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Universal DNA methylation age across mammalian tissues.


ABSTRACT: Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

SUBMITTER: Lu AT 

PROVIDER: S-EPMC10501909 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Universal DNA methylation age across mammalian tissues.

Lu A T AT   Fei Z Z   Haghani A A   Robeck T R TR   Zoller J A JA   Li C Z CZ   Lowe R R   Yan Q Q   Zhang J J   Vu H H   Ablaeva J J   Acosta-Rodriguez V A VA   Adams D M DM   Almunia J J   Aloysius A A   Ardehali R R   Arneson A A   Baker C S CS   Banks G G   Belov K K   Bennett N C NC   Black P P   Blumstein D T DT   Bors E K EK   Breeze C E CE   Brooke R T RT   Brown J L JL   Carter G G GG   Caulton A A   Cavin J M JM   Chakrabarti L L   Chatzistamou I I   Chen H H   Cheng K K   Chiavellini P P   Choi O W OW   Clarke S M SM   Cooper L N LN   Cossette M L ML   Day J J   DeYoung J J   DiRocco S S   Dold C C   Ehmke E E EE   Emmons C K CK   Emmrich S S   Erbay E E   Erlacher-Reid C C   Faulkes C G CG   Ferguson S H SH   Finno C J CJ   Flower J E JE   Gaillard J M JM   Garde E E   Gerber L L   Gladyshev V N VN   Gorbunova V V   Goya R G RG   Grant M J MJ   Green C B CB   Hales E N EN   Hanson M B MB   Hart D W DW   Haulena M M   Herrick K K   Hogan A N AN   Hogg C J CJ   Hore T A TA   Huang T T   Izpisua Belmonte J C JC   Jasinska A J AJ   Jones G G   Jourdain E E   Kashpur O O   Katcher H H   Katsumata E E   Kaza V V   Kiaris H H   Kobor M S MS   Kordowitzki P P   Koski W R WR   Krützen M M   Kwon S B SB   Larison B B   Lee S G SG   Lehmann M M   Lemaitre J F JF   Levine A J AJ   Li C C   Li X X   Lim A R AR   Lin D T S DTS   Lindemann D M DM   Little T J TJ   Macoretta N N   Maddox D D   Matkin C O CO   Mattison J A JA   McClure M M   Mergl J J   Meudt J J JJ   Montano G A GA   Mozhui K K   Munshi-South J J   Naderi A A   Nagy M M   Narayan P P   Nathanielsz P W PW   Nguyen N B NB   Niehrs C C   O'Brien J K JK   O'Tierney Ginn P P   Odom D T DT   Ophir A G AG   Osborn S S   Ostrander E A EA   Parsons K M KM   Paul K C KC   Pellegrini M M   Peters K J KJ   Pedersen A B AB   Petersen J L JL   Pietersen D W DW   Pinho G M GM   Plassais J J   Poganik J R JR   Prado N A NA   Reddy P P   Rey B B   Ritz B R BR   Robbins J J   Rodriguez M M   Russell J J   Rydkina E E   Sailer L L LL   Salmon A B AB   Sanghavi A A   Schachtschneider K M KM   Schmitt D D   Schmitt T T   Schomacher L L   Schook L B LB   Sears K E KE   Seifert A W AW   Seluanov A A   Shafer A B A ABA   Shanmuganayagam D D   Shindyapina A V AV   Simmons M M   Singh K K   Sinha I I   Slone J J   Snell R G RG   Soltanmaohammadi E E   Spangler M L ML   Spriggs M C MC   Staggs L L   Stedman N N   Steinman K J KJ   Stewart D T DT   Sugrue V J VJ   Szladovits B B   Takahashi J S JS   Takasugi M M   Teeling E C EC   Thompson M J MJ   Van Bonn B B   Vernes S C SC   Villar D D   Vinters H V HV   Wallingford M C MC   Wang N N   Wayne R K RK   Wilkinson G S GS   Williams C K CK   Williams R W RW   Yang X W XW   Yao M M   Young B G BG   Zhang B B   Zhang Z Z   Zhao P P   Zhao Y Y   Zhou W W   Zimmermann J J   Ernst J J   Raj K K   Horvath S S  

Nature aging 20230810 9


Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mo  ...[more]

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