monkeys. Primates 20, 371–397 (1979).
Google Scholar
WILkINSON, G. S. & Brunet-Rossinni, A. K. Methods for age estimation and the study of senescence in bats. In Ecological and behavioral methods for the study of bats 315–325 (Johns Hopkins University Press, 2009).
Hartman, K. L., Wittich, A., Cai, J. J., van der Meulen, F. H. & Azevedo, J. M. N. Estimating the age of Risso’s dolphins (Grampus griseus) based on skin appearance. J. Mammal. 97, 490–502 (2016).
Google Scholar
Chevallier, C., Gauthier, G. & Berteaux, D. Age estimation of live arctic foxes Vulpes lagopus based on teeth condition. Wildl. Biol. 4, 1–6 (2017).
White, P. A. et al. Age estimation of African lions Panthera leo by ratio of tooth areas. PloS One 11, e0153648 (2016).
Google Scholar
Siegal-Willott, J., Isaza, R., Johnson, R. & Blaik, M. Distal limb radiography, ossification, and growth plate closure in the juvenile Asian elephant (Elephas maximus). J. Zoo Wildl. Med. 39, 320–334 (2008).
Google Scholar
Paoli-Iseppi, D. et al. Measuring animal age with DNA methylation: From humans to wild animals. Front. Genet. 8, 106 (2017).
Google Scholar
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).
Google Scholar
Schübeler, D. Function and information content of DNA methylation. Nature 517, 321–326 (2015).
Google Scholar
Field, A. E. et al. DNA methylation clocks in aging: Categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).
Google Scholar
Weidner, C. I. et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 15, 1–12 (2014).
Google Scholar
Bocklandt, S. et al. Epigenetic predictor of age. PloS One 6, e14821 (2011).
Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 (2017).
Google Scholar
Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 1–14 (2017).
Google Scholar
Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging (Albany NY) 9, 1055–1068 (2017).
Google Scholar
Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19, 22 (2018).
Google Scholar
Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 1–5 (2018).
Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).
Google Scholar
Wright, P. G. et al. Application of a novel molecular method to age free-living wild Bechstein’s bats. Mol. Ecol. Resour. 18, 1374–1380 (2018).
Google Scholar
Park, K. et al. Determining the age of cats by pulp cavity/tooth width ratio using dental radiography. J. Vet. Sci. 15, 557 (2014).
Google Scholar
Yoshimura, H. et al. The relationship between plant-eating and hair evacuation in snow leopards (Panthera uncia). PLOS ONE 15, e0236635 (2020).
Kinoshita, K. et al. Long-term monitoring of fecal steroid hormones in female snow leopards (Panthera uncia) during pregnancy or pseudopregnancy. PLOS ONE 6, e19314 (2011).
Li, G., Davis, B. W., Eizirik, E. & Murphy, W. J. Phylogenomic evidence for ancient hybridization in the genomes of living cats (Felidae). Genome Res. 26, 1–11 (2016).
Google Scholar
Marino, C. L., Lascelles, B. D. X., Vaden, S. L., Gruen, M. E. & Marks, S. L. Prevalence and classification of chronic kidney disease in cats randomly selected from four age groups and in cats recruited for degenerative joint disease studies. J. Feline Med. Surg. 16, 465–472 (2014).
Google Scholar
Sparkes, A. H. et al. ISFM consensus guidelines on the diagnosis and management of feline chronic kidney disease. J. Feline Med. Surg. 18, 219–239 (2016).
Google Scholar
Hamano, Y. et al. Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting. Leg. Med. 21, 5–10 (2016).
Google Scholar
Hamano, Y., Manabe, S., Morimoto, C., Fujimoto, S. & Tamaki, K. Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory application for cigarette butts. Sci. Rep. 7, 10444 (2017).
Google Scholar
Bekaert, B., Kamalandua, A., Zapico, S. C., Van de Voorde, W. & Decorte, R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics 10, 922–930 (2015).
Google Scholar
Hussmann, D. & Hansen, L. L. Methylation-sensitive high resolution melting (MS-HRM). In DNA Methylation Protocols (ed. Tost, J.) vol. 1708, pp. 551–571 (Springer New York, 2018).
Wojdacz, T. K. & Dobrovic, A. Methylation-sensitive high resolution melting (MS-HRM): A new approach for sensitive and high-throughput assessment of methylation. Nucleic Acids Res. 35, e41 (2007).
Mawlood, S. K., Dennany, L., Watson, N. & Pickard, B. S. The EpiTect methyl qPCR assay as novel age estimation method in forensic biology. Forens. Sci. Int. 264, 132–138 (2016).
Google Scholar
Migheli, F. et al. Comparison study of MS-HRM and pyrosequencing techniques for quantification of APC and CDKN2A gene methylation. PLOS ONE 8, e52501 (2013).
Xiao, Z. et al. Validation of methylation-sensitive high-resolution melting (MS-HRM) for the detection of stool DNA methylation in colorectal neoplasms. Clin. Chim. Acta 431, 154–163 (2014).
Google Scholar
Šestáková, Š, Šálek, C. & Remešová, H. DNA methylation validation methods: A coherent review with practical comparison. Biol. Proc. Online 21, 19 (2019).
Google Scholar
Fleming, P. A., Crawford, H. M., Auckland, C. & Calver, M. C. Nine ways to score nine lives—Identifying appropriate methods to age domestic cats (Felis catus). J. Zool.
Smyth, L. J., McKay, G. J., Maxwell, A. P. & McKnight, A. J. DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease. Epigenetics 9, 366–376 (2014).
Google Scholar
Chen, J. et al. Elevated Klotho promoter methylation is associated with severity of chronic kidney disease. PloS One 8, e79856 (2013).
White, J. D., Norris, J. M., Baral, R. M. & Malik, R. Naturally-occurring chronic renal disease in Australian cats: A prospective study of 184 cases. Aust. Vet. J. 84, 188–194 (2006).
Google Scholar
Snow Leopard Trust. Snow leopard facts/life cycle. Snow Leopard Trust http://snowleopard.org/snow-leopard-facts/life-cycle/.
Dhingra, R., Nwanaji-Enwerem, J. C., Samet, M. & Ward-Caviness, C. K. DNA methylation age—Environmental influences, health impacts, and its role in environmental epidemiology. Curr. Environ. Health Rep. 5, 317–327 (2018).
Google Scholar
Lea, A. J., Altmann, J., Alberts, S. C. & Tung, J. Resource base influences genome-wide DNA methylation levels in wild baboons (Papio cynocephalus). Mol. Ecol. 25, 1681–1696 (2016).
Google Scholar
IRIS. IRIS Kidney—Guidelines—IRIS Staging of CKD. http://www.iris-kidney.com/guidelines/staging.html (2019).
Spiers, H. et al. Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model. Mech. Ageing Dev. 154, 20–23 (2016).
Google Scholar
Vignettes, C.-B. Proceedings from the 2015 Annual Meeting of the American College of Physicians, Wisconsin Chapter. WMJ (2015).
Zhang, X. et al. Genome-wide analysis of cell-free DNA methylation profiling with MeDIP-Seq identified potential biomarkers for colorectal cancer (2021).
MD, B., US, N. L. of M. & US, N. C. for B. I. National Center for Biotechnology Information (NCBI). https://www.ncbi.nlm.nih.gov/.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Google Scholar
Xu, C. et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci. Rep. 5, 17788 (2015).
Google Scholar
Chang, C.-C. & Lin, C.-J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 1–27 (2011).
Google Scholar
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).
Google Scholar
Source: Ecology - nature.com