Inferring DNA methylation in non-skeletal tissues of ancient specimens

Genome-wide premortem DNA methylation patterns can be computationally reconstructed from high-coverage DNA sequences of ancient samples. Because DNA methylation is more conserved across species than across tissues, and ancient DNA is typically extracted from bones and teeth, previous works utilizing...

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Autores: Mathov, Yoav, Nissim-Rafinia, Malka, Leibson, Chen, Galun, Nir, Marqués-Bonet, Tomàs, Kandel, Arye, Liebergal, Meir, Meshorer, Eran, Carmel, Liran
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/392326
Acesso em linha:http://hdl.handle.net/10261/392326
https://api.elsevier.com/content/abstract/scopus_id/85209652121
Access Level:Acceso aberto
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dc.title.none.fl_str_mv Inferring DNA methylation in non-skeletal tissues of ancient specimens
title Inferring DNA methylation in non-skeletal tissues of ancient specimens
spellingShingle Inferring DNA methylation in non-skeletal tissues of ancient specimens
Mathov, Yoav
title_short Inferring DNA methylation in non-skeletal tissues of ancient specimens
title_full Inferring DNA methylation in non-skeletal tissues of ancient specimens
title_fullStr Inferring DNA methylation in non-skeletal tissues of ancient specimens
title_full_unstemmed Inferring DNA methylation in non-skeletal tissues of ancient specimens
title_sort Inferring DNA methylation in non-skeletal tissues of ancient specimens
dc.creator.none.fl_str_mv Mathov, Yoav
Nissim-Rafinia, Malka
Leibson, Chen
Galun, Nir
Marqués-Bonet, Tomàs
Kandel, Arye
Liebergal, Meir
Meshorer, Eran
Carmel, Liran
author Mathov, Yoav
author_facet Mathov, Yoav
Nissim-Rafinia, Malka
Leibson, Chen
Galun, Nir
Marqués-Bonet, Tomàs
Kandel, Arye
Liebergal, Meir
Meshorer, Eran
Carmel, Liran
author_role author
author2 Nissim-Rafinia, Malka
Leibson, Chen
Galun, Nir
Marqués-Bonet, Tomàs
Kandel, Arye
Liebergal, Meir
Meshorer, Eran
Carmel, Liran
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv John Templeton Foundation
Israel Science Foundation
Ministry of Innovation, Science and Technology (Israel)
European Research Council
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
European Commission
Generalitat de Catalunya
Leibson, Chen [0000-0002-1333-1381]
Marqués-Bonet, Tomàs [0000-0002-5597-3075]
Meshorer, Eran [0000-0003-4777-986X]
Carmel, Liran [0000-0003-0225-8550]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
description Genome-wide premortem DNA methylation patterns can be computationally reconstructed from high-coverage DNA sequences of ancient samples. Because DNA methylation is more conserved across species than across tissues, and ancient DNA is typically extracted from bones and teeth, previous works utilizing ancient DNA methylation maps focused on studying evolutionary changes in the skeletal system. Here we suggest that DNA methylation patterns in one tissue may, under certain conditions, be informative on DNA methylation patterns in other tissues of the same individual. Using the fact that tissue-specific DNA methylation builds up during embryonic development, we identified the conditions that allow for such cross-tissue inference and devised an algorithm that carries it out. We trained the algorithm on methylation data from extant species and reached high precisions of up to 0.92 for validation datasets. We then used the algorithm on archaic humans, and identified more than 1,850 positions for which we were able to observe differential DNA methylation in prefrontal cortex neurons. These positions are linked to hundreds of genes, many of which are involved in neural functions such as structural and developmental processes. Six positions are located in the neuroblastoma breaking point family (NBPF) gene family, which probably played a role in human brain evolution. The algorithm we present here allows for the examination of epigenetic changes in tissues and cell types that are absent from the palaeontological record, and therefore provides new ways to study the evolutionary impacts of epigenetic changes.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/392326
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dc.language.none.fl_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126004NB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2018-000792-M
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1038/s41559-024-02571-w
The MATLAB code can be downloaded from http://carmelab.huji.ac.il/software.html
https://doi.org/10.1038/s41559-024-02571-w
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spelling Inferring DNA methylation in non-skeletal tissues of ancient specimensMathov, YoavNissim-Rafinia, MalkaLeibson, ChenGalun, NirMarqués-Bonet, TomàsKandel, AryeLiebergal, MeirMeshorer, EranCarmel, LiranGenome-wide premortem DNA methylation patterns can be computationally reconstructed from high-coverage DNA sequences of ancient samples. Because DNA methylation is more conserved across species than across tissues, and ancient DNA is typically extracted from bones and teeth, previous works utilizing ancient DNA methylation maps focused on studying evolutionary changes in the skeletal system. Here we suggest that DNA methylation patterns in one tissue may, under certain conditions, be informative on DNA methylation patterns in other tissues of the same individual. Using the fact that tissue-specific DNA methylation builds up during embryonic development, we identified the conditions that allow for such cross-tissue inference and devised an algorithm that carries it out. We trained the algorithm on methylation data from extant species and reached high precisions of up to 0.92 for validation datasets. We then used the algorithm on archaic humans, and identified more than 1,850 positions for which we were able to observe differential DNA methylation in prefrontal cortex neurons. These positions are linked to hundreds of genes, many of which are involved in neural functions such as structural and developmental processes. Six positions are located in the neuroblastoma breaking point family (NBPF) gene family, which probably played a role in human brain evolution. The algorithm we present here allows for the examination of epigenetic changes in tissues and cell types that are absent from the palaeontological record, and therefore provides new ways to study the evolutionary impacts of epigenetic changes.This publication was made possible through the support of a grant from the John Templeton Foundation (grant ID 61739 to L.C. and E.M.). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. This study was also funded by the Israel Science Foundation (grant no. 2436/22 to L.C.) and by the Ministry of Innovation, Science & Technology (grant no. grant 1001584586 to L.C. and E.M.). T.M.-B. is supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 864203), PID2021-126004NB-100 (MICIIN/FEDER, UE), ‘Unidad de Excelencia María de Maeztu’, funded by the AEI (CEX2018-000792-M), NIH 1R01HG010898-01A1 and Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2021 SGR 00177).With funding from the Spanish government through the "Maria de Maeztu Centre of Excellence" accreditation (CEX2018-000792-M).Peer reviewedNature Publishing GroupJohn Templeton FoundationIsrael Science FoundationMinistry of Innovation, Science and Technology (Israel)European Research CouncilMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)European CommissionGeneralitat de CatalunyaLeibson, Chen [0000-0002-1333-1381]Marqués-Bonet, Tomàs [0000-0002-5597-3075]Meshorer, Eran [0000-0003-4777-986X]Carmel, Liran [0000-0003-0225-8550]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/392326https://api.elsevier.com/content/abstract/scopus_id/85209652121reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/864203info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126004NB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2018-000792-MThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1038/s41559-024-02571-wThe MATLAB code can be downloaded from http://carmelab.huji.ac.il/software.htmlhttps://doi.org/10.1038/s41559-024-02571-wSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3923262026-05-22T06:33:51Z
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