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Hierarchy and control of ageing-related methylation networks
DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480875/ https://www.ncbi.nlm.nih.gov/pubmed/34534207 http://dx.doi.org/10.1371/journal.pcbi.1009327 |
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author | Palla, Gergely Pollner, Péter Börcsök, Judit Major, András Molnár, Béla Csabai, István |
author_facet | Palla, Gergely Pollner, Péter Börcsök, Judit Major, András Molnár, Béla Csabai, István |
author_sort | Palla, Gergely |
collection | PubMed |
description | DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state. A prominent example of methylation-based age estimators is provided by Horvath’s clock, based on 353 CpG dinucleotides, showing a high correlation (not necessarily causation) with chronological age across multiple tissue types. On this small subset of CpG dinucleotides we demonstrate how the adjustment of one methylation level leads to a cascade of changes at other sites. Among the studied subset, we locate the most important CpGs (and related genes) that may have a large influence on the rest of the sub-system. According to our analysis, the structure of this network is way more hierarchical compared to what one would expect based on ensembles of uncorrelated connections. Therefore, only a handful of CpGs is enough to modify the system towards a desired state. When propagation of the change over the network is taken into account, the resulting modification in the predicted age can be significantly larger compared to the effect of isolated CpG perturbations. By adjusting the most influential single CpG site and following the propagation of methylation level changes we can reach up to 5.74 years in virtual age reduction, significantly larger than without taking into account of the network control. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process. |
format | Online Article Text |
id | pubmed-8480875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84808752021-09-30 Hierarchy and control of ageing-related methylation networks Palla, Gergely Pollner, Péter Börcsök, Judit Major, András Molnár, Béla Csabai, István PLoS Comput Biol Research Article DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state. A prominent example of methylation-based age estimators is provided by Horvath’s clock, based on 353 CpG dinucleotides, showing a high correlation (not necessarily causation) with chronological age across multiple tissue types. On this small subset of CpG dinucleotides we demonstrate how the adjustment of one methylation level leads to a cascade of changes at other sites. Among the studied subset, we locate the most important CpGs (and related genes) that may have a large influence on the rest of the sub-system. According to our analysis, the structure of this network is way more hierarchical compared to what one would expect based on ensembles of uncorrelated connections. Therefore, only a handful of CpGs is enough to modify the system towards a desired state. When propagation of the change over the network is taken into account, the resulting modification in the predicted age can be significantly larger compared to the effect of isolated CpG perturbations. By adjusting the most influential single CpG site and following the propagation of methylation level changes we can reach up to 5.74 years in virtual age reduction, significantly larger than without taking into account of the network control. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process. Public Library of Science 2021-09-17 /pmc/articles/PMC8480875/ /pubmed/34534207 http://dx.doi.org/10.1371/journal.pcbi.1009327 Text en © 2021 Palla et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Palla, Gergely Pollner, Péter Börcsök, Judit Major, András Molnár, Béla Csabai, István Hierarchy and control of ageing-related methylation networks |
title | Hierarchy and control of ageing-related methylation networks |
title_full | Hierarchy and control of ageing-related methylation networks |
title_fullStr | Hierarchy and control of ageing-related methylation networks |
title_full_unstemmed | Hierarchy and control of ageing-related methylation networks |
title_short | Hierarchy and control of ageing-related methylation networks |
title_sort | hierarchy and control of ageing-related methylation networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480875/ https://www.ncbi.nlm.nih.gov/pubmed/34534207 http://dx.doi.org/10.1371/journal.pcbi.1009327 |
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