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A Genomic Predictor of Lifespan in Vertebrates Benjamin Mayne1*, Oliver Berry 1, Campbell Davies 2, Jessica Farley2 & Simon Jarman1,3

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A Genomic Predictor of Lifespan in Vertebrates Benjamin Mayne1*, Oliver Berry 1, Campbell Davies 2, Jessica Farley2 & Simon Jarman1,3 www.nature.com/scientificreports OPEN A genomic predictor of lifespan in vertebrates Benjamin Mayne1*, Oliver Berry 1, Campbell Davies 2, Jessica Farley2 & Simon Jarman1,3 Biological ageing and its mechanistic underpinnings are of immense biomedical and ecological signifcance. Ageing involves the decline of diverse biological functions and places a limit on a species’ maximum lifespan. Ageing is associated with epigenetic changes involving DNA methylation. Furthermore, an analysis of mammals showed that the density of CpG sites in gene promoters, which are targets for DNA methylation, is correlated with lifespan. Using 252 whole genomes and databases of animal age and promotor sequences, we show a pattern across vertebrates. We also derive a predictive lifespan clock based on CpG density in a selected set of promoters. The lifespan clock accurately predicts maximum lifespan in vertebrates (R2 = 0.76) from the density of CpG sites within only 42 selected promoters. Our lifespan clock provides a wholly new method for accurately estimating lifespan using genome sequences alone and enables estimation of this challenging parameter for both poorly understood and extinct species. Biological ageing is observed in almost all animal species1,2. Ageing involves the decline of diverse biological func- tions and the dynamics of this process limit a species’ maximum lifespan3. Longevity of individuals is strongly linked to specifc alleles in genetic model organisms4–6. Ageing is also associated with several epigenetic changes involving DNA methylation (DNAm)7,8. DNAm of cytosine-phosphate-guanosine (CpG) sites, involves a cova- lent modifcation to cytosine to form 5-methylcytosine. Tis modifcation to DNA has the potential to regulate gene expression, including of genes critical for longevity, without altering the underlying sequence. Te obser- vation that DNAm at promotor CpG sites can accumulate or decline predictably with age, over and above the more random process of epigenetic drif [19], has enabled the development of “clock like” biomarkers for age9–11. Individual human age, for example, can be predicted with great accuracy (R2 = 0.92) in a range of tissues by an epigenetic clock12. Similar epigenetic clocks have been created in a range of mammal and bird species13–17. Although it’s ofen reported, maximum lifespan for a species is difcult to defne. It is frequently the highest reported value for captive animals because of the difculty in estimating age for wild individuals. Alternatively, it is an accepted consensus value for the majority of individuals within a species18, or is based on records from a small number of wild individuals that have an age estimate as a result of exceptional circumstances14,19. Maximum lifespans difer greatly among species, even among fairly closely-related species20. In vertebrates, species such as the pygmy goby (Eviota sigillata) live for only eight weeks21, while the Greenland shark (Somniosus microcepha- lus) may live for more than 400 years22. In mammals, the forest shrew (Myosorex varius) has one of the shortest reported lifespans at 2.1 years23, whereas some bowhead whales (Balaena mysticeta) have been reported to be older than 200 years19,23. Te diferences in lifespan between species has ecological signifcance because age regu- lates fundamental aspects of animal life cycles and demography such as probability of mortality24. Consequently lifespan is central to estimating risk of animal extinction25, evaluating biosecurity risks26 and estimating the sustainable yield in fsheries and other harvested organisms27. Yet, despite this profound practical importance, lifespan is poorly characterised for most wild animals because it is difcult to estimate28. Maximum lifespan is believed to be under genetic control23–25, but so far, no gene variants can account for diferences in lifespan among species. Because ageing is characterised by changes in gene expression caused by DNAm, another potential controller of lifespan is genomic changes that accommodate DNAm’s efects on regu- lation of gene expression. Specifcally, clusters of high density CpG sites, also known as CpG islands, are highly conserved within promoter sequences29,30 and well known for regulating gene expression31. CpG sites are also prone to mutation32 and their function in regulating gene expression may make them prime targets for evolution- ary pressures to vary lifespans. 1Environomics Future Science Platform, Indian Oceans Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation, Crawley, Western Australia, Australia. 2Oceans and Atmosphere, Commonwealth Scientifc and Industrial Research Organisation, Hobart, Tasmania, Australia. 3School of Biological Sciences, University of Western Australia, 35 Stirling Highway, Perth, Western Australia, Australia. *email: [email protected] SCIENTIFIC REPORTS | (2019) 9:17866 | https://doi.org/10.1038/s41598-019-54447-w 1 www.nature.com/scientificreports/ www.nature.com/scientificreports abTraining data, R2 = 0.78, p-value < 2.2 x 10-16 Testing data, R2 = 0.76, p-value < 2.2 x 10-16 6 6 ● 5 5 ● ● ● ● ● 4 ●● 4 ● ●●● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ●●●●● ●● ● ● ●●● ●●● ● espan (ln) ● ● ● espan (ln) ● ● ● ●●● ●●●●● ● ● ● ● ● ● ● ● ●●●●●● ●●●● ● ● ●● ● ● ● 3 ●● ●●● ●● ●● ● ● 3 ● ● ●● ●● ● ●●●●● ●● ● ● ● ● ● ●● ●●●● ● ●●●●●● ●●● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● Predicted Lif 2 ● ● Predicted Lif 2 ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● Amphibia ● ● ● Aves 1 1 ● ● Fish ● ● Mammalia ● Reptilia 0 0 012345 012345 Known Lifespan (ln) Known Lifespan (ln) Figure 1. Lifespan Estimation from CpG density with lifespan loci. Te correlation between the known and predicted lifespan in the (a) training and (b) testing data set. Colours denote the class of each species. Te R2 value and p-value are given above each plot. Tis hypothesis was strongly supported in an investigation showing that CpG density is correlated with lifespan among a set of conserved mammalian promoters26. McLain and Faulk33 identifed 1,079 promoters where the CpG density correlated with increasing lifespan (q < 0.05). Tis suggests a functional role for CpG density in the maximum lifespan of mammalian species. Highly dense CpG regions may ofer greater bufering to long-lived species against dysregulation caused by accumulated methylation33. Despite promoters being conserved across vertebrates34, it is unknown if CpG density within promoters are a key driver of the expression of genes related to lifespan. Moreover, although many studies have explored using the predictive power of methylation at specifc CpG sites, no such study has investigated the predictive power of CpG density to estimate lifespan. Here, we extend observations of the correlation between promoter CpG density and lifespan in mammals to produce a predictive model for lifespan in all vertebrates. We use reference genomes of animals with known lifespans to identify promoters that can be predictive of lifespan. We combined data from major databases includ- ing NCBI Genomes35, the Eukaryotic Promoter Database (EPD)36, Animal Ageing and Longevity Database (AnAge)23 and TimeTree37 to build a predictive model that estimates lifespan. Our results show CpG density in selected promoters is highly predictive of lifespan across vertebrates. To our knowledge this is the frst study which has built a genetic predictive model to estimate the lifespan of vertebrate species from genetic markers. Results Lifespan estimation from CpG density. We identifed all vertebrate species that had reference genomes available in NCBI35, known maximum lifespans in the AnAge database23 and evolutionary divergence times in TimeTree37. Tis primary data set contained 252 species from fve vertebrate classes (Supplementary Table 1), with lifespans ranging from 1.1 years, a Turquoise killifsh (Nothobranchius furzeri) to 205 years, a Rougheye rockfsh (Sebastes aleutianus). We removed humans (Homo sapiens) from the data set as they were listed with a maximum lifespan of 120 years, which does not refect the variability and the true global average lifespan (60.9– 86.3 years)38. Mammals comprised the most represented class of vertebrates in the data set (Supplementary Table 1), and the average BLAST length of promoters from EPD was 374 bp (Supplementary Fig. 2). The BLAST hit length decreased with increasing evolutionary distance from humans (R2 = −0.85, p-value < 2.20 × 10−16; Supplementary Fig. 2), which is most likely a refection of using human promoter sequences. We also identifed a positive correlation (R2 = 0.64, p-value < 2.2 × 10−16) between total CpG sites and genome size across animal species (Supplementary Fig. 3). It has been suggested that an increase rate of recombination prevents the loss of CpG island density during increased chromosome numbers and genome size39. Tis suggests that CpG density genome wide is maintained across diferent sized animal genomes. Te fnal lifespan predictor was based on 42 promoters (Supplementary Table 2) afer a 10-fold cross val- idation to optimise the model (see Methods). From here on, promoters in the model will be referred to as the lifespan loci and the model itself as the lifespan clock. As expected, the lifespan clock returned a regres- sion coefcient between the known and predicted lifespan of species within the training data set (R2 = 0.78, p-value < 2.20 × 10−16) (Fig. 1a). Furthermore, using the independent set of samples in the testing
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