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Petersma FT, Thomas L, Harris D, Bradley D, Papastamatiou YP. Age is not just a number: How incorrect ageing impacts close-kin mark-recapture estimates of population size. Ecol Evol 2024; 14:e11352. [PMID: 38840589 PMCID: PMC11150428 DOI: 10.1002/ece3.11352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/07/2024] Open
Abstract
Population size is a key parameter for the conservation of animal species. Close-kin mark-recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age-length relationship (a 'growth curve'). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex-specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over- and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference.
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Affiliation(s)
- Felix T. Petersma
- Centre for Research into Environmental and Ecological ModellingUniversity of St AndrewsSt AndrewsUK
| | - Len Thomas
- Centre for Research into Environmental and Ecological ModellingUniversity of St AndrewsSt AndrewsUK
| | - Danielle Harris
- Centre for Research into Environmental and Ecological ModellingUniversity of St AndrewsSt AndrewsUK
| | - Darcy Bradley
- Bren School of Environmental Science & ManagementUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Yannis P. Papastamatiou
- Department of Biological Sciences, Institute of EnvironmentFlorida International UniversityNorth MiamiFloridaUSA
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2
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Mayne B, Chandler D, Noune C, Espinoza T, Roberts D, Anderson C, Berry O. Increased scalability and sequencing quality of an epigenetic age prediction assay. PLoS One 2024; 19:e0297006. [PMID: 38743704 PMCID: PMC11093300 DOI: 10.1371/journal.pone.0297006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 12/22/2023] [Indexed: 05/16/2024] Open
Abstract
Epigenetic ageing in a human context, has been used to better understand the relationship between age and factors such as lifestyle and genetics. In an ecological setting, it has been used to predict the age of individual animals for wildlife management. Despite the importance of epigenetic ageing in a range of research fields, the assays to measure epigenetic ageing are either expensive on a large scale or complex. In this study, we aimed to improve the efficiency and sequencing quality of an existing epigenetic ageing assay for the Australian Lungfish (Neoceratodus forsteri). We used an enzyme-based alternative to bisulfite conversion to reduce DNA fragmentation and evaluated its performance relative to bisulfite conversion. We found the sequencing quality to be 12% higher with the enzymatic alternative compared to bisulfite treatment (p-value < 0.01). This new enzymatic based approach, although currently double the cost of bisulfite treatment can increases the throughput and sequencing quality. We envisage this assay setup being adopted increasingly as the scope and scale of epigenetic ageing research continues to grow.
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Affiliation(s)
- Benjamin Mayne
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, Western Australia, Australia
| | - David Chandler
- Australian Genome Research Facility, Perth, WA, Australia
| | | | | | | | - Chloe Anderson
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, Western Australia, Australia
| | - Oliver Berry
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, Western Australia, Australia
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3
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Jin K, McCoy BM, Goldman EA, Usova V, Tkachev V, Chitsazan AD, Kakebeen A, Jeffery U, Creevy KE, Wills A, Snyder‐Mackler N, Promislow DEL. DNA methylation and chromatin accessibility predict age in the domestic dog. Aging Cell 2024; 23:e14079. [PMID: 38263575 PMCID: PMC11019125 DOI: 10.1111/acel.14079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/25/2024] Open
Abstract
Across mammals, the epigenome is highly predictive of chronological age. These "epigenetic clocks," most of which have been built using DNA methylation (DNAm) profiles, have gained traction as biomarkers of aging and organismal health. While the ability of DNAm to predict chronological age has been repeatedly demonstrated, the ability of other epigenetic features to predict age remains unclear. Here, we use two types of epigenetic information-DNAm, and chromatin accessibility as measured by ATAC-seq-to develop age predictors in peripheral blood mononuclear cells sampled from a population of domesticated dogs. We measured DNAm and ATAC-seq profiles for 71 dogs, building separate predictive clocks from each, as well as the combined dataset. We also use fluorescence-assisted cell sorting to quantify major lymphoid populations for each sample. We found that chromatin accessibility can accurately predict chronological age (R2 ATAC = 26%), though less accurately than the DNAm clock (R2 DNAm = 33%), and the clock built from the combined datasets was comparable to both (R2 combined = 29%). We also observed various populations of CD62L+ T cells significantly correlated with dog age. Finally, we found that all three clocks selected features that were in or near at least two protein-coding genes: BAIAP2 and SCARF2, both previously implicated in processes related to cognitive or neurological impairment. Taken together, these results highlight the potential of chromatin accessibility as a complementary epigenetic resource for modeling and investigating biologic age.
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Affiliation(s)
- Kelly Jin
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
| | - Brianah M. McCoy
- Center for Evolution and MedicineArizona State UniversityTempeArizonaUSA
- School of Life SciencesArizona State UniversityTempeArizonaUSA
| | | | - Viktoria Usova
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
| | - Victor Tkachev
- Division of Pediatric Hematology/OncologyBoston Children's HospitalBostonMassachusettsUSA
- Dana Farber Cancer InstituteBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Alex D. Chitsazan
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Anneke Kakebeen
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Unity Jeffery
- College of Veterinary MedicineTexas A & M UniversityCollege StationTexasUSA
| | - Kate E. Creevy
- College of Veterinary MedicineTexas A & M UniversityCollege StationTexasUSA
| | - Andrea Wills
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Noah Snyder‐Mackler
- Center for Evolution and MedicineArizona State UniversityTempeArizonaUSA
- School of Life SciencesArizona State UniversityTempeArizonaUSA
| | - Daniel E. L. Promislow
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
- Department of BiologyUniversity of WashingtonSeattleWashingtonUSA
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4
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Swenson JD, Brooks EN, Kacev D, Boyd C, Kinney MJ, Marcy‐Quay B, Sévêque A, Feldheim KA, Komoroske LM. Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments. Ecol Evol 2024; 14:e10854. [PMID: 38327683 PMCID: PMC10847890 DOI: 10.1002/ece3.10854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 01/01/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024] Open
Abstract
Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.
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Affiliation(s)
- John D. Swenson
- Department of Environmental ConservationThe University of Massachusetts AmherstAmherstMassachusettsUSA
| | - Elizabeth N. Brooks
- Population Dynamics Branch, Northeast Fisheries Science Center, National Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationWoods HoleMassachusettsUSA
| | - Dovi Kacev
- Marine Biology Research DivisionScripps Institution of OceanographySan DiegoCaliforniaUSA
| | - Charlotte Boyd
- International Union for Conservation of NatureNorth America OfficeWashington DCMarylandUSA
| | - Michael J. Kinney
- NOAA FisheriesPacific Island Fisheries Science CenterHonoluluHawaiiUSA
| | - Benjamin Marcy‐Quay
- Rubenstein Ecosystem Science LaboratoryUniversity of VermontBurlingtonVermontUSA
| | - Anthony Sévêque
- Department of Wildlife, Fisheries and Aquaculture, Forest and Wildlife Research CenterMississippi State UniversityMississippi StateMississippiUSA
| | - Kevin A. Feldheim
- Pritzker Laboratory for Molecular Systematics and EvolutionThe Field MuseumChicagoIllinoisUSA
| | - Lisa M. Komoroske
- Department of Environmental ConservationThe University of Massachusetts AmherstAmherstMassachusettsUSA
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5
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Thavornkanlapachai R, Armstrong KN, Knuckey C, Huntley B, Hanrahan N, Ottewell K. Species-specific SNP arrays for non-invasive genetic monitoring of a vulnerable bat. Sci Rep 2024; 14:1847. [PMID: 38253562 PMCID: PMC10803360 DOI: 10.1038/s41598-024-51461-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Genetic tagging from scats is one of the minimally invasive sampling (MIS) monitoring approaches commonly used to guide management decisions and evaluate conservation efforts. Microsatellite markers have traditionally been used but are prone to genotyping errors. Here, we present a novel method for individual identification in the Threatened ghost bat Macroderma gigas using custom-designed Single Nucleotide Polymorphism (SNP) arrays on the MassARRAY system. We identified 611 informative SNPs from DArTseq data from which three SNP panels (44-50 SNPs per panel) were designed. We applied SNP genotyping and molecular sexing to 209 M. gigas scats collected from seven caves in the Pilbara, Western Australia, employing a two-step genotyping protocol and identifying unique genotypes using a custom-made R package, ScatMatch. Following data cleaning, the average amplification rate was 0.90 ± 0.01 and SNP genotyping errors were low (allelic dropout 0.003 ± 0.000) allowing clustering of scats based on one or fewer allelic mismatches. We identified 19 unique bats (9 confirmed/likely males and 10 confirmed/likely females) from a maternity and multiple transitory roosts, with two male bats detected using roosts, 9 km and 47 m apart. The accuracy of our SNP panels enabled a high level of confidence in the identification of individual bats. Targeted SNP genotyping is a valuable tool for monitoring and tracking of non-model species through a minimally invasive sampling approach.
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Affiliation(s)
- Rujiporn Thavornkanlapachai
- Department of Biodiversity, Conservation and Attractions, Bentley Delivery Centre, Locked Bag 104, Bentley, WA, 6983, Australia.
| | - Kyle N Armstrong
- School of Biological Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
- South Australian Museum, Adelaide, SA, 5000, Australia
| | - Chris Knuckey
- Biologic Environmental, 24 Wickham Street, East Perth, WA, 6004, Australia
| | - Bart Huntley
- Department of Biodiversity, Conservation and Attractions, Bentley Delivery Centre, Locked Bag 104, Bentley, WA, 6983, Australia
| | - Nicola Hanrahan
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, 2753, Australia
- Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, 0815, Australia
| | - Kym Ottewell
- Department of Biodiversity, Conservation and Attractions, Bentley Delivery Centre, Locked Bag 104, Bentley, WA, 6983, Australia
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6
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Arai K, Qi H, Inoue-Murayama M. Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM). PLoS One 2023; 18:e0294994. [PMID: 38079426 PMCID: PMC10712859 DOI: 10.1371/journal.pone.0294994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Age is an important parameter for bettering the understanding of biodemographic trends-development, survival, reproduction and environmental effects-critical for conservation. However, current age estimation methods are challenging to apply to many species, and no standardised technique has been adopted yet. This study examined the potential use of methylation-sensitive high-resolution melting (MS-HRM), a labour-, time-, and cost-effective method to estimate chronological age from DNA methylation in Asian elephants (Elephas maximus). The objective of this study was to investigate the accuracy and validation of MS-HRM use for age determination in long-lived species, such as Asian elephants. The average lifespan of Asian elephants is between 50-70 years but some have been known to survive for more than 80 years. DNA was extracted from 53 blood samples of captive Asian elephants across 11 zoos in Japan, with known ages ranging from a few months to 65 years. Methylation rates of two candidate age-related epigenetic genes, RALYL and TET2, were significantly correlated with chronological age. Finally, we established a linear, unisex age estimation model with a mean absolute error (MAE) of 7.36 years. This exploratory study suggests an avenue to further explore MS-HRM as an alternative method to estimate the chronological age of Asian elephants.
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Affiliation(s)
- Kana Arai
- Wildlife Research Center, Kyoto University, Kyoto, Japan
| | - Huiyuan Qi
- Wildlife Research Center, Kyoto University, Kyoto, Japan
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7
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Le Clercq LS, Kotzé A, Grobler JP, Dalton DL. Biological clocks as age estimation markers in animals: a systematic review and meta-analysis. Biol Rev Camb Philos Soc 2023; 98:1972-2011. [PMID: 37356823 DOI: 10.1111/brv.12992] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 06/04/2023] [Accepted: 06/08/2023] [Indexed: 06/27/2023]
Abstract
Various biological attributes associated with individual fitness in animals change predictably over the lifespan of an organism. Therefore, the study of animal ecology and the work of conservationists frequently relies upon the ability to assign animals to functionally relevant age classes to model population fitness. Several approaches have been applied to determining individual age and, while these methods have proved useful, they are not without limitations and often lack standardisation or are only applicable to specific species. For these reasons, scientists have explored the potential use of biological clocks towards creating a universal age-determination method. Two biological clocks, tooth layer annulation and otolith layering have found universal appeal. Both methods are highly invasive and most appropriate for post-mortem age-at-death estimation. More recently, attributes of cellular ageing previously explored in humans have been adapted to studying ageing in animals for the use of less-invasive molecular methods for determining age. Here, we review two such methods, assessment of methylation and telomere length, describing (i) what they are, (ii) how they change with age, and providing (iii) a summary and meta-analysis of studies that have explored their utility in animal age determination. We found that both attributes have been studied across multiple vertebrate classes, however, telomere studies were used before methylation studies and telomere length has been modelled in nearly twice as many studies. Telomere length studies included in the review often related changes to stress responses and illustrated that telomere length is sensitive to environmental and social stressors and, in the absence of repair mechanisms such as telomerase or alternative lengthening modes, lacks the ability to recover. Methylation studies, however, while also detecting sensitivity to stressors and toxins, illustrated the ability to recover from such stresses after a period of accelerated ageing, likely due to constitutive expression or reactivation of repair enzymes such as DNA methyl transferases. We also found that both studied attributes have parentally heritable features, but the mode of inheritance differs among taxa and may relate to heterogamy. Our meta-analysis included more than 40 species in common for methylation and telomere length, although both analyses included at least 60 age-estimation models. We found that methylation outperforms telomere length in terms of predictive power evidenced from effect sizes (more than double that observed for telomeres) and smaller prediction intervals. Both methods produced age correlation models using similar sample sizes and were able to classify individuals into young, middle, or old age classes with high accuracy. Our review and meta-analysis illustrate that both methods are well suited to studying age in animals and do not suffer significantly from variation due to differences in the lifespan of the species, genome size, karyotype, or tissue type but rather that quantitative method, patterns of inheritance, and environmental factors should be the main considerations. Thus, provided that complex factors affecting the measured trait can be accounted for, both methylation and telomere length are promising targets to develop as biomarkers for age determination in animals.
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Affiliation(s)
- Louis-Stéphane Le Clercq
- South African National Biodiversity Institute, P.O. Box 754, Pretoria, 0001, South Africa
- Department of Genetics, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South Africa
| | - Antoinette Kotzé
- South African National Biodiversity Institute, P.O. Box 754, Pretoria, 0001, South Africa
- Department of Genetics, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South Africa
| | - J Paul Grobler
- Department of Genetics, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South Africa
| | - Desiré Lee Dalton
- School of Health and Life Sciences, Teesside University, Middlesbrough, TS1 3BA, UK
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8
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Laine VN, Sepers B, Lindner M, Gawehns F, Ruuskanen S, van Oers K. An ecologist's guide for studying DNA methylation variation in wild vertebrates. Mol Ecol Resour 2023; 23:1488-1508. [PMID: 35466564 DOI: 10.1111/1755-0998.13624] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 11/30/2022]
Abstract
The field of molecular biology is advancing fast with new powerful technologies, sequencing methods and analysis software being developed constantly. Commonly used tools originally developed for research on humans and model species are now regularly used in ecological and evolutionary research. There is also a growing interest in the causes and consequences of epigenetic variation in natural populations. Studying ecological epigenetics is currently challenging, especially for vertebrate systems, because of the required technical expertise, complications with analyses and interpretation, and limitations in acquiring sufficiently high sample sizes. Importantly, neglecting the limitations of the experimental setup, technology and analyses may affect the reliability and reproducibility, and the extent to which unbiased conclusions can be drawn from these studies. Here, we provide a practical guide for researchers aiming to study DNA methylation variation in wild vertebrates. We review the technical aspects of epigenetic research, concentrating on DNA methylation using bisulfite sequencing, discuss the limitations and possible pitfalls, and how to overcome them through rigid and reproducible data analysis. This review provides a solid foundation for the proper design of epigenetic studies, a clear roadmap on the best practices for correct data analysis and a realistic view on the limitations for studying ecological epigenetics in vertebrates. This review will help researchers studying the ecological and evolutionary implications of epigenetic variation in wild populations.
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Affiliation(s)
- Veronika N Laine
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | - Bernice Sepers
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
- Behavioural Ecology Group, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Melanie Lindner
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
- Chronobiology Unit, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, The Netherlands
| | - Fleur Gawehns
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Suvi Ruuskanen
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
- Department of Biology, University of Turku, Finland
| | - Kees van Oers
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
- Behavioural Ecology Group, Wageningen University & Research (WUR), Wageningen, The Netherlands
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9
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Mayne B, Espinoza T, Crook DA, Anderson C, Korbie D, Marshall JC, Kennard MJ, Harding DJ, Butler GL, Roberts B, Whiley J, Marshall S. Accurate, non-destructive, and high-throughput age estimation for Golden perch (Macquaria ambigua spp.) using DNA methylation. Sci Rep 2023; 13:9547. [PMID: 37308782 PMCID: PMC10260977 DOI: 10.1038/s41598-023-36773-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 06/09/2023] [Indexed: 06/14/2023] Open
Abstract
Age structure information of animal populations is fundamental to their conservation and management. In fisheries, age is routinely obtained by counting daily or annual increments in calcified structures (e.g., otoliths) which requires lethal sampling. Recently, DNA methylation has been shown to estimate age using DNA extracted from fin tissue without the need to kill the fish. In this study we used conserved known age-associated sites from the zebrafish (Danio rerio) genome to predict the age of golden perch (Macquaria ambigua), a large-bodied native fish from eastern Australia. Individuals aged using validated otolith techniques from across the species' distribution were used to calibrate three epigenetic clocks. One clock was calibrated using daily (daily clock) and another with annual (annual clock) otolith increment counts, respectively. A third used both daily and annual increments (universal clock). We found a high correlation between the otolith and epigenetic age (Pearson correlation > 0.94) across all clocks. The median absolute error was 2.4 days in the daily clock, 184.6 days in the annual clock, and 74.5 days in the universal clock. Our study demonstrates the emerging utility of epigenetic clocks as non-lethal and high-throughput tools for obtaining age estimates to support the management of fish populations and fisheries.
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Affiliation(s)
- Benjamin Mayne
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, WA, Australia.
| | - Tom Espinoza
- Department of Regional Development, Manufacturing and Water, Brisbane, QLD, Australia
| | - David A Crook
- Department of Primary Industries, Narrandera Fisheries Centre, Narrandera, NSW, Australia
| | - Chloe Anderson
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, WA, Australia
| | - Darren Korbie
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jonathan C Marshall
- Queensland Department of Environment and Science, Brisbane, QLD, Australia
- Australian Rivers Institute and Griffith School of Environment and Science, Griffith University, Nathan, QLD, 4111, Australia
| | - Mark J Kennard
- Australian Rivers Institute and Griffith School of Environment and Science, Griffith University, Nathan, QLD, 4111, Australia
| | - Doug J Harding
- Department of Regional Development, Manufacturing and Water, Brisbane, QLD, Australia
| | - Gavin L Butler
- NSW Department of Primary Industries (Fisheries), Grafton, NSW, Australia
| | - Brien Roberts
- Fisheries Division, Department of Industry, Tourism and Trade, Darwin, NT, Australia
| | - Josh Whiley
- Australian Rivers Institute and Griffith School of Environment and Science, Griffith University, Nathan, QLD, 4111, Australia
| | - Sharon Marshall
- Department of Regional Development, Manufacturing and Water, Brisbane, QLD, Australia
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10
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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11
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Li J, Han F, Yuan T, Li W, Li Y, Wu HX, Wei H, Niu S. The methylation landscape of giga-genome and the epigenetic timer of age in Chinese pine. Nat Commun 2023; 14:1947. [PMID: 37029142 PMCID: PMC10082083 DOI: 10.1038/s41467-023-37684-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 03/27/2023] [Indexed: 04/09/2023] Open
Abstract
Epigenetics has been revealed to play a crucial role in the long-term memory in plants. However, little is known about whether the epigenetic modifications occur with age progressively in conifers. Here, we present the single-base resolution DNA methylation landscapes of the 25-gigabase Chinese pine (Pinus tabuliformis) genome at different ages. The result shows that DNA methylation is closely coupled with the regulation of gene transcription. The age-dependent methylation profile with a linearly increasing trend is the most significant pattern of DMRs between ages. Two segments at the five-prime end of the first ultra-long intron in DAL1, a conservative age biomarker in conifers, shows a gradual decline of CHG methylation as the age increased, which is highly correlated with its expression profile. Similar high correlation is also observed in nine other age marker genes. Our results suggest that DNA methylation serves as an important epigenetic signature of developmental age in conifers.
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Affiliation(s)
- Jiang Li
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, PR China
| | - Fangxu Han
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, PR China
| | - Tongqi Yuan
- College of Material Science and Technology, Beijing Forestry University, Beijing, 100083, P. R. China
| | - Wei Li
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, PR China
| | - Yue Li
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, PR China
| | - Harry X Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Linnaeus väg 6, SE-901 83, Umeå, Sweden
- CSIRO National Research Collection Australia, Black Mountain Laboratory, Canberra, ACT, 2601, Australia
| | - Hairong Wei
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA
| | - Shihui Niu
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, PR China.
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12
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Robeck TR, Haghani A, Fei Z, Lindemann DM, Russell J, Herrick KES, Montano G, Steinman KJ, Katsumata E, Zoller JA, Horvath S. Multi-tissue DNA methylation aging clocks for sea lions, walruses and seals. Commun Biol 2023; 6:359. [PMID: 37005462 PMCID: PMC10067968 DOI: 10.1038/s42003-023-04734-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
Age determination of wild animals, including pinnipeds, is critical for accurate population assessment and management. For most pinnipeds, current age estimation methodologies utilize tooth or bone sectioning which makes antemortem estimations problematic. We leveraged recent advances in the development of epigenetic age estimators (epigenetic clocks) to develop highly accurate pinniped epigenetic clocks. For clock development, we applied the mammalian methylation array to profile 37,492 cytosine-guanine sites (CpGs) across highly conserved stretches of DNA in blood and skin samples (n = 171) from primarily three pinniped species representing the three phylogenetic families: Otariidae, Phocidae and Odobenidae. We built an elastic net model with Leave-One-Out-Cross Validation (LOOCV) and one with a Leave-One-Species-Out-Cross-Validation (LOSOCV). After identifying the top 30 CpGs, the LOOCV produced a highly correlated (r = 0.95) and accurate (median absolute error = 1.7 years) age estimation clock. The LOSOCV elastic net results indicated that blood and skin clock (r = 0.84) and blood (r = 0.88) pinniped clocks could predict age of animals from pinniped species not used for clock development to within 3.6 and 4.4 years, respectively. These epigenetic clocks provide an improved and relatively non-invasive tool to determine age in skin or blood samples from all pinniped species.
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Affiliation(s)
- Todd R Robeck
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA.
- Species Preservation Lab, SeaWorld Parks and Entertainment, San Diego, CA, USA.
| | - Amin Haghani
- Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Altos Labs, San Diego, USA
| | - Zhe Fei
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA
| | | | | | | | - Gisele Montano
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
- Species Preservation Lab, SeaWorld Parks and Entertainment, San Diego, CA, USA
| | - Karen J Steinman
- Species Preservation Lab, SeaWorld Parks and Entertainment, San Diego, CA, USA
| | | | - Joseph A Zoller
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA
| | - Steve Horvath
- Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, Los Angeles, CA, USA.
- Altos Labs, San Diego, USA.
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA.
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13
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Pallin L, Bierlich KC, Durban J, Fearnbach H, Savenko O, Baker CS, Bell E, Double MC, de la Mare W, Goldbogen J, Johnston D, Kellar N, Nichols R, Nowacek D, Read AJ, Steel D, Friedlaender A. Demography of an ice-obligate mysticete in a region of rapid environmental change. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220724. [PMID: 36397972 PMCID: PMC9626259 DOI: 10.1098/rsos.220724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/13/2022] [Indexed: 06/01/2023]
Abstract
Antarctic minke whales (Balaenoptera bonaerensis, AMW) are an abundant, ice-dependent species susceptible to rapid climatic changes occurring in parts of the Antarctic. Here, we used remote biopsy samples and estimates of length derived from unoccupied aircraft system (UAS) to characterize for the first time the sex ratio, maturity, and pregnancy rates of AMWs around the Western Antarctic Peninsula (WAP). DNA profiling of 82 biopsy samples (2013-2020) identified 29 individual males and 40 individual females. Blubber progesterone levels indicated 59% of all sampled females were pregnant, irrespective of maturity. When corrected for sexual maturity, the median pregnancy rate was 92.3%, indicating that most mature females become pregnant each year. We measured 68 individuals by UAS (mean = 8.04 m) and estimated that 66.5% of females were mature. This study provides the first data on the demography of AMWs along the WAP and represents the first use of non-lethal approaches to studying this species. Furthermore, these results provide baselines against which future changes in population status can be assessed in this rapidly changing marine ecosystem.
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Affiliation(s)
- L. Pallin
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, USA
| | - K. C. Bierlich
- Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
- Marine Mammal Institute, Department of Fisheries, Wildlife, & Conservation Sciences, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Drive, Newport, OR, USA
| | - J. Durban
- Marine Mammal Institute, Department of Fisheries, Wildlife, & Conservation Sciences, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Drive, Newport, OR, USA
- SeaLife Response, Rehabilitation, and Research, Des Moines, WA 98198, USA
| | - H. Fearnbach
- SeaLife Response, Rehabilitation, and Research, Des Moines, WA 98198, USA
| | - O. Savenko
- National Antarctic Scientific Center of Ukraine, 16 Taras Shevchenko Blvd, 01601, Kyiv, Ukraine
- Ukrainian Scientific Center of Ecology of the Sea, 89 Frantsuzsky Blvd, 65009, Odesa, Ukraine
| | - C. S. Baker
- Marine Mammal Institute, Department of Fisheries, Wildlife, & Conservation Sciences, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Drive, Newport, OR, USA
| | - E. Bell
- Australian Antarctic Division, 203 Channel Highway, Kingston, Tas 7050, Australia
| | - M. C. Double
- Australian Antarctic Division, 203 Channel Highway, Kingston, Tas 7050, Australia
| | - W. de la Mare
- Australian Antarctic Division, 203 Channel Highway, Kingston, Tas 7050, Australia
| | - J. Goldbogen
- Hopkins Marine Station, Department of Biology, Stanford University, 120 Ocean View Blvd, Pacific Grove, CA 93950, USA
| | - D. Johnston
- Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
| | - N. Kellar
- Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA
| | - R. Nichols
- Institute for Marine Science, University of California Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, USA
- Department of Ocean Sciences, University of California Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, USA
| | - D. Nowacek
- Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
| | - A. J. Read
- Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
| | - D. Steel
- Marine Mammal Institute, Department of Fisheries, Wildlife, & Conservation Sciences, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Drive, Newport, OR, USA
| | - A. Friedlaender
- Institute for Marine Science, University of California Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, USA
- Department of Ocean Sciences, University of California Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, USA
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14
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Epigenetic clock: A promising biomarker and practical tool in aging. Ageing Res Rev 2022; 81:101743. [PMID: 36206857 DOI: 10.1016/j.arr.2022.101743] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/13/2022] [Accepted: 09/30/2022] [Indexed: 01/31/2023]
Abstract
As a complicated process, aging is characterized by various changes at the cellular, subcellular and nuclear levels, one of which is epigenetic aging. With increasing awareness of the critical role that epigenetic alternations play in aging, DNA methylation patterns have been employed as a measure of biological age, currently referred to as the epigenetic clock. This review provides a comprehensive overview of the epigenetic clock as a biomarker of aging and a useful tool to manage healthy aging. In this burgeoning scientific field, various kinds of epigenetic clocks continue to emerge, including Horvath's clock, Hannum's clock, DNA PhenoAge, and DNA GrimAge. We hereby present the most classic epigenetic clocks, as well as their differences. Correlations of epigenetic age with morbidity, mortality and other factors suggest the potential of epigenetic clocks for risk prediction and identification in the context of aging. In particular, we summarize studies on promising age-reversing interventions, with epigenetic clocks employed as a practical tool in the efficacy evaluation. We also discuss how the lack of higher-quality information poses a major challenge, and offer some suggestions to address existing obstacles. Hopefully, our review will help provide an appropriate understanding of the epigenetic clocks, thereby enabling novel insights into the aging process and how it can be manipulated to promote healthy aging.
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15
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Beal AP, Hackerott S, Feldheim K, Gruber SH, Eirin‐Lopez JM. Age group DNA methylation differences in lemon sharks ( Negaprion brevirostris): Implications for future age estimation tools. Ecol Evol 2022; 12:e9226. [PMID: 36052296 PMCID: PMC9425014 DOI: 10.1002/ece3.9226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/11/2022] Open
Abstract
Age information is often non-existent for most shark populations due to a lack of measurable physiological and morphological traits that can be used to estimate age. Recently, epigenetic clocks have been found to accurately estimate age for mammals, birds, and fish. However, since these clocks rely, among other things, on the availability of reference genomes, their application is hampered in non-traditional model organisms lacking such molecular resources. The technique known as Methyl-Sensitive Amplified Polymorphism (MSAP) has emerged as a valid alternative for studying DNA methylation biomarkers when reference genome information is missing, and large numbers of samples need to be processed. Accordingly, the MSAP technique was used in the present study to characterize global DNA methylation patterns in lemon sharks from three different age groups (juveniles, subadults, and adults). The obtained results reveal that, while MSAP analyses lack enough resolution as a standalone approach to infer age in these organisms, the global DNA methylation patterns observed using this technique displayed significant differences between age groups. Overall, these results confer that DNA methylation does change with age in sharks like what has been seen for other vertebrates and that MSAP could be useful as part of an epigenetics pipeline to infer the broad range of ages found in large samples sizes.
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Affiliation(s)
- Andria Paige Beal
- Environmental Epigenetics Laboratory, Institute of EnvironmentFlorida International UniversityMiamiFloridaUSA
| | - Serena Hackerott
- Environmental Epigenetics Laboratory, Institute of EnvironmentFlorida International UniversityMiamiFloridaUSA
| | - Kevin Feldheim
- Pritzker Laboratory for Molecular Systematics and EvolutionField Museum of Natural HistoryChicagoIllinoisUSA
| | | | - Jose M. Eirin‐Lopez
- Environmental Epigenetics Laboratory, Institute of EnvironmentFlorida International UniversityMiamiFloridaUSA
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16
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Lamka GF, Harder AM, Sundaram M, Schwartz TS, Christie MR, DeWoody JA, Willoughby JR. Epigenetics in Ecology, Evolution, and Conservation. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.871791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Epigenetic variation is often characterized by modifications to DNA that do not alter the underlying nucleotide sequence, but can influence behavior, morphology, and physiological phenotypes by affecting gene expression and protein synthesis. In this review, we consider how the emerging field of ecological epigenetics (eco-epi) aims to use epigenetic variation to explain ecologically relevant phenotypic variation and predict evolutionary trajectories that are important in conservation. Here, we focus on how epigenetic data have contributed to our understanding of wild populations, including plants, animals, and fungi. First, we identified published eco-epi literature and found that there was limited taxonomic and ecosystem coverage and that, by necessity of available technology, these studies have most often focused on the summarized epigenome rather than locus- or nucleotide-level epigenome characteristics. We also found that while many studies focused on adaptation and heritability of the epigenome, the field has thematically expanded into topics such as disease ecology and epigenome-based ageing of individuals. In the second part of our synthesis, we discuss key insights that have emerged from the epigenetic field broadly and use these to preview the path toward integration of epigenetics into ecology. Specifically, we suggest moving focus to nucleotide-level differences in the epigenome rather than whole-epigenome data and that we incorporate several facets of epigenome characterization (e.g., methylation, chromatin structure). Finally, we also suggest that incorporation of behavior and stress data will be critical to the process of fully integrating eco-epi data into ecology, conservation, and evolutionary biology.
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17
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Hibernation slows epigenetic ageing in yellow-bellied marmots. Nat Ecol Evol 2022; 6:418-426. [PMID: 35256811 PMCID: PMC8986532 DOI: 10.1038/s41559-022-01679-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023]
Abstract
Species that hibernate generally live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor–arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause ageing suppression. Metabolic rate is also a known correlate of longevity; we thus proposed the ‘hibernation–ageing hypothesis’ whereby ageing is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7–8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modelled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic ageing over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (two years old). With respect to circannual patterns, the epigenetic age increased during the active season and essentially stalled during the hibernation period. Taken together, our results are consistent with the hibernation–ageing hypothesis and may explain the enhanced longevity in hibernators. Species that hibernate generally have longer lifespans than expected based on their body size. The authors show epigenetic ageing patterns from a natural population of hibernating yellow-bellied marmots consistent with the hypothesis that ageing is suspended during hibernation.
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18
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Neveceralova P, Carroll EL, Steel D, Vermeulen E, Elwen S, Zidek J, Stafford JK, Chivell W, Hulva P. Population Changes in a Whale Breeding Ground Revealed by Citizen Science Noninvasive Genetics. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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19
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Lemaître J, Rey B, Gaillard J, Régis C, Gilot‐Fromont E, Débias F, Duhayer J, Pardonnet S, Pellerin M, Haghani A, Zoller JA, Li CZ, Horvath S. DNA methylation as a tool to explore ageing in wild roe deer populations. Mol Ecol Resour 2022; 22:1002-1015. [PMID: 34665921 PMCID: PMC9297961 DOI: 10.1111/1755-0998.13533] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/24/2021] [Accepted: 10/11/2021] [Indexed: 12/12/2022]
Abstract
DNA methylation-based biomarkers of ageing (epigenetic clocks) promise to lead to new insights into evolutionary biology of ageing. Relatively little is known about how the natural environment affects epigenetic ageing effects in wild species. In this study, we took advantage of a unique long-term (>40 years) longitudinal monitoring of individual roe deer (Capreolus capreolus) living in two wild populations (Chizé and Trois-Fontaines, France) facing different ecological contexts, to investigate the relationship between chronological age and levels of DNA methylation (DNAm). We generated novel DNA methylation data from n = 94 blood samples, from which we extracted leucocyte DNA, using a custom methylation array (HorvathMammalMethylChip40). We present three DNA methylation-based estimators of age (DNAm or epigenetic age), which were trained in males, females, and both sexes combined. We investigated how sex differences influenced the relationship between DNAm age and chronological age using sex-specific epigenetic clocks. Our results highlight that old females may display a lower degree of biological ageing than males. Further, we identify the main sites of epigenetic alteration that have distinct ageing patterns between the two sexes. These findings open the door to promising avenues of research at the crossroads of evolutionary biology and biogerontology.
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Affiliation(s)
- Jean‐François Lemaître
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Benjamin Rey
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Jean‐Michel Gaillard
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Corinne Régis
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Emmanuelle Gilot‐Fromont
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
- Université de LyonVetAgro SupMarcy‐l'EtoileFrance
| | - François Débias
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Jeanne Duhayer
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Sylvia Pardonnet
- Laboratoire de Biométrie et Biologie EvolutiveUMR5558Université de LyonUniversité Lyon 1CNRSVilleurbanneFrance
| | - Maryline Pellerin
- Direction de la Recherche et de l'Appui ScientifiqueOffice Français de la BiodiversitéUnité Ongulés SauvagesGapFrance
| | - Amin Haghani
- Human GeneticsDavid Geffen School of MedicineUniversity of CaliforniaLos Angeles CaliforniaUSA
| | - Joseph A. Zoller
- Department of BiostatisticsFielding School of Public HealthUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Caesar Z. Li
- Department of BiostatisticsFielding School of Public HealthUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Steve Horvath
- Human GeneticsDavid Geffen School of MedicineUniversity of CaliforniaLos Angeles CaliforniaUSA
- Department of BiostatisticsFielding School of Public HealthUniversity of CaliforniaLos AngelesCaliforniaUSA
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20
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Larison B, Pinho GM, Haghani A, Zoller JA, Li CZ, Finno CJ, Farrell C, Kaelin CB, Barsh GS, Wooding B, Robeck TR, Maddox D, Pellegrini M, Horvath S. Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids. Commun Biol 2021; 4:1412. [PMID: 34921240 PMCID: PMC8683477 DOI: 10.1038/s42003-021-02935-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/02/2021] [Indexed: 01/09/2023] Open
Abstract
Effective conservation and management of threatened wildlife populations require an accurate assessment of age structure to estimate demographic trends and population viability. Epigenetic aging models are promising developments because they estimate individual age with high accuracy, accurately predict age in related species, and do not require invasive sampling or intensive long-term studies. Using blood and biopsy samples from known age plains zebras (Equus quagga), we model epigenetic aging using two approaches: the epigenetic clock (EC) and the epigenetic pacemaker (EPM). The plains zebra EC has the potential for broad application within the genus Equus given that five of the seven extant wild species of the genus are threatened. We test the EC's ability to predict age in sister taxa, including two endangered species and the more distantly related domestic horse, demonstrating high accuracy in all cases. By comparing chronological and estimated age in plains zebras, we investigate age acceleration as a proxy of health status. An interaction between chronological age and inbreeding is associated with age acceleration estimated by the EPM, suggesting a cumulative effect of inbreeding on biological aging throughout life.
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Affiliation(s)
- Brenda Larison
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, 90095, USA.
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, 90095, USA.
| | - Gabriela M Pinho
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, 90095, USA
| | - Amin Haghani
- Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Joseph A Zoller
- Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Caesar Z Li
- Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Carrie J Finno
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA, 95616, USA
| | - Colin Farrell
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Christopher B Kaelin
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Gregory S Barsh
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Bernard Wooding
- Quagga Project, Elandsberg Farms, Hermon, 7308, South Africa
| | - Todd R Robeck
- Zoological Operations, SeaWorld Parks and Entertainment, 7007 SeaWorld Drive, Orlando, FL, USA
| | - Dewey Maddox
- White Oak Conservation, 581705 White Oak Road, Yulee, FL, 32097, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Steve Horvath
- Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego, CA, USA.
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21
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Heydenrych MJ, Saunders BJ, Bunce M, Jarman SN. Epigenetic Measurement of Key Vertebrate Population Biology Parameters. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.617376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The age, sex, and sexual maturity of individual animals are key parameters in assessing wild populations and informing conservation management strategies. These parameters represent the reproductive potential of a population and can indicate recovery rates or vulnerabilities. Natural populations of wild animals are difficult to study; logistically, economically, and due to the impacts of invasive biomonitoring. Genetic and epigenetic analyses offer a low impact, low cost, and information-rich alternative. As epigenetic mechanisms are intrinsically linked with both biological aging and reproductive processes, DNA methylation can be used as a suitable biomarker for population biology study. This review assesses published research utilizing DNA methylation analysis in relation to three key population parameters: age, sex, and sexual maturity. We review studies on wild vertebrates that investigate epigenetic age relationships, with successful age estimation assays designed for mammals, birds, and fish. For both determination of sex and identification of sexual maturity, very little has been explored regarding DNA methylation-based assays. Related research, however, confirms the links between DNA methylation and these processes. Future development of age estimation assays for underrepresented and key conservation taxa is suggested, as is the experimental development and design of DNA methylation-based assays for both sex and sexual maturity identification, further expanding the genomics toolkit for population biology studies.
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22
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Dalle Luche G, Boggs ASP, Kucklick JR, Hawker DW, Wisse JH, Bengtson Nash S. Steroid hormone profiles and body conditions of migrating male humpback whales (Megaptera novaeangliae). Gen Comp Endocrinol 2021; 313:113888. [PMID: 34425085 DOI: 10.1016/j.ygcen.2021.113888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 08/08/2021] [Accepted: 08/18/2021] [Indexed: 11/21/2022]
Abstract
Simultaneous analysis of multiple steroid hormones from remotely obtained blubber biopsies has the potential to concurrently provide information regarding stress and reproductive status from free-swimming cetaceans, while also investigating correlations between hormone concentrations and other health biomarkers. In this study we measured blubber concentration profiles of eight reproductive and adrenal steroid hormones (17α-hydroxy-progesterone, testosterone, androstenedione, progesterone, cortisol, 11-deoxy-corticosterone, oestrone, and oestradiol) together with body condition, as determined by the inverse Adipocyte Index, of 101 male humpback whales. Whales were sampled randomly at two time points, while migrating to and from their northeast Australian breeding grounds, allowing for intra- and inter-seasonal profile analysis. Testosterone, progesterone and cortisol together with androstenedione 17α-hydroxyprogesterone, and oestrone concentrations (the latter quantified for the first time in live biopsied male humpback whales) decreased between the northward and southward migrations. Decreasing testosterone levels during the height of humpback whale conceptions suggests asynchronicity between blubber testosterone levels and the expected peak of male fertility. Statistically significant relationships between levels of certain steroid analytes were observed and appeared to change between the early and late breeding seasons. During the northward migration, testosterone, progesterone, androstenedione, oestrone and 17α-hydroxyprogesterone levels were positively correlated. Cortisol concentrations correlated positively with those of testosterone during the northward migration, but negatively during the southward migration. Androstenedione and testosterone were positively correlated with adiposity during the late breeding season. These hormone-hormone and hormone-adiposity correlations may be reflective of the activation of certain steroid hormone synthesis pathways, or alternatively, of concomitant physiological stimuli. As steroid hormones work in concert, information on multiple steroid hormones is needed to interpret endocrinological status and understand the relationships between these compounds and ancillary health markers. This study provides steroid hormone profiles of wild male humpback whales, as well as the first insight into seasonal male endocrinology as a function of adiposity.
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Affiliation(s)
- Greta Dalle Luche
- Environmental Futures Research Institute, Griffith University, Brisbane, QLD 4111, Australia.
| | - Ashley S P Boggs
- National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA
| | - John R Kucklick
- National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA
| | - Darryl W Hawker
- School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia
| | - Jillian H Wisse
- Nicholas School of the Environment, Duke University, Durham, NC 27710, USA
| | - Susan Bengtson Nash
- Environmental Futures Research Institute, Griffith University, Brisbane, QLD 4111, Australia
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23
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Bertucci EM, Mason MW, Rhodes OE, Parrott BB. Exposure to ionizing radiation disrupts normal epigenetic aging in Japanese medaka. Aging (Albany NY) 2021; 13:22752-22771. [PMID: 34644261 PMCID: PMC8544305 DOI: 10.18632/aging.203624] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/03/2021] [Indexed: 02/06/2023]
Abstract
Alterations to the epigenome are a hallmark of biological aging and age-dependent patterning of the DNA methylome ("epigenetic aging") can be modeled to produce epigenetic age predictors. Rates of epigenetic aging vary amongst individuals and correlate to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not empirically resolved. Here, we investigate the relationship between aging, DNA methylation, and environmental exposures in Japanese medaka (Oryzias latipes). We find age-associated DNA methylation patterning enriched in genomic regions of low CpG density and that, similar to mammals, most age-related changes occur during early life. We construct an epigenetic clock capable of predicting chronological age with a mean error of 61.1 days (~8.4% of average lifespan). To test the role of environmental factors in driving epigenetic age variation, we exposed medaka to chronic, environmentally relevant doses of ionizing radiation. Because most organisms share an evolutionary history with ionizing radiation, we hypothesized that exposure would reveal fundamental insights into environment-by-epigenetic aging interactions. Radiation exposure disrupted epigenetic aging by accelerating and decelerating normal age-associated patterning and was most pronounced in cytosines that were moderately associated with age. These findings empirically demonstrate the role of DNA methylation in integrating environmental factors into aging trajectories.
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Affiliation(s)
- Emily M. Bertucci
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802, USA
| | - Marilyn W. Mason
- Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802, USA
| | - Olin E. Rhodes
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802, USA
| | - Benjamin B. Parrott
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802, USA
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24
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Buddhachat K, Brown JL, Kaewkool M, Poommouang A, Kaewmong P, Kittiwattanawong K, Nganvongpanit K. Life Expectancy in Marine Mammals Is Unrelated to Telomere Length but Is Associated With Body Size. Front Genet 2021; 12:737860. [PMID: 34630527 PMCID: PMC8498114 DOI: 10.3389/fgene.2021.737860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 08/31/2021] [Indexed: 11/28/2022] Open
Abstract
Marine mammals vary greatly in size and lifespan across species. This study determined whether measures of adult body weight, length and relative telomere length were related to lifespan. Skin tissue samples (n = 338) were obtained from 23 marine mammal species, including four Mysticeti, 19 Odontoceti and one dugong species, and the DNA extracted to measure relative telomere length using real-time PCR. Life span, adult body weight, and adult body length of each species were retrieved from existing databases. The phylogenetic signal analysis revealed that body length might be a significant factor for shaping evolutionary processes of cetacean species through time, especially for genus Balaenoptera that have an enormous size. Further, our study found correlations between lifespan and adult body weight (R2 = 0.6465, p < 0.001) and adult body length (R2 = 0.6142, p ≤0.001), but no correlations with relative telomere length (R2 = −0.0476, p = 0.9826). While data support our hypothesis that larger marine mammals live longer, relative telomere length is not a good predictor of species longevity.
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Affiliation(s)
- Kittisak Buddhachat
- Department of Biology, Faculty of Science, Naresuan University, Phitsanulok, Thailand.,Excellence Center in Veterinary Bioscience, Chiang Mai University, Chiang Mai, Thailand
| | - Janine L Brown
- Smithsonian Conservation Biology Institute, Center for Species Survival, Front Royal, VA, United States
| | - Manthanee Kaewkool
- Department of Veterinary Biosciences and Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Anocha Poommouang
- Department of Veterinary Biosciences and Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand
| | | | | | - Korakot Nganvongpanit
- Excellence Center in Veterinary Bioscience, Chiang Mai University, Chiang Mai, Thailand.,Department of Veterinary Biosciences and Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand
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25
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Qi H, Kinoshita K, Mori T, Matsumoto K, Matsui Y, Inoue-Murayama M. Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease. Sci Rep 2021; 11:19963. [PMID: 34620957 PMCID: PMC8497492 DOI: 10.1038/s41598-021-99424-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/22/2021] [Indexed: 11/12/2022] Open
Abstract
Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines-most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique-as an alternative to current age-estimation methods-for two feline species that share a relatively long genetic distance with each other: domestic cat (Felis catus; 79 blood samples) and an endangered Panthera, the snow leopard (Panthera uncia; 11 blood samples). We measured the methylation rates of two gene regions using methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.
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Affiliation(s)
- Huiyuan Qi
- Wildlife Research Center, Kyoto University, Kyoto, 606-8203, Japan
| | - Kodzue Kinoshita
- Wildlife Research Center, Kyoto University, Kyoto, 606-8203, Japan
| | - Takashi Mori
- Kyoto Medical Center, Daktari Animal Hospital, Kyoto, 615-8234, Japan
| | - Kaori Matsumoto
- Kyoto Medical Center, Daktari Animal Hospital, Kyoto, 615-8234, Japan
- Miyazaki Prefectural Miyakonojo Livestock Hygiene Service Center, Miyazaki, 889-4505, Japan
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26
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Simpson DJ, Chandra T. Epigenetic age prediction. Aging Cell 2021; 20:e13452. [PMID: 34415665 PMCID: PMC8441394 DOI: 10.1111/acel.13452] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.
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Affiliation(s)
- Daniel J. Simpson
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Tamir Chandra
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
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27
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Fairfield EA, Richardson DS, Daniels CL, Butler CL, Bell E, Taylor MI. Ageing European lobsters ( Homarus gammarus) using DNA methylation of evolutionarily conserved ribosomal DNA. Evol Appl 2021; 14:2305-2318. [PMID: 34603500 PMCID: PMC8477595 DOI: 10.1111/eva.13296] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/30/2022] Open
Abstract
Crustaceans are notoriously difficult to age because of their indeterminate growth and the moulting of their exoskeleton throughout life. The poor knowledge of population age structure in crustaceans therefore hampers accurate assessment of population dynamics and consequently sustainable fisheries management. Quantification of DNA methylation of the evolutionarily conserved ribosomal DNA (rDNA) may allow for age prediction across diverse species. However, the rDNA epigenetic clock remains to be tested in crustaceans, despite its potential to inform both ecological and evolutionary understanding, as well as conservation and management practices. Here, patterns of rDNA methylation with age were measured across 5154 bp of rDNA corresponding to 355 quality-filtered loci in the economically important European lobster (Homarus gammarus). Across 0- to 51-month-old lobsters (n = 155), there was a significant linear relationship between age and percentage rDNA methylation in claw tissue at 60% of quality-filtered loci (n = 214). An Elastic Net regression model using 46 loci allowed for the accurate and precise age estimation of individuals (R 2 = 0.98; standard deviation = 1.6 months). Applying this ageing model to antennal DNA from wild lobsters of unknown age (n = 38) resulted in predicted ages that are concordant with estimates of minimum size at age in the wild (mean estimated age = 40.1 months; range 32.8-55.7 months). Overall, the rDNA epigenetic clock shows potential as a novel, nonlethal ageing technique for European lobsters. However, further validation is required across a wider range of known-age individuals and tissue types before the model can be used in fisheries management.
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Affiliation(s)
| | | | | | | | - Ewen Bell
- The Centre for Environment, Fisheries and Aquaculture ScienceLowestoftUK
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28
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Narayan V, McMahon M, O'Brien JJ, McAllister F, Buffenstein R. Insights into the Molecular Basis of Genome Stability and Pristine Proteostasis in Naked Mole-Rats. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1319:287-314. [PMID: 34424521 DOI: 10.1007/978-3-030-65943-1_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The naked mole-rat (Heterocephalus glaber) is the longest-lived rodent, with a maximal reported lifespan of 37 years. In addition to its long lifespan - which is much greater than predicted based on its small body size (longevity quotient of ~4.2) - naked mole-rats are also remarkably healthy well into old age. This is reflected in a striking resistance to tumorigenesis and minimal declines in cardiovascular, neurological and reproductive function in older animals. Over the past two decades, researchers have been investigating the molecular mechanisms regulating the extended life- and health- span of this animal, and since the sequencing and assembly of the naked mole-rat genome in 2011, progress has been rapid. Here, we summarize findings from published studies exploring the unique molecular biology of the naked mole-rat, with a focus on mechanisms and pathways contributing to genome stability and maintenance of proteostasis during aging. We also present new data from our laboratory relevant to the topic and discuss our findings in the context of the published literature.
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Affiliation(s)
| | - Mary McMahon
- Calico Life Sciences, LLC, South San Francisco, CA, USA
| | | | | | - Rochelle Buffenstein
- Calico Life Sciences, LLC, South San Francisco, CA, USA. .,Department of Pharmacology, University of Texas Health at San Antonio, San Antonio, TX, USA.
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29
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Mayne B, Espinoza T, Roberts D, Butler GL, Brooks S, Korbie D, Jarman S. Nonlethal age estimation of three threatened fish species using DNA methylation: Australian lungfish, Murray cod and Mary River cod. Mol Ecol Resour 2021; 21:2324-2332. [PMID: 34161658 PMCID: PMC8518777 DOI: 10.1111/1755-0998.13440] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/16/2021] [Accepted: 06/01/2021] [Indexed: 11/29/2022]
Abstract
Age‐based demography is fundamental to management of wild fish populations. Age estimates for individuals can determine rates of change in key life‐history parameters such as length, maturity, mortality and fecundity. These age‐based characteristics are critical for population viability analysis in endangered species and for developing sustainable harvest strategies. For teleost fish, age has traditionally been determined by counting increments formed in calcified structures such as otoliths. However, the collection of otoliths is lethal and therefore undesirable for threatened species. At a molecular level, age can be predicted by measuring DNA methylation. Here, we use previously identified age‐associated sites of DNA methylation in zebrafish (Danio rerio) to develop two epigenetic clocks for three threatened freshwater fish species. One epigenetic clock was developed for the Australian lungfish (Neoceratodus forsteri) and the second for the Murray cod (Maccullochellapeelii) and Mary River cod (Maccullochellamariensis). Age estimation models were calibrated using either known‐age individuals, ages derived from otoliths or bomb radiocarbon dating of scales. We demonstrate a high Pearson's correlation between the chronological and predicted age in both the Lungfish clock (cor = .98) and Maccullochella clock (cor = .92). The median absolute error rate for both epigenetic clocks was also low (Lungfish = 0.86 years; Maccullochella = 0.34 years). This study demonstrates the transferability of DNA methylation sites for age prediction between highly phylogenetically divergent fish species. Given the method is nonlethal and suited to automation, age prediction by DNA methylation has the potential to improve fisheries and other wildlife management settings.
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Affiliation(s)
- Benjamin Mayne
- Environomics Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Indian Ocean Marine Research Centre, Crawley, WA, Australia
| | - Thomas Espinoza
- Department of Natural Resources, Mines and Energy, Bundaberg, Qld, Australia
| | | | - Gavin L Butler
- Department of Primary Industries, Grafton Fisheries Centre, Grafton, NSW, Australia
| | - Steven Brooks
- Department of Agriculture, Fisheries and Forestry, Brisbane, Qld, Australia
| | - Darren Korbie
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Qld, Australia
| | - Simon Jarman
- School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
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30
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Zupkovitz G, Kabiljo J, Kothmayer M, Schlick K, Schöfer C, Lagger S, Pusch O. Analysis of Methylation Dynamics Reveals a Tissue-Specific, Age-Dependent Decline in 5-Methylcytosine Within the Genome of the Vertebrate Aging Model Nothobranchius furzeri. Front Mol Biosci 2021; 8:627143. [PMID: 34222326 PMCID: PMC8242171 DOI: 10.3389/fmolb.2021.627143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 06/02/2021] [Indexed: 12/12/2022] Open
Abstract
Erosion of the epigenetic DNA methylation landscape is a widely recognized hallmark of aging. Emerging advances in high throughput sequencing techniques, in particular DNA methylation data analysis, have resulted in the establishment of precise human and murine age prediction tools. In vertebrates, methylation of cytosine at the C5 position of CpG dinucleotides is executed by DNA methyltransferases (DNMTs) whereas the process of enzymatic demethylation is highly dependent on the activity of the ten-eleven translocation methylcytosine dioxygenase (TET) family of enzymes. Here, we report the identification of the key players constituting the DNA methylation machinery in the short-lived teleost aging model Nothobranchius furzeri. We present a comprehensive spatio-temporal expression profile of the methylation-associated enzymes from embryogenesis into late adulthood, thereby covering the complete killifish life cycle. Data mining of the N. furzeri genome produced five dnmt gene family orthologues corresponding to the mammalian DNMTs (DNMT1, 2, 3A, and 3B). Comparable to other teleost species, N. furzeri harbors multiple genomic copies of the de novo DNA methylation subfamily. A related search for the DNMT1 recruitment factor UHRF1 and TET family members resulted in the identification of N. furzeri uhrf1, tet1, tet2, and tet3. Phylogenetic analysis revealed high cross-species similarity on the amino acid level of all individual dnmts, tets, and uhrf1, emphasizing a high degree of functional conservation. During early killifish development all analyzed dnmts and tets showed a similar expression profile characterized by a strong increase in transcript levels after fertilization, peaking either at embryonic day 6 or at the black eye stage of embryonic development. In adult N. furzeri, DNA methylation regulating enzymes showed a ubiquitous tissue distribution. Specifically, we observed an age-dependent downregulation of dnmts, and to some extent uhrf1, which correlated with a significant decrease in global DNA methylation levels in the aging killifish liver and muscle. The age-dependent DNA methylation profile and spatio-temporal expression characteristics of its enzymatic machinery reported here may serve as an essential platform for the identification of an epigenetic aging clock in the new vertebrate model system N. furzeri.
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Affiliation(s)
- Gordin Zupkovitz
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria.,Department of Life Science Engineering, University of Applied Sciences Technikum Wien, Vienna, Austria.,City of Vienna Competence Team Aging Tissue, Vienna, Austria
| | - Julijan Kabiljo
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria.,Department of General Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Michael Kothmayer
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
| | - Katharina Schlick
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
| | - Christian Schöfer
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
| | - Sabine Lagger
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Oliver Pusch
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
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31
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Robeck TR, Fei Z, Lu AT, Haghani A, Jourdain E, Zoller JA, Li CZ, Steinman KJ, DiRocco S, Schmitt T, Osborn S, Van Bonn B, Katsumata E, Mergl J, Almunia J, Rodriguez M, Haulena M, Dold C, Horvath S. Multi-species and multi-tissue methylation clocks for age estimation in toothed whales and dolphins. Commun Biol 2021; 4:642. [PMID: 34059764 PMCID: PMC8167141 DOI: 10.1038/s42003-021-02179-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/04/2021] [Indexed: 02/05/2023] Open
Abstract
The development of a precise blood or skin tissue DNA Epigenetic Aging Clock for Odontocete (OEAC) would solve current age estimation inaccuracies for wild odontocetes. Therefore, we determined genome-wide DNA methylation profiles using a custom array (HorvathMammalMethyl40) across skin and blood samples (n = 446) from known age animals representing nine odontocete species within 4 phylogenetic families to identify age associated CG dinucleotides (CpGs). The top CpGs were used to create a cross-validated OEAC clock which was highly correlated for individuals (r = 0.94) and for unique species (median r = 0.93). Finally, we applied the OEAC for estimating the age and sex of 22 wild Norwegian killer whales. DNA methylation patterns of age associated CpGs are highly conserved across odontocetes. These similarities allowed us to develop an odontocete epigenetic aging clock (OEAC) which can be used for species conservation efforts by provide a mechanism for estimating the age of free ranging odontocetes from either blood or skin samples.
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Affiliation(s)
- Todd R Robeck
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA.
| | - Zhe Fei
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Joseph A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Caesar Z Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Karen J Steinman
- Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA
| | | | | | | | | | | | - June Mergl
- Marineland of Canada, Niagara Falls, ON, Canada
| | - Javier Almunia
- Loro Parque Fundación, SA, Avenida Loro Parque, Puerto de la Cruz, Santa Cruz de Tenerife, Spain
| | | | | | - Christopher Dold
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - Steve Horvath
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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32
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Mayne B, Berry O, Jarman S. Optimal sample size for calibrating DNA methylation age estimators. Mol Ecol Resour 2021; 21:2316-2323. [PMID: 34053192 PMCID: PMC8518423 DOI: 10.1111/1755-0998.13437] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 05/13/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022]
Abstract
Age is a fundamental parameter in wildlife management as it is used to determine the risk of extinction, manage invasive species, and regulate sustainable harvest. In a broad variety of vertebrates species, age can be determined by measuring DNA methylation. Animals with known ages are initially required during development, calibration, and validation of these epigenetic clocks. However, wild animals with known ages are frequently difficult to obtain. Here, we perform Monte‐Carlo simulations to determine the optimal sample size required to create an accurate calibration model for age estimation by elastic net regression modelling of cytosine‐phosphate‐guanine methylation data. Our results suggest a minimum calibration population size of 70, but ideally 134 individuals or more for accurate and precise models. We also provide estimates to the extent a model can be extrapolated beyond a distribution of ages that was used during calibration. The findings can assist researchers to better design age estimation models and decide if their model is adequate for determining key population attributes.
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Affiliation(s)
- Benjamin Mayne
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia
| | - Oliver Berry
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia
| | - Simon Jarman
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia
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33
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Bors EK, Baker CS, Wade PR, O'Neill KB, Shelden KEW, Thompson MJ, Fei Z, Jarman S, Horvath S. An epigenetic clock to estimate the age of living beluga whales. Evol Appl 2021; 14:1263-1273. [PMID: 34025766 PMCID: PMC8127720 DOI: 10.1111/eva.13195] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 12/20/2022] Open
Abstract
DNA methylation data facilitate the development of accurate molecular estimators of chronological age or "epigenetic clocks." We present a robust epigenetic clock for the beluga whale, Delphinapterus leucas, developed for an endangered population in Cook Inlet, Alaska, USA. We used a custom methylation array to measure methylation levels at 37,491 cytosine-guanine sites (CpGs) from skin samples of dead whales (n = 67) whose chronological ages were estimated based on tooth growth layer groups. Using these calibration data, a penalized regression model selected 23 CpGs, providing an R 2 = 0.92 for the training data; and an R 2 = 0.74 and median absolute age error = 2.9 years for the leave one out cross-validation. We applied the epigenetic clock to an independent dataset of 38 skin samples collected with a biopsy dart from living whales between 2016 and 2018. Age estimates ranged from 11 to 27 years. We also report sex correlations in CpG data and describe an approach of identifying the sex of an animal using DNA methylation. The epigenetic estimators of age and sex presented here have broad applications for conservation and management of Cook Inlet beluga whales and potentially other cetaceans.
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Affiliation(s)
| | - C. Scott Baker
- Marine Mammal InstituteOregon State UniversityNewportORUSA
| | - Paul R. Wade
- Marine Mammal LaboratoryAlaska Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanographic and Atmospheric AdministrationSeattleWAUSA
| | | | - Kim E. W. Shelden
- Marine Mammal LaboratoryAlaska Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanographic and Atmospheric AdministrationSeattleWAUSA
| | - Michael J. Thompson
- Molecular, Cell and Developmental BiologyUniversity of California Los AngelesLos AngelesCAUSA
| | - Zhe Fei
- Department of BiostatisticsSchool of Public HealthUniversity of California‐Los AngelesLos AngelesCAUSA
| | - Simon Jarman
- School of Biological SciencesUniversity of Western AustraliaPerthWAAustralia
| | - Steve Horvath
- Department of BiostatisticsSchool of Public HealthUniversity of California‐Los AngelesLos AngelesCAUSA
- Department of Human GeneticsGonda Research CenterDavid Geffen School of MedicineLos AngelesCAUSA
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34
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Anderson JA, Johnston RA, Lea AJ, Campos FA, Voyles TN, Akinyi MY, Alberts SC, Archie EA, Tung J. High social status males experience accelerated epigenetic aging in wild baboons. eLife 2021; 10:e66128. [PMID: 33821798 PMCID: PMC8087445 DOI: 10.7554/elife.66128] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting 'epigenetic clock' closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons - the best predictor of reproductive success - imposes costs consistent with a 'live fast, die young' life-history strategy.
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Affiliation(s)
- Jordan A Anderson
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
| | - Rachel A Johnston
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
| | - Amanda J Lea
- Department of Biology, Duke UniversityDurhamUnited States
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton UniversityPrincetonUnited States
- Department of Ecology and Evolution, Princeton UniversityPrincetonUnited States
| | - Fernando A Campos
- Department of Biology, Duke UniversityDurhamUnited States
- Department of Anthropology, University of Texas at San AntonioSan AntonioUnited States
| | - Tawni N Voyles
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
| | - Mercy Y Akinyi
- Institute of Primate Research, National Museums of KenyaNairobiKenya
| | - Susan C Alberts
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
- Department of Biology, Duke UniversityDurhamUnited States
| | - Elizabeth A Archie
- Department of Biological Sciences, University of Notre DameNotre DameUnited States
| | - Jenny Tung
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
- Department of Biology, Duke UniversityDurhamUnited States
- Duke Population Research Institute, Duke UniversityDurhamUnited States
- Canadian Institute for Advanced ResearchTorontoCanada
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35
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DNA methylation predicts age and provides insight into exceptional longevity of bats. Nat Commun 2021; 12:1615. [PMID: 33712580 PMCID: PMC7955057 DOI: 10.1038/s41467-021-21900-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/08/2021] [Indexed: 01/31/2023] Open
Abstract
Exceptionally long-lived species, including many bats, rarely show overt signs of aging, making it difficult to determine why species differ in lifespan. Here, we use DNA methylation (DNAm) profiles from 712 known-age bats, representing 26 species, to identify epigenetic changes associated with age and longevity. We demonstrate that DNAm accurately predicts chronological age. Across species, longevity is negatively associated with the rate of DNAm change at age-associated sites. Furthermore, analysis of several bat genomes reveals that hypermethylated age- and longevity-associated sites are disproportionately located in promoter regions of key transcription factors (TF) and enriched for histone and chromatin features associated with transcriptional regulation. Predicted TF binding site motifs and enrichment analyses indicate that age-related methylation change is influenced by developmental processes, while longevity-related DNAm change is associated with innate immunity or tumorigenesis genes, suggesting that bat longevity results from augmented immune response and cancer suppression.
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36
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Yang Y, Fan X, Yan J, Chen M, Zhu M, Tang Y, Liu S, Tang Z. A comprehensive epigenome atlas reveals DNA methylation regulating skeletal muscle development. Nucleic Acids Res 2021; 49:1313-1329. [PMID: 33434283 PMCID: PMC7897484 DOI: 10.1093/nar/gkaa1203] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 11/20/2020] [Accepted: 11/26/2020] [Indexed: 12/22/2022] Open
Abstract
DNA methylation is important for the epigenetic regulation of gene expression and plays a critical role in mammalian development. However, the dynamic regulation of genome-wide DNA methylation in skeletal muscle development remains largely unknown. Here, we generated the first single-base resolution DNA methylome and transcriptome maps of porcine skeletal muscle across 27 developmental stages. The overall methylation level decreased from the embryo to the adult, which was highly correlated with the downregulated expression of DNMT1 and an increase in partially methylated domains. Notably, we identified over 40 000 developmentally differentially methylated CpGs (dDMCs) that reconstitute the developmental trajectory of skeletal muscle and associate with muscle developmental genes and transcription factors (TFs). The dDMCs were significantly under-represented in promoter regulatory regions but strongly enriched as enhancer histone markers and in chromatin-accessible regions. Integrative analysis revealed the negative regulation of both promoter and gene body methylation in genes associated with muscle contraction and insulin signaling during skeletal muscle development. Mechanistically, DNA methylation affected the expression of muscle-related genes by modulating the accessibly of upstream myogenesis TF binding, indicating the involvement of the DNA methylation/SP1/IGF2BP3 axis in skeletal myogenesis. Our results highlight the function and regulation of dynamic DNA methylation in skeletal muscle development.
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Affiliation(s)
- Yalan Yang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Xinhao Fan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Junyu Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Muya Chen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Min Zhu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yijie Tang
- Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Siyuan Liu
- Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Research Centre of Animal Nutritional Genomics, State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.,Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.,GuangXi Engineering Centre for Resource Development of Bama Xiang Pig, Bama 547500, China
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37
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Hearn J, Plenderleith F, Little TJ. DNA methylation differs extensively between strains of the same geographical origin and changes with age in Daphnia magna. Epigenetics Chromatin 2021; 14:4. [PMID: 33407738 PMCID: PMC7789248 DOI: 10.1186/s13072-020-00379-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/12/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Patterns of methylation influence lifespan, but methylation and lifespan may also depend on diet, or differ between genotypes. Prior to this study, interactions between diet and genotype have not been explored together to determine their influence on methylation. The invertebrate Daphnia magna is an excellent choice for testing the epigenetic response to the environment: parthenogenetic offspring are identical to their siblings (making for powerful genetic comparisons), they are relatively short lived and have well-characterised inter-strain life-history trait differences. We performed a survival analysis in response to caloric restriction and then undertook a 47-replicate experiment testing the DNA methylation response to ageing and caloric restriction of two strains of D. magna. RESULTS Methylated cytosines (CpGs) were most prevalent in exons two to five of gene bodies. One strain exhibited a significantly increased lifespan in response to caloric restriction, but there was no effect of food-level CpG methylation status. Inter-strain differences dominated the methylation experiment with over 15,000 differently methylated CpGs. One gene, Me31b, was hypermethylated extensively in one strain and is a key regulator of embryonic expression. Sixty-one CpGs were differentially methylated between young and old individuals, including multiple CpGs within the histone H3 gene, which were hypermethylated in old individuals. Across all age-related CpGs, we identified a set that are highly correlated with chronological age. CONCLUSIONS Methylated cytosines are concentrated in early exons of gene sequences indicative of a directed, non-random, process despite the low overall DNA methylation percentage in this species. We identify no effect of caloric restriction on DNA methylation, contrary to our previous results, and established impacts of caloric restriction on phenotype and gene expression. We propose our approach here is more robust in invertebrates given genome-wide CpG distributions. For both strain and ageing, a single gene emerges as differentially methylated that for each factor could have widespread phenotypic effects. Our data showed the potential for an epigenetic clock at a subset of age positions, which is exciting but requires confirmation.
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Affiliation(s)
- Jack Hearn
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Fiona Plenderleith
- The James Hutton Institute, Craigiebuckler, Aberdeen, UK
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
| | - Tom J. Little
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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38
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Enabling pinniped conservation by means of non-invasive genetic population analysis. CONSERV GENET RESOUR 2021. [DOI: 10.1007/s12686-020-01182-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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39
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Anastasiadi D, Shao C, Chen S, Piferrer F. Footprints of global change in marine life: Inferring past environment based on DNA methylation and gene expression marks. Mol Ecol 2020; 30:747-760. [PMID: 33372368 DOI: 10.1111/mec.15764] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/17/2020] [Accepted: 11/26/2020] [Indexed: 12/14/2022]
Abstract
Ocean global warming affects the distribution, life history and physiology of marine life. Extreme events, like marine heatwaves, are increasing in frequency and intensity. During sensitive stages of early fish development, the consequences may be long-lasting and mediated by epigenetic mechanisms. Here, we used European sea bass as a model to study the possible long-lasting effects of a marine heatwave during early development. We measured DNA methylation and gene expression in four tissues (brain, muscle, liver and testis) and detected differentially methylated regions (DMRs). Six genes were differentially expressed and contained DMRs three years after exposure to increased temperature, indicating direct phenotypic consequences and representing persistent changes. Interestingly, nine genes contained DMRs around the same genomic regions across tissues, therefore consisting of common footprints of developmental temperature in environmentally responsive loci. These loci are, to our knowledge, the first metastable epialleles (MEs) described in fish. MEs may serve as biomarkers to infer past life history events linked with persistent consequences. These results highlight the importance of subtle phenotypic changes mediated by epigenetics to extreme weather events during sensitive life stages. Also, to our knowledge, it is the first time the molecular effects of a marine heatwave during the lifetime of individuals are assessed. MEs could be used in surveillance programs aimed at determining the footprints of climate change on marine life. Our study paves the way for the identification of conserved MEs that respond equally to environmental perturbations across species. Conserved MEs would constitute a tool of assessment of global change effects in marine life at a large scale.
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Affiliation(s)
- Dafni Anastasiadi
- Institut de Ciències del Mar, Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, Spain
| | - Changwei Shao
- Key Lab of Sustainable Development of Marine Fisheries, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (CAFS), Qingdao, China.,Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao, China
| | - Songlin Chen
- Key Lab of Sustainable Development of Marine Fisheries, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (CAFS), Qingdao, China.,Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao, China
| | - Francesc Piferrer
- Institut de Ciències del Mar, Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, Spain
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40
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Mayne B, Korbie D, Kenchington L, Ezzy B, Berry O, Jarman S. A DNA methylation age predictor for zebrafish. Aging (Albany NY) 2020; 12:24817-24835. [PMID: 33353889 PMCID: PMC7803548 DOI: 10.18632/aging.202400] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022]
Abstract
Changes in DNA methylation at specific CpG sites have been used to build predictive models to estimate animal age, predominantly in mammals. Little testing for this effect has been conducted in other vertebrate groups, such as bony fish, the largest vertebrate class. The development of most age-predictive models has relied on a genome-wide sequencing method to obtain a DNA methylation level, which makes it costly to deploy as an assay to estimate age in many samples. Here, we have generated a reduced representation bisulfite sequencing data set of caudal fin tissue from a model fish species, zebrafish (Danio rerio), aged from 11.9-60.1 weeks. We identified changes in methylation at specific CpG sites that correlated strongly with increasing age. Using an optimised unique set of 26 CpG sites we developed a multiplex PCR assay that predicts age with an average median absolute error rate of 3.2 weeks in zebrafish between 10.9-78.1 weeks of age. We also demonstrate the use of multiplex PCR as an efficient quantitative approach to measure DNA methylation for the use of age estimation. This study highlights the potential further use of DNA methylation as an age estimation method in non-mammalian vertebrate species.
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Affiliation(s)
- Benjamin Mayne
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, Western Australia, Australia
| | - Darren Korbie
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, Australia
| | - Lisa Kenchington
- Western Australian Zebrafish Experimental Research Centre (WAZERC), University of Western Australia, Perth, Western Australia, Australia
| | - Ben Ezzy
- Western Australian Zebrafish Experimental Research Centre (WAZERC), University of Western Australia, Perth, Western Australia, Australia
| | - Oliver Berry
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, Western Australia, Australia
| | - Simon Jarman
- School of Biological Sciences, The University of Western Australia, Perth, Western Australia, Australia
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41
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Remili A, Gallego P, Pinzone M, Castro C, Jauniaux T, Garigliany MM, Malarvannan G, Covaci A, Das K. Humpback whales (Megaptera novaeangliae) breeding off Mozambique and Ecuador show geographic variation of persistent organic pollutants and isotopic niches. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 267:115575. [PMID: 33254700 DOI: 10.1016/j.envpol.2020.115575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/27/2020] [Accepted: 08/28/2020] [Indexed: 06/12/2023]
Abstract
Humpback whales (Megaptera novaeangliae) from the Southern Hemisphere carry information on persistent organic pollutants (POPs) from their feeding zones in Antarctica to their breeding grounds, making this species a sentinel of contaminants accumulation in the Southern Ocean. This study aimed to evaluate driving factors, namely feeding areas, trophic level, and sex, affecting POP concentrations in the blubber of humpback whales breeding off Mozambique and off Ecuador. Biopsies of free-ranging humpback whales including blubber and skin were collected in 2014 and 2015 from Ecuador (n = 59) and in 2017 from Mozambique (n = 89). In both populations, HCB was the major contaminant followed by DDTs > CHLs > PCBs > HCHs > PBDEs. POP concentrations were significantly higher in males compared to females. HCB, DDTs, HCHs and PBDEs were significantly different between whales from the Mozambique population and the Ecuador population. Sex and feeding habits were important driving factors accounting for POP concentrations in Ecuador whales. The whales from our study had some of the lowest POP concentrations measured for humpback whales in the world. These whales fed predominantly on krill as reflected from the low δ13C and δ15N values measured in the skin. However, the isotopic niches of whales from Mozambique and Ecuador did not overlap indicating that the two populations are feeding in different areas of the Southern Ocean.
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Affiliation(s)
- Anaïs Remili
- Freshwater and Oceanic Sciences Unit of ReSearch (FOCUS - Oceanology), University of Liege, 4000, Liege, Belgium; Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada
| | - Pierre Gallego
- Freshwater and Oceanic Sciences Unit of ReSearch (FOCUS - Oceanology), University of Liege, 4000, Liege, Belgium; Odyssea asbl., 37 rue du Nord, L-4260, Esch-sur-Alzette, Luxembourg
| | - Marianna Pinzone
- Freshwater and Oceanic Sciences Unit of ReSearch (FOCUS - Oceanology), University of Liege, 4000, Liege, Belgium
| | - Cristina Castro
- Pacific Whale Foundation Ecuador, Malecón Julio Izurieta y Abdón Calderón. Palo Santo Travel, Puerto López - Manabí - Ecuador
| | - Thierry Jauniaux
- Department of Pathology, Veterinary College, University of Liege, Sart Tilman B43, 4000, Liege, Belgium
| | - Mutien-Marie Garigliany
- Department of Pathology, Veterinary College, University of Liege, Sart Tilman B43, 4000, Liege, Belgium
| | - Govindan Malarvannan
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Krishna Das
- Freshwater and Oceanic Sciences Unit of ReSearch (FOCUS - Oceanology), University of Liege, 4000, Liege, Belgium.
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42
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Marcy‐Quay B, Sethi SA, Therkildsen NO, Kraft CE. Expanding the feasibility of fish and wildlife assessments with close‐kin mark–recapture. Ecosphere 2020. [DOI: 10.1002/ecs2.3259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
| | - Suresh A. Sethi
- U.S. Geological Survey New York Cooperative Fish and Wildlife Research Unit Cornell University Ithaca New York14853USA
| | | | - Clifford E. Kraft
- Department of Natural Resources Cornell University Ithaca New York14853USA
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43
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Guevara EE, Lawler RR, Staes N, White CM, Sherwood CC, Ely JJ, Hopkins WD, Bradley BJ. Age-associated epigenetic change in chimpanzees and humans. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190616. [PMID: 32951551 DOI: 10.1098/rstb.2019.0616] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Methylation levels have been shown to change with age at sites across the human genome. Change at some of these sites is so consistent across individuals that it can be used as an 'epigenetic clock' to predict an individual's chronological age to within a few years. Here, we examined how the pattern of epigenetic ageing in chimpanzees compares with humans. We profiled genome-wide blood methylation levels by microarray for 113 samples from 83 chimpanzees aged 1-58 years (26 chimpanzees were sampled at multiple ages during their lifespan). Many sites (greater than 65 000) showed significant change in methylation with age and around one-third (32%) of these overlap with sites showing significant age-related change in humans. At over 80% of sites showing age-related change in both species, chimpanzees displayed a significantly faster rate of age-related change in methylation than humans. We also built a chimpanzee-specific epigenetic clock that predicted age in our test dataset with a median absolute deviation from known age of only 2.4 years. However, our chimpanzee clock showed little overlap with previously constructed human clocks. Methylation at CpGs comprising our chimpanzee clock showed moderate heritability. Although the use of a human microarray for profiling chimpanzees biases our results towards regions with shared genomic sequence between the species, nevertheless, our results indicate that there is considerable conservation in epigenetic ageing between chimpanzees and humans, but also substantial divergence in both rate and genomic distribution of ageing-associated sites. This article is part of the theme issue 'Evolution of the primate ageing process'.
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Affiliation(s)
- Elaine E Guevara
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA.,Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC 20052, USA
| | - Richard R Lawler
- Department of Sociology and Anthropology, James Madison University, Harrisonburg, VA 22807, USA
| | - Nicky Staes
- Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC 20052, USA.,Behavioural Ecology and Ecophysiology Group, Department of Biology, University of Antwerp, Wilrijk, Belgium.,Centre for Research and Conservation, Royal Zoological Society of Antwerp, Antwerp, Belgium
| | - Cassandra M White
- Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC 20052, USA
| | - Chet C Sherwood
- Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC 20052, USA
| | | | - William D Hopkins
- Keeling Center for Comparative Medicine and Research, University of Texas MD Anderson Cancer Center, Bastrop, TX 78602, USA
| | - Brenda J Bradley
- Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC 20052, USA
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44
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Lowe R, Danson AF, Rakyan VK, Yildizoglu S, Saldmann F, Viltard M, Friedlander G, Faulkes CG. DNA methylation clocks as a predictor for ageing and age estimation in naked mole-rats, Heterocephalus glaber. Aging (Albany NY) 2020; 12:4394-4406. [PMID: 32126024 PMCID: PMC7093186 DOI: 10.18632/aging.102892] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 02/25/2020] [Indexed: 01/07/2023]
Abstract
The naked mole-rat, Heterocephalus glaber (NMR), the longest-lived rodent, is of significance and interest in the study of biomarkers for ageing. Recent breakthroughs in this field have revealed ‘epigenetic clocks’ that are based on the temporal accumulation of DNA methylation at specific genomic sites. Here, we validate the hypothesis of an epigenetic clock in NMRs based on changes in methylation of targeted CpG sites. We initially analysed 51 CpGs in NMR livers spanning an age range of 39-1,144 weeks and found 23 to be significantly associated with age (p<0.05). We then built a predictor of age using these sites. To test the accuracy of this model, we analysed an additional set of liver samples, and were successfully able to predict their age with a root mean squared error of 166 weeks. We also profiled skin samples with the same age range, finding a striking correlation between their predicted age versus their actual age (R=0.93), but which was lower when compared to the liver, suggesting that skin ages slower than the liver in NMRs. Our model will enable the prediction of age in wild-caught and captive NMRs of unknown age, and will be invaluable for further mechanistic studies of mammalian ageing.
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Affiliation(s)
- Robert Lowe
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Amy F Danson
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Vardhman K Rakyan
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Centre for Genomic Health, Queen Mary University of London, London, UK
| | - Selin Yildizoglu
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Frédéric Saldmann
- Fondation pour la Recherche en Physiologie, Brussels, Belgium.,Service de Physiologie et Explorations Fonctionnelles, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Melanie Viltard
- Fondation pour la Recherche en Physiologie, Brussels, Belgium
| | - Gérard Friedlander
- Service de Physiologie et Explorations Fonctionnelles, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France.,Université Paris Descartes, Faculté de Médecine, Paris, France.,INSERM UMR_S1151 CNRS UMR8253 Institut Necker-Enfants Malades (INEM), Paris, France
| | - Chris G Faulkes
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
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45
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Abstract
Biological ageing and its mechanistic underpinnings are of immense biomedical and ecological significance. 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.
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Anastasiadi D, Piferrer F. A clockwork fish: Age prediction using DNA methylation‐based biomarkers in the European seabass. Mol Ecol Resour 2019; 20:387-397. [DOI: 10.1111/1755-0998.13111] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/22/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Dafni Anastasiadi
- Institut de Ciències del Mar (ICM) Spanish National Research Council (CSIC) Barcelona Spain
| | - Francesc Piferrer
- Institut de Ciències del Mar (ICM) Spanish National Research Council (CSIC) Barcelona Spain
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Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, Christensen BC, Gladyshev VN, Heijmans BT, Horvath S, Ideker T, Issa JPJ, Kelsey KT, Marioni RE, Reik W, Relton CL, Schalkwyk LC, Teschendorff AE, Wagner W, Zhang K, Rakyan VK. DNA methylation aging clocks: challenges and recommendations. Genome Biol 2019; 20:249. [PMID: 31767039 PMCID: PMC6876109 DOI: 10.1186/s13059-019-1824-y] [Citation(s) in RCA: 459] [Impact Index Per Article: 91.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 09/16/2019] [Indexed: 12/15/2022] Open
Abstract
Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Robert Lowe
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
- Beatson Institute for Cancer Research and University of Glasgow, Glasgow, UK.
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
| | - Stephan Beck
- Medical Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London, UK.
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Steve Horvath
- Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, Los Angeles, CA, USA.
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA.
| | - Trey Ideker
- San Diego Center for Systems Biology, University of California-San Diego, San Diego, CA, USA.
| | - Jean-Pierre J Issa
- Fels Institute for Cancer Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
| | - Karl T Kelsey
- Department of Epidemiology, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
| | - Wolf Reik
- Epigenetics Programme, The Babraham Institute, Cambridge, UK.
- The Wellcome Trust Sanger Institute, Cambridge, UK.
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | | | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen Faculty of Medicine, Aachen, Germany.
| | - Kang Zhang
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau.
| | - Vardhman K Rakyan
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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48
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Piferrer F, Anastasiadi D, Valdivieso A, Sánchez-Baizán N, Moraleda-Prados J, Ribas L. The Model of the Conserved Epigenetic Regulation of Sex. Front Genet 2019; 10:857. [PMID: 31616469 PMCID: PMC6775248 DOI: 10.3389/fgene.2019.00857] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 08/16/2019] [Indexed: 12/18/2022] Open
Abstract
Epigenetics integrates genomic and environmental information to produce a given phenotype. Here, the model of Conserved Epigenetic Regulation of Sex (CERS) is discussed. This model is based on our knowledge on genes involved in sexual development and on epigenetic regulation of gene expression activation and silencing. This model was recently postulated to be applied to the sexual development of fish, and it states that epigenetic and gene expression patterns are more associated with the development of a particular gonadal phenotype, e.g., testis differentiation, rather than with the intrinsic or extrinsic causes that lead to the development of this phenotype. This requires the existence of genes with different epigenetic modifications, for example, changes in DNA methylation levels associated with the development of a particular sex. Focusing on DNA methylation, the identification of CpGs, the methylation of which is linked to sex, constitutes the basis for the identification of Essential Epigenetic Marks (EEM). EEMs are defined as the number and identity of informative epigenetic marks that are strictly necessary, albeit perhaps not sufficient, to bring about a specific, measurable, phenotype of interest. Here, we provide a summary of the genes where DNA methylation has been investigated so far, focusing on fish. We found that cyp19a1a and dmrt1, two key genes for ovary and testis development, respectively, consistently show an inverse relationship between their DNA methylation and expression levels, thus following CERS predictions. However, in foxl2a, a pro-female gene, and amh, a pro-male gene, such relationship is not clear. The available data of other genes related to sexual development such as sox9, gsdf, and amhr2 are also discussed. Next, we discuss the use of CERS to make testable predictions of how sex is epigenetically regulated and to better understand sexual development, as well as the use of EEMs as tools for the diagnosis and prognosis of sex. We argue that CERS can aid in focusing research on the epigenetic regulation of sexual development not only in fish but also in vertebrates in general, particularly in reptiles with temperature sex-determination, and can be the basis for possible practical applications including sex control in aquaculture and also in conservation biology.
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Affiliation(s)
- Francesc Piferrer
- Institut de Ciències del Mar (ICM), Spanish National Research Council (CSIC), Barcelona, Spain
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49
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Parrott BB, Bertucci EM. Epigenetic Aging Clocks in Ecology and Evolution. Trends Ecol Evol 2019; 34:767-770. [DOI: 10.1016/j.tree.2019.06.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/11/2019] [Accepted: 06/14/2019] [Indexed: 11/16/2022]
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Rey O, Eizaguirre C, Angers B, Baltazar‐Soares M, Sagonas K, Prunier JG, Blanchet S. Linking epigenetics and biological conservation: Towards a
conservation epigenetics
perspective. Funct Ecol 2019. [DOI: 10.1111/1365-2435.13429] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Olivier Rey
- CNRS UMR 5244, Interactions Hôtes‐Pathogènes‐Environnements (IHPE) Université de Perpignan Via Domitia Perpignan France
| | - Christophe Eizaguirre
- School of Biological and Chemical Sciences Queen Mary University of London London UK
| | - Bernard Angers
- Department of Biological Sciences Université de Montréal Montreal QC Canada
| | | | - Kostas Sagonas
- School of Biological and Chemical Sciences Queen Mary University of London London UK
| | - Jérôme G. Prunier
- Evolution et Diversité Biologique, École Nationale Supérieure de Formation de l'Enseignement Agricole (ENSFEA), CNRS, UPS, UMR5174 Institut de Recherche pour le Développement (IRD) Toulouse France
| | - Simon Blanchet
- Evolution et Diversité Biologique, École Nationale Supérieure de Formation de l'Enseignement Agricole (ENSFEA), CNRS, UPS, UMR5174 Institut de Recherche pour le Développement (IRD) Toulouse France
- Station d'Ecologie Théorique et Expérimentale, UMR5321, CNRS Université Paul Sabatier (UP) Moulis France
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