Genetic loci and metabolic states associated with murine epigenetic aging
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Curated by eLife
Evaluation Summary:
In this manuscript, Mozhui and colleagues used several epigenetic predictors, of which most come from other manuscript that have not yet been peer reviewed, to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan). They also identified several quantitative trait loci for the different predictors, using linkage analysis, which could shed some light on the underlying biology of epigenetic mouse ageing. One of the question that remains is how generalizable (some of) the findings are given that the follow-up analyses were only done using liver tissue.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)
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Abstract
Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a ‘pan-mammalian’ microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4 , NKX2- 3, and CUTC . Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.
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Evaluation Summary:
In this manuscript, Mozhui and colleagues used several epigenetic predictors, of which most come from other manuscript that have not yet been peer reviewed, to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan). They also identified several quantitative trait loci for the different predictors, using linkage analysis, which could shed some light on the underlying biology of epigenetic mouse ageing. One of the question that remains is how generalizable (some of) the findings are given that the follow-up analyses were only done using liver tissue.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to …
Evaluation Summary:
In this manuscript, Mozhui and colleagues used several epigenetic predictors, of which most come from other manuscript that have not yet been peer reviewed, to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan). They also identified several quantitative trait loci for the different predictors, using linkage analysis, which could shed some light on the underlying biology of epigenetic mouse ageing. One of the question that remains is how generalizable (some of) the findings are given that the follow-up analyses were only done using liver tissue.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
The study presents one of many recent studies trying to find potential functional meaning of epigenetic clock. For that, different measurements were performed and correlated with DNA methylation biomarkers. Further the changes in the epigenetic age were assessed depending on diet, therefore metabolic status of the animal. Two major observation has been made 1. the diet is affecting the epigenetic clock and 2. Specific QTLs were uncovered highlighting the importance of metabolism and cell cycle in aging. Interestingly, same loci were previously associated with epigenetic age acceleration in human further confirming the relevance of the association and study itself.
It is of highest importance to understand the correlation of epigenetic changes with biology and physiology. Recent study of Levine lab has shown …
Reviewer #1 (Public Review):
The study presents one of many recent studies trying to find potential functional meaning of epigenetic clock. For that, different measurements were performed and correlated with DNA methylation biomarkers. Further the changes in the epigenetic age were assessed depending on diet, therefore metabolic status of the animal. Two major observation has been made 1. the diet is affecting the epigenetic clock and 2. Specific QTLs were uncovered highlighting the importance of metabolism and cell cycle in aging. Interestingly, same loci were previously associated with epigenetic age acceleration in human further confirming the relevance of the association and study itself.
It is of highest importance to understand the correlation of epigenetic changes with biology and physiology. Recent study of Levine lab has shown the similar work on large sample of human samples from UK Biobank (Kuo et al 2020). Here, the number of samples is significantly smaller but the defined genetic, phenotypic, and full genome sequence of mice used in the study is an advantage in distilling the correlations.
This reviewer finds the observation that metabolic state is inducing changes in epigenetic age very interesting and worth studying. Series of correlations with acceleration of epigenetic age is well presented and quantified. However, some observations, although in lower numbers, were previously presented by the laboratory in another paper.
Description of QTLs is one of the most interesting parts of the study. Correlations of metabolically relevant loci with age acceleration is highly suggestive of molecular mechanism, probably based on negative feedback loop, that moderates DNAm. This idea is, however, not discussed in the paper.
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Reviewer #2 (Public Review):
The manuscript titled "Genetic Analyses of Epigenetic Predictors that Estimate Aging, Metabolic Traits, and Lifespan" by Mozhui et al. extended the application of the DNA methylation microarray technology to the liver of the BXD mouse cohort. This study examines on the relationship between the epigenome aging parameters such as epigenetic age and phenotypic parameters such as diet and metabolic traits. It also explored potential genetic mechanisms responsible for epigenetic age acceleration by identifying QTLs on chromosomes 11 and 19. Overall, this is an informative and well-organized study of importance to aging research, and it is inspiring as it explores mechanisms of epigenetic age acceleration.
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Reviewer #3 (Public Review):
The manuscript by Mozhui et al. investigates genetic and environmental modifiers of DNAm based biomarkers of ageing. The authors use a number of interesting measures to evaluate these effects, from epigenetic age, to maximum predicted lifespan, to methylome entropy. The manuscript then presents and discusses the effects of genetics and environment on these measures.
Methylome entropy: The study shows that "Entropy was also significantly higher in the HFD group" - the difference between the two groups is very small (Table 2). Is this difference possibly meaningful? And assuming it is, are any of the enzymes relevant for maintaining or changing DNAm (DNMTs, TETs, ...) differentially expressed between the groups which could explain the difference? This question would also be interesting in the context of the …
Reviewer #3 (Public Review):
The manuscript by Mozhui et al. investigates genetic and environmental modifiers of DNAm based biomarkers of ageing. The authors use a number of interesting measures to evaluate these effects, from epigenetic age, to maximum predicted lifespan, to methylome entropy. The manuscript then presents and discusses the effects of genetics and environment on these measures.
Methylome entropy: The study shows that "Entropy was also significantly higher in the HFD group" - the difference between the two groups is very small (Table 2). Is this difference possibly meaningful? And assuming it is, are any of the enzymes relevant for maintaining or changing DNAm (DNMTs, TETs, ...) differentially expressed between the groups which could explain the difference? This question would also be interesting in the context of the finding that "entropy had an inverse correlation with body weight ", which somewhat is in conflict with the HFD results.
The finding of "lower age acceleration with higher glucose" may be due to the focus on liver tissue? Are there maybe any expression data pointing to healthier samples or other types of effects which might affect ageing, e.g. lower basal metabolic rate (-> less glucose drop during fasting)?
A very interesting part represents the "Genetic analysis of epigenetic age acceleration and predicted-maxLS". Both genomic loci on Chr 11 and Chr 19 harbour various interesting genes. In extension of the current analysis, the authors could have looked at existing HiC datasets to identify if several of the found SNPs are within long-ranging genome - interactions and may also play a regulatory role towards more distant genes.
Also, the finding of HFD, BW, glucose levels, etc affecting several of the measures used prompts the question if any of the genes present in the Eaaq11 or 19 have been implicated with these metabolic phenotypes? Are mouse models available of genes found in these regions to ask whether OVX or KD would have an effect on ageing? Also, given that the readout is primarily DNA methylation and not physiology, would any of genes in these loci affect DNA methylation itself? This may be a potential confounder.
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