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- Hierarchy and control of ageing-related methylation networks
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Figures Abstract DNA methylation provides one of best foundation for mature skin 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.
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Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state. On this small subset of CpG francoprovencal svájc anti aging 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.
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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. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process.
Author summary Aging affects all living organisms. In humans, the chronological age correlates with the methylation level of some locations of the DNA. Here we extract an interaction network between these ageing related sites, which shows signs of hierarchical organisation.
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In addition, modifications in the methylation of single sites of the DNA can impose cascades of changes at other sites over this network.
When modifying the most influential locations, the resulting cascades of changes can set back the estimated biological age by more than 5 years.
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Our study also shows that compared to single site methylation perturbations, the propagation of the change over the interaction network leads to methylation change profiles which are more aligned with the natural direction of ageing in a high dimensional representation of the methylation levels. PLoS Comput Biol 17 9 : e This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist. Introduction An ancient desire of humanity is to understand, slow, or even halt and reverse ageing. In related studies it was soon realised that certain biomarkers can rather precisely predict the functional capability of tissues, organs and even patients [ 12 ].
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In addition, age-related biomarkers enable the introduction of biological age [ 34 ], bringing additive information in the risk assessments for age-related conditions on top of chronological age. Individuals of the same chronological age can still show great heterogeneity in the tissue and organismal functions, and thus, could possess different risks for age-associated diseases as judged from their biological ages.
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However, the predictive value of biological age estimators is usually decreasing at old age due to the increased biological heterogeneity among elderly individuals [ 3 ]. Probably the most promising age-predictive biomarkers are the ones based on DNA-methylation [ 5 — 7 ], which can be used for basically any source of DNA from sorted cells through tissues to organs, and can predict the biological age across the whole life span from prenatal tissues to tissues obtained from centenarians [ 5 ].
DNA methylation-related markers are also important in endocrinology [ 8 ], cell biology [ 9 ], biodemography [ 10 ], lifestyle factors [ 11 ], and medicine [ 12 ].
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The research of DNA methylation dates back to the s, when it was first observed that the methylation level of the CpG dinucleotides in the DNA is changing genome-wide with the chronological age [ 1314 ]. Later on, thanks to the developments in methylation array technologies, specific CpG dinucleotides were located in the genome, based on which the age of the DNA source e. The involved change can be random fashion due to epigenomic drift [ 23 ] directional, or show increased variability with age [ 24 ].
The research on DNA-methylation related biomarkers has been a huge success [ 25 ], with still some important challenges remaining, such as the dissection of the regulators and drivers of age-related changes in single-cell, tissue- and disease-specific models, the analysis of further epigenomic marks, the implementation of longitudinal and diverse population studies, and the exploration of non-human models [ 26 ].
In general, when the goal is to estimate the age based on the methylome, the usual framework couples a set of CpGs with a mathematical algorithm, where the observed methylation levels of the CpG dinucleotides are combined in some way to yield the estimated age in years [ 25 ]. The obtained estimated age is referred to as DNAm age, or epigenetic age, which is highly correlated with the chronological age, but is also effected by other biological factors such best foundation for mature skin the health status as well.
The above mentioned DNA methylation-based age estimators are usually built using supervised machine learning techniques such as penalized regression models, which automatically select the most informative CpGs for the age estimation [ 25 ]. The first DNA methylation-based age estimators in the scientific literature were concentrating on a single tissue [ 2728 ], and therefore, were tailor-made for just one type of DNA source, leading to biased estimates for other tissues.
However, the construction of multi-tissue DNA methylation-based age estimators is non-trivial, due to the significant differences between the DNA methylation patterns among different tissues [ 2930 ], the specific ways in which the DNA methylation patterns change with age across the different cell types [ 1731 ], the fact that distinct biological processes drive the observed age-related hypermethylation and hypomethylation, and that baseline DNA methylation state is strongly driven by genetics being highly CpG density dependent [ 32 ].
In a recent study, together with similar clocks derived by Hannum et.
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The estimation of age based on DNA methylation across multiple tissues is indeed a complex problem. Furthermore, when considered individually, the methylation state for most of the CpGs is only weakly correlated with age, e. Thus, these were selected not for their individual strength, but rather to their power to work collectively to parsimoniously capture ageing over the life-course [ 25 ].
This also means that we cannot really point out any of these CpGs as being more important than others for measuring the molecular age.
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These properties are in full consistency with the way the elastic net regression method is selecting among the feature variables by penalising the coefficients both in quadratic and absolute forms. In this approach by combining the penalty of the summed absolute value of the coefficients from Lasso regression that is known to turn the coefficients of unimportant variables to zero with the quadratic penalty of the coefficients from Ridge regression we obtain a convex loss function with a unique minimum, making the selection more stable.
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When considering the modifications best foundation for mature skin the methylation profile during the life-course, the known ageing effects are leading to coordinated changes across the entire DNA methylome, including those driven by cell-type specific epigenomics, where changes in cell proportion will led to variation including the age-related myeloid skew [ 40 ], T cell exhaustion [ 41 ]polycomb target hypermethylation [ 20 ], bivalent domain hypermethylation [ 42 ], etc.
Such systemic effects can be seen as networks of age-related change, where the methylation level change of any CpG is accompanied by changes in the levels of related other CpGs as well.
Hierarchy and control of ageing-related methylation networks
The tool best foundation for mature skin use to reveal the links is given by Lasso cross-validation Lasso-CV regression, which is a simple and robust approach to extract the most relevant inter relations between a given outcome or response variable and a larger set of regressors or feature variables. Due to the complex nature of the problem, instead of focusing solely on the revealed pairwise interactions, we gather the obtained connections links into a methylation network, and analyse the properties of the system using techniques from complex network theory.
The network approach for studying the structure and dynamics of complex systems has become ubiquitous over the last two decades, and studies of networks ranging from gene interactions to the level of the society have shown that the statistical analysis of the underlying graph structure can highlight non-trivial properties and reveal previously unseen relations [ 43 — 46 ].
In the present work our network analysis is focusing on the hierarchical and control properties of the web of connections between the CpG dinucleotides. Signs of hierarchical organization were observed in complex networks of diverse types [ 47 ], ranging from transcriptional regulatory networks within cells [ 48 ], through flocks of various animal species [ 4950 ], to the level of on-line news content [ 51 ], scientific journals [ 52 ], social interactions [ 53 — 55 ], best foundation for mature skin systems [ 56 ], and evolution [ 57 ].
In a hierarchical network, nodes close to the top of the hierarchy usually have a larger influence compared to nodes at the bottom levels.
One of the related questions we address in this paper is whether we can detect signs of hierarchy in case of the methylation network too, and if so, which CpGs are on the top of the hierarchy? Besides the hierarchical properties, another important aspect we investigate is given by the control properties of the network.
The control theory of networks is based on the framework of structural controllability of linear dynamical systems [ 58 ], exploiting the connections between graph combinatorics and linear algebra.
The importance of the nodes from the point of view of control can be characterised by the control centrality [ 59 ], corresponding to the number of nodes we can drive by controlling the given node using an external signal. By combining control centrality with the results from the hierarchy analysis, we can locate the CpGs having the largest influence on the rest of the system, which may also play a crucial role in the process of ageing. The basic idea is initiating a small change on the methylation of one CpG, and then propagate the effect over the methylation network according to the regression coefficients defining the link weights.
This provides a minimal model for tracking the changes in the methylation profile, in which the complex interrelations between the CpGs are taken into account.
9 A legjobb alapozó érett bőrre 2021, beleértve a Bare Minerals, No7 és Laura Mercier termékeket
In our view, treating the set of ageing-related CpGs as a complex, interacting system can provide more realistic change profiles in DNA methylation compared to isolated individual shifts in the methylation of single CpGs. Nevertheless, there can still be differences between the obtained shifts in the DNAm age depending on which CpG was chosen for the initiating perturbation, and naturally, one of the most interesting questions is for which initiating CpG do the methylation changes accumulate in such a way that the resulting shift in the DNAm age is maximal.
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A flow chart summarising the investigations carried in the paper are given in Fig 1.