Evidence map›Paper›PMID 34534207›Full record

ArticlePLoS computational biology2021

Hierarchy and control of ageing-related methylation networks.

Gergely Palla, Péter Pollner, Judit Börcsök, András Major, Béla Molnár, István Csabai

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Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Gergely PallaHealth Services Management Training Centre, Semmelweis University, Budapest, Hungary.ORCID 0000-0002-3406-4200
Péter PollnerHealth Services Management Training Centre, Semmelweis University, Budapest, Hungary.ORCID 0000-0003-0464-4893
Judit BörcsökDanish Cancer Society Research Center, Copenhagen, Denmark.ORCID 0000-0002-3290-3971
András MajorDept. of Physics of Complex Systems, ELTE Eötvös University, Budapest, Hungary.ORCID 0000-0001-5146-0670
Béla MolnárMolecular Medicine Research Group, Hungarian Academy of Sciences, Budapest, Hungary.
István CsabaiDept. of Physics of Complex Systems, ELTE Eötvös University, Budapest, Hungary.ORCID 0000-0001-9232-9898

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

DNA MethylationAgingCpG IslandsHumans

Identifiers

PMID34534207
PMCPMC8480875

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.