in

Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease

  • 1.

    Blomqvist, L. & Sten, I. Reproductive Biology of the Snow Leopard. Panthera Books, London (1982).

  • 2.

    Kirkwood, T. B. & Austad, S. N. Why do we age?. Nature 408, 233–238 (2000).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 3.

    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).

    Article 

    Google Scholar 

  • 4.

    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).

    Article 

    Google Scholar 

  • 5.

    Mori, A. Analysis of population changes by measurement of body weight in the Koshima troop of Japanese monkeys. Primates 20, 371–397 (1979).

    Article 

    Google Scholar 

  • 6.

    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).

    Google Scholar 

  • 7.

    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).

    Article 

    Google Scholar 

  • 8.

    Chevallier, C., Gauthier, G. & Berteaux, D. Age estimation of live arctic foxes Vulpes lagopus based on teeth condition. Wildl. Biol. 4, 1–6 (2017).

    Google Scholar 

  • 9.

    White, P. A. et al. Age estimation of African lions Panthera leo by ratio of tooth areas. PloS One 11, e0153648 (2016).

    Article 

    Google Scholar 

  • 10.

    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).

    Article 

    Google Scholar 

  • 11.

    Paoli-Iseppi, D. et al. Measuring animal age with DNA methylation: From humans to wild animals. Front. Genet. 8, 106 (2017).

    Article 

    Google Scholar 

  • 12.

    Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).

    Article 

    Google Scholar 

  • 13.

    Schübeler, D. Function and information content of DNA methylation. Nature 517, 321–326 (2015).

    ADS 
    Article 

    Google Scholar 

  • 14.

    Field, A. E. et al. DNA methylation clocks in aging: Categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).

    CAS 
    Article 

    Google Scholar 

  • 15.

    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).

    Article 

    Google Scholar 

  • 16.

    Bocklandt, S. et al. Epigenetic predictor of age. PloS One 6, e14821 (2011).

  • 17.

    Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 (2017).

    CAS 
    Article 

    Google Scholar 

  • 18.

    Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 1–14 (2017).

    Article 

    Google Scholar 

  • 19.

    Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging (Albany NY) 9, 1055–1068 (2017).

    CAS 
    Article 

    Google Scholar 

  • 20.

    Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19, 22 (2018).

    Article 

    Google Scholar 

  • 21.

    Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 1–5 (2018).

    Google Scholar 

  • 22.

    Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).

    CAS 
    Article 

    Google Scholar 

  • 23.

    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).

    CAS 
    Article 

    Google Scholar 

  • 24.

    Park, K. et al. Determining the age of cats by pulp cavity/tooth width ratio using dental radiography. J. Vet. Sci. 15, 557 (2014).

    Article 

    Google Scholar 

  • 25.

    Yoshimura, H. et al. The relationship between plant-eating and hair evacuation in snow leopards (Panthera uncia). PLOS ONE 15, e0236635 (2020).

  • 26.

    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).

  • 27.

    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).

    Article 

    Google Scholar 

  • 28.

    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).

    Article 

    Google Scholar 

  • 29.

    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).

    Article 

    Google Scholar 

  • 30.

    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).

    CAS 
    Article 

    Google Scholar 

  • 31.

    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).

    ADS 
    Article 

    Google Scholar 

  • 32.

    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).

    Article 

    Google Scholar 

  • 33.

    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).

  • 34.

    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).

  • 35.

    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).

    CAS 
    Article 

    Google Scholar 

  • 36.

    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).

  • 37.

    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).

    CAS 
    Article 

    Google Scholar 

  • 38.

    Šestáková, Š, Šálek, C. & Remešová, H. DNA methylation validation methods: A coherent review with practical comparison. Biol. Proc. Online 21, 19 (2019).

    Article 

    Google Scholar 

  • 39.

    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.

  • 40.

    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).

    CAS 
    Article 

    Google Scholar 

  • 41.

    Chen, J. et al. Elevated Klotho promoter methylation is associated with severity of chronic kidney disease. PloS One 8, e79856 (2013).

  • 42.

    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).

    CAS 
    Article 

    Google Scholar 

  • 43.

    Snow Leopard Trust. Snow leopard facts/life cycle. Snow Leopard Trust http://snowleopard.org/snow-leopard-facts/life-cycle/.

  • 44.

    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).

    CAS 
    Article 

    Google Scholar 

  • 45.

    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).

    CAS 
    Article 

    Google Scholar 

  • 46.

    IRIS. IRIS Kidney—Guidelines—IRIS Staging of CKD. http://www.iris-kidney.com/guidelines/staging.html (2019).

  • 47.

    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).

    CAS 
    Article 

    Google Scholar 

  • 48.

    Vignettes, C.-B. Proceedings from the 2015 Annual Meeting of the American College of Physicians, Wisconsin Chapter. WMJ (2015).

  • 49.

    Zhang, X. et al. Genome-wide analysis of cell-free DNA methylation profiling with MeDIP-Seq identified potential biomarkers for colorectal cancer (2021).

  • 50.

    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/.

  • 51.

    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).

    CAS 
    Article 

    Google Scholar 

  • 52.

    Xu, C. et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci. Rep. 5, 17788 (2015).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 53.

    Chang, C.-C. & Lin, C.-J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 1–27 (2011).

    Article 

    Google Scholar 

  • 54.

    Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Phytoplankton biodiversity and the inverted paradox

    Rover images confirm Jezero crater is an ancient Martian lake